MedWorm.com provides a medical RSS filtering service. Over 6000 RSS medical sources are combined and output via different filters. This feed contains the latest items from the 'Genetic Epidemiology' source. Thu, 09 Feb 2012 11:21:03 +0100 http://www.medworm.com/index.php?rid=5672685&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21612 In a commentary on the evolution of the field of genetic epidemiology over the past 10 years, Khoury et al. (2011) highlight several important developments, including the emergence of evaluation of genetic discoveries for their translational utility and of standards for reporting genetic findings. In this companion to their article, I reflect on some of these trends and speculate about the direction of the field in the future. In particular, I emphasize the opportunities posed by novel technologies like next‐generation sequencing and the biological insights emerging from integrative genomics, but I also question the utility of large consortia. The basic principles of population‐based research and the importance of taking account of the environment remain important to the field. (Source... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5672685 Mon, 06 Feb 2012 05:00:00 +0100 5672685 http://www.medworm.com/index.php?rid=5672684&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21613 It has been suggested that children with larger brains tend to perform better on IQ tests or cognitive function tests. Prenatal head growth and head growth in infancy are two crucial periods for subsequent intelligence. Studies have shown that environmental exposure to air pollutants during pregnancy is associated with fetal growth reduction, developmental delay, and reduced IQ. Meanwhile, genetic polymorphisms may modify the effect of environment on head growth. However, studies on gene–environment or gene–gene interactions on growth trajectories have been quite limited partly due to the difficulty to quantitatively measure interactions on growth trajectories. Moreover, it is known that assessing the significance of gene–environment or gene–gene interactions on cross‐sectional o... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5672684 Wed, 01 Feb 2012 05:00:00 +0100 5672684 http://www.medworm.com/index.php?rid=5634030&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21606 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5634030 Fri, 27 Jan 2012 16:19:06 +0100 5634030 http://www.medworm.com/index.php?rid=5634029&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21605 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5634029 Fri, 27 Jan 2012 16:19:05 +0100 5634029 http://www.medworm.com/index.php?rid=5634028&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21604 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5634028 Fri, 27 Jan 2012 16:19:03 +0100 5634028 http://www.medworm.com/index.php?rid=5634027&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21603 Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study‐specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005–0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome‐wide association studies (GWAS) follow‐up studies using dense genotyping or sequencing in large samples of non‐European individuals. In this work, we constructed a study‐specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5634027 Fri, 27 Jan 2012 16:19:02 +0100 5634027 http://www.medworm.com/index.php?rid=5634026&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21608 We present a new decision tree algorithm denoted as Bagged Alternating Decision Trees (BADTrees) that is based on identifying common structural elements in a bootstrapped set of Alternating Decision Trees (ADTrees). The algorithm is order , where n is the number of SNPs considered and k is the number of SNPs in the tree constructed. Our simulation study suggests that BADTrees have higher power and lower type I error rates than ADTrees alone and comparable power with lower type I error rates compared to logistic regression. We illustrate the application of these data using simulated data as well as from the Lupus Large Association Study 1 (7,822 SNPs in 3,548 individuals). Our results suggest that BADTrees hold promise as a low computational order algorithm for detecting complex combination... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5634026 Fri, 27 Jan 2012 16:19:00 +0100 5634026 http://www.medworm.com/index.php?rid=5634025&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21602 In this study, we have developed an extended support vector machine (SVM) method and an SVM‐based pedigree‐based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM‐based PGMDR approach to improve performance in detecting gene‐gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5634025 Fri, 27 Jan 2012 16:18:59 +0100 5634025 http://www.medworm.com/index.php?rid=5634024&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21607 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5634024 Fri, 27 Jan 2012 16:18:58 +0100 5634024 http://www.medworm.com/index.php?rid=5556238&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21600 We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single‐nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)‐replicated SNPs. The second and third methods investig... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5556238 Sun, 01 Jan 2012 05:00:00 +0100 5556238 http://www.medworm.com/index.php?rid=5556237&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.21609 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5556237 Sun, 01 Jan 2012 05:00:00 +0100 5556237 http://www.medworm.com/index.php?rid=5481870&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20632 AbstractGene‐set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene‐set, have plagued current approaches, often leading to ad hoc “fixes.” To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene‐level and gene‐set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene‐sets. However, other types of gene‐set structures can be used, such as the popula... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5481870 Wed, 07 Dec 2011 05:00:00 +0100 5481870 http://www.medworm.com/index.php?rid=5481872&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20640 AbstractPatterns of linkage disequilibrium are often depicted pictorially by using tools that rely on visualizations of raw data or pairwise correlations among individual markers. Such approaches can fail to highlight some of the more interesting and complex features of haplotype structure. To enable natural visual comparisons of haplotype structure across subgroups of a population (e.g. isolated subpopulations or cases and controls), we propose an alternative visualization that provides a novel graphical representation of haplotype frequencies. We introduce Haploscope, a tool for visualizing the haplotype cluster frequencies that are produced by statistical models for population haplotype variation. We demonstrate the utility of our technique by examining haplotypes around the LCT gene, a... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5481872 Tue, 06 Dec 2011 05:00:00 +0100 5481872 http://www.medworm.com/index.php?rid=5481871&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20652 AbstractOver the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributi... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5481871 Tue, 06 Dec 2011 05:00:00 +0100 5481871 http://www.medworm.com/index.php?rid=5481869&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20662 The objective of this study is to investigate the prevalence of Down syndrome (DS) associated with Chernobyl fallout. Maternal age‐adjusted DS data and corresponding live birth data from the following seven European countries or regions were analyzed: Bavaria and West Berlin in Germany, Belarus, Hungary, the Lothian Region of Scotland, North West England, and Sweden from 1981 to 1992. To assess the underlying time trends in the DS occurrence, and to investigate whether there have been significant changes in the trend functions after Chernobyl, we applied logistic regression allowing for peaks and jumps from January 1987 onward. The majority of the trisomy 21 cases of the previously reported, highly significant January 1987 clusters in Belarus and West Berlin were conceived when the radio... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5481869 Thu, 01 Dec 2011 05:00:00 +0100 5481869 http://www.medworm.com/index.php?rid=5463818&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20664 AbstractThe ongoing controversy surrounding direct‐to‐consumer (DTC) personal genomic tests intensified last year when the U.S. Government Accountability Office released results of an undercover investigation of four companies that offer such testing. Among their findings, they reported that some of their donors received DNA‐based predictions that conflicted with their actual medical histories. We aimed to more rigorously evaluate the relationship between DTC genomic risk estimates and self‐reported disease by leveraging data from the Scripps Genomic Health Initiative. We prospectively collected self‐reported personal and family health history data for 3,416 individuals, who went on to purchase a commercially available DTC genomic test. For 5 out of 15 total conditions studied, w... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5463818 Thu, 01 Dec 2011 05:00:00 +0100 5463818 http://www.medworm.com/index.php?rid=5463820&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20663 AbstractPoly(ADP‐ribose) polymerase‐1 (PARP‐1 catalyzes poly(ADP‐ribosyl)ation to various proteins involved in many cellular processes, including DNA damage detection and repair and cell proliferation and death. PARP‐1 has been implicated in human carcinogenesis, but the association between the most‐studied PARP‐1 V762A polymorphism (rs1136410) and risk of various cancers was reported with inconclusive results. The aim of this study was to assess the association between the PARP‐1 V762A polymorphism and cancer risk. A meta‐analysis of 21 studies with 12,027 cancer patients and 14,106 cancer‐free controls was conducted to evaluate the strength of the association using odds ratio (OR) with 95% confidence interval (CI). Overall, no significant association was found between... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5463820 Tue, 29 Nov 2011 05:00:00 +0100 5463820 http://www.medworm.com/index.php?rid=5463819&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20661 We present a novel Bayesian method for automatic detection of multivariant joint association in genome‐wide case‐control studies. Our method has improved power and specificity over existing tools. We fit a joint probabilistic model to the entire data and identify disease variants simultaneously. The method dynamically accounts for the strong linkage disequilibrium (LD) between variants. As a result, only the primary disease variants will be identified, with all secondary associations due to LD effects filtered out. Our method better pinpoints the disease variants with improved resolution. The method is also computationally efficient for genome‐wide studies. When applied to a real data set of inflammatory bowel disease (IBD) containing 401,473 variants in 4,720 individuals, our method... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5463819 Tue, 29 Nov 2011 05:00:00 +0100 5463819 http://www.medworm.com/index.php?rid=5453348&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20639 AbstractGenome‐wide association studies (GWAS) are a useful approach in the study of the genetic components of complex phenotypes. Aside from large cohorts, GWAS have generally been limited to the study of one or a few diseases or traits. The emergence of biobanks linked to electronic medical records (EMRs) allows the efficient reuse of genetic data to yield meaningful genotype–phenotype associations for multiple phenotypes or traits. Phase I of the electronic MEdical Records and GEnomics (eMERGE‐I) Network is a National Human Genome Research Institute‐supported consortium composed of five sites to perform various genetic association studies using DNA repositories and EMR systems. Each eMERGE site has developed EMR‐based algorithms to comprise a core set of 14 phenotypes for extr... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453348 Tue, 29 Nov 2011 04:59:32 +0100 5453348 http://www.medworm.com/index.php?rid=5453347&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20638 AbstractDespite the numerous and successful applications of genome‐wide association studies (GWASs), there has been a lot of difficulty in discovering disease susceptibility loci (DSLs). This is due to the fact that the GWAS approach is an indirect mapping technique, often identifying markers. For the identification of DSLs, which is required for the understanding of the genetic pathways for complex diseases, sequencing data that examines every genetic locus directly is necessary. Yet, there is currently a lack of methodology targeted at the identification of the DSLs in sequencing data: existing methods localize the causal variant to a region but not to a single variant, and therefore do not allow one to identify unique loci that cause the phenotype association. Here, we have developed ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453347 Tue, 29 Nov 2011 04:59:31 +0100 5453347 http://www.medworm.com/index.php?rid=5453346&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20637 AbstractMeta‐analyses of genome‐wide association studies require numerous study partners to conduct pre‐defined analyses and thus simple but efficient analyses plans. Potential differences between strata (e.g. men and women) are usually ignored, but often the question arises whether stratified analyses help to unravel the genetics of a phenotype or if they unnecessarily increase the burden of analyses. To decide whether to stratify or not to stratify, we compare general analytical power computations for the overall analysis with those of stratified analyses considering quantitative trait analyses and two strata. We also relate the stratification problem to interaction modeling and exemplify theoretical considerations on obesity and renal function genetics. We demonstrate that the ove... