A unified mixed-effects model for rare-variant association in sequencing studies

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Monoallelic pathogenic variants in LEPR do not cause obesity

Individuals with obesity caused by biallelic pathogenic LEPR (leptin receptor) variants can benefit from setmelanotide, the novel MC4R agonist. An ongoing phase 3 clinical trial (NCT05093634) includes individuals with obesity who carry a heterozygous LEPR variant, although the obesogenic impact of these variants remains incompletely evaluated. The aim of this study was to functionally assess heterozygous variants in LEPR and to evaluate their effect on obesity. We sequenced LEPR in ∼10,000 participants from the French RaDiO study. We found 86 rare heterozygous variants. Each identified variant was then investigated in vitro using luciferase and western blot assays. Using the criteria of the American College of Medical Genetics and Genomics (ACMG), including the strong criterion related to functional assays, we found 12 pathogenic LEPR variants. Most heterozygotes did not present with obesity, and we found no association between these pathogenic variants and body mass index (BMI). This lack of association between pathogenic LEPR variants and obesity risk or BMI was confirmed using exome data from 200,000 individuals in the UK Biobank. In the literature, among 55 reported heterozygotes for of a rare pathogenic LEPR variant, only 27% had obesity. In conclusion, monoallelic pathogenic LEPR variants were functionally tested, and they do not elevate the risk of obesity or BMI levels. This raises questions about the use of setmelanotide, a costly drug with potential side effects, based solely on the presence of a heterozygous LEPR variant.

PNLIPRP1 Hypermethylation in Exocrine Pancreas Links Type 2 Diabetes and Cholesterol Metabolism

We postulated that type 2 diabetes (T2D) predisposes patients to exocrine pancreatic diseases through (epi)genetic mechanisms. We explored the methylome (using MethylationEPIC arrays) of the exocrine pancreas in 141 donors, assessing the impact of T2D. An epigenome-wide association study of T2D identified hypermethylation in an enhancer of the pancreatic lipase-related protein 1 (PNLIPRP1) gene, associated with decreased PNLIPRP1 expression. PNLIPRP1 null variants (found in 191,000 participants in the UK Biobank) were associated with elevated glycemia and LDL cholesterol. Mendelian randomization using 2.5M SNP Omni arrays in 111 donors revealed that T2D was causal of PNLIPRP1 hypermethylation, which in turn was causal of LDL cholesterol. Additional AR42J rat exocrine cell analyses demonstrated that Pnliprp1 knockdown induced acinar-to-ductal metaplasia, a known prepancreatic cancer state, and increased cholesterol levels, reversible with statin. This (epi)genetic study suggests a role for PNLIPRP1 in human metabolism and exocrine pancreatic function, with potential implications for pancreatic diseases.

ARTICLE HIGHLIGHTS

Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature

BACKGROUND

In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.

METHODS

We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.

RESULTS

We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.

CONCLUSIONS

We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.

Integration of variant annotations using deep set networks boosts rare variant association testing

Rare genetic variants can have strong effects on phenotypes, yet accounting for rare variants in genetic analyses is statistically challenging due to the limited number of allele carriers and the burden of multiple testing. While rich variant annotations promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here we propose deep rare variant association testing (DeepRVAT), a model based on set neural networks that learns a trait-agnostic gene impairment score from rare variant annotations and phenotypes, enabling both gene discovery and trait prediction. On 34 quantitative and 63 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT yields substantial gains in gene discoveries and improved detection of individuals at high genetic risk. Finally, we demonstrate how DeepRVAT enables calibrated and computationally efficient rare variant tests at biobank scale, aiding the discovery of genetic risk factors for human disease traits.

Functional genetics reveals the contribution of delta opioid receptor to type 2 diabetes and beta-cell function

Functional genetics has identified drug targets for metabolic disorders. Opioid use impacts metabolic homeostasis, although mechanisms remain elusive. Here, we explore the OPRD1 gene (encoding delta opioid receptor, DOP) to understand its impact on type 2 diabetes. Large-scale sequencing of OPRD1 and in vitro analysis reveal that loss-of-function variants are associated with higher adiposity and lower hyperglycemia risk, whereas gain-of-function variants are associated with lower adiposity and higher type 2 diabetes risk. These findings align with studies of opium addicts. OPRD1 is expressed in human islets and beta cells, with decreased expression under type 2 diabetes conditions. DOP inhibition by an antagonist enhances insulin secretion from human beta cells and islets. RNA-sequencing identifies pathways regulated by DOP antagonism, including nerve growth factor, circadian clock, and nuclear receptor pathways. Our study highlights DOP as a key player between opioids and metabolic homeostasis, suggesting its potential as a therapeutic target for type 2 diabetes.

