Whole genome sequencing (WGS) and whole exome sequencing studies are used to test the association of rare genetic variants with health traits. Many existing WGS efforts now aggregate data from heterogeneous groups, for example, combining sets of individuals of European and African ancestries. We here investigate the statistical implications on rare variant association testing with a binary trait when

Risk genes influence the chance of an individual developing disease over their lifetime, although the age at onset (AAO) genes influence disease timing. These two categories are not disjoint; a gene that influences AAO might also appear to influence the risk. When an allele influences both AAO and risk, a reasonable question is whether we would have more power to detect association using a statistical

In silico simulations play an indispensable role in the development and application of statistical models and methods for genetic studies. Simulation tools allow for the evaluation of methods and investigation of models in a controlled manner. With the growing popularity of evolutionary models and simulation‐based statistical methods, genetic simulations have been applied to a wide variety of research

With rapid advancements of sequencing technologies and accumulations of electronic health records, a large number of genetic variants and multiple correlated human complex traits have become available in many genetic association studies. Thus, it becomes necessary and important to develop new methods that can jointly analyze the association between multiple genetic variants and multiple traits. Compared

Multiple methods have been proposed to aggregate genetic variants in a gene or a region and jointly test their association with a trait of interest. However, these joint tests do not provide estimates of the individual effect of each variant. Moreover, few methods have evaluated the joint association of multiple variants with DNA methylation. We propose a method based on linear mixed models to estimate

Germline mutations in many genes have been shown to increase the risk of developing cancer. This risk can vary across families who carry mutations in the same gene due to differences in the specific variants, gene–gene interactions, other susceptibility mutations, environmental factors, and behavioral factors. We develop an analytic tool to explore this heterogeneity using family history data. We propose

The intensities from genotyping array data can be used to detect copy number variants (CNVs) but a high level of noise in the data and overlap between different copy‐number intensity distributions produces unreliable calls, particularly when only a few probes are covered by the CNV. We present a novel pipeline (CamCNV) with a series of steps to reduce noise and detect more reliably CNVs covering as

Estimating the prevalence of rare germline genetic mutations in the general population is of interest as it can inform genetic counseling and risk management. Most studies that estimate the prevalence of mutations are performed in high‐risk populations, and each study is designed with differing inclusion criteria, resulting in ascertained populations. Quantifying the effects of ascertainment is necessary

Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between

In this paper, we develop TWO‐SIGMA, a TWO‐component SInGle cell Model‐based Association method for differential expression (DE) analyses in single‐cell RNA‐seq (scRNA‐seq) data. The first component models the probability of “drop‐out” with a mixed‐effects logistic regression model and the second component models the (conditional) mean expression with a mixed‐effects negative binomial regression model

Bronchodilator (BD) drugs are commonly prescribed for treatment and management of obstructive lung function present with diseases such as asthma. Administration of BD medication can partially or fully restore lung function as measured by pulmonary function tests. The genetics of baseline lung function measures taken before BD medication have been extensively studied, and the genetics of the BD response

Carotid artery atherosclerotic disease (CAAD) is a risk factor for stroke. We used a genome‐wide association (GWAS) approach to discover genetic variants associated with CAAD in participants in the electronic Medical Records and Genomics (eMERGE) Network. We identified adult CAAD cases with unilateral or bilateral carotid artery stenosis and controls without evidence of stenosis from electronic health

locStra is an ‐package for the analysis of regional and global population stratification in whole‐genome sequencing (WGS) studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard

Clinical trial results have recently demonstrated that inhibiting inflammation by targeting the interleukin‐1β pathway can offer a significant reduction in lung cancer incidence and mortality, highlighting a pressing and unmet need to understand the benefits of inflammation‐focused lung cancer therapies at the genetic level. While numerous genome‐wide association studies (GWAS) have explored the genetic

Mendelian randomization (MR) is an established approach for assessing the causal effects of heritable exposures on outcomes. Outcomes of interest often include binary clinical endpoints, but may also include censored survival times. We explore the implications of both the Cox proportional hazard model and the additive hazard model in the context of MR, with a specific emphasis on two‐stage methods

Vitamin D has been intensively studied for its association with human health, but the scope of such association and the causal role of vitamin D remain controversial. We aim to comprehensively investigate the links between vitamin D and human health through both epidemiological and Mendelian randomization (MR) analyses. We examined the epidemiological associations between serum 25‐hydroxyvitamin D

The integration of transcriptomic studies and genome‐wide association studies (GWAS) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies

There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative

Variance component models have gained popularity for genetic analyses, driven by their flexibility to simultaneously analyze multiple genetic variants in a gene by kernel statistics, and their ability to account for population stratification via genomic relationship matrices. For exploratory analyses with modest sample sizes and a potentially large number of variance components, it can be challenging

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to

In spite of the tremendous success of genome‐wide association studies (GWAS) in identifying genetic variants associated with complex traits and common diseases, many more are yet to be discovered. Hence, it is always desirable to improve the statistical power of GWAS. Paralleling with the intensive efforts of integrating GWAS with functional annotations or other omic data, we propose leveraging other

Genome‐wide associations studies have repeatedly identified the major histocompatibility complex genomic region (6p21.3) as key in immune pathologies. Researchers have also aimed to extend the biological interpretation of associations by focusing directly on human leukocyte antigen (HLA) polymorphisms and their combination as haplotypes. To circumvent the effort and high costs of HLA typing, statistical

An important aspect of disease gene mapping is replication, that is, a putative finding in one group of individuals is confirmed in another set of individuals. As it can happen by chance that individuals share an estimated disease position, we developed a statistical approach to determine the p‐value for multiple individuals or families to share a possibly small number of candidate susceptibility variants

Smoking is a major contributor to lung cancer and chronic obstructive pulmonary disease (COPD). Two of the strongest genetic associations of smoking‐related phenotypes are the chromosomal regions 15q25.1, encompassing the nicotinic acetylcholine receptor subunit genes CHRNA5‐CHRNA3‐CHRNB4, and 19q13.2, encompassing the nicotine metabolizing gene CYP2A6. In this study, we examined genetic relations

Imaging technology and machine learning algorithms for disease classification set the stage for high‐throughput phenotyping and promising new avenues for genome‐wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning‐based phenotyping in genetic association analysis as misclassification problem. To evaluate

Family history and body mass index (BMI) are well‐known risk factors for colorectal cancer (CRC), however, their joint effects are not well described. Using linked data for genealogy, self‐reported height and weight from driver's licenses, and the Utah Surveillance, Epidemiology, and End‐Results cancer registry, we found that an increasing number of first‐degree relatives (FDR) with CRC is associated

Noncoding DNA contains gene regulatory elements that alter gene expression, and the function of these elements can be modified by genetic variation. Massively parallel reporter assays (MPRA) enable high‐throughput identification and characterization of functional genetic variants, but the statistical methods to identify allelic effects in MPRA data have not been fully developed. In this study, we demonstrate

With advancements in high‐throughout technologies, studies have been conducted to investigate the role of massive genetic variants in human diseases. While set‐based tests have been developed for binary and continuous disease outcomes, there are few computationally efficient set‐based tests available for time‐to‐event outcomes. To facilitate the genetic association and interaction analyses of time‐to‐event

Gene–gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single‐nucleotide polymorphisms. The current tools for exploring G × G were often developed for case‐control designs with less considerations for their applications in families. Family‐based studies are robust against bias led from

The breakthroughs in next generation sequencing have allowed us to access data consisting of both common and rare variants, and in particular to investigate the impact of rare genetic variation on complex diseases. Although rare genetic variants are thought to be important components in explaining genetic mechanisms of many diseases, discovering these variants remains challenging, and most studies

Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene–environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank‐scale sequencing studies with correlated samples. We propose efficient Mixed‐model Association tests for GEne–Environment

In genome‐wide association studies, signals associated with rare variants and interactions between genes are hard to detect even when the sample size is in tens of thousands. To overcome these problems, we examine the concept of supervariant. Like the classic concept of the gene, a supervariant is a combination of alleles in multiple loci, but the contributing loci can be anywhere in the genome. We

A key aim for current genome‐wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole‐genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can

There are numerous statistical models used to identify individuals at high risk of cancer due to inherited mutations. Mendelian models predict future risk of cancer by using family history with estimated cancer penetrances (age‐ and sex‐specific risk of cancer given the genotype of the mutations) and mutation prevalences. However, there is often residual risk heterogeneity across families even after

Multiple linear regression is commonly used to test for association between genetic variants and continuous traits and estimate genetic effect sizes. Confounding variables are controlled for by including them as additional covariates. An alternative technique that is increasingly used is to regress out covariates from the raw trait and then perform regression analysis with only the genetic variants

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high‐risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide

In observational genomics data sets, there is often confounding of the effect of an exposure on gene expression. To adjust for confounding when estimating the exposure effect, a common approach involves including potential confounders as covariates with the exposure in a regression model of gene expression. However, when the exposure and confounders interact to influence gene expression, the fitted