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453346 Tue, 29 Nov 2011 04:59:30 +0100 5453346 http://www.medworm.com/index.php?rid=5453345&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20634 AbstractMore than a decade after Duncan Thomas gave his presidential address at the International Society for Genetic Epidemiology entitled “Genetic Epidemiology with a Capital E,” genetic epidemiology has gone mainstream. Epidemiology has taken its place not only in gene discovery studies but also in characterizing genetic effects and gene‐environment interactions in populations. Furthermore, epidemiologic principles are being applied to the evaluation of genetic tests. We used an online informatics tool, the HuGE Navigator, to describe the growth in the field in the past decade. We developed the HuGE Navigator as a means to continuously monitor the evolving information obtained from epidemiologic studies of the human genome. Between 2001 and 2010, the HuGE Navigator included 57,005... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453345 Tue, 29 Nov 2011 04:59:26 +0100 5453345 http://www.medworm.com/index.php?rid=5453344&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20633 We present a statistical model for allele‐specific patterns of copy number polymorphisms (CNPs) in commercial single nucleotide polymorphism (SNP) array data. This model is based on the observation that fluorescent signal intensities tend to cluster into clouds of similar allele‐specific copy number (ASCN) genotypes at each SNP locus. To capture the tendency of this clustering to be made vague by instrumental errors, our model allows for cluster memberships to overlap each other, according to a Bayesian Gaussian mixture model (GMM). This approach is flexible, allowing for both absolute scale differences and X/Y scale imbalances of fluorescent signal intensities. The resulting model is also robust toward unobserved ASCN genotypes, which can be problematic for ordinary GMMs. We illustrat... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453344 Tue, 29 Nov 2011 04:59:25 +0100 5453344 http://www.medworm.com/index.php?rid=5453343&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20630 AbstractThe detection of loci contributing effects to complex human traits, and their subsequent fine‐mapping for the location of causal variants, remains a considerable challenge for the genetics research community. Meta‐analyses of genomewide association studies, primarily ascertained from European‐descent populations, have made considerable advances in our understanding of complex trait genetics, although much of their heritability is still unexplained. With the increasing availability of genomewide association data from diverse populations, transethnic meta‐analysis may offer an exciting opportunity to increase the power to detect novel complex trait loci and to improve the resolution of fine‐mapping of causal variants by leveraging differences in local linkage disequilibrium... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453343 Tue, 29 Nov 2011 04:59:23 +0100 5453343 http://www.medworm.com/index.php?rid=5453342&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20626 AbstractSub‐Saharan Africa has been identified as the part of the world with the greatest human genetic diversity. This high level of diversity causes difficulties for genome‐wide association (GWA) studies in African populations—for example, by reducing the accuracy of genotype imputation in African populations compared to non‐African populations. Here, we investigate haplotype variation and imputation in Africa, using 253 unrelated individuals from 15 Sub‐Saharan African populations. We identify the populations that provide the greatest potential for serving as reference panels for imputing genotypes in the remaining groups. Considering reference panels comprising samples of recent African descent in Phase 3 of the HapMap Project, we identify mixtures of reference groups that pr... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453342 Tue, 29 Nov 2011 04:59:18 +0100 5453342 http://www.medworm.com/index.php?rid=5453341&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20622 AbstractThe curse of multiple testing has led to the adoption of a stringent Bonferroni threshold for declaring genome‐wide statistical significance for any one SNP as standard practice. Although justified in avoiding false positives, this conservative approach has the potential to miss true associations as most studies are drastically underpowered. As an alternative to increasing sample size, we compare results from a typical SNP‐by‐SNP analysis with three other methods that incorporate regional information in order to boost or dampen an otherwise noisy signal: the haplotype method (Schaid et al. [2002] Am J Hum Genet 70:425–434), the gene‐based method (Liu et al. [2010] Am J Hum Genet 87:139–145), and a new method (interaction count) that uses genome‐wide screening of pairw... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5453341 Tue, 29 Nov 2011 04:59:16 +0100 5453341 http://www.medworm.com/index.php?rid=5329246&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20621 AbstractFor complex diseases, the relationship between genotypes, environment factors, and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene‐gene and gene‐environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy‐based approach of information theory. In this article, we first develop entropy‐based test statistics to identify two‐way and higher order gene‐gene and gene‐environment interactions. We then apply these methods to a bladder cancer data set and thereby test th... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5329246 Wed, 19 Oct 2011 15:02:42 +0100 5329246 http://www.medworm.com/index.php?rid=5329243&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20631 AbstractExtreme‐value sampling design that samples subjects with extremely large or small quantitative trait values is commonly used in genetic association studies. Samples in such designs are often treated as “cases” and “controls” and analyzed using logistic regression. Such a case‐control analysis ignores the potential dose‐response relationship between the quantitative trait and the underlying trait locus and thus may lead to loss of power in detecting genetic association. An alternative approach to analyzing such data is to model the dose‐response relationship by a linear regression model. However, parameter estimation from this model can be biased, which may lead to inflated type I errors. We propose a robust and efficient approach that takes into consideration of bot... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5329243 Wed, 19 Oct 2011 15:02:18 +0100 5329243 http://www.medworm.com/index.php?rid=5329245&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20635 In this study, we present a new computational model for haplotype phasing based on pairwise sharing of haplotypes inferred to be Identical‐By‐Descent (IBD). We apply the Bayesian network based model in a new phasing algorithm, called systematic long‐range phasing (SLRP), that can capitalize on the close genetic relationships in isolated founder populations, and show with simulated and real genome‐wide genotype data that SLRP substantially reduces the rate of phasing errors compared to previous phasing algorithms. Furthermore, the method accurately identifies regions of IBD, enabling linkage‐like studies without pedigrees, and can be used to impute most genotypes with very low error rate. Genet. Epidemiol. 2011.  © 2011 Wiley Periodicals, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5329245 Mon, 17 Oct 2011 04:00:00 +0100 5329245 http://www.medworm.com/index.php?rid=5329244&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20636 We describe implementation of a set‐based method to assess the significance of findings from genomewide association study data. Our method, implemented in PLINK, is based on theoretical approximation of Fisher's statistics such that the combination of P‐vales at a gene or across a pathway is carried out in a manner that accounts for the correlation structure, or linkage disequilibrium, between single nucleotide polymorphisms. We compare our method to a permutation‐based product of P‐values approach and show a typical correlation in excess of 0.98 for a number of comparisons. The method gives Type I error rates that are less than or equal to the corresponding nominal significance levels, making it robust to the effects of false positives. We show that in broadly similar populations,... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5329244 Mon, 17 Oct 2011 04:00:00 +0100 5329244 http://www.medworm.com/index.php?rid=5231588&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20612 AbstractTesting for Hardy‐Weinberg equilibrium (HWE) is commonly used as a quality control filter in genome‐wide scans for markers with experimentally determined genotypes. In contrast, for markers with imputed genotypes, there are post‐imputation metrics of quality that can be used as screens but there are no formal tests of deviation from HWE. Similarly, there are no formal tests of deviation from HWE for probabilistic genotypes that are generated by sequencing projects. Here, I describe generalizations of the approximate χ2 and exact tests of HWE for use with uncertain genotypes. The tests fully account for the probabilities of all possible genotypes at a marker for each individual. By computer simulation, the approximate and exact tests are shown to maintain valid control of the... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5231588 Thu, 15 Sep 2011 04:00:00 +0100 5231588 http://www.medworm.com/index.php?rid=5231587&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20627 AbstractLarge‐scale meta‐analyses of genome‐wide association scans (GWAS) have been successful in discovering common risk variants with modest and small effects. The detection of lower frequency signals will undoubtedly require concerted efforts of at least similar scale. We investigate the sample size‐dictated power limits of GWAS meta‐analyses, in the presence and absence of modest levels of heterogeneity and across a range of different allelic architectures. We find that data combination through large‐scale collaboration is vital in the quest for complex trait susceptibility loci, but that effect size heterogeneity across meta‐analyzed studies drawn from similar populations does not appear to have a profound effect on sample size requirements. Genet. Epidemiol. 2011. © 20... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5231587 Thu, 15 Sep 2011 04:00:00 +0100 5231587 http://www.medworm.com/index.php?rid=5231586&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20628 AbstractVariants identified in recent genome‐wide association studies based on the common‐disease common‐variant hypothesis are far from fully explaining the hereditability of complex traits. Rare variants may, in part, explain some of the missing hereditability. Here, we explored the advantage of the extreme phenotype sampling in rare‐variant analysis and refined this design framework for future large‐scale association studies on quantitative traits. We first proposed a power calculation approach for a likelihood‐based analysis method. We then used this approach to demonstrate the potential advantages of extreme phenotype sampling for rare variants. Next, we discussed how this design can influence future sequencing‐based association studies from a cost‐efficiency (with the... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5231586 Thu, 15 Sep 2011 04:00:00 +0100 5231586 http://www.medworm.com/index.php?rid=5231585&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20603 We describe the weights that maximize the power of the summary statistics. For small effect‐sizes, any choice of weights yields summary Wald and score statistics with the same power, and the optimal weights are proportional to the square roots of the sites' Fisher information for the SNP's regression coefficient. When SNP effect size is constant across sites, the optimal summary Wald statistic is the well‐known inverse‐variance‐weighted combination of estimated regression coefficients, divided by its standard deviation. We give simple approximations to the optimal weights for various phenotypes, and show that weights proportional to the square roots of study sizes are suboptimal for data from case‐control studies with varying case‐control ratios, for quantitative trait data whe... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5231585 Thu, 15 Sep 2011 04:00:00 +0100 5231585 http://www.medworm.com/index.php?rid=5231584&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20625 AbstractIn multilocus association analysis, since some markers may not be associated with a trait, it seems attractive to use penalized regression with the capability of automatic variable selection. On the other hand, in spite of a rapidly growing body of literature on penalized regression, most focus on variable selection and outcome prediction, for which penalized methods are generally more effective than their nonpenalized counterparts. However, for statistical inference, i.e. hypothesis testing and interval estimation, it is less clear how penalized methods would perform, or even how to best apply them, largely due to lack of studies on this topic. In our motivating data for a cohort of kidney transplant recipients, it is of primary interest to assess whether a group of genetic varian... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5231584 Thu, 15 Sep 2011 04:00:00 +0100 5231584 http://www.medworm.com/index.php?rid=5244972&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20629 AbstractGenome‐wide association (GWA) studies have been extremely successful in identifying novel loci contributing effects to a wide range of complex human traits. However, despite this success, the joint marginal effects of these loci account for only a small proportion of the heritability of these traits. Interactions between variants in different loci are not typically modelled in traditional GWA analysis, but may account for some of the missing heritability in humans, as they do in other model organisms. One of the key challenges in performing gene‐gene interaction studies is the computational burden of the analysis. We propose a two‐stage interaction analysis strategy to address this challenge in the context of both quantitative traits and dichotomous phenotypes. We have perfor... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5244972 Thu, 01 Sep 2011 04:00:00 +0100 5244972 http://www.medworm.com/index.php?rid=5231583&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20624 In this report, we present such an algorithm, the Gene Environment Wide Interaction Search Threshold (GEWIST), and show that the use of GEWIST will increase power under a variety of interaction scenarios. Furthermore, by integrating over possible interaction effect sizes, we provide a framework to optimize prioritization in situations where interactions are a priori unknown. Genet. Epidemiol. 2011.© 2011 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5231583 Thu, 01 Sep 2011 04:00:00 +0100 5231583 http://www.medworm.com/index.php?rid=5167719&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20615 AbstractIn case‐control genetic association studies, the robust procedure, Pearson's Chi‐square test, is commonly used for testing association between disease status and genetic markers. However, this test does not take the possible trend of relative risks, which are due to genotype, into account. On the contrary, although Cochran‐Armitage trend test with optimal scores is more powerful; it is usually difficult to assign the correct scores in advance since the true genetic model is rarely known in practice. If the unknown underlying genetic models are misspecified, the trend test may lose power dramatically. Therefore, it is desirable to find a powerful yet robust statistical test for genome‐wide association studies. In this paper, we propose a new test based on the partition of Pe... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5167719 Sun, 28 Aug 2011 00:32:43 +0100 5167719 http://www.medworm.com/index.php?rid=5167722&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20613 AbstractWe are interested in investigating the involvement of multiple rare variants within a given region by conducting analyses of individual regions with two goals: (1) to determine if regional rare variation in aggregate is associated with risk; and (2) conditional upon the region being associated, to identify specific genetic variants within the region that are driving the association. In particular, we seek a formal integrated analysis that achieves both of our goals. For rare variants with low minor allele frequencies, there is very little power to statistically test the null hypothesis of equal allele or genotype counts for each variant. Thus, genetic association studies are often limited to detecting association within a subset of the common genetic markers. However, it is very li... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5167722 Thu, 25 Aug 2011 23:00:00 +0100 5167722 http://www.medworm.com/index.php?rid=5167721&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20620 AbstractThe trend test under the additive model is commonly used when a case‐control genetic association study is carried out. However, for many complex diseases, the underlying genetic models are unknown and a mis‐specification of the genetic model may result in a substantial loss of power. MAX3 has been proposed as an efficiency robust test against genetic model uncertainty which takes the maximum absolute value of the trend test statistics under the recessive, additive, and dominant models. Besides its popularity, little attention has been paid to the adjustment of covariates in this test and existing approaches all depend on the estimators of parameters of interest which may be seriously biased if the individuals are divided into a large number of partial tables stratified by covar... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5167721 Thu, 25 Aug 2011 23:00:00 +0100 5167721 http://www.medworm.com/index.php?rid=5167720&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20623 This article applies the recently proposed “stability selection” procedure of Meinshausen and Bühlmann to the problem of variable selection in genome‐wide association. In particular, it explores whether stability selection can identify new regions of interest originally missed or can call into legitimate question regions originally flagged. Our analysis of the seven data sets of the Wellcome Trust Case‐Control Consortium suggests that stability selection effectively controls the family‐wise error rate but suffers a loss of power. The extensive correlation structure among SNP markers induced by linkage disequilibrium renders the procedure too conservative, causing it to miss regions known to be highly significant from simple marginal analyses. As a remedy one can aggregate nearby... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5167720 Thu, 25 Aug 2011 23:00:00 +0100 5167720 http://www.medworm.com/index.php?rid=5101882&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20614 AbstractGenome‐wide association studies (GWAS) have been frequently conducted on general or isolated populations with related individuals. However, there is a lack of consensus on which strategy is most appropriate for analyzing dichotomous phenotypes in general pedigrees. Using simulation studies, we compared several strategies including generalized estimating equations (GEE) strategies with various working correlation structures, generalized linear mixed model (GLMM), and a variance component strategy (denoted LMEBIN) that treats dichotomous outcomes as continuous with special attentions to their performance with rare variants, rare diseases, and small sample sizes. In our simulations, when the sample size is not small, for type I error, only GEE and LMEBIN maintain nominal type I erro... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5101882 Wed, 03 Aug 2011 23:00:00 +0100 5101882 http://www.medworm.com/index.php?rid=5101881&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20610 AbstractIn this article, we develop a powerful test for identifying single nucleotide polymorphism (SNP)‐sets that are predictive of survival with data from genome‐wide association studies. We first group typed SNPs into SNP‐sets based on genomic features and then apply a score test to assess the overall effect of each SNP‐set on the survival outcome through a kernel machine Cox regression framework. This approach uses genetic information from all SNPs in the SNP‐set simultaneously and accounts for linkage disequilibrium (LD), leading to a powerful test with reduced degrees of freedom when the typed SNPs are in LD with each other. This type of test also has the advantage of capturing the potentially nonlinear effects of the SNPs, SNP‐SNP interactions (epistasis), and the joint ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5101881 Wed, 03 Aug 2011 23:00:00 +0100 5101881 http://www.medworm.com/index.php?rid=5101880&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20616 AbstractGenome wide association studies (GWAS) have revealed many fascinating insights into complex diseases even from simple, single‐marker statistical tests. Most of these tests are designed for testing of associations between a phenotype and an autosomal genotype and are therefore not applicable to X chromosome data. Testing for association on the X chromosome raises unique challenges that have motivated the development of X‐specific statistical tests in the literature. However, to date there has been no study of these methods under a wide range of realistic study designs, allele frequencies and disease models to assess the size and power of each test. To address this, we have performed an extensive simulation study to investigate the effects of the sex ratios in the case and contro... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5101880 Wed, 03 Aug 2011 23:00:00 +0100 5101880 http://www.medworm.com/index.php?rid=5101879&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20619 AbstractIt is now understood that many human cancer types are the result of the accumulation of both genetic and epigenetic changes. DNA methylation is a molecular modification of DNA that is crucial for normal development. Genes that are rich in CpG dinucleotides are usually not methylated in normal tissues, but are frequently hypermethylated in cancer. With the advent of high‐throughput platforms, large‐scale structure of genomic methylation patterns is available through genome‐wide scans and tremendous amount of DNA methylation data have been recently generated. However, sophisticated statistical methods to handle complex DNA methylation data are very limited. Here, we developed a likelihood based Uniform‐Normal‐mixture model to select differentially methylated loci between ca... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5101879 Wed, 03 Aug 2011 23:00:00 +0100 5101879 http://www.medworm.com/index.php?rid=5101878&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20618 AbstractCollapsing multiple variants into one variable and testing their collective effect is a useful strategy for rare variant association analysis. Direct collapsing, however, is not valid or may significantly lose power when a group of variants to be collapsed have heterogeneous effects on target traits (i.e. some positive and some negative). This could be especially true for quantitative traits (such as blood pressure and body mass index), regardless of whether subjects are sampled randomly from a population or selectively from two extreme tails of the trait distribution. To deal with this problem, we propose a novel, data‐driven method, the P‐value Weighted Sum Test (PWST), which allows each variant to be individually weighted according to the evidence of association from the dat... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5101878 Wed, 03 Aug 2011 23:00:00 +0100 5101878 http://www.medworm.com/index.php?rid=5046697&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20604 AbstractAccurate assignment of copy number at known copy number variant (CNV) loci is important for both increasing understanding of the structural evolution of genomes as well as for carrying out association studies of copy number with disease. As with calling SNP genotypes, the task can be framed as a clustering problem but for a number of reasons assigning copy number is much more challenging. CNV assays have lower signal‐to‐noise ratios than SNP assays, often display heavy tailed and asymmetric intensity distributions, contain outlying observations and may exhibit systematic technical differences among different cohorts. In addition, the number of copy‐number classes at a CNV in the population may be unknown a priori. Due to these complications, automatic and robust assignment of... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046697 Sun, 17 Jul 2011 23:00:00 +0100 5046697 http://www.medworm.com/index.php?rid=5046696&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20606 We present a novel method, IBDLD, for estimating the probability of identity by descent (IBD) for a pair of related individuals at a locus, given dense genotype data and a pedigree of arbitrary size and complexity. IBDLD overcomes the challenges of exact multipoint estimation of IBD in pedigrees of potentially large size and eliminates the difficulty of accommodating the background linkage disequilibrium (LD) that is present in high‐density genotype data. We show that IBDLD is much more accurate at estimating the true IBD sharing than methods that remove LD by pruning SNPs and is highly robust to pedigree errors or other forms of misspecified relationships. The method is fast and can be used to estimate the probability for each possible IBD sharing state at every SNP from a high‐densit... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046696 Sun, 17 Jul 2011 23:00:00 +0100 5046696 http://www.medworm.com/index.php?rid=5046695&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20608 AbstractIn genome‐wide association studies (GWAS), it is a common practice to impute the genotypes of untyped single nucleotide polymorphism (SNP) by exploiting the linkage disequilibrium structure among SNPs. The use of imputed genotypes improves genome coverage and makes it possible to perform meta‐analysis combining results from studies genotyped on different platforms. A popular way of using imputed data is the “expectation‐substitution” method, which treats the imputed dosage as if it were the true genotype. In current practice, the estimates given by the expectation‐substitution method are usually combined using inverse variance weighting (IVM) scheme in meta‐analysis. However, the IVM is not optimal as the estimates given by the expectation‐substitution method are ge... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046695 Sun, 17 Jul 2011 23:00:00 +0100 5046695 http://www.medworm.com/index.php?rid=5046694&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20609 AbstractIn anticipation of the availability of next‐generation sequencing data, there is increasing interest in investigating association between complex traits and rare variants (RVs). In contrast to association studies for common variants (CVs), due to the low frequencies of RVs, common wisdom suggests that existing statistical tests for CVs might not work, motivating the recent development of several new tests for analyzing RVs, most of which are based on the idea of pooling/collapsing RVs. However, there is a lack of evaluations of, and thus guidance on the use of, existing tests. Here we provide a comprehensive comparison of various statistical tests using simulated data. We consider both independent and correlated rare mutations, and representative tests for both CVs and RVs. As ex... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046694 Sun, 17 Jul 2011 23:00:00 +0100 5046694 http://www.medworm.com/index.php?rid=5046693&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20605 AbstractGenome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population‐based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where . Based on published studies of 26 SNPs that are current... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046693 Sun, 17 Jul 2011 23:00:00 +0100 5046693 http://www.medworm.com/index.php?rid=5046692&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20607 AbstractAssociation studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two‐stage regression analysis, sometimes referred to as residual‐ or adjusted‐outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual‐outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted‐outcome and the SNP is evaluated by a simple linear regression of the adjusted‐outcome on the SNP. In this article, we examine the performance of this two‐stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are co... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046692 Sun, 17 Jul 2011 23:00:00 +0100 5046692 http://www.medworm.com/index.php?rid=5046691&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20611 We examined the empirical distribution of the natural logarithm of the corrected OR obtained from the bias correction approach and found it to be normally distributed under the null hypothesis. On the basis of the simulation study results, we found that the logistic regression approaches that adjust or do not adjust for the primary disease status had low power for detecting secondary phenotype associated variants and highly inflated type I error probabilities, whereas our approach was more powerful for identifying the SNP‐secondary phenotype associations and had better‐controlled type I error probabilities. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046691 Sun, 17 Jul 2011 23:00:00 +0100 5046691 http://www.medworm.com/index.php?rid=5046690&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20601 AbstractAllelic expression (AE) imbalance between the two alleles of a gene can be used to detect cis‐acting regulatory SNPs (rSNPs) in individuals heterozygous for a transcribed SNP (tSNP). In this paper, we propose three tests for AE analysis focusing on phase‐unknown data and any degree of linkage disequilibrium (LD) between the rSNP and tSNP: a test based on the minimum P‐value of a one‐sided F test and a two‐sided t test (proposed previously for phase‐unknown data), a test the combines the F and t tests, and a mixture‐model‐based test. We compare these three tests to the F and t tests and an existing regression‐based test for phase‐known data. We show that the ranking of the tests based on power depends most strongly on the magnitude of the LD between the rSNP and ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046690 Sun, 17 Jul 2011 23:00:00 +0100 5046690 http://www.medworm.com/index.php?rid=5046689&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20602 In this study, we developed a novel approach to measuring MA and, using HapMap mate‐pairs of European and African descent, carried out a genome‐wide investigation and characterization of MA. We further investigated the impact of observed levels of MA on maternal association tests through simulation experiments. For the first time, we showed that non‐negligible levels of MA are detected in human populations, such that subtle genotype frequency differences between individuals mating in the population are sufficient to induce spurious maternal genotype associations. Though the underlying mechanisms driving the asymmetry within these populations remain elusive, our findings provide consequential evidence for the occurrence of MA in humans and highlight the importance of controlling for M... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046689 Sun, 17 Jul 2011 23:00:00 +0100 5046689 http://www.medworm.com/index.php?rid=5101877&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20617 AbstractTesting for Hardy‐Weinberg equilibrium (HWE) is used routinely as an important initial step for genotype data quality checking. Departure from HWE can be caused by many factors, such as genotyping errors, population stratification, and disease association, if we use affected individuals only. In a structured population, even if a marker is in HWE in each subpopulation, data may show departure from HWE if allele frequencies are different in different subpopulations and such a departure can be misinterpreted as a potential problem in genotyping quality, resulting in false exclusion from future analysis. In this article, we propose a new HWE test, a test for HWE in structured populations (HWES) that can assess departure from HWE and take into account of population stratification at ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5101877 Thu, 30 Jun 2011 23:00:00 +0100 5101877 http://www.medworm.com/index.php?rid=5046688&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20598 AbstractLinkage disequilibrium (LD) of genetic loci is routinely estimated and graphically illustrated in genetic association studies. It has been suggested that the information in LD is also useful for association mapping and genetic association can be detected by comparing LD patterns between cases and controls. Here, we extend this idea to analyze case‐parents data by comparing LD patterns between transmitted and nontransmitted genotypes. We provide the condition when contrasting LD is valid for testing gene‐gene interactions. A permutation procedure is given to assess statistical significance. One advantage of our proposed methods is that haplotype information is not required. Thus, the implementation of our methods is straightforward and the resulted tests are free from potential ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=5046688 Thu, 30 Jun 2011 23:00:00 +0100 5046688 http://www.medworm.com/index.php?rid=4930750&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20596 AbstractAlthough genome‐wide association studies have led to the identifications of hundreds of genes underlying dozens of traits in recent years, most published studies have primarily used single marker‐based analysis. Intuitively, more information may be utilized when multiple markers are jointly analyzed. Therefore, many methods have been proposed in the literature for association analysis between traits and multiple markers. Among these methods, simulation and real data analyses have shown that it is often more effective to reduce the dimensionality of the markers in a region through principal components analysis of all the markers first, and then to perform association analysis between traits and those principal components that account for most of the genetic variations in the reg... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4930750 Thu, 16 Jun 2011 17:24:32 +0100 4930750 http://www.medworm.com/index.php?rid=4869673&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20594 In this study, we propose a novel Forward U‐Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene‐gene/gene‐environment interactions. In this new approach, a U‐Statistic‐based forward algorithm is first used to select potential disease‐susceptibility loci and then a weighted U‐statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U‐Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high‐dimensional gene‐gene/gene‐environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4869673 Wed, 25 May 2011 23:00:00 +0100 4869673 http://www.medworm.com/index.php?rid=4869672&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20595 AbstractNonsyndromic cleft palate (CP) is a common birth defect with a complex and heterogeneous etiology involving both genetic and environmental risk factors. We conducted a genome‐wide association study (GWAS) using 550 case‐parent trios, ascertained through a CP case collected in an international consortium. Family‐based association tests of single nucleotide polymorphisms (SNP) and three common maternal exposures (maternal smoking, alcohol consumption, and multivitamin supplementation) were used in a combined 2 df test for gene (G) and gene‐environment (G × E) interaction simultaneously, plus a separate 1 df test for G × E interaction alone. Conditional logistic regression models were used to estimate effects on risk to exposed and unexposed children. While no SNP achieved g... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4869672 Wed, 25 May 2011 23:00:00 +0100 4869672 http://www.medworm.com/index.php?rid=4869671&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20600 AbstractRecently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome‐wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome‐wide association data on 1,145 BCa cases and 1,142 controls from ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4869671 Wed, 25 May 2011 23:00:00 +0100 4869671 http://www.medworm.com/index.php?rid=4869670&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20599 AbstractWe are interested in detecting genetic variants that influence transition between discrete stages of a disease progression process, such as the natural history of progression to cervical cancer with the following four stages: (1) normal‐human papillomavirus (HPV) exposed, (2) persistent infection with oncogenic HPV, (3) cervical intraepithelial neoplasia grades 2 or 3 (CIN2/3), and (4) cervical cancer. Standard statistical tests derived from the proportional odds model or polytomous regression model can be used to study this type of ordinal outcome. But these methods are either too sensitive to the proportion odds assumption or fail to take advantage of the restriction on the parameter space for the genetic variants. Two alternative tests, the maximum score test (MAX) and the ada... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4869670 Wed, 25 May 2011 23:00:00 +0100 4869670 http://www.medworm.com/index.php?rid=4869669&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20597 AbstractRecent advances in next‐generation sequencing technologies make it affordable to search for rare and functional variants for common complex diseases systematically. We investigated strategies for enriching rare variants in the samples selected for sequencing so as to optimize the power for their discovery. In particular, we investigated the roles of alternative sources of enrichment in families through computer simulations. We showed that linkage information, extreme phenotype, and nonrandom ascertainment, such as multiply affected families, constitute different sources for enriching rare and functional variants in a sequencing study design. Linkage is well known to have limited power for detecting small genetic effects, and hence not considered to be a powerful tool for discover... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4869669 Wed, 25 May 2011 23:00:00 +0100 4869669 http://www.medworm.com/index.php?rid=4869668&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20590 AbstractMethods to account for population structure (PS) in genome‐wide association studies have been well developed in samples of unrelated individuals, but when a sample is composed of families, the task of finding and accounting for PS is not as straight forward. Family‐based tests that condition on parental genotypes or their sufficient statistics are immune to biases due to PS, but are known to have low power, particularly for unselected samples. Population‐based approaches that use all available data are an attractive alternative, but the methods have not been evaluated for continuous outcomes when a sample has both family and PS. Therefore, we compare through simulation the performance of population‐based regression models that account for family and PS with continuous outco... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4869668 Wed, 25 May 2011 23:00:00 +0100 4869668 http://www.medworm.com/index.php?rid=4838385&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20592 AbstractStatistical equivalence methods have been in development since the late 1980s in order to provide an appropriate statistical methodology to address nondifferences in biological experiments. This is analogous to genetic association studies in which a polymorphism “is not associated” with a trait. We applied the equivalence method to genetic data to confirm that an association between the KIF1B (kinesin family member1B) rs10492972 allele and multiple sclerosis (MS), reported in Nature Genetics in 2008, is present neither in eight data sets of cases and controls nor in three independent data sets of the International Multiple Sclerosis Genetic Consortium. When the data sets are considered together, a nonsuperiority test excludes the rs10492972*C allele as a major “risk” allele... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4838385 Tue, 17 May 2011 23:00:00 +0100 4838385 http://www.medworm.com/index.php?rid=4838384&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20589 AbstractThe field of phenomics has been investigating network structure among large arrays of phenotypes, and genome‐wide association studies (GWAS) have been used to investigate the relationship between genetic variation and single diseases/outcomes. A novel approach has emerged combining both the exploration of phenotypic structure and genotypic variation, known as the phenome‐wide association study (PheWAS). The Population Architecture using Genomics and Epidemiology (PAGE) network is a National Human Genome Research Institute (NHGRI)‐supported collaboration of four groups accessing eight extensively characterized epidemiologic studies. The primary focus of PAGE is deep characterization of well‐replicated GWAS variants and their relationships to various phenotypes and traits in ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4838384 Tue, 17 May 2011 23:00:00 +0100 4838384 http://www.medworm.com/index.php?rid=4890479&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20591 AbstractWith the failure of common variants alone to explain the bulk of trait heritability, it becomes more important to understand the contribution of maternally inherited effects, prenatal effects, and postnatal environmental effects. These effects can be disentangled by studying families containing children conceived by assisted reproductive technologies (ART). We propose and develop a model that is an extension of the variance component model commonly used in pedigree analysis. Our model is flexible enough to allow any number of family members and degrees of relationship; thus, researchers can use both small and extended families simultaneously. Simulations demonstrate that our method has appropriate statistical properties and is robust to model misspecification and accurate in the pr... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4890479 Sat, 30 Apr 2011 23:00:00 +0100 4890479 http://www.medworm.com/index.php?rid=4869667&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20593 In this study, we propose a simple and fast statistical framework to estimate the total heritability explained by all true susceptibility variants in a GWAS. It is expected that many true risk variants will not be detected in a GWAS due to limited power. The proposed framework aims at recovering the “hidden” heritability. Importantly, only the summary z‐statistics are required as input and no raw genotype data are needed. The strategy is to recover the true effect sizes from the observed z‐statistics. The methodology does not rely on any distributional assumptions of the effect sizes of variants. Both binary and quantitative traits can be handled and covariates may be included. Population‐based or family‐based designs are allowed as long as the summary statistics are available.... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4869667 Sat, 30 Apr 2011 23:00:00 +0100 4869667 http://www.medworm.com/index.php?rid=4838383&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20588 AbstractIt is generally known that risk variants segregate together with a disease within families, but this information has not been used in the existing statistical methods for detecting rare variants. Here we introduce two weighted sum statistics that can apply to either genome‐wide association data or resequencing data for identifying rare disease variants: weights calculated based on sibpairs and odd ratios, respectively. We evaluated the two methods via extensive simulations under different disease models. We compared the proposed methods with the weighted sum statistic (WSS) proposed by Madsen and Browning, keeping the same genotyping or resequencing cost. Our methods clearly demonstrate more statistical power than the WSS. In addition, we found that using sibpair information can ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4838383 Sat, 30 Apr 2011 23:00:00 +0100 4838383 http://www.medworm.com/index.php?rid=4752242&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20585 AbstractMulti‐symptom diseases without a consistent continuous measurement of severity may be best understood with a categorical interpretation. In this paper, we present LOCate v.2, a fast, exact algorithm for linkage analysis of all types of categorical traits, both ordinal and nominal. Our method is able to incorporate missing data and analyze complex genealogical structure, including inbreeding loops. LOCate v.2 computes exact likelihoods efficiently through an elimination algorithm, similar to that used by Superlink for binary traits. We compare LOCate v.2 to LOT and QTLlink, two existing methods of linkage analysis for ordinal traits. We find that LOCate v.2 outperforms both methods when used to analyze simulated nominal traits. In addition, LOCate v.2 performs as well as QTLlink o... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4752242 Tue, 26 Apr 2011 23:26:56 +0100 4752242 http://www.medworm.com/index.php?rid=4752244&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20587 AbstractCurrent evidence suggests that the genetic risk of breast cancer may be caused primarily by rare variants. However, while classification of protein‐truncating mutations as deleterious is relatively straightforward, distinguishing as deleterious or neutral the large number of rare missense variants is a difficult on‐going task. In this article, we present one approach to this problem, hierarchical statistical modeling of data observed in a case‐control study of contralateral breast cancer (CBC) in which all the participants were genotyped for variants in BRCA1 and BRCA2. Hierarchical modeling permits leverage of information from observed correlations of characteristics of groups of variants with case‐control status to infer with greater precision the risks of individual rare... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4752244 Sun, 24 Apr 2011 23:00:00 +0100 4752244 http://www.medworm.com/index.php?rid=4752243&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20586 In this study, we generalize the VT test of Price et al. in several aspects. We propose a general class of adaptive tests that covers the VT test of Price et al. as a special case. In particular, we show that some of our proposed adaptive tests may substantially improve the power over the pooled association tests, including the VT test of Price et al., especially so in the presence of many neutral RVs and/or of causal RVs with opposite association directions, in which cases most of the existing pooled association tests suffer from significant loss of power. Our proposed tests are also general and flexible with the ability to incorporate weights on RVs and to adjust for covariates. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4752243 Sun, 24 Apr 2011 23:00:00 +0100 4752243 http://www.medworm.com/index.php?rid=4702613&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20580 We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e−3) with an odds ratio of 1.28 [1.12–1.48], which replicates a previous case‐control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.c... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4702613 Wed, 13 Apr 2011 03:39:22 +0100 4702613 http://www.medworm.com/index.php?rid=4702614&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20583 AbstractLarge‐scale genome‐wide association studies (GWAS) have become feasible recently because of the development of bead and chip technology. However, the success of GWAS partially depends on the statistical methods that are able to manage and analyze this sort of large‐scale data. Currently, the commonly used tests for GWAS include the Cochran–Armitage trend test, the allelic χ2 test, the genotypic χ2 test, the haplotypic χ2 test, and the multi‐marker genotypic χ2 test among others. From a methodological point of view, it is a great challenge to improve the power of commonly used tests, since these tests are commonly used precisely because they are already among the most powerful tests. In this article, we propose an improved score test that is uniformly more powerful tha... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4702614 Sun, 10 Apr 2011 23:00:00 +0100 4702614 http://www.medworm.com/index.php?rid=4675429&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20577 AbstractUnderstanding and modeling genetic or nongenetic factors that influence susceptibility to complex traits has been the focus of many genetic studies. Large pedigrees with known complex structure may be advantageous in epidemiological studies since they can significantly increase the number of factors whose influence on the trait can be estimated. We propose a likelihood approach, developed in the context of generalized linear mixed models, for modeling dichotomous traits based on data from hundreds of individuals all of whom are potentially correlated through either a known pedigree or an estimated covariance matrix. Our approach is based on a hierarchical model where we first assess the probability of each individual having the trait and then formulate a likelihood assuming conditi... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4675429 Sun, 03 Apr 2011 23:00:00 +0100 4675429 http://www.medworm.com/index.php?rid=4675428&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20584 We present new fast and accurate algorithms for estimating global and local kinship coefficients from dense SNP genotypes. These algorithms require only a single pass through the SNP genotype data. We also show that these estimates can be used to cluster individuals into pedigrees. With these estimates in hand, quantitative trait locus linkage analysis proceeds via traditional variance components methods without any prior relationship information. We demonstrate the success of our algorithms on simulated and real data sets. Our procedures make linkage analysis as easy as a typical genomewide association study. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4675428 Sun, 03 Apr 2011 23:00:00 +0100 4675428 http://www.medworm.com/index.php?rid=4675427&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20582 AbstractGenome‐wide association studies (GWAS) are conducted with the promise to discover novel genetic variants associated with diverse traits. For most traits, associated markers individually explain just a modest fraction of the phenotypic variation, but their number can well be in the hundreds. We developed a maximum likelihood method that allows us to infer the distribution of associated variants even when many of them were missed by chance. Compared to previous approaches, the novelty of our method is that it (a) does not require having an independent (unbiased) estimate of the effect sizes; (b) makes use of the complete distribution of P‐values while allowing for the false discovery rate; (c) takes into account allelic heterogeneity and the SNP pruning strategy. We applied our m... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4675427 Thu, 31 Mar 2011 23:00:00 +0100 4675427 http://www.medworm.com/index.php?rid=4604624&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20576 AbstractMost findings from genome‐wide association studies (GWAS) are consistent with a simple disease model at a single nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have noted, departures from this multiplicative model are difficult to detect. Here, we seek to quantify this both analytically and empirically. We show that imperfect linkage disequilibrium (LD) between causal and marker loci distorts disease models, with the power to detect such departures dropping off very quickly: decaying as a function of r4, where r2 is the usual correlation between the causal and marker loci, in contrast to the well‐known result that power to detect a multiplicati... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4604624 Fri, 18 Mar 2011 02:26:23 +0100 4604624 http://www.medworm.com/index.php?rid=4573942&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20581 We present a Bayesian semiparametric model for the meta‐analysis of candidate gene studies with a binary outcome. Such studies often report results from association tests for different, possibly study‐specific and non‐overlapping genetic markers in the same genetic region. Meta‐analyses of the results at each marker in isolation are seldom appropriate as they ignore the correlation that may exist between markers due to linkage disequilibrium (LD) and cannot assess the relative importance of variants at each marker. Also such marker‐wise meta‐analyses are restricted to only those studies that have typed the marker in question, with a potential loss of power. A better strategy is one which incorporates information about the LD between markers so that any combined estimate of the ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4573942 Sat, 12 Mar 2011 00:42:40 +0100 4573942 http://www.medworm.com/index.php?rid=4543949&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20578 AbstractEven in large‐scale genome‐wide association studies (GWASs), only a fraction of the true associations are detected at the genome‐wide significance level. When few or no associations reach the significance threshold, one strategy is to follow up on the most promising candidates, i.e. the single nucleotide polymorphisms (SNPs) with the smallest association‐test P‐values, by genotyping them in additional studies. In this communication, we propose an overall test for GWASs that analyzes the SNPs with the most promising P‐values simultaneously and therefore allows an early assessment of whether the follow‐up of the selected SNPs is likely promising. We theoretically derive the properties of the proposed overall test under the null hypothesis and assess its power based on s... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4543949 Thu, 03 Mar 2011 00:00:00 +0100 4543949 http://www.medworm.com/index.php?rid=4539648&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20575 AbstractNext Generation Sequencing represents a powerful tool for detecting genetic variation associated with human disease. Because of the high cost of this technology, it is critical that we develop efficient study designs that consider the trade‐off between the number of subjects (n) and the coverage depth (µ). How we divide our resources between the two can greatly impact study success, particularly in pilot studies. We propose a strategy for selecting the optimal combination of n and µ for studies aimed at detecting rare variants and for studies aimed at detecting associations between rare or uncommon variants and disease. For detecting rare variants, we find the optimal coverage depth to be between 2 and 8 reads when using the likelihood ratio test. For association studies, we fi... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4539648 Wed, 02 Mar 2011 00:00:00 +0100 4539648 http://www.medworm.com/index.php?rid=4488136&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20565 AbstractCurrent common wisdom posits that association analyses using family‐based designs have inflated type 1 error rates (if relationships are ignored) and independent controls are more powerful than familial controls. We explore these suppositions. We show theoretically that family‐based designs can have deflated type‐error rates. Through simulation, we examine the validity and power of family designs for several scenarios: cases from randomly or selectively ascertained pedigrees; and familial or independent controls. Family structures considered are as follows: sibships, nuclear families, moderate‐sized and extended pedigrees. Three methods were considered with the χ2 test for trend: variance correction (VC), weighted (weights assigned to account for genetic similarity), and n... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4488136 Wed, 16 Feb 2011 00:00:00 +0100 4488136 http://www.medworm.com/index.php?rid=4488135&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20574 AbstractNext‐generation DNA sequencing technologies are facilitating large‐scale association studies of rare genetic variants. The depth of the sequence read coverage is an important experimental variable in the next‐generation technologies and it is a major determinant of the quality of genotype calls generated from sequence data. When case and control samples are sequenced separately or in different proportions across batches, they are unlikely to be matched on sequencing read depth and a differential misclassification of genotypes can result, causing confounding and an increased false‐positive rate. Data from Pilot Study 3 of the 1000 Genomes project was used to demonstrate that a difference between the mean sequencing read depth of case and control samples can result in false�... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4488135 Wed, 16 Feb 2011 00:00:00 +0100 4488135 http://www.medworm.com/index.php?rid=4454332&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20566 AbstractClinical diagnoses of complex diseases may often encompass multiple genetically heterogeneous disorders. One way of dissecting this heterogeneity is to apply latent class (LC) analysis to measurements related to the diagnosis, such as detailed symptoms, to define more homogeneous disease sub‐types, influenced by a smaller number of genes that will thus be more easily detectable. We have previously developed a LC model allowing dependence between the latent disease class status of relatives within families. We have also proposed a strategy to incorporate the posterior probability of class membership of each subject in parametric linkage analysis, which is not directly transferable to genetic association methods. Under the framework of family‐based association tests (FBAT), we no... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4454332 Wed, 09 Feb 2011 00:00:00 +0100 4454332 http://www.medworm.com/index.php?rid=4454331&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20569 AbstractMany complex diseases are likely to be a result of the interplay of genes and environmental exposures. The standard analysis in a genome‐wide association study (GWAS) scans for main effects and ignores the potentially useful information in the available exposure data. Two recently proposed methods that exploit environmental exposure information involve a two‐step analysis aimed at prioritizing the large number of SNPs tested to highlight those most likely to be involved in a G × E interaction. For example, Murcray et al. ([2009] Am J Epidemiol 169:219–226) proposed screening on a test that models the G‐E association induced by an interaction in the combined case‐control sample. Alternatively, Kooperberg and LeBlanc ([2008] Genet Epidemiol 32:255–263) suggested screenin... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4454331 Wed, 09 Feb 2011 00:00:00 +0100 4454331 http://www.medworm.com/index.php?rid=4454330&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20567 In this report, we show that, under the condition that there is no other covariate, there is a striking correspondence between the two approaches for a quantitative or a binary trait: if the same positive semi‐definite matrix is used as the centered similarity matrix in GDBR and as the kernel matrix in KMR, the F‐test statistic in GDBR and the score test statistic in KMR are equal (up to some ignorable constants). The result is based on the connections of both methods to linear or logistic (random‐effects) regression models. Genet. Epidemiol. 2011.   © 2011 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4454330 Wed, 09 Feb 2011 00:00:00 +0100 4454330 http://www.medworm.com/index.php?rid=4454329&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20571 AbstractAlthough it is recognized that many common complex diseases are a result of multiple genetic and environmental risk factors, studies of gene‐environment interaction remain a challenge and have had limited success to date. Given the current state‐of‐the‐science, NIH sought input on ways to accelerate investigations of gene‐environment interplay in health and disease by inviting experts from a variety of disciplines to give advice about the future direction of gene‐environment interaction studies. Participants of the NIH Gene‐Environment Interplay Workshop agreed that there is a need for continued emphasis on studies of the interplay between genetic and environmental factors in disease and that studies need to be designed around a multifaceted approach to reflect differ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4454329 Wed, 09 Feb 2011 00:00:00 +0100 4454329 http://www.medworm.com/index.php?rid=4454328&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20572 We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false‐negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well‐cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4454328 Wed, 09 Feb 2011 00:00:00 +0100 4454328 http://www.medworm.com/index.php?rid=4543948&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20579 In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus‐specific heritability. The method is extended to deal with haplotypes, multi‐allelic markers, multi‐locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimer's disease, bipolar disorder, breast cancer, coronary artery disease, Crohn's disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4543948 Tue, 01 Feb 2011 00:00:00 +0100 4543948 http://www.medworm.com/index.php?rid=4539647&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20570 AbstractGenome‐wide association studies succeeded in finding genetic variants associated with various phenotypes, but a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some missing variation is due to rare variants. Latest sequencing technology facilitates the investigation of such rare variants, but their statistical analysis remains challenging. For quantitative traits, a commonly used approach is to contrast the frequency of putatively functional rare variants between subjects in the two tails of the trait distribution. The contrast is usually performed by Fisher's exact or similar test. These tests are conservative as they discard trait rank information and are most useful under the unrealistic homogeneity assumpti... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4539647 Tue, 01 Feb 2011 00:00:00 +0100 4539647 http://www.medworm.com/index.php?rid=4488134&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20562 AbstractPower, i.e. sample size, is a crucial issue in genome‐wide association studies (GWAS) on disorders generated by a multitude of weak genetic effects. Here, we examine the influence of sampling cases and/or controls from populations that are subjected to an external risk factor (such as smoking or nutritional factors). We use an additive threshold model and derive the necessary sample size as function of the external risk factor's strength and of the sampling scheme. If both cases and controls are sampled from the risk population, a loss of power must be expected. The loss of power (i.e. the increase of the necessary sample size) is even larger if only the cases are sampled from the risk population, whereas the inverse scheme (nonrisk cases and risk controls) provides a gain of pow... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4488134 Tue, 01 Feb 2011 00:00:00 +0100 4488134 http://www.medworm.com/index.php?rid=4458778&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20573 AbstractDetection of loss of heterozygosity (LOH) plays an important role in genetic, genomic and cancer research. We develop computational methods to estimate the proportion of homozygous SNP calls, identify samples with structural alterations and/or unusual genotypic patterns, cluster samples with close LOH structures and map the genomic segments bearing LOH by analyzing data of genome‐wide SNP arrays or customized SNP arrays. In addition to cancer genetics/genomics, we also apply the methods to study long contiguous stretches of homozygosity (LCSH) in general populations. The LCSH analysis aids in the identification of samples with complex LCSH patterns indicative of nonrandom mating and/or meiotic recombination cold spots, separation of samples with different genetic backgrounds and ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4458778 Tue, 01 Feb 2011 00:00:00 +0100 4458778 http://www.medworm.com/index.php?rid=4454327&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20568 AbstractGenetic association studies for binary diseases are designed as case‐control studies: the cases are those affected with the primary disease and the controls are free of the disease. At the time of case‐control collection, information about secondary phenotypes is also collected. Association studies of secondary phenotype and genetic variants have received a great deal of interest recently. To study the secondary phenotypes, investigators use standard regression approaches, where individuals with secondary phenotypes are coded as cases and those without secondary phenotypes are coded as controls. However, using the secondary phenotype as an outcome variable in a case‐control study might lead to a biased estimate of odds ratios (ORs) for genetic variants. The secondary phenotyp... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4454327 Tue, 01 Feb 2011 00:00:00 +0100 4454327 http://www.medworm.com/index.php?rid=4418078&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20551 AbstractQuantitative traits (QT) are an important focus of human genetic studies both because of interest in the traits themselves and because of their role as risk factors for many human diseases. For large‐scale QT association studies including genome‐wide association studies, investigators usually focus on genetic loci showing significant evidence for SNP‐QT association, and genetic effect size tends to be overestimated as a consequence of the winner's curse. In this paper, we study the impact of the winner's curse on QT association studies in which the genetic effect size is parameterized as the slope in a linear regression model. We demonstrate by analytical calculation that the overestimation in the regression slope estimate decreases as power increases. To reduce the ascertain... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4418078 Mon, 31 Jan 2011 23:11:24 +0100 4418078 http://www.medworm.com/index.php?rid=4418079&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20564 We describe here some of the strategies and pitfalls associated with combining phenotype data from varying studies. Using the Gene Environment Association Studies (GENEVA) multi‐site GWAS consortium as an example, this paper provides an illustration to guide GWAS consortia through the process of phenotype harmonization and describes key issues that arise when sharing data across disparate studies. GENEVA is unusual in the diversity of disease endpoints and so the issues it faces as its participating studies share data will be informative for many collaborations. Phenotype harmonization requires identifying common phenotypes, determining the feasibility of cross‐study analysis for each, preparing common definitions, and applying appropriate algorithms. Other issues to be considered incl... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4418079 Mon, 31 Jan 2011 00:00:00 +0100 4418079 http://www.medworm.com/index.php?rid=4369573&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20558 AbstractAssociation analysis has led to the identification of many genetic variants for complex diseases. While assessing the association between genes and a disease, other factors can play an important role. The consequence of not considering covariates (such as population stratification and environmental factors) is well‐documented in genetic studies. We introduce a nonparametric test of association that adjusts for covariate effects. Specifically, the adjustment is realized through weights that are constructed from genomic propensity scores that summarize the contribution of all covariates. The benefit of our test is demonstrated through an important data set on bipolar disorder (BD) collected by the Wellcome Trust Case Control Consortium. When compared to other tests, our test identi... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4369573 Wed, 19 Jan 2011 22:08:55 +0100 4369573 http://www.medworm.com/index.php?rid=4369572&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20552 AbstractThe availability of extensively genotyped reference samples, such as “The HapMap” and 1,000 Genomes Project reference panels, together with advances in statistical methodology, have allowed for the imputation of genotypes at single nucleotide polymorphism (SNP) markers that are untyped in a cohort or case‐control study. These imputation procedures facilitate the interpretation and meta‐analyses of genome‐wide association studies. A natural question when implementing these procedures concerns how best to take into account uncertainty in imputed genotypes. Here we compare the performance of the following three strategies: least‐squares regression on the “best‐guess” imputed genotype; regression on the expected genotype score or “dosage”; and mixture regression m... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4369572 Wed, 19 Jan 2011 22:08:54 +0100 4369572 http://www.medworm.com/index.php?rid=4369571&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20560 We describe the effect of self‐reported race on a gene expression study of lung function in asthma. We generated gene expression profiles for 254 young adults (205 non‐Hispanic whites and 49 African Americans) with asthma on whom concurrent total RNA derived from peripheral blood CD4+ lymphocytes and lung function measurements were obtained. We identified four principal components that explained 62% of the variance in gene expression. The dominant principal component, which explained 29% of the total variance in gene expression, was strongly associated with self‐identified race (P<10−16). The impact of these racial differences was observed when we performed differential gene expression analysis of lung function. Using multivariate linear models, we tested whether gene expression... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4369571 Wed, 19 Jan 2011 22:08:52 +0100 4369571 http://www.medworm.com/index.php?rid=4369568&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20559 We describe an important yet unreported source of bias in case‐control studies due to variations in chip technology between different commercial array releases. As cases are commonly genotyped with newer arrays and freely available control resources are frequently used for comparison, there exists an important potential for false associations which are robust to standard quality control and replication design. Genet. Epidemiol. 2011.  © 2011 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4369568 Wed, 19 Jan 2011 22:08:48 +0100 4369568 http://www.medworm.com/index.php?rid=4369570&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20561 AbstractThe advent of next‐generation sequencing technologies has facilitated the detection of rare variants. Despite the significant cost reduction, sequencing cost is still high for large‐scale studies. In this article, we examine DNA pooling as a cost‐effective strategy for rare variant detection. We consider the optimal number of individuals in a DNA pool to detect an allele with a specific minor allele frequency (MAF) under a given coverage depth and detection threshold. We found that the optimal number of individuals in a pool is indifferent to the MAF at the same coverage depth and detection threshold. In addition, when the individual contributions to each pool are equal, the total number of individuals across different pools required in an optimal design to detect a variant w... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4369570 Wed, 19 Jan 2011 00:00:00 +0100 4369570 http://www.medworm.com/index.php?rid=4369569&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20563 In this study, we compare recently published multiple testing correction methods using 2.5M estimated allelic dosages. We also derive permutation significance levels based on 10,000 GWAS results under the null hypothesis of no association. Our results show that the simpleM method works well with estimated allelic dosages and gives the closest approximation to the permutation threshold while requiring the least computation time. Genet. Epidemiol. 2011.  © 2011 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4369569 Wed, 19 Jan 2011 00:00:00 +0100 4369569 http://www.medworm.com/index.php?rid=4301710&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20556 AbstractWe propose a two‐stage approach to analyze genome‐wide association data in order to identify a set of promising single‐nucleotide polymorphisms (SNPs). In stage one, we select a list of top signals from single SNP analyses by controlling false discovery rate. In stage two, we use the least absolute shrinkage and selection operator (LASSO) regression to reduce false positives. The proposed approach was evaluated using simulated quantitative traits based on genome‐wide SNP data on 8,861 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC) Study. Our first stage, targeted at controlling false negatives, yields better power than using Bonferroni‐corrected significance level. The LASSO regression reduces the number of significant SNPs in stage two: it redu... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4301710 Sat, 01 Jan 2011 00:00:00 +0100 4301710 http://www.medworm.com/index.php?rid=4301712&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20549 AbstractRecent advances in genotyping technologies have enabled genomewide association studies (GWAS) of many complex traits including autoimmune disease, infectious disease, cancer and heart disease. To facilitate interpretations and establish biological basis, it could be advantageous to identify alleles of functional genes, beyond just single nucleotide polymorphisms (SNPs) within or nearby genes. Leslie et al. ([2008] Am J Hum Genet 82:48–56) have proposed an Identity‐by‐Decent method (IBD‐based) for predicting human leukocyte antigen (HLA) alleles (multiallelic and highly polymorphic) with SNP data, and predictions have achieved a satisfactory accuracy on the order of 97%. Building upon their success, we introduce a complementary method for predicting highly polymorphic allele... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4301712 Fri, 31 Dec 2010 00:00:00 +0100 4301712 http://www.medworm.com/index.php?rid=4301711&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20557 AbstractAge‐at‐onset phenotypes are important traits in genetic association analyses. Often, intermediate phenotypes that are related to the age‐at‐onset phenotype are also associated with the marker loci that are associated with the age‐at‐onset phenotype. In order to understand the genetic etiology of the observed associations, statistical methodology is needed to distinguish between a direct genetic effect on the age‐at‐onset phenotype and an indirect effect induced by the genetic association with the endo‐phenotype that is correlated with the age‐at‐onset phenotype. In this communication, we introduce a new statistical approach to detect causal genetic effects on survival data in the presence of genetic associations with secondary phenotypes that might influence s... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4301711 Fri, 31 Dec 2010 00:00:00 +0100 4301711 http://www.medworm.com/index.php?rid=4280270&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20550 AbstractAdmixture mapping is a widely used method for localizing disease genes in African Americans. Most current methods for inferring ancestry at each locus in the genome use a few thousand single nucleotide polymorphisms (SNPs) that are very different in frequency between West Africans and European Americans, and that are required to not be in linkage disequilibrium in the ancestral populations. Modern SNP arrays provide data on hundreds of thousands of SNPs per sample, and to use these to infer ancestry, using many of the standard methods, it is necessary to choose subsets of the SNPs for analysis. Here we present panels of about 4,300 ancestry informative markers (AIMs) that are subsets respectively of SNPs on the Illumina 1 M, Illumina 650, Illumina 610, Affymetrix 6.0 and Affymetrix... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4280270 Thu, 23 Dec 2010 01:20:22 +0100 4280270 http://www.medworm.com/index.php?rid=4280269&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20555 We present a new method Pnorm to correct for multiple hypothesis testing in case‐control association studies in which some individuals are related. The adjustment with Pnorm simultaneously accounts for two sources of correlations of the test statistics: (1) LD among genetic markers (2) dependence among genotypes across related individuals. Using simulated data based on the International HapMap Project, we demonstrate that it has better control of type I error and is more powerful than some of the recently developed methods. We apply the method to a genome‐wide association study of alcoholism in the GAW 14 COGA data set and detect genome‐wide significant association. Genet. Epidemiol. 35:70–79, 2011. © 2010 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4280269 Thu, 23 Dec 2010 01:20:05 +0100 4280269 http://www.medworm.com/index.php?rid=4280268&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20554 In this study, we propose a novel Bayesian generalized linear model for analyzing multiple rare variants within a gene or genomic region in genetic association studies. Our model can deal with complicated situations that have not been fully addressed by existing methods, including issues of disparate effects and nonfunctional variants. Our method jointly models the overall effect and the weights of multiple rare variants and estimates them from the data. This approach produces different weights to different variants based on their contributions to the phenotype, yielding an effective summary of the information across variants. We evaluate the proposed method and compare its performance to existing methods on extensive simulated data. The results show that the proposed method performs well ... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4280268 Thu, 23 Dec 2010 01:19:40 +0100 4280268 http://www.medworm.com/index.php?rid=4280267&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20548 AbstractModel‐free linkage analysis methods, based on identity‐by‐descent allele sharing, are commonly used for complex trait analysis. The Maximum‐Likelihood‐Binomial (MLB) approach, which is based on the hypothesis that parental alleles are binomially distributed among affected sibs, is particularly popular. An extension of this method to quantitative traits (QT) has been proposed (MLB‐QTL), based on the introduction of a latent binary variable capturing information about the linkage between the QT and the marker. Interestingly, the MLB‐QTL method does not require the decomposition of sibships into constituent sibpairs and requires no prior assumption about the distribution of the QT. We propose a new formulation of the MLB method for quantitative traits (nMLB‐QTL) that e... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4280267 Thu, 23 Dec 2010 01:19:14 +0100 4280267 http://www.medworm.com/index.php?rid=4280266&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20547 In this study, we describe a novel implementation of a multinomial modeling approach that allows the estimation of such genetic effects using either case/mother duos or case/parent trios. We investigate the performance of our approach using computer simulations and explore the sample sizes and data structures required to provide high power for detection of effects and accurate estimation of the relative risks conferred. Through the incorporation of additional assumptions (such as Hardy‐Weinberg equilibrium, random mating and known allele frequencies) and/or the incorporation of additional types of control sample (such as unrelated controls, controls and their mothers, or both parents of controls), we show that the (relative risk) parameters of interest are identifiable and well estimated... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4280266 Thu, 23 Dec 2010 01:18:45 +0100 4280266 http://www.medworm.com/index.php?rid=4280265&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20546 We describe a method of joint meta‐analysis (JMA) of SNP and SNP by Environment (SNP × E) regression coefficients for use in gene‐environment interaction studies. Methods: In testing SNP × E interactions, one approach uses a two degree of freedom test to identify genetic variants that influence the trait of interest. This approach detects both main and interaction effects between the trait and the SNP. We propose a method to jointly meta‐analyze the SNP and SNP × E coefficients using multivariate generalized least squares. This approach provides confidence intervals of the two estimates, a joint significance test for SNP and SNP × E terms, and a test of homogeneity across samples. Results: We present a simulation study comparing this method to four other methods of meta‐analysi... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4280265 Thu, 23 Dec 2010 01:18:17 +0100 4280265 http://www.medworm.com/index.php?rid=4280264&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20544 AbstractIn genetic association studies, quality control (QC) filters are applied to remove potentially problematic markers before the markers are tested for statistical associations. However, spurious associations can still occur after QC. We introduce Post‐Association Cleaning (PAC) approach that can complement QC by capturing spurious associations using the information in the post‐association results. Specifically, we propose a PAC filter based on the linkage disequilibrium (LD) information. The intuition is that if the association is caused by a true genetic effect, neighboring markers in LD should show comparably significant P‐values. If not, it may be evidence of spurious association. Previous studies have applied the same idea but only manually without a formal statistical fram... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4280264 Thu, 23 Dec 2010 01:17:36 +0100 4280264 http://www.medworm.com/index.php?rid=4188835&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20553 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188835 Tue, 23 Nov 2010 01:54:05 +0100 4188835 http://www.medworm.com/index.php?rid=4188834&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20535 AbstractThe distribution of two‐point heterogeneity lod scores (HLOD) has been intensively investigated because the conventional χ2 approximation to the likelihood ratio test is not directly applicable. However, there was no study investigating th e distribution of the multipoint HLOD despite its wide application. Here we want to point out that, compared with the two‐point HLOD, the multipoint HLOD essentially tests for homogeneity given linkage and follows a relatively simple limiting distribution , which can be obtained by established statistical theory. We further examine the theoretical result by simulation studies. Genet. Epidemiol. 34: 912‐916, 2010.© 2010 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188834 Tue, 23 Nov 2010 01:54:04 +0100 4188834 http://www.medworm.com/index.php?rid=4188833&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20545 AbstractPenalized likelihood methods have become increasingly popular in recent years for evaluating haplotype‐phenotype association in case‐control studies. Although a retrospective likelihood is dictated by the sampling scheme, these penalized methods are typically built on prospective likelihoods due to their modeling simplicity and computational feasibility. It has been well documented that for unpenalized methods, prospective analyses of case‐control data can be valid but less efficient than their retrospective counterparts when testing for association, and result in substantial bias when estimating the haplotype effects. For penalized methods, which combine effect estimation and testing in one step, the impact of using a prospective likelihood is not clear. In this work, we exa... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188833 Tue, 23 Nov 2010 01:54:03 +0100 4188833 http://www.medworm.com/index.php?rid=4188832&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20543 We examined how markers enter the model as penalties and P‐value thresholds are varied, and report the sensitivity and specificity of each of the methods. Results show that penalized methods outperform single marker analysis, with the main difference being that penalized methods allow the simultaneous inclusion of a number of markers, and generally do not allow correlated variables to enter the model, producing a sparse model in which most of the identified explanatory markers are accounted for. Genet. Epidemiol. 