Structured testing of genetic association with mixed clinical outcomes

Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we proposed a structured test statistic for testing genetic association with mixed types of survival, binary, and continuous outcomes. The structured testing incorporates known biological information of variants while allowing for their heterogeneous effects and is a powerful strategy for analyzing infrequent genetic factors. Simulation studies show that the proposed test statistic has correct type I error and is highly effective in detecting significant genetic variants. We applied our approach to a uterine corpus endometrial carcinoma study and identified several genetic pathways associated with the clinical outcomes.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

BACKGROUND

The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.

METHODS

We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.

RESULTS

We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.

CONCLUSION

Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

Pathogenic, Total Loss-of-Function DYRK1B Variants Cause Monogenic Obesity Associated With Type 2 Diabetes

OBJECTIVE

Rare variants in DYRK1B have been described in some patients with central obesity, type 2 diabetes, and early-onset coronary disease. Owing to the limited number of conducted studies, the broader impact of DYRK1B variants on a larger scale has yet to be investigated.

RESEARCH DESIGN AND METHODS

DYRK1B was sequenced in 9,353 participants from a case-control study for obesity and type 2 diabetes. Each DYRK1B variant was functionally assessed in vitro. Variant pathogenicity was determined using criteria from the American College of Medical Genetics and Genomics (ACMG). The effect of pathogenic or likely pathogenic (P/LP) variants on metabolic traits was assessed using adjusted mixed-effects score tests.

RESULTS

Sixty-five rare, heterozygous DYRK1B variants were identified and were not associated with obesity or type 2 diabetes. Following functional analyses, 20 P/LP variants were pinpointed, including 6 variants that exhibited a fully inhibitory effect (P/LP-null) on DYRK1B activity. P/LP and P/LP-null DYRK1B variants were associated with increased BMI and obesity risk; however, the impact was notably more pronounced for the P/LP-null variants (effect of 8.0 ± 3.2 and odds ratio of 7.9 [95% CI 1.2-155]). Furthermore, P/LP-null variants were associated with higher fasting glucose and type 2 diabetes risk (effect of 2.9 ± 1.0 and odds ratio of 4.8 [95% CI 0.85-37]), while P/LP variants had no effect on glucose homeostasis.

CONCLUSIONS

P/LP, total loss-of-function DYRK1B variants cause monogenic obesity associated with type 2 diabetes. This study underscores the significance of conducting functional assessments in order to accurately ascertain the tangible effects of P/LP DYRK1B variants.

RAVAR: a curated repository for rare variant-trait associations

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level. To address the issue, we present the Rare Variant Association Repository (RAVAR), a comprehensive collection of rare variant associations. RAVAR includes 95 047 high-quality rare variant associations (76186 gene-level and 18 861 variant-level associations) for 4429 reported traits which are manually curated from 245 publications. RAVAR is the first resource to collect and curate published rare variant associations in an interactive web interface with integrated visualization, search, and download features. Detailed gene and SNP information are provided for each association, and users can conveniently search for related studies by exploring the EFO tree structure and interactive Manhattan plots. RAVAR could vastly improve the accessibility of rare variant studies. RAVAR is freely available for all users without login requirement at http://www.ravar.bio.

FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.

Human GLP1R variants affecting GLP1R cell surface expression are associated with impaired glucose control and increased adiposity

The glucagon-like peptide 1 receptor (GLP1R) is a major drug target with several agonists being prescribed in individuals with type 2 diabetes and obesity1,2. The impact of genetic variability of GLP1R on receptor function and its association with metabolic traits are unclear with conflicting reports. Here, we show an unexpected diversity of phenotypes ranging from defective cell surface expression to complete or pathway-specific gain of function (GoF) and loss of function (LoF), after performing a functional profiling of 60 GLP1R variants across four signalling pathways. The defective insulin secretion of GLP1R LoF variants is rescued by allosteric GLP1R ligands or high concentrations of exendin-4/semaglutide in INS-1 823/3 cells. Genetic association studies in 200,000 participants from the UK Biobank show that impaired GLP1R cell surface expression contributes to poor glucose control and increased adiposity with increased glycated haemoglobin A1c and body mass index. This study defines impaired GLP1R cell surface expression as a risk factor for traits associated with type 2 diabetes and obesity and provides potential treatment options for GLP1R LoF variant carriers.

Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set

Rare variant association studies with multiple traits or diseases have drawn a lot of attention since association signals of rare variants can be boosted if more than one phenotype outcome is associated with the same rare variants. Most of the existing statistical methods to identify rare variants associated with multiple phenotypes are based on a group test, where a pre-specified genetic region is tested one at a time. However, these methods are not designed to locate susceptible rare variants within the genetic region. In this article, we propose new statistical methods to prioritize rare variants within a genetic region when a group test for the genetic region identifies a statistical association with multiple phenotypes. It computes the weighted selection probability (WSP) of individual rare variants and ranks them from largest to smallest according to their WSP. In simulation studies, we demonstrated that the proposed method outperforms other statistical methods in terms of true positive selection, when multiple phenotypes are correlated with each other. We also applied it to our soybean single nucleotide polymorphism (SNP) data with 13 highly correlated amino acids, where we identified some potentially susceptible rare variants in chromosome 19.

Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data

The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.

Biallelic Mutations in P4HTM Cause Syndromic Obesity

We previously demonstrated that 50% of children with obesity from consanguineous families from Pakistan carry pathogenic variants in known monogenic obesity genes. Here, we have discovered a novel monogenetic recessive form of severe childhood obesity using an in-house computational staged approach. The analysis included whole-exome sequencing data of 366 children with severe obesity, 1,000 individuals of the Pakistan Risk of Myocardial Infarction Study (PROMIS) study, and 200,000 participants of the UK Biobank to prioritize genes harboring rare homozygous variants with putative effect on human obesity. We identified five rare or novel homozygous missense mutations predicted deleterious in five consanguineous families in P4HTM encoding prolyl 4-hydroxylase transmembrane (P4H-TM). We further found two additional homozygous missense mutations in children with severe obesity of Indian and Moroccan origin. Molecular dynamics simulation suggested that these mutations destabilized the active conformation of the substrate binding domain. Most carriers also presented with hypotonia, cognitive impairment, and/or developmental delay. Three of the five probands died of pneumonia during the first 2 years of the follow-up. P4HTM deficiency is a novel form of syndromic obesity, affecting 1.5% of our children with obesity associated with high mortality. P4H-TM is a hypoxia-inducible factor that is necessary for survival and adaptation under oxygen deprivation, but the role of this pathway in energy homeostasis and obesity pathophysiology remains to be elucidated.

Heterozygous pathogenic variants in POMC are not responsible for monogenic obesity: Implication for MC4R agonist use

PURPOSE

Recessive deficiency of proopiomelanocortin (POMC) causes childhood-onset severe obesity. Cases can now benefit from the melanocortin 4 receptor agonist setmelanotide. Furthermore, a phase 3 clinical trial is evaluating setmelanotide in heterozygotes for POMC. We performed a large-scale genetic analysis to assess the effect of heterozygous, pathogenic POMC variants on obesity.

METHODS

A genetic analysis was performed in a family including 2 cousins with childhood-onset obesity. We analyzed the obesity status of heterozygotes for pathogenic POMC variants in the Human Gene Mutation Database. The association between heterozygous pathogenic POMC variants and obesity risk was assessed using 190,000 exome samples from UK Biobank.

RESULTS

The 2 cousins carried a compound heterozygous pathogenic variant in POMC. Six siblings were heterozygotes; only 1 of them had obesity. In Human Gene Mutation Database, we identified 60 heterozygotes for pathogenic POMC variants, of whom 14 had obesity. In UK Biobank, heterozygous pathogenic POMC variants were not associated with obesity risk, but they modestly increased body mass index levels.

CONCLUSION

Heterozygous pathogenic POMC variants do not contribute to monogenic obesity, but they slightly increase body mass index. Setmelanotide use in patients with obesity, which would only be based on the presence of a heterozygous POMC variant, can be questioned.

A Varying Coefficient Model to Jointly Test Genetic and Gene-Environment Interaction Effects

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE.

Leveraging trans-ethnic genetic risk scores to improve association power for complex traits in underrepresented populations

Trans-ethnic genome-wide association studies have revealed that many loci identified in European populations can be reproducible in non-European populations, indicating widespread trans-ethnic genetic similarity. However, how to leverage such shared information more efficiently in association analysis is less investigated for traits in underrepresented populations. We here propose a statistical framework, trans-ethnic genetic risk score informed gene-based association mixed model (GAMM), by hierarchically modeling single-nucleotide polymorphism effects in the target population as a function of effects of the same trait in well-studied populations. GAMM powerfully integrates genetic similarity across distinct ancestral groups to enhance power in understudied populations, as confirmed by extensive simulations. We illustrate the usefulness of GAMM via the application to 13 blood cell traits (i.e. basophil count, eosinophil count, hematocrit, hemoglobin concentration, lymphocyte count, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, mean corpuscular volume, monocyte count, neutrophil count, platelet count, red blood cell count and total white blood cell count) in Africans of the UK Biobank (n = 3204) while utilizing genetic overlap shared in Europeans (n = 746 667) and East Asians (n = 162 255). We discovered multiple new associated genes, which had otherwise been missed by existing methods, and revealed that the trans-ethnic information indirectly contributed much to the phenotypic variance. Overall, GAMM represents a flexible and powerful statistical framework of association analysis for complex traits in underrepresented populations by integrating trans-ethnic genetic similarity across well-studied populations, and helps attenuate health inequities in current genetics research for people of minority populations.

