A fully adjusted two-stage procedure for rank-normalization in genetic association studies

Predictors of upstream inflammation and oxidative stress pathways during early pregnancy

BACKGROUND

Inflammation and oxidative stress are critical to pregnancy, but most human study has focused on downstream, non-causal indicators. Oxylipins are lipid mediators of inflammation and oxidative stress that act through many biological pathways. Our aim was to characterize predictors of circulating oxylipin concentrations based on maternal characteristics.

METHODS

Our study was conducted among 901 singleton pregnancies in the LIFECODES Fetal Growth Study, a nested case-cohort with recruitment from 2007 to 2018. We measured a targeted panel of oxylipins in early pregnancy plasma and urine samples from several biosynthetic pathways, defined by the polyunsaturated fatty acid (PUFA) precursor and enzyme group. We evaluated levels across predictors, including characteristics of participants' pregnancy, socioeconomic determinants, and obstetric and medical history.

RESULTS

Current pregnancy and sociodemographic characteristics were the most important predictors of circulating oxylipins concentrations. Plasma oxylipins were lower and urinary oxylipins higher for participants with a later gestational age at sampling (13-23 weeks), higher prepregnancy BMI (obesity class I, II, or III), Black or Hispanic race and ethnicity, and lower socioeconomic status (younger age, lower education, and uninsured). For example, compared to those with normal or underweight prepregnancy BMI, participants with class III prepregnancy obesity had 45-46% lower plasma epoxy-eicosatrienoic acids, the anti-inflammatory oxylipins produced from arachidonic acid (AA) by cytochrome P450, and had 81% higher urinary 15-series F2-isoprostanes, an indicator of oxidative stress produced from non-enzymatic AA oxidation. Similarly, in urine, Black participants had 92% higher prostaglandin E2 metabolite, a pro-inflammatory oxylipin, and 41% higher 5-series F2-isoprostane, an oxidative stress indicator.

CONCLUSIONS

In this large pregnancy study, we found that circulating levels of oxylipins were different for participants of lower socioeconomic status or of a systematically marginalized racial and ethnic groups. Given associations differed along biosynthetic pathways, results provide insight into etiologic links between maternal predictors and inflammation and oxidative stress.

Structural models of genome-wide covariance identify multiple common dimensions in autism

Common genetic variation has been associated with multiple phenotypic features in Autism Spectrum Disorder (ASD). However, our knowledge of shared genetic factor structures contributing to this highly heterogeneous phenotypic spectrum is limited. Here, we developed and implemented a structural equation modelling framework to directly model genomic covariance across core and non-core ASD phenotypes, studying autistic individuals of European descent with a case-only design. We identified three independent genetic factors most strongly linked to language performance, behaviour and developmental motor delay, respectively, studying an autism community sample (N = 5331). The three-factorial structure was largely confirmed in independent ASD-simplex families (N = 1946), although we uncovered, in addition, simplex-specific genetic overlap between behaviour and language phenotypes. Multivariate models across cohorts revealed novel associations, including links between language and early mastering of self-feeding. Thus, the common genetic architecture in ASD is multi-dimensional with overarching genetic factors contributing, in combination with ascertainment-specific patterns, to phenotypic heterogeneity.

A high-resolution PheWAS approach to alcohol-related polygenic risk scores reveals mechanistic influences of alcohol reinforcing value and drinking motives

AIMS

This study uses a high-resolution phenome-wide approach to evaluate the motivational mechanisms of polygenic risk scores (PRSs) that have been robustly associated with coarse alcohol phenotypes in large-scale studies.

METHODS

In a community-based sample of 1534 Europeans, we examined genome-wide PRSs for the Alcohol Use Disorders Identification Test (AUDIT), drinks per week, alcohol use disorder (AUD), problematic alcohol use (PAU), and general addiction, in relation to 42 curated phenotypes. The curated phenotypes were in seven categories: alcohol consumption, alcohol reinforcing value, drinking motives, other addictive behaviors, commonly comorbid psychiatric syndromes, impulsivity, and personality traits.

RESULTS

The PRS for each alcohol phenotype was validated via its within-sample association with the corresponding phenotype (adjusted R2s = 0.35-1.68%, Ps = 0.012-3.6 × 10-7) with the exception of AUD. All PRSs were positively associated with alcohol reinforcing value and drinking motives, with the strongest effects from AUDIT-consumption (adjusted R2s = 0.45-1.33%, Ps = 0.006-3.6 × 10-5) and drinks per week PRSs (adjusted R2s = 0.52-2.28%, Ps = 0.004-6.6 × 10-9). Furthermore, the PAU and drinks per week PRSs were positively associated with adverse childhood experiences (adjusted R2s = 0.6-0.7%, Ps = 0.0001-4.8 × 10-4).