34:879–891, 2010. © 2010 Wiley‐Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188832 Tue, 23 Nov 2010 01:54:03 +0100 4188832 http://www.medworm.com/index.php?rid=4188831&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20542 AbstractThe recent successes of genome‐wide association studies (GWAS) have revealed that many of the replicated findings have explained only a small fraction of the heritability of common diseases. One hypothesis that investigators have suggested is that higher order interactions between SNPs or SNPs and environmental risk factors may account for some of this missing heritability. Searching for these interactions poses great statistical and computational challenges. In this article, we propose a novel method that addresses these challenges by incorporating external biological knowledge into a fully Bayesian analysis. The method is designed to be scalable for high‐dimensional search spaces (where it supports interactions of any order) because priors that use such knowledge focus the se... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188831 Tue, 23 Nov 2010 01:54:02 +0100 4188831 http://www.medworm.com/index.php?rid=4188830&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20541 AbstractThe impact of erroneous genotypes having passed standard quality control (QC) can be severe in genome‐wide association studies, genotype imputation, and estimation of heritability and prediction of genetic risk based on single nucleotide polymorphisms (SNP). To detect such genotyping errors, a simple two‐locus QC method, based on the difference in test statistic of association between single SNPs and pairs of SNPs, was developed and applied. The proposed approach could detect many problematic SNPs with statistical significance even when standard single SNP QC analyses fail to detect them in real data. Depending on the data set used, the number of erroneous SNPs that were not filtered out by standard single SNP QC but detected by the proposed approach varied from a few hundred t... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188830 Tue, 23 Nov 2010 01:54:01 +0100 4188830 http://www.medworm.com/index.php?rid=4188829&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20540 AbstractDespite the success of genome‐wide association studies, much of the genetic contribution to complex human traits is still unexplained. One potential source of genetic variation that may contribute to this “missing heritability” is that which differs in magnitude and/or direction between males and females, which could result from sexual dimorphism in gene expression. Such sex‐differentiated effects are common in model organisms, and are becoming increasingly evident in human complex traits through large‐scale male‐ and female‐specific meta‐analyses. In this article, we review the methodology for meta‐analysis of sex‐specific genome‐wide association studies, and propose a sex‐differentiated test of association with quantitative or dichotomous traits, which all... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188829 Tue, 23 Nov 2010 01:53:59 +0100 4188829 http://www.medworm.com/index.php?rid=4188828&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20527 We present two approaches for using the linkage disequilibrium structure of an external reference panel to infer the unknown value of an untyped SNP from the observed genotypes of typed SNPs. The maximum‐likelihood approach integrates the prediction of untyped genotypes and estimation of association parameters into a single framework and yields consistent and efficient estimators of genetic effects and gene‐environment interactions with proper variance estimators. The imputation approach is a two‐stage strategy, which first imputes the untyped genotypes by either the most likely genotypes or the expected genotype counts and then uses the imputed values in a downstream association analysis. The latter approach has proper control of type I error in single‐SNP tests with possible cova... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4188828 Tue, 23 Nov 2010 01:53:57 +0100 4188828 http://www.medworm.com/index.php?rid=4137920&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20505 AbstractAn important component in the analysis of genome‐wide association studies involves the imputation of genotypes that have not been measured directly in the studied samples. The imputation procedure uses the linkage disequilibrium (LD) structure in the population to infer the genotype of an unobserved single nucleotide polymorphism. The LD structure is normally learned from a dense genotype map of a reference population that matches the studied population. In many instances there is no reference population that exactly matches the studied population, and a natural question arises as to how to choose the reference population for the imputation. Here we present a Coalescent‐based method that addresses this issue. In contrast to the current paradigm of imputation methods, our method... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4137920 Fri, 05 Nov 2010 00:00:00 +0100 4137920 http://www.medworm.com/index.php?rid=4137919&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20533 AbstractGenome‐wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously implemented a computationally efficient Markov Chain framework for genotype imputation and haplotyping in the freely available MaCH software package. The approach describes sampled chromosomes as mosaics of each other and uses available genotype and shotgun sequence data to estimate unobserved genotypes and haplotypes, together with useful measures of the quality of these estimates. Our approach is already widely used to facilitate comparison of results across studi... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4137919 Fri, 05 Nov 2010 00:00:00 +0100 4137919 http://www.medworm.com/index.php?rid=4137918&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20537 AbstractIn this paper, we extend the PPL framework to the analysis of case‐control (CC) data and introduce three new linkage disequilibrium (LD) statistics. These statistics measure the evidence for or against LD, rather than testing the null hypothesis of no LD, and they therefore avoid the need for multiple testing corrections. They are suitable not only for CC designs but also can be used in application to family data, ranging from trios to complex pedigrees, all under the same statistical framework, allowing for the seamless analysis of disparate data structures. They also provide other core advantages of the PPL framework, including the use of sequential updating to accumulate LD evidence across potentially heterogeneous sets or subsets of data; parameterization in terms of a very g... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4137918 Mon, 01 Nov 2010 00:00:00 +0100 4137918 http://www.medworm.com/index.php?rid=4115498&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20520 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4115498 Sat, 30 Oct 2010 00:00:53 +0100 4115498 http://www.medworm.com/index.php?rid=4104004&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20524 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4104004 Tue, 26 Oct 2010 09:25:10 +0100 4104004 http://www.medworm.com/index.php?rid=4104003&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20536 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4104003 Tue, 26 Oct 2010 09:25:07 +0100 4104003 http://www.medworm.com/index.php?rid=4104002&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20530 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4104002 Tue, 26 Oct 2010 09:25:03 +0100 4104002 http://www.medworm.com/index.php?rid=4104001&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20529 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4104001 Tue, 26 Oct 2010 09:25:03 +0100 4104001 http://www.medworm.com/index.php?rid=4010424&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20538 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=4010424 Tue, 31 Aug 2010 23:00:00 +0100 4010424 http://www.medworm.com/index.php?rid=3979653&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20531 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3979653 Tue, 31 Aug 2010 23:00:00 +0100 3979653 http://www.medworm.com/index.php?rid=3968077&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20532 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3968077 Tue, 31 Aug 2010 23:00:00 +0100 3968077 http://www.medworm.com/index.php?rid=3964581&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20525 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3964581 Tue, 31 Aug 2010 23:00:00 +0100 3964581 http://www.medworm.com/index.php?rid=3919280&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20513 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3919280 Tue, 31 Aug 2010 23:00:00 +0100 3919280 http://www.medworm.com/index.php?rid=3919279&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20510 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3919279 Tue, 31 Aug 2010 23:00:00 +0100 3919279 http://www.medworm.com/index.php?rid=3879799&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20515 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3879799 Wed, 30 Jun 2010 23:00:00 +0100 3879799 http://www.medworm.com/index.php?rid=3841005&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20508 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3841005 Wed, 30 Jun 2010 23:00:00 +0100 3841005 http://www.medworm.com/index.php?rid=3841004&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20506 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3841004 Wed, 30 Jun 2010 23:00:00 +0100 3841004 http://www.medworm.com/index.php?rid=3841003&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20501 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3841003 Wed, 30 Jun 2010 23:00:00 +0100 3841003 http://www.medworm.com/index.php?rid=3841002&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20500 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3841002 Wed, 30 Jun 2010 23:00:00 +0100 3841002 http://www.medworm.com/index.php?rid=3841001&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20504 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3841001 Wed, 30 Jun 2010 23:00:00 +0100 3841001 http://www.medworm.com/index.php?rid=3841000&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20497 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3841000 Wed, 30 Jun 2010 23:00:00 +0100 3841000 http://www.medworm.com/index.php?rid=3840999&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20496 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3840999 Wed, 30 Jun 2010 23:00:00 +0100 3840999 http://www.medworm.com/index.php?rid=3840998&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20495 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3840998 Wed, 30 Jun 2010 23:00:00 +0100 3840998 http://www.medworm.com/index.php?rid=3840997&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20494 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3840997 Wed, 30 Jun 2010 23:00:00 +0100 3840997 http://www.medworm.com/index.php?rid=3840996&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20489 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3840996 Wed, 30 Jun 2010 23:00:00 +0100 3840996 http://www.medworm.com/index.php?rid=3840995&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20488 Abstract (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3840995 Wed, 30 Jun 2010 23:00:00 +0100 3840995 http://www.medworm.com/index.php?rid=3840994&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20487 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3840994 Wed, 30 Jun 2010 23:00:00 +0100 3840994 http://www.medworm.com/index.php?rid=3840992&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20514 (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3840992 Wed, 30 Jun 2010 23:00:00 +0100 3840992 http://www.medworm.com/index.php?rid=3694357&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20502 A combination of common and rare variants is thought to contribute to genetic susceptibility to complex diseases. Recently, next-generation sequencers have greatly lowered sequencing costs, providing an opportunity to identify rare disease variants in large genetic epidemiology studies. At present, it is still expensive and time consuming to resequence large number of individual genomes. However, given that next-generation sequencing technology can provide accurate estimates of allele frequencies from pooled DNA samples, it is possible to detect associations of rare variants using pooled DNA sequencing. Current statistical approaches to the analysis of associations with rare variants are not designed for use with pooled next-generation sequencing data. Hence, they may not be optimal in ter... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3694357 Thu, 24 Jun 2010 23:00:00 +0100 3694357 http://www.medworm.com/index.php?rid=3686117&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20519 Conclusion: The utility of a genetic risk variant for risk categorization depends on both the magnitude of the genotype relative risk and the accuracy with which this, and other elements of risk calculation, are known. Genetic risk calculations should include an assessment of the accuracy of the risk estimation. Genet. Epidemiol. XX:1-9, 2010. © 2010 Wiley-Liss, Inc. (Source: Genetic Epidemiology) Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3686117 Tue, 22 Jun 2010 23:00:00 +0100 3686117 http://www.medworm.com/index.php?rid=3686124&cid=s_33629_54_f&fid=33629&url=http%3A%2F%2Fdx.doi.org%2F10.1002%252Fgepi.20489 Genetic heterogeneity, which may manifest on a population level as different frequencies of a specific disease susceptibility allele in different subsets of patients, is a common problem for candidate gene and genome-wide association studies of complex human diseases. The ordered subset analysis (OSA) was originally developed as a method to reduce genetic heterogeneity in the context of family-based linkage studies. Here, we have extended a previously proposed method (OSACC) for applying the OSA methodology to case-control datasets. We have evaluated the type I error and power of different OSACC permutation tests with an extensive simulation study. Case-control datasets were generated under two different models by which continuous clinical or environmental covariates may influence the rela... Genetic Epidemiology journals http://www.medworm.com/rss/comments.php?id=3686124 Sun, 20 Jun 2010 23:00:00 +0100 3686124