StocSum: stochastic summary statistics for whole genome sequencing studies

Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.

MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits

The Transcriptome-Wide Association Study (TWAS) is a widely used approach which integrates gene expression and Genome Wide Association Study (GWAS) data to study the role of cis-regulated gene expression (GEx) in complex traits. However, the genetic architecture of GEx varies across populations, and recent findings point to possible ancestral heterogeneity in the effects of GEx on complex traits, which may be amplified in TWAS by modeling GEx as a function of cis-eQTLs. Here, we present a novel extension to TWAS to account for heterogeneity in the effects of cis-regulated GEx which are correlated with ancestry. Our proposed Multi-Ancestry TwaS (MATS) framework jointly analyzes samples from multiple populations and distinguishes between shared, ancestry-specific and/or subject-specific expression-trait associations. As such, MATS amplifies power to detect shared GEx associations over ancestry-stratified TWAS through increased sample sizes, and facilitates the detection of genes with subgroup-specific associations which may be masked by standard TWAS. Our simulations highlight the improved Type-I error conservation and power of MATS compared with competing approaches. Our real data applications to Alzheimer's disease (AD) case-control genotypes from the Alzheimer's Disease Sequencing Project (ADSP) and continuous phenotypes from the UK Biobank (UKBB) identify a number of unique gene-trait associations which were not discovered through standard and/or ancestry-stratified TWAS. Ultimately, these findings promote MATS as a powerful method for detecting and estimating significant gene expression effects on complex traits within multi-ancestry cohorts and corroborates the mounting evidence for inter-population heterogeneity in gene-trait associations.

Contribution of heterozygous PCSK1 variants to obesity and implications for precision medicine: a case-control study

BACKGROUND

Rare biallelic pathogenic mutations in PCSK1 (encoding proprotein convertase subtilisin/kexin type 1 [PC1/3]) cause early-onset obesity associated with various endocrinopathies. Setmelanotide has been approved for carriers of these biallelic mutations in the past 3 years. We aimed to perform a large-scale functional genomic study focusing on rare heterozygous variants of PCSK1 to decipher their putative impact on obesity risk.

METHODS

This case-control study included all participants with overweight and obesity (ie, cases) or healthy weight (ie, controls) from the RaDiO study of three community-based and one hospital-based cohort in France recruited between Jan 1, 1995, and Dec 31, 2000. In adults older than 18 years, healthy weight was defined as BMI of less than 25·0 kg/m², overweight as 25·0-29·9 kg/m², and obesity as 30·0 kg/m² or higher. Participants with type 2 diabetes had fasting glucose of 7·0 mmol/L or higher or used treatment for hyperglycaemia (or both) and were negative for islet or insulin autoantibodies. Functional assessment of rare missense variants of PCSK1 was performed. Pathogenicity clusters of variants were determined with machine learning. The effect of each cluster of PCSK1 variants on obesity was assessed using the adjusted mixed-effects score test.

FINDINGS

All 13 coding exons of PCSK1 were sequenced in 9320 participants (including 7260 adults and 2060 children and adolescents) recruited from the RaDiO study. We detected 65 rare heterozygous PCSK1 variants, including four null variants and 61 missense variants that were analysed in vitro and clustered into five groups (A-E), according to enzymatic activity. Compared with the wild-type, 15 missense variants led to complete PC1/3 loss of function (group A; reference) and rare exome variant ensemble learner (REVEL) led to 15 (25%) false positives and four (7%) false negatives. Carrying complete loss-of-function or null PCSK1 variants was significantly associated with obesity (six [86%] of seven carriers vs 1518 [35%] of 4395 non-carriers; OR 9·3 [95% CI 1·5-177·4]; p=0·014) and higher BMI (32·0 kg/m² [SD 9·3] in carriers vs 27·3 kg/m² [6·5] in non-carriers; mean effect π 6·94 [SE 1·95]; p=0·00029). Clusters of PCSK1 variants with partial or neutral effect on PC1/3 activity did not have an effect on obesity or overweight and on BMI.

INTERPRETATION

Only carriers of heterozygous, null, or complete loss-of-function PCSK1 variants cause monogenic obesity and, therefore, might be eligible for setmelanotide. In silico tests were unable to accurately detect these variants, which suggests that in vitro assays are necessary to determine the variant pathogenicity for genetic diagnosis and precision medicine purposes.

