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Microbiome scientists critically need modern tools to explore and analyze microbial evolution. Often this involves studying the evolution of microbial genomes as a whole. However, different genes in a single genome can be subject to different evolutionary pressures, which can result in distinct gene-level evolutionary histories. To address this challenge, we propose to treat estimated gene-level phylogenies as data objects, and present an interactive method for the analysis of a collection of gene phylogenies. We use a local linear approximation of phylogenetic tree space to visualize estimated gene trees as points in low-dimensional Euclidean space, and address important practical limitations of existing related approaches, allowing an intuitive visualization of complex data objects. We demonstrate the utility of our proposed approach through microbial data analyses, including by identifying outlying gene histories in strains of Prevotella, and by contrasting Streptococcus phylogenies estimated using different gene sets. Our method is available as an open-source R package, and assists with estimating, visualizing, and interacting with a collection of bacterial gene phylogenies., (© The Author 2023. Published by Oxford University Press.)

Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5–3.1% for an array of one million sites and 0.7–7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health.

Explosive growth in the amount of genomic data is matched by increasing power of consumer-grade computers. Even applications that require powerful servers can be quickly tested on desktop or laptop machines if we can generate representative samples from large data sets. I describe a fast and memory-efficient implementation of an on-line sampling method developed for tape drives 30 years ago. Focusing on genotype files, I test the performance of this technique on modern solid-state and spinning hard drives, and show that it performs well compared to a simple sampling scheme. I illustrate its utility by developing a method to quickly estimate genome-wide patterns of linkage disequilibrium (LD) decay with distance. I provide open-source software that samples loci from several variant format files, a separate program that performs LD decay estimates, and a C++ library that lets developers incorporate these methods into their own projects.

Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-sequencing data in GTEx V8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased whole genome sequencing data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants., Competing Interests: Conflicts of interest statement TL is a paid advisor to GSK, Pfizer, Goldfinch Bio, and Variant Bio and has equity in Variant Bio., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Mitochondrial DNA heteroplasmy samples can shed light on vital developmental and genetic processes shaping mitochondrial DNA populations. The sample means and sample variance of a set of heteroplasmy observations are typically used both to estimate bottleneck sizes and to perform fits to the theoretical "Kimura" distribution in seeking evidence for mitochondrial DNA selection. However, each of these applications raises problems. Sample statistics do not generally provide optimal fits to the Kimura distribution and so can give misleading results in hypothesis testing, including false positive signals of selection. Using sample variance can give misleading results for bottleneck size estimates, particularly for small samples. These issues can and do lead to false positive results for mitochondrial DNA mechanisms-all published experimental datasets we re-analyzed, reported as displaying departures from the Kimura model, do not in fact give evidence for such departures. Here we outline a maximum likelihood approach that is simple to implement computationally and addresses all of these issues. We advocate the use of maximum likelihood fits and explicit hypothesis tests, not fits and Kolmogorov-Smirnov tests via summary statistics, for ongoing work with mitochondrial DNA heteroplasmy., Competing Interests: Conflicts of interest The author(s) declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

