Analyses of biomarker traits in diverse UK biobank participants identify associations missed by European-centric analysis strategies

A genome-wide association study of alloimmunization in the TOPMed OMG-SCD cohort identifies a locus on chromosome 12

BACKGROUND

Red cell alloimmunization after exposure to donor red cells is a very common complication of transfusion for patients with sickle cell disease (SCD), resulting frequently in accelerated donor red blood cell destruction. Patients show substantial differences in their predisposition to alloimmunization, and genetic variability is one proposed component. Although several genetic association studies have been conducted for alloimmunization, the results have been inconsistent, and the genetic determinants of alloimmunization remain largely unknown.

STUDY DESIGN AND METHODS

We performed a genome-wide association study (GWAS) in 236 African American (AA) SCD patients from the Outcome Modifying Genes in Sickle Cell Disease (OMG-SCD) cohort, which is part of Trans-Omics for Precision Medicine (TOPMed), with whole-genome sequencing data available. We also performed sensitivity analyses adjusting for different sets of covariates and applied different sample grouping strategies based on the number of alloantibodies patients developed.

RESULTS

We identified one genome-wide significant locus on chr12 (p = 3.1e-9) with no evidence of genomic inflation (lambda = 1.003). Further leveraging QTL evidence from GTEx whole blood and/or Jackson Heart Study PBMC RNA-Seq data, we identified a number of potential genes, such as ARHGAP9, STAT6, and ATP23, that may be driving the association signal. We also discovered some suggestive loci using different analysis strategies.

DISCUSSION

We call for the community to collect additional alloantibody information within SCD cohorts to further the understanding of the genetic basis of alloimmunization in order to improve transfusion outcomes.

EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

ABSTRACT

Coagulation factor VIII (FVIII) and its carrier protein von Willebrand factor (VWF) are critical to coagulation and platelet aggregation. We leveraged whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program along with TOPMed-based imputation of genotypes in additional samples to identify genetic associations with circulating FVIII and VWF levels in a single-variant meta-analysis, including up to 45 289 participants. Gene-based aggregate tests were implemented in TOPMed. We identified 3 candidate causal genes and tested their functional effect on FVIII release from human liver endothelial cells (HLECs) and VWF release from human umbilical vein endothelial cells. Mendelian randomization was also performed to provide evidence for causal associations of FVIII and VWF with thrombotic outcomes. We identified associations (P < 5 × 10-9) at 7 new loci for FVIII (ST3GAL4, CLEC4M, B3GNT2, ASGR1, F12, KNG1, and TREM1/NCR2) and 1 for VWF (B3GNT2). VWF, ABO, and STAB2 were associated with FVIII and VWF in gene-based analyses. Multiphenotype analysis of FVIII and VWF identified another 3 new loci, including PDIA3. Silencing of B3GNT2 and the previously reported CD36 gene decreased release of FVIII by HLECs, whereas silencing of B3GNT2, CD36, and PDIA3 decreased release of VWF by HVECs. Mendelian randomization supports causal association of higher FVIII and VWF with increased risk of thrombotic outcomes. Seven new loci were identified for FVIII and 1 for VWF, with evidence supporting causal associations of FVIII and VWF with thrombotic outcomes. B3GNT2, CD36, and PDIA3 modulate the release of FVIII and/or VWF in vitro.

Diversity of CFTR variants across ancestries characterized using 454,727 UK biobank whole exome sequences

BACKGROUND

Limited understanding of the diversity of variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene across ancestries hampers efforts to advance molecular diagnosis of cystic fibrosis (CF). The consequences pose a risk of delayed diagnoses and subsequently worsened health outcomes for patients. Therefore, characterizing the spectrum of CFTR variants across ancestries is critical for revolutionizing molecular diagnoses of CF.

METHODS

We analyzed 454,727 UK Biobank (UKBB) whole-exome sequences to characterize the diversity of CFTR variants across ancestries. Using the PanUKBB classification, the participants were assigned into six major groups: African (AFR), American/American Admixed (AMR), Central South Asia (CSA), East Asian (EAS), European (EUR), and Middle East (MID). We segregated ancestry-specific CFTR variants, including those that are CF-causing or clinically relevant. The ages of certain CF-causing variants were determined and analyzed for selective pressure effects, and curated phenotype analysis was performed for participants with clinically relevant CFTR genotypes.