Distance‐based regression model has become a powerful approach to identifying phenotypic associations in many fields. It is found to be particularly useful for high‐dimensional biological and genetic data with proper distance or similarity measures being available. The pseudo F statistic used in this model accumulates information and is effective when the signals, that is the variations represented

It is challenging to estimate the phenotypic impact of the structural genome changes known as copy‐number variations (CNVs), since there are many unique CNVs which are nonrecurrent, and most are too rare to be studied individually. In recent work, we found that CNV‐aggregated genomic annotations, that is, specifically the intolerance to mutation as measured by the pLI score (probability of being loss‐of‐function

Genetic matching between transplant donor and recipient pairs has traditionally focused on the human leukocyte antigen (HLA) regions of the genome, but recent studies suggest that matching for non‐HLA regions may be important as well. We assess four genetic matching scores for use in association analyses of transplant outcomes. These scores describe genetic ancestry distance using identity‐by‐state

Many variants with low frequencies or with low to modest effects likely remain unidentified in genome‐wide association studies (GWAS) because of stringent genome‐wide thresholds for detection. To improve the power of detection, variant prioritization based on their functional annotations and epigenetic landmarks has been used successfully. Here, we propose a novel method of prioritization of a GWAS

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene‐by‐environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular

Mendelian randomization is the use of genetic variants to assess the effect of intervening on a risk factor using observational data. We consider the scenario in which there is a pharmacomimetic (i.e., treatment‐mimicking) genetic variant that can be used as a proxy for a particular pharmacological treatment that changes the level of the risk factor. If the association of the pharmacomimetic genetic

It has been hypothesised that nonsyndromic cleft lip/palate (nsCL/P) and cancer may share aetiological risk factors. Population studies have found inconsistent evidence for increased incidence of cancer in nsCL/P cases, but several genes (e.g., CDH1 , AXIN2 ) have been implicated in the aetiologies of both phenotypes. We aimed to evaluate shared genetic aetiology between nsCL/P and oral cavity/oropharyngeal

Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false‐discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate

Population stratification may cause an inflated type‐I error and spurious association when assessing the association between genetic variations with an outcome. Many genetic association studies are now using exonic variants, which captures only 1% of the genome, however, population stratification adjustments have not been evaluated in the context of exonic variants. We compare the performance of two

To date, thousands of genetic variants to be associated with numerous human traits and diseases have been identified by genome‐wide association studies (GWASs). The GWASs focus on testing the association between single trait and genetic variants. However, the analysis of multiple traits and single nucleotide polymorphisms (SNPs) might reflect physiological process of complex diseases and the corresponding

Although genomewide association studies (GWASs) have identified many genetic variants underlying complex traits, a large fraction of heritability still remains unexplained. Integrative analysis that incorporates additional information, such as expression quantitativetrait locus (eQTL) data into sequencing studies (denoted as transcriptomewide association study [TWAS]), can aid the discovery of trait‐associated

Mediation analysis attempts to determine whether the relationship between an independent variable (e.g., exposure) and an outcome variable can be explained, at least partially, by an intermediate variable, called a mediator. Most methods for mediation analysis focus on one mediator at a time, although multiple mediators can be jointly analyzed by structural equation models (SEMs) that account for correlations

The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust

Next generation sequencing technologies have made it possible to investigate the role of rare variants (RVs) in disease etiology. Because RVs associated with disease susceptibility tend to be enriched in families with affected individuals, study designs based on affected sib pairs (ASP) can be more powerful than case-control studies. We construct tests of RV-set association in ASPs for single genomic

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene–multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating

We present an important characteristic of trio models which may lead to bias and loss of power when one parent is unmodeled in trio analyses. Motivated by recent interest in estimating parental effects on postnatal and later-life phenotypes, we consider a causal model where each parent has both an effect on their child's phenotype which is mediated through the genotype transmitted to the child and

Genome‐wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single‐nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans‐eQTLs

In transcriptome‐wide association studies (TWAS), gene expression values are predicted using genotype data and tested for association with a phenotype. The power of this approach to detect associations relies, at least in part, on the accuracy of the prediction. Here we compare the prediction accuracy of six different methods—LASSO, Ridge regression, Elastic net, Best Linear Unbiased Predictor, Bayesian

Previous transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis

We propose a novel variant set test for rare-variant association studies, which leverages multiple single-nucleotide variant (SNV) annotations. Our approach optimizes a convex combination of different sequence kernel association test (SKAT) statistics, where each statistic is constructed from a different annotation and combination weights are optimized through a multiple kernel learning algorithm.