Genomic regions subject to purifying selection are more likely to carry disease-causing mutations than regions not under selection. Cross species conservation is often used to identify such regions but with limited resolution to detect selection on short evolutionary timescales such as that occurring in only one species. In contrast, genetic intolerance looks for depletion of variation relative to expectation within a species, allowing species-specific features to be identified. When estimating the intolerance of noncoding sequence, methods strongly leverage variant frequency distributions. As the expected distributions depend on ancestry, if not properly controlled for, ancestral population source may obfuscate signals of selection. We demonstrate that properly incorporating ancestry in intolerance estimation greatly improved variant classification. We provide a genome-wide intolerance map that is conditional on ancestry and likely to be particularly valuable for variant prioritization., (© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The success of transcriptome-wide association studies (TWAS) has led to substantial research toward improving the predictive accuracy of its core component of genetically regulated expression (GReX). GReX links expression information with genotype and phenotype by playing two roles simultaneously: it acts as both the outcome of the genotype-based predictive models (for predicting expressions) and the linear combination of genotypes (as the predicted expressions) for association tests. From the perspective of machine learning (considering SNPs as features), these are actually two separable steps-feature selection and feature aggregation-which can be independently conducted. In this study, we show that the single approach of GReX limits the adaptability of TWAS methodology and practice. By conducting simulations and real data analysis, we demonstrate that disentangled protocols adapting straightforward approaches for feature selection (e.g., simple marker test) and aggregation (e.g., kernel machines) outperform the standard TWAS protocols that rely on GReX. Our development provides more powerful novel tools for conducting TWAS. More importantly, our characterization of the exact nature of TWAS suggests that, instead of questionably binding two distinct steps into the same statistical form (GReX), methodological research focusing on optimal combinations of feature selection and aggregation approaches will bring higher power to TWAS protocols., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Structural neuroimaging studies of individuals with chronic pain conditions have often observed decreased regional grey matter at a phenotypic level. However, it is not known if this association can be attributed to genetic factors. Here we employed a novel integrative data-driven and hypothesis-testing approach to determine whether there is a genetic basis to grey matter morphology differences in chronic pain. Using publicly available genome-wide association study summary statistics for regional chronic pain conditions (n = 196 963) and structural neuroimaging measures (n = 19 629-34 000), we applied bivariate linkage disequilibrium-score regression and latent causal variable analyses to determine the genetic correlations (rG) and genetic causal proportion (GCP) between these complex traits, respectively. Five a priori brain regions (i.e. prefrontal cortex, cingulate cortex, insula, thalamus and superior temporal gyrus) were selected based on systematic reviews of grey matter morphology studies in chronic pain. Across this evidence-based selection of five brain regions, 10 significant negative genetic correlations (out of 369) were found (false discovery rate < 5%), suggesting a shared genetic basis to both reduced regional grey matter morphology and the presence of chronic pain. Specifically, negative genetic correlations were observed between reduced insula grey matter morphology and chronic pain in the abdomen (mean insula cortical thickness), hips (left insula volume) and neck/shoulders (left and right insula volume). Similarly, a shared genetic basis was found for reduced posterior cingulate cortex volume in chronic pain of the hip (left and right posterior cingulate), neck/shoulder (left posterior cingulate) and chronic pain at any site (left posterior cingulate); and for reduced pars triangularis volume in chronic neck/shoulder (left pars triangularis) and widespread pain (right pars triangularis). Across these negative genetic correlations, a significant genetic causal proportion was only found between mean insula thickness and chronic abdominal pain [rG (standard error, SE) = -0.25 (0.08), P = 1.06 × 10-3; GCP (SE) = -0.69 (0.20), P = 4.96 × 10-4]. This finding suggests that the genes underlying reduced cortical thickness of the insula causally contribute to an increased risk of chronic abdominal pain. Altogether, these results provide independent corroborating evidence for observational reports of decreased grey matter of particular brain regions in chronic pain. Further, we show for the first time that this association is mediated (in part) by genetic factors. These novel findings warrant further investigation into the neurogenetic pathways that underlie the development and prolongation of chronic pain conditions., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier's accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that the probability of detecting differences between two distributions is lower for accuracy-based statistics. We examine several candidate causes for the low power of accuracy-tests. These causes include: the discrete nature of the accuracy-test statistic, the type of signal accuracy-tests are designed to detect, their inefficient use of the data, and their suboptimal regularization. When the purpose of the analysis is the evaluation of a particular classifier, not signal detection, we suggest several improvements to increase power. In particular, to replace V-fold cross-validation with the Leave-One-Out Bootstrap., (© The Author 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CpG islands (CGIs), their methylation states, CpG spacing and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be explained at individual gene and gene-family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI) and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA box on HK genes, remains to be elucidated. First, this study introduces such a problem-solving protocol for human-genome annotation, which is based on a combination of GTEx, JBLA and Gene Ontology (GO) analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; the HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic based on GO enrichment analysis. Finally, we demonstrate that Hadoop MapReduce-based MR-JBLA algorithm is more efficient than the original JBLA in k-mer counting and CGI-associated gene analysis., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Recent reports have identified differences in the mutational spectra across human populations. Although some of these reports have been replicated in other cohorts, most have been reported only in the 1000 Genomes Project (1kGP) data. While investigating an intriguing putative population stratification within the Japanese population, we identified a previously unreported batch effect leading to spurious mutation calls in the 1kGP data and to the apparent population stratification. Because the 1kGP data are used extensively, we find that the batch effects also lead to incorrect imputation by leading imputation servers and a small number of suspicious GWAS associations. Lower quality data from the early phases of the 1kGP thus continue to contaminate modern studies in hidden ways. It may be time to retire or upgrade such legacy sequencing data., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript-trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative "reference" traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation ( R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates., (Copyright © 2019 by the Genetics Society of America.)