RESULTS

We detected over 4000 CFTR variants, including novel ancestry-specific variants, across six ancestries. Europeans had the most unique CFTR variants [n = 2212], while the American group had the least unique variants [n = 23]. F508del was the most prevalent CF-causing variant found in all ancestries, except in EAS, where V520F was the most prevalent. Common EAS variants such as 3600G > A, V456A, and V520, which appeared approximately 270, 215, and 338 generations ago, respectively, did not show evidence of selective pressure. Sixteen participants had two CF-causing variants, with two being diagnosed with CF. We found 154 participants harboring a CF-causing and varying clinical consequences (VCC) variant. Phenotype analysis performed for participants with multiple clinically relevant variants returned significant associations with CF and its pulmonary phenotypes [Bonferroni-adjusted p < 0.05].

CONCLUSIONS

We leveraged the UKBB database to comprehensively characterize the broad spectrum of CFTR variants across ancestries. The detection of over 4000 CFTR variants, including several ancestry-specific and uncharacterized CFTR variants, warrants the need for further characterization of their functional and clinical relevance. Overall, the presentation of classical CF phenotypes seen in non-CF diagnosed participants with more than one CF-causing variant indicates that they may benefit from current CFTR modulator therapies.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

MOTIVATION

Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions.

RESULTS

Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

AVAILABILITY AND IMPLEMENTATION

The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Relative predictive value of sociodemographic factors for chronic diseases among All of Us participants: a descriptive analysis

BACKGROUND

Although sociodemographic characteristics are associated with health disparities, the relative predictive value of different social and demographic factors remains largely unknown. This study aimed to describe the sociodemographic characteristics of All of Us participants and evaluate the predictive value of each factor for chronic diseases associated with high morbidity and mortality.

METHODS

We performed a cross-sectional analysis using de-identified survey data from the All of Us Research Program, which has collected social, demographic, and health information from adults living in the United States since May 2018. Sociodemographic data included self-reported age, sex, gender, sexual orientation, race/ethnicity, income, education, health insurance, primary care provider (PCP) status, and health literacy scores. We analyzed the self-reported prevalence of hypertension, coronary artery disease, any cancer, skin cancer, lung disease, diabetes, obesity, and chronic kidney disease. Finally, we assessed the relative importance of each sociodemographic factor for predicting each chronic disease using the adequacy index for each predictor from logistic regression.

RESULTS

Among the 372,050 participants in this analysis, the median age was 53 years, 59.8% reported female sex, and the most common racial/ethnic categories were White (54.0%), Black (19.9%), and Hispanic/Latino (16.7%). Participants who identified as Asian, Middle Eastern/North African, and White were the most likely to report annual incomes greater than $200,000, advanced degrees, and employer or union insurance, while participants who identified as Black, Hispanic, and Native Hawaiian/Pacific Islander were the most likely to report annual incomes less than $10,000, less than a high school education, and Medicaid insurance. We found that age was most predictive of hypertension, coronary artery disease, any cancer, skin cancer, diabetes, obesity, and chronic kidney disease. Insurance type was most predictive of lung disease. Notably, no two health conditions had the same order of importance for sociodemographic factors.

CONCLUSIONS

Age was the best predictor for the assessed chronic diseases, but the relative predictive value of income, education, health insurance, PCP status, race/ethnicity, and sexual orientation was highly variable across health conditions. Identifying the sociodemographic groups with the largest disparities in a specific disease can guide future interventions to promote health equity.

Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS and impact on esophageal cancer risk in Japanese people

An East Asian-specific variant on aldehyde dehydrogenase 2 (ALDH2 rs671, G>A) is the major genetic determinant of alcohol consumption. We performed an rs671 genotype-stratified genome-wide association study meta-analysis of alcohol consumption in 175,672 Japanese individuals to explore gene-gene interactions with rs671 behind drinking behavior. The analysis identified three genome-wide significant loci (GCKR, KLB, and ADH1B) in wild-type homozygotes and six (GCKR, ADH1B, ALDH1B1, ALDH1A1, ALDH2, and GOT2) in heterozygotes, with five showing genome-wide significant interaction with rs671. Genetic correlation analyses revealed ancestry-specific genetic architecture in heterozygotes. Of the discovered loci, four (GCKR, ADH1B, ALDH1A1, and ALDH2) were suggested to interact with rs671 in the risk of esophageal cancer, a representative alcohol-related disease. Our results identify the genotype-specific genetic architecture of alcohol consumption and reveal its potential impact on alcohol-related disease risk.

Genetic Variants Associated With Hidradenitis Suppurativa

Importance

Hidradenitis suppurativa (HS) is a common and severely morbid chronic inflammatory skin disease that is reported to be highly heritable. However, the genetic understanding of HS is insufficient, and limited genome-wide association studies (GWASs) have been performed for HS, which have not identified significant risk loci.

Objective

To identify genetic variants associated with HS and to shed light on the underlying genes and genetic mechanisms.

Design, Setting, and Participants

This genetic association study recruited 753 patients with HS in the HS Program for Research and Care Excellence (HS ProCARE) at the University of North Carolina Department of Dermatology from August 2018 to July 2021. A GWAS was performed for 720 patients (after quality control) with controls from the Add Health study and then meta-analyzed with 2 large biobanks, UK Biobank (247 cases) and FinnGen (673 cases). Variants at 3 loci were tested for replication in the BioVU biobank (290 cases). Data analysis was performed from September 2021 to December 2022.

Main Outcomes and Measures

Main outcome measures are loci identified, with association of P < 1 × 10-8 considered significant.

Results

A total of 753 patients were recruited, with 720 included in the analysis. Mean (SD) age at symptom onset was 20.3 (10.57) years and at enrollment was 35.3 (13.52) years; 360 (50.0%) patients were Black, and 575 (79.7%) were female. In a meta-analysis of the 4 studies, 2 HS-associated loci were identified and replicated, with lead variants rs10512572 (P = 2.3 × 10-11) and rs17090189 (P = 2.1 × 10-8) near the SOX9 and KLF5 genes, respectively. Variants at these loci are located in enhancer regulatory elements detected in skin tissue.

Conclusions and Relevance

In this genetic association study, common variants associated with HS located near the SOX9 and KLF5 genes were associated with risk of HS. These or other nearby genes may be associated with genetic risk of disease and the development of clinical features, such as cysts, comedones, and inflammatory tunnels, that are unique to HS. New insights into disease pathogenesis related to these genes may help predict disease progression and novel treatment approaches in the future.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

Leveraging base-pair mammalian constraint to understand genetic variation and human disease

Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

Differences and commonalities in the genetic architecture of protein quantitative trait loci in European and Arab populations

Polygenic scores (PGS) can identify individuals at risk of adverse health events and guide genetics-based personalized medicine. However, it is not clear how well PGS translate between different populations, limiting their application to well-studied ethnicities. Proteins are intermediate traits linking genetic predisposition and environmental factors to disease, with numerous blood circulating protein levels representing functional readouts of disease-related processes. We hypothesized that studying the genetic architecture of a comprehensive set of blood-circulating proteins between a European and an Arab population could shed fresh light on the translatability of PGS to understudied populations. We therefore conducted a genome-wide association study with whole-genome sequencing data using 1301 proteins measured on the SOMAscan aptamer-based affinity proteomics platform in 2935 samples of Qatar Biobank and evaluated the replication of protein quantitative traits (pQTLs) from European studies in an Arab population. Then, we investigated the colocalization of shared pQTL signals between the two populations. Finally, we compared the performance of protein PGS derived from a Caucasian population in a European and an Arab cohort. We found that the majority of shared pQTL signals (81.8%) colocalized between both populations. About one-third of the genetic protein heritability was explained by protein PGS derived from a European cohort, with protein PGS performing ~20% better in Europeans when compared to Arabs. Our results are relevant for the translation of PGS to non-Caucasian populations, as well as for future efforts to extend genetic research to understudied populations.