FUNDING

Agence Nationale de la Recherche, European Research Council, National Center for Precision Diabetic Medicine, European Regional Development Fund, Hauts-de-France Regional Council, and the European Metropolis of Lille.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Gene-Level Associations in Patients With and Without Pathogenic Germline Variants in CDKN2A and Pancreatic Cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is a component of familial melanoma due to germline pathogenic variants (GPVs) in CDKN2A. However, it is unclear what role this gene or other genes play in its etiology.

MATERIALS AND METHODS

We analyzed 189 cancer predisposition genes using parametric rare-variant association (RVA) tests and nonparametric permutation tests to identify gene-level associations in PDAC for patients with (CDKN2A+) and without (CDKN2A-) GPV. Exome sequencing was performed on 84 patients with PDAC, 47 CDKN2A+ and 37 CDKN2A-. After variant filtering, various RVA tests and permutation tests were run separately by CDKN2A status. Genes with the strongest nominal associations were evaluated in patients with PDAC from The Cancer Genome Atlas and the UK Biobank (UKB). A secondary analysis including only GPV from UKB was also performed.

RESULTS

In RVA tests, ERCC4 and RET showed the most compelling evidence as plausible PDAC candidate genes for CDKN2A+ patients. In contrast, the findings in CDKN2A- patients provided evidence for HMBS, EPCAM, and MRE11 as potential new candidate genes and confirmed ATM, BRCA2, and PALB2 as PDAC genes, consistent with findings in The Cancer Genome Atlas and the UKB. As expected, CDKN2A- patients were more likely to harbor GPVs from the 189 genes investigated. When including only GPVs from UKB, significant associations with PDAC were seen for ATM, BRCA2, and CDKN2A.

CONCLUSION

These results suggest that variants in other genes likely play a role in PDAC in all patients and that PDAC in CDKN2A+ patients has a distinct etiology from PDAC in CDKN2A- patients.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

BACKGROUND

Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods.

RESULTS

We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow.

CONCLUSION

In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genetic Regulation of DNA Methylation Yields Novel Discoveries in GWAS of Colorectal Cancer

BACKGROUND

Colorectal cancer has a strong epigenetic component that is accompanied by frequent DNA methylation (DNAm) alterations in addition to heritable genetic risk. It is of interest to understand the interrelationship of germline genetics, DNAm, and colorectal cancer risk.

METHODS

We performed a genome-wide methylation quantitative trait locus (meQTL) analysis in 1,355 people, assessing the pairwise associations between genetic variants and lymphocytes methylation data. In addition, we used penalized regression with cis-genetic variants ± 1 Mb of methylation to identify genome-wide heritable DNAm. We evaluated the association of genetically predicted methylation with colorectal cancer risk based on genome-wide association studies (GWAS) of over 125,000 cases and controls using the multivariate sMiST as well as univariately via examination of marginal association with colorectal cancer risk.

RESULTS

Of the 142 known colorectal cancer GWAS loci, 47 were identified as meQTLs. We identified four novel colorectal cancer-associated loci (NID2, ATXN10, KLHDC10, and CEP41) that reside over 1 Mb outside of known colorectal cancer loci and 10 secondary signals within 1 Mb of known loci.

CONCLUSIONS

Leveraging information of DNAm regulation into genetic association of colorectal cancer risk reveals novel pathways in colorectal cancer tumorigenesis. Our summary statistics-based framework sMiST provides a powerful approach by combining information from the effect through methylation and residual direct effects of the meQTLs on disease risk. Further validation and functional follow-up of these novel pathways are needed.

IMPACT

Using genotype, DNAm, and GWAS, we identified four new colorectal cancer risk loci. We studied the landscape of genetic regulation of DNAm via single-SNP and multi-SNP meQTL analyses.

Statistical inference for high-dimensional pathway analysis with multiple responses

Pathway analysis, i.e., grouping analysis, has important applications in genomic studies. Existing pathway analysis approaches are mostly focused on a single response and are not suitable for analyzing complex diseases that are often related with multiple response variables. Although a handful of approaches have been developed for multiple responses, these methods are mainly designed for pathways with a moderate number of features. A multi-response pathway analysis approach that is able to conduct statistical inference when the dimension is potentially higher than sample size is introduced. Asymptotical properties of the test statistic are established and theoretical investigation of the statistical power is conducted. Simulation studies and real data analysis show that the proposed approach performs well in identifying important pathways that influence multiple expression quantitative trait loci (eQTL).

TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes

Background

T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed.

Methods

We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined.

Results

Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients.

Conclusion

The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.