Our understanding of the human mutation rate helps us build evolutionary models and interpret patterns of genetic variation observed in human populations. Recent work indicates that the frequencies of specific polymorphism types have been elevated in Europe, and that many more, subtler signatures of global polymorphism variation may yet remain unidentified. Here, we present an analysis of the 1000 Genomes Project supported by analysis in the Simons Genome Diversity Panel, suggesting additional putative signatures of mutation rate variation across populations and the extent to which they are shaped by local sequence context. First, we compiled a list of the most significantly variable polymorphism types in a cross-continental statistical test. Clustering polymorphisms together, we observe three sets that showed distinct shared patterns of relative enrichment among ancestral populations, and we characterize each one of these putative "signatures" of polymorphism variation. For three of these signatures, we found that a single flanking base pair of sequence context was sufficient to determine the majority of enrichment or depletion of a polymorphism type. However, local genetic context up to 2-3 bp away contributes additional variability and may help to interpret a previously noted enrichment of certain polymorphism types in some East Asian groups. Moreover, considering broader local genetic context highlights patterns of polymorphism variation, which were not captured by previous approaches. Building our understanding of mutation rate in this way can help us to construct more accurate evolutionary models and better understand the mechanisms that underlie genetic change., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

The emergence of very large cohorts in genomic research has facilitated a focus on genotype-imputation strategies to power rare variant association. These strategies have benefited from improvements in imputation methods and association tests, however little attention has been paid to ways in which array design can increase rare variant association power. Therefore, we developed a novel framework to select tag SNPs using the reference panel of 26 populations from Phase 3 of the 1000 Genomes Project. We evaluate tag SNP performance via mean imputed r 2 at untyped sites using leave-one-out internal validation and standard imputation methods, rather than pairwise linkage disequilibrium. Moving beyond pairwise metrics allows us to account for haplotype diversity across the genome for improve imputation accuracy and demonstrates population-specific biases from pairwise estimates. We also examine array design strategies that contrast multi-ethnic cohorts vs. single populations, and show a boost in performance for the former can be obtained by prioritizing tag SNPs that contribute information across multiple populations simultaneously. Using our framework, we demonstrate increased imputation accuracy for rare variants (frequency < 1%) by 0.5-3.1% for an array of one million sites and 0.7-7.1% for an array of 500,000 sites, depending on the population. Finally, we show how recent explosive growth in non-African populations means tag SNPs capture on average 30% fewer other variants than in African populations. The unified framework presented here will enable investigators to make informed decisions for the design of new arrays, and help empower the next phase of rare variant association for global health., (Copyright © 2018 Wojcik et al.)

Explosive growth in the amount of genomic data is matched by increasing power of consumer-grade computers. Even applications that require powerful servers can be quickly tested on desktop or laptop machines if we can generate representative samples from large data sets. I describe a fast and memory-efficient implementation of an on-line sampling method developed for tape drives 30 years ago. Focusing on genotype files, I test the performance of this technique on modern solid-state and spinning hard drives, and show that it performs well compared to a simple sampling scheme. I illustrate its utility by developing a method to quickly estimate genome-wide patterns of linkage disequilibrium (LD) decay with distance. I provide open-source software that samples loci from several variant format files, a separate program that performs LD decay estimates, and a C++ library that lets developers incorporate these methods into their own projects., (Copyright © 2018 Greenberg.)

Testing for the existence of variance components in linear mixed models is a fundamental task in many applicative fields. In statistical genetics, the score test has recently become instrumental in the task of testing an association between a set of genetic markers and a phenotype. With few markers, this amounts to set-based variance component tests, which attempt to increase power in association studies by aggregating weak individual effects. When the entire genome is considered, it allows testing for the heritability of a phenotype, defined as the proportion of phenotypic variance explained by genetics. In the popular score-based Sequence Kernel Association Test (SKAT) method, the assumed distribution of the score test statistic is uncalibrated in small samples, with a correction being computationally expensive. This may cause severe inflation or deflation of P -values, even when the null hypothesis is true. Here, we characterize the conditions under which this discrepancy holds, and show it may occur also in large real datasets, such as a dataset from the Wellcome Trust Case Control Consortium 2 ( n  = 13,950) study, and, in particular, when the individuals in the sample are unrelated. In these cases, the SKAT approximation tends to be highly overconservative and therefore underpowered. To address this limitation, we suggest an efficient method to calculate exact P -values for the score test in the case of a single variance component and a continuous response vector, which can speed up the analysis by orders of magnitude. Our results enable fast and accurate application of the score test in heritability and in set-based association tests. Our method is available in http://github.com/cozygene/RL-SKAT., (Copyright © 2017 by the Genetics Society of America.)