The future of sickle cell disease therapeutics rests in genomics

Sickle cell disease (SCD) is the most-common monogenic recessive disease in humans, annually affecting almost 300,000 newborns worldwide, 75% of whom live in Africa. Genomics research can accelerate the development of curative therapies for SCD in three ways. First, research should explore the missing heritability of foetal haemoglobin (HbF) - the strongest known modifier of SCD clinical expression - among highly genetically heterogenous and understudied African populations, to provide novel therapeutics targets for HbF induction. Second, SCD research should invest in RNA therapies, either by using microRNA to target the production of HbF proteins by binding to the transcription machinery in a cell, or by directly mediating production of HbF or adult haemoglobin through injection of messenger RNA. Third, investigators should aim to identify currently unknown genetic risk factors for SCD cardiovascular complications, which will address mortality, particularly in adults. Now is the time for global research programs to uncover genomic keys to unlock SCD therapeutics.

MagicalRsq: Machine-learning-based genotype imputation quality calibration

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

Can genomic research make a useful contribution to social policy?

As genetic research into outcomes beyond health gathers pace, largely through the use of genome-wide association studies, interest from policy-makers has grown. In the last year, two UK reports have explored the policy implications of genomic research, one from the UK Government Office for Science and one from the Early Intervention Foundation. In this article, we explore areas of consensus between these two reports and use them to propose priorities for policy-makers as we prepare for what some have termed a 'genetic revolution'. Both reports agree on two clear recommendations for science and policy communities. One of these relates to public education and engagement, and the other to ensuring that genomic databases are ancestrally diverse. Both reports agree that-even if it is found to be a viable and ethical idea in the medium-term future-DNA data should not be incorporated into social policy before these two issues have been comprehensively addressed. In the article, we argue that scientists are taking the lead on tackling the diversity deficit but that there is a clear role for policy-makers to play in addressing low genetic literacy in society, and that this is a matter of urgency.

Genetic Determinants of 25-Hydroxyvitamin D Concentrations and Their Relevance to Public Health

Twin studies suggest a considerable genetic contribution to the variability in 25-hydroxyvitamin D (25(OH)D) concentrations, reporting heritability estimates up to 80% in some studies. While genome-wide association studies (GWAS) suggest notably lower rates (13−16%), they have identified many independent variants that associate with serum 25(OH)D concentrations. These discoveries have provided some novel insight into the metabolic pathway, and in this review we outline findings from GWAS studies to date with a particular focus on 35 variants which have provided replicating evidence for an association with 25(OH)D across independent large-scale analyses. Some of the 25(OH)D associating variants are linked directly to the vitamin D metabolic pathway, while others may reflect differences in storage capacity, lipid metabolism, and pathways reflecting skin properties. By constructing a genetic score including these 25(OH)D associated variants we show that genetic differences in 25(OH)D concentrations persist across the seasons, and the odds of having low concentrations (<50 nmol/L) are about halved for individuals in the highest 20% of vitamin D genetic score compared to the lowest quintile, an impact which may have notable influences on retaining adequate levels. We also discuss recent studies on personalized approaches to vitamin D supplementation and show how Mendelian randomization studies can help inform public health strategies to reduce adverse health impacts of vitamin D deficiency.

Unique attributes of obesity in India: A narrative review

Obesity has become a burgeoning epidemic in India, even though the country is still dealing with undernutrition. As a significant determinant of the Metabolic Syndrome (MetS) and non-communicable diseases (NCDs) such as type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD), understanding the Indian context of the problem and learning how to deal with the obesity epidemic in this country has gained paramount importance. This narrative review points to the unique features of the obesity epidemic in India and its associated contributing factors, including the evolving nature of the Indian diet, the peculiarity of the increased adiposity at lower BMIs, unique obesity-associated genetic variants in Indians, the contribution of the gut microbiome, the impact of chronic inflammation and the role of ambient air pollution, and the contribution of decreased physical activity levels concerning the rapid urbanisation and the built environment. We believe that disseminating our insights into these unique features influencing the development of obesity in India will help increase global awareness and pave the way for better control and management of this obesity epidemic.