Simultaneous test and estimation of total genetic effect in eQTL integrative analysis through mixed models

Integration of expression quantitative trait loci (eQTL) into genome-wide association studies (GWASs) is a promising manner to reveal functional roles of associated single-nucleotide polymorphisms (SNPs) in complex phenotypes and has become an active research field in post-GWAS era. However, how to efficiently incorporate eQTL mapping study into GWAS for prioritization of causal genes remains elusive. We herein proposed a novel method termed as Mixed transcriptome-wide association studies (TWAS) and mediated Variance estimation (MTV) by modeling the effects of cis-SNPs of a gene as a function of eQTL. MTV formulates the integrative method and TWAS within a unified framework via mixed models and therefore includes many prior methods/tests as special cases. We further justified MTV from another two statistical perspectives of mediation analysis and two-stage Mendelian randomization. Relative to existing methods, MTV is superior for pronounced features including the processing of direct effects of cis-SNPs on phenotypes, the powerful likelihood ratio test for assessment of joint effects of cis-SNPs and genetically regulated gene expression (GReX), two useful quantities to measure relative genetic contributions of GReX and cis-SNPs to phenotypic variance, and the computationally efferent parameter expansion expectation maximum algorithm. With extensive simulations, we identified that MTV correctly controlled the type I error in joint evaluation of the total genetic effect and proved more powerful to discover true association signals across various scenarios compared to existing methods. We finally applied MTV to 41 complex traits/diseases available from three GWASs and discovered many new associated genes that had otherwise been missed by existing methods. We also revealed that a small but substantial fraction of phenotypic variation was mediated by GReX. Overall, MTV constructs a robust and realistic modeling foundation for integrative omics analysis and has the advantage of offering more attractive biological interpretations of GWAS results.

Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression

With the increasing volume of human sequencing data available, analysis incorporating external controls becomes a popular and cost-effective approach to boost statistical power in disease association studies. To prevent spurious association due to population stratification, it is important to match the ancestry backgrounds of cases and controls. However, rare variant association tests based on a standard logistic regression model are conservative when all ancestry-matched strata have the same case-control ratio and might become anti-conservative when case-control ratio varies across strata. Under the conditional logistic regression (CLR) model, we propose a weighted burden test (CLR-Burden), a variance component test (CLR-SKAT) and a hybrid test (CLR-MiST). We show that the CLR model coupled with ancestry matching is a general approach to control for population stratification, regardless of the spatial distribution of disease risks. Through extensive simulation studies, we demonstrate that the CLR-based tests robustly control type 1 errors under different matching schemes and are more powerful than the standard Burden, SKAT and MiST tests. Furthermore, because CLR-based tests allow for different case-control ratios across strata, a full-matching scheme can be employed to efficiently utilize all available cases and controls to accelerate the discovery of disease associated genes.

A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.

Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies

BACKGROUND

Integrating functional annotations into SNP-set association studies has been proven a powerful analysis strategy. Statistical methods for such integration have been developed for continuous and binary phenotypes; however, the SNP-set integrative approaches for time-to-event or survival outcomes are lacking.

METHODS

We here propose IEHC, an integrative eQTL (expression quantitative trait loci) hierarchical Cox regression, for SNP-set based survival association analysis by modeling effect sizes of genetic variants as a function of eQTL via a hierarchical manner. Three p-values combination tests are developed to examine the joint effects of eQTL and genetic variants after a novel decorrelated modification of statistics for the two components. An omnibus test (IEHC-ACAT) is further adapted to aggregate the strengths of all available tests.

RESULTS

Simulations demonstrated that the IEHC joint tests were more powerful if both eQTL and genetic variants contributed to association signal, while IEHC-ACAT was robust and often outperformed other approaches across various simulation scenarios. When applying IEHC to ten TCGA cancers by incorporating eQTL from relevant tissues of GTEx, we revealed that substantial correlations existed between the two types of effect sizes of genetic variants from TCGA and GTEx, and identified 21 (9 unique) cancer-associated genes which would otherwise be missed by approaches not incorporating eQTL.

CONCLUSION

IEHC represents a flexible, robust, and powerful approach to integrate functional omics information to enhance the power of identifying association signals for the survival risk of complex human cancers.

Impact of rare and common genetic variation in the interleukin-1 pathway on human cytokine responses

Background

The interleukin (IL)-1 pathway is primarily associated with innate immunological defense and plays a major role in the induction and regulation of inflammation. Both common and rare genetic variation in this pathway underlies various inflammation-mediated diseases, but the role of rare variants relative to common variants in immune response variability in healthy individuals remains unclear.

Methods

We performed molecular inversion probe sequencing on 48 IL-1 pathway-related genes in 463 healthy individuals from the Human Functional Genomics Project. We functionally grouped common and rare variants, over gene, subpathway, and inflammatory levels and performed the Sequence Kernel Association Test to test for association with in vitro stimulation-induced cytokine responses; specifically, IL-1β and IL-6 cytokine measurements upon stimulations that represent an array of microbial infections: lipopolysaccharide (LPS), phytohaemagglutinin (PHA), Candida albicans (C. albicans), and Staphylococcus aureus (S. aureus).