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes., (© The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Genomic phenotypes, such as DNA methylation and chromatin accessibility, can be used to characterize the transcriptional and regulatory activity of DNA within a cell. Recent technological advances have made it possible to measure such phenotypes very densely. This density often results in spatial structure, in the sense that measurements at nearby sites are very similar. In this article, we consider the task of comparing genomic phenotypes across experimental conditions, cell types, or disease subgroups. We propose a new method, Joint Adaptive Differential Estimation (JADE), which leverages the spatial structure inherent to genomic phenotypes. JADE simultaneously estimates smooth underlying group average genomic phenotype profiles and detects regions in which the average profile differs between groups. We evaluate JADE's performance in several biologically plausible simulation settings. We also consider an application to the detection of regions with differential methylation between mature skeletal muscle cells, myotubes, and myoblasts., (© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Close relatives can share large segments of their genome identical by descent (IBD) that can be identified in genome-wide polymorphism data sets. There are a range of methods to use these IBD segments to identify relatives and estimate their relationship. These methods have focused on sharing on the autosomes, as they provide a rich source of information about genealogical relationships. We hope to learn additional information about recent ancestry through shared IBD segments on the X chromosome, but currently lack the theoretical framework to use this information fully. Here, we fill this gap by developing probability distributions for the number and length of X chromosome segments shared IBD between an individual and an ancestor k generations back, as well as between half- and full-cousin relationships. Due to the inheritance pattern of the X and the fact that X homologous recombination occurs only in females (outside of the pseudoautosomal regions), the number of females along a genealogical lineage is a key quantity for understanding the number and length of the IBD segments shared among relatives. When inferring relationships among individuals, the number of female ancestors along a genealogical lineage will often be unknown. Therefore, our IBD segment length and number distributions marginalize over this unknown number of recombinational meioses through a distribution of recombinational meioses we derive. By using Bayes' theorem to invert these distributions, we can estimate the number of female ancestors between two relatives, giving us details about the genealogical relations between individuals not possible with autosomal data alone., (Copyright © 2016 by the Genetics Society of America.)

There is mounting evidence that complex human phenotypes are highly polygenic, with many loci harboring multiple causal variants, yet most genetic association studies examine each SNP in isolation. While this has led to the discovery of thousands of disease associations, discovered variants account for only a small fraction of disease heritability. Alternative multi-SNP methods have been proposed, but issues such as multiple-testing correction, sensitivity to genotyping error, and optimization for the underlying genetic architectures remain. Here we describe a local joint-testing procedure, complete with multiple-testing correction, that leverages a genetic phenomenon we call linkage masking wherein linkage disequilibrium between SNPs hides their signal under standard association methods. We show that local joint testing on the original Wellcome Trust Case Control Consortium (WTCCC) data set leads to the discovery of 22 associated loci, 5 more than the marginal approach. These loci were later found in follow-up studies containing thousands of additional individuals. We find that these loci significantly increase the heritability explained by genome-wide significant associations in the WTCCC data set. Furthermore, we show that local joint testing in a cis-expression QTL (eQTL) study of the gEUVADIS data set increases the number of genes containing significant eQTL by 10.7% over marginal analyses. Our multiple-hypothesis correction and joint-testing framework are available in a python software package called Jester, available at github.com/brielin/Jester., (Copyright © 2016 by the Genetics Society of America.)

With the completion of the entire human genome sequence and remarkable advances in genotyping technologies, there has been an increased interest in the application of genetics and genomics in biomedical research over the last decade. Large-scale population-based genetic association studies have now become routine and their application to several multifactorial diseases such as cardiovascular disorders has led to the identification of a number of novel susceptibility genes. However, to be able to interpret results from such studies, clinicians need to have a basic understanding of unique concepts and issues related to this fast-moving area of research. In this primer, we provide a broad overview of design, analysis, and methodological issues with a focus on population-based study design.

Modern case-control studies typically involve the collection of data on a large number of outcomes, often at considerable logistical and monetary expense. These data are of potentially great value to subsequent researchers, who, although not necessarily concerned with the disease that defined the case series in the original study, may want to use the available information for a regression analysis involving a secondary outcome. Because cases and controls are selected with unequal probability, regression analysis involving a secondary outcome generally must acknowledge the sampling design. In this paper, the author presents a new framework for the analysis of secondary outcomes in case-control studies. The approach is based on a careful re-parameterization of the conditional model for the secondary outcome given the case-control outcome and regression covariates, in terms of (a) the population regression of interest of the secondary outcome given covariates and (b) the population regression of the case-control outcome on covariates. The error distribution for the secondary outcome given covariates and case-control status is otherwise unrestricted. For a continuous outcome, the approach sometimes reduces to extending model (a) by including a residual of (b) as a covariate. However, the framework is general in the sense that models (a) and (b) can take any functional form, and the methodology allows for an identity, log or logit link function for model (a).