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

The sequences of 150,119 genomes in the UK Biobank

Detailed knowledge of how diversity in the sequence of the human genome affects phenotypic diversity depends on a comprehensive and reliable characterization of both sequences and phenotypic variation. Over the past decade, insights into this relationship have been obtained from whole-exome sequencing or whole-genome sequencing of large cohorts with rich phenotypic data^1,2. Here we describe the analysis of whole-genome sequencing of 150,119 individuals from the UK Biobank³. This constitutes a set of high-quality variants, including 585,040,410 single-nucleotide polymorphisms, representing 7.0% of all possible human single-nucleotide polymorphisms, and 58,707,036 indels. This large set of variants allows us to characterize selection based on sequence variation within a population through a depletion rank score of windows along the genome. Depletion rank analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation. We define three cohorts within the UK Biobank: a large British Irish cohort, a smaller African cohort and a South Asian cohort. A haplotype reference panel is provided that allows reliable imputation of most variants carried by three or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large-scale whole-genome sequencing studies. Using this formidable new resource, we provide several examples of trait associations for rare variants with large effects not found previously through studies based on whole-exome sequencing and/or imputation.

TOP-LD: A tool to explore linkage disequilibrium with TOPMed whole-genome sequence data

Current publicly available tools that allow rapid exploration of linkage disequilibrium (LD) between markers (e.g., HaploReg and LDlink) are based on whole-genome sequence (WGS) data from 2,504 individuals in the 1000 Genomes Project. Here, we present TOP-LD, an online tool to explore LD inferred with high-coverage (∼30×) WGS data from 15,578 individuals in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. TOP-LD provides a significant upgrade compared to current LD tools, as the TOPMed WGS data provide a more comprehensive representation of genetic variation than the 1000 Genomes data, particularly for rare variants and in the specific populations that we analyzed. For example, TOP-LD encompasses LD information for 150.3, 62.2, and 36.7 million variants for European, African, and East Asian ancestral samples, respectively, offering 2.6- to 9.1-fold increase in variant coverage compared to HaploReg 4.0 or LDlink. In addition, TOP-LD includes tens of thousands of structural variants (SVs). We demonstrate the value of TOP-LD in fine-mapping at the GGT1 locus associated with gamma glutamyltransferase in the African ancestry participants in UK Biobank. Beyond fine-mapping, TOP-LD can facilitate a wide range of applications that are based on summary statistics and estimates of LD. TOP-LD is freely available online.

Epigenomics as a paradigm to understand the nuances of phenotypes

Quantifying the relative importance of genomic and epigenomic modulators of phenotype is a focal challenge in comparative physiology, but progress is constrained by availability of data and analytic methods. Previous studies have linked physiological features to coding DNA sequence, regulatory DNA sequence, and epigenetic state, but few have disentangled their relative contributions or unambiguously distinguished causative effects ('drivers') from correlations. Progress has been limited by several factors, including the classical approach of treating continuous and fluid phenotypes as discrete and static across time and environment, and difficulty in considering the full diversity of mechanisms that can modulate phenotype, such as gene accessibility, transcription, mRNA processing and translation. We argue that attention to phenotype nuance, progressing to association with epigenetic marks and then causal analyses of the epigenetic mechanism, will enable clearer evaluation of the evolutionary path. This would underlie an essential paradigm shift, and power the search for links between genomic and epigenomic features and physiology. Here, we review the growing knowledge base of gene-regulatory mechanisms and describe their links to phenotype, proposing strategies to address widely recognized challenges.

Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

The role of the X chromosome in infectious diseases

Many infectious diseases in humans present with a sex bias. This bias arises from a combination of environmental factors, hormones and genetics. In this study, we review the contribution of the X chromosome to the genetic factor associated with infectious diseases. First, we give an overview of the X-linked genes that have been described in the context of infectious diseases and group them in four main pathways that seem to be dysregulated in infectious diseases: nuclear factor kappa-B, interleukin 2 and interferon γ cascade, toll-like receptors and programmed death ligand 1. Then, we review the infectious disease associations in existing genome-wide association studies (GWAS) from the GWAS Catalog and the Pan-UK Biobank, describing the main associations and their possible implications for the disease. Finally, we highlight the importance of including the X chromosome in GWAS analysis and the importance of sex-specific analysis.