Results

We identified a burden of NCF4 rare variants with PHA-induced IL-6 cytokine and showed that the respective carriers are in the 1% lowest IL-6 producers. Collapsing rare variants in IL-1 subpathway genes produces a bidirectional association with LPS-induced IL-1β cytokine levels, which is reflected by a significant Spearman correlation. On the inflammatory level, we identified a burden of rare variants in genes encoding for proteins with an anti-inflammatory function with S. aureus-induced IL-6 cytokine. In contrast to these rare variant findings which were based on different types of stimuli, common variant associations were exclusively identified with C. albicans-induced cytokine over various levels of grouping, from the gene, to subpathway, to inflammatory level.

Conclusions

In conclusion, this study shows that functionally grouping common and rare genetic variants enables the elucidation IL-1-mediated biological mechanisms, specifically, for IL-1β and IL-6 cytokine responses induced by various stimuli. The framework used in this study may allow for the analysis of rare and common genetic variants in a wider variety of (non-immune) complex phenotypes and therefore has the potential to contribute to better understanding of unresolved, complex traits and diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00907-w.

A method for subtype analysis with somatic mutations

MOTIVATION

Cancer is a highly heterogeneous disease, and virtually all types of cancer have subtypes. Understanding the association between cancer subtypes and genetic variations is fundamental to the development of targeted therapies for patients. Somatic mutation plays important roles in tumor development and has emerged as a new type of genetic variations for studying the association with cancer subtypes. However, the low prevalence of individual mutations poses a tremendous challenge to the related statistical analysis.

RESULTS

In this article, we propose an approach, subtype analysis with somatic mutations (SASOM), for the association analysis of cancer subtypes with somatic mutations. Our approach tests the association between a set of somatic mutations (from a genetic pathway) and subtypes, while incorporating functional information of the mutations into the analysis. We further propose a robust p-value combination procedure, DAPC, to synthesize statistical significance from different sources. Simulation studies show that the proposed approach has correct type I error and tends to be more powerful than possible alternative methods. In a real data application, we examine the somatic mutations from a cutaneous melanoma dataset, and identify a genetic pathway that is associated with immune-related subtypes.

AVAILABILITY AND IMPLEMENTATION

The SASOM R package is available at https://github.com/rksyouyou/SASOM-pkg. R scripts and data are available at https://github.com/rksyouyou/SASOM-analysis.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Variant-set association test for generalized linear mixed model

Advances in high-throughput biotechnologies have culminated in a wide range of omics (such as genomics, epigenomics, transcriptomics, metabolomics, and metagenomics) studies, and increasing evidence in these studies indicates that the biological architecture of complex traits involves a large number of omics variants each with minor effects but collectively accounting for the full phenotypic variability. Thus, a major challenge in many "ome-wide" association analyses is to achieve adequate statistical power to identify multiple variants of small effect sizes, which is notoriously difficult for studies with relatively small-sample sizes. A small-sample adjustment incorporated in the kernel machine regression framework was proposed to solve this for association studies under various settings. However, such an adjustment in the generalized linear mixed model (GLMM) framework, which accounts for both sample relatedness and non-Gaussian outcomes, has not yet been attempted. In this study, we fill this gap by extending small-sample adjustment in kernel machine association test to GLMM. We propose a new Variant-Set Association Test (VSAT), a powerful and efficient analysis tool in GLMM, to examine the association between a set of omics variants and correlated phenotypes. The usefulness of VSAT is demonstrated using both numerical simulation studies and applications to data collected from multiple association studies. The software for implementing the proposed method in R is available at https://www.github.com/jchen1981/SSKAT.

Identifying Differentially Expressed Genes of Zero Inflated Single Cell RNA Sequencing Data Using Mixed Model Score Tests

Single cell RNA sequencing (scRNA-seq) allows quantitative measurement and comparison of gene expression at the resolution of single cells. Ignoring the batch effects and zero inflation of scRNA-seq data, many proposed differentially expressed (DE) methods might generate bias. We propose a method, single cell mixed model score tests (scMMSTs), to efficiently identify DE genes of scRNA-seq data with batch effects using the generalized linear mixed model (GLMM). scMMSTs treat the batch effect as a random effect. For zero inflation, scMMSTs use a weighting strategy to calculate observational weights for counts independently under zero-inflated and zero-truncated distributions. Counts data with calculated weights were subsequently analyzed using weighted GLMMs. The theoretical null distributions of the score statistics were constructed by mixed Chi-square distributions. Intensive simulations and two real datasets were used to compare edgeR-zinbwave, DESeq2-zinbwave, and scMMSTs. Our study demonstrates that scMMSTs, as supplement to standard methods, are advantageous to define DE genes of zero-inflated scRNA-seq data with batch effects.

Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

Pathogenic variants in actionable MODY genes are associated with type 2 diabetes

Genome-wide association studies have identified 240 independent loci associated with type 2 diabetes (T2D) risk, but this knowledge has not advanced precision medicine. In contrast, the genetic diagnosis of monogenic forms of diabetes (including maturity-onset diabetes of the young (MODY)) are textbook cases of genomic medicine. Recent studies trying to bridge the gap between monogenic diabetes and T2D have been inconclusive. Here, we show a significant burden of pathogenic variants in genes linked with monogenic diabetes among people with common T2D, particularly in actionable MODY genes, thus implying that there should be a substantial change in care for carriers with T2D. We show that, among 74,629 individuals, this burden is probably driven by the pathogenic variants found in GCK, and to a lesser extent in HNF4A, KCNJ11, HNF1B and ABCC8. The carriers with T2D are leaner, which evidences a functional metabolic effect of these mutations. Pathogenic variants in actionable MODY genes are more frequent than was previously expected in common T2D. These results open avenues for future interventions assessing the clinical interest of these pathogenic mutations in precision medicine.

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

Efficient gene-environment interaction tests for large biobank-scale sequencing 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 interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.

A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study

Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with various phenotypes, but together they explain only a fraction of heritability, suggesting many variants have yet to be discovered. Recently it has been recognized that incorporating functional information of genetic variants can improve power for identifying novel loci. For example, S-PrediXcan and TWAS tested the association of predicted gene expression with phenotypes based on GWAS summary statistics by leveraging the information on genetic regulation of gene expression and found many novel loci. However, as genetic variants may have effects on more than one gene and through different mechanisms, these methods likely only capture part of the total effects of these variants. In this paper, we propose a summary statistics-based mixed effects score test (sMiST) that tests for the total effect of both the effect of the mediator by imputing genetically predicted gene expression, like S-PrediXcan and TWAS, and the direct effects of individual variants. It allows for multiple functional annotations and multiple genetically predicted mediators. It can also perform conditional association analysis while adjusting for other genetic variants (e.g., known loci for the phenotype). Extensive simulation and real data analyses demonstrate that sMiST yields p-values that agree well with those obtained from individual level data but with substantively improved computational speed. Importantly, a broad application of sMiST to GWAS is possible, as only summary statistics of genetic variant associations are required. We apply sMiST to a large-scale GWAS of colorectal cancer using summary statistics from ∼120, 000 study participants and gene expression data from the Genotype-Tissue Expression (GTEx) project. We identify several novel and secondary independent genetic loci.

A hierarchical testing approach for detecting safety signals in clinical trials

Detecting safety signals in clinical trial safety data is known to be challenging due to high dimensionality, rare occurrence, weak signal, and complex dependence. We propose a new hierarchical testing approach for analyzing safety data from a typical randomized clinical trial. This approach accounts for the hierarchical structure of adverse events (AEs), that is, AEs are categorized by system organ class (SOC). Our approach contains two steps: the first step tests, for each SOC, whether any AEs within this SOC are differently distributed between treatment arms; and the second step identifies signal AEs from SOCs passing the first step tests. We show the superiority, in terms of power of detecting safety signals given controlled false discovery rate, of the new approach comparing with currently available approaches through simulation studies. We also demonstrate this approach with two real data examples.

Retrospective versus prospective score tests for genetic association with case-control data

Since the seminal work of Prentice and Pyke, the prospective logistic likelihood has become the standard method of analysis for retrospectively collected case-control data, in particular for testing the association between a single genetic marker and a disease outcome in genetic case-control studies. In the study of multiple genetic markers with relatively small effects, especially those with rare variants, various aggregated approaches based on the same prospective likelihood have been developed to integrate subtle association evidence among all the markers considered. Many of the commonly used tests are derived from the prospective likelihood under a common-random-effect assumption, which assumes a common random effect for all subjects. We develop the locally most powerful aggregation test based on the retrospective likelihood under an independent-random-effect assumption, which allows the genetic effect to vary among subjects. In contrast to the fact that disease prevalence information cannot be used to improve efficiency for the estimation of odds ratio parameters in logistic regression models, we show that it can be utilized to enhance the testing power in genetic association studies. Extensive simulations demonstrate the advantages of the proposed method over the existing ones. A real genome-wide association study is analyzed for illustration.

A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics

Cancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose to test for genetic associations using a mixed-effect two-stage polytomous model score test (MTOP). In the first stage, a standard polytomous model is used to specify all possible subtypes defined by the cross-classification of the tumor characteristics. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization algorithm to account for missing data on tumor markers. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. The proposed methods have been implemented in a user-friendly and high-speed R statistical package called TOP (https://github.com/andrewhaoyu/TOP).