CONCLUSIONS

These results implicate alcohol reinforcing value and drinking motives as genetically-influenced mechanisms using PRSs for the first time. The findings also highlight the value of dissecting genetic influence on alcohol involvement through diverse phenotypic risk pathways but also the need for future studies with both phenotypic richness and larger samples.

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.

Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

Genetic loci of beta-aminoisobutyric acid are associated with aging-related mild cognitive impairment

We studied the genetic associations of a previously developed Metabolomic Risk Score (MRS) for Mild Cognitive Impairment (MCI) and beta-aminoisobutyric acid metabolite (BAIBA)-the metabolite highlighted by results from a genome-wide association study (GWAS) of the MCI-MRS, and assessed their association with MCI in datasets of diverse race/ethnicities. We first performed a GWAS for the MCI-MRS and BAIBA, in Hispanic/Latino adults (n = 3890) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We identified ten independent genome-wide significant (p value <5 × 10-8) variants associated with MCI-MRS or BAIBA. Variants associated with the MCI-MRS are located in the Alanine-Glyoxylate Aminotransferase 2 (AGXT2 gene), which is known to be associated with BAIBA metabolism. Variants associated with BAIBA are located in the AGXT2 gene and in the SLC6A13 gene. Next, we tested the variants' association with MCI in independent datasets of n = 3178 HCHS/SOL older individuals, n = 3775 European Americans, and n = 1032 African Americans from the Atherosclerosis Risk In Communities (ARIC) study. Variants were considered associated with MCI if their p value <0.05 in the meta-analysis of the three datasets and their direction of association was consistent with expectation. Rs16899972 and rs37369 from the AGXT2 region were associated with MCI. Mediation analysis supported the mediation effect of BAIBA between the two genetic variants and MCI (p value = 0.004 for causal mediated effect). In summary, genetic variants in the AGXT2 region are associated with MCI in Hispanic/Latino, African, and European American populations in the USA, and their effect is likely mediated by changes in BAIBA levels.

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.

An integrated multi-omics analysis of sleep-disordered breathing traits implicates P2XR4 purinergic signaling

Sleep Disordered Breathing (SDB) is a common disease associated with increased risk for cardiometabolic, cardiovascular, and cognitive diseases. How SDB affects the molecular environment is still poorly understood. We study the association of three SDB measures with gene expression measured using RNA-seq in multiple blood tissues from the Multi-Ethnic Study of Atherosclerosis. We develop genetic instrumental variables for the associated transcripts as polygenic risk scores (tPRS), then generalize and validate the tPRS in the Women's Health Initiative. We measure the associations of the validated tPRS with SDB and serum metabolites in Hispanic Community Health Study/Study of Latinos. Here we find differential gene expression by blood cell type in relation to SDB traits and link P2XR4 expression to average oxyhemoglobin saturation during sleep and butyrylcarnitine (C4) levels. These findings can be used to develop interventions to alleviate the effect of SDB on the human molecular environment.

A stop-gain variant in BTNL9 is associated with atherogenic lipid profiles

Current understanding of lipid genetics has come mainly from studies in European-ancestry populations; limited effort has focused on Polynesian populations, whose unique population history and high prevalence of dyslipidemia may provide insight into the biological foundations of variation in lipid levels. Here, we performed an association study to fine map a suggestive association on 5q35 with high-density lipoprotein cholesterol (HDL-C) seen in Micronesian and Polynesian populations. Fine-mapping analyses in a cohort of 2,851 Samoan adults highlighted an association between a stop-gain variant (rs200884524; c.652C>T, p.R218∗; posterior probability = 0.9987) in BTNL9 and both lower HDL-C and greater triglycerides (TGs). Meta-analysis across this and several other cohorts of Polynesian ancestry from Samoa, American Samoa, and Aotearoa New Zealand confirmed the presence of this association (βHDL-C = -1.60 mg/dL, p HDL-C = 7.63 × 10-10; βTG = 12.00 mg/dL, p TG = 3.82 × 10-7). While this variant appears to be Polynesian specific, there is also evidence of association from other multiancestry analyses in this region. This work provides evidence of a previously unexplored contributor to the genetic architecture of lipid levels and underscores the importance of genetic analyses in understudied populations.

Multi-omic association study identifies DNA methylation-mediated genotype and smoking exposure effects on lung function in children living in urban settings

Impaired lung function in early life is associated with the subsequent development of chronic respiratory disease. Most genetic associations with lung function have been identified in adults of European descent and therefore may not represent those most relevant to pediatric populations and populations of different ancestries. In this study, we performed genome-wide association analyses of lung function in a multiethnic cohort of children (n = 1,035) living in low-income urban neighborhoods. We identified one novel locus at the TDRD9 gene in chromosome 14q32.33 associated with percent predicted forced expiratory volume in one second (FEV1) (p = 2.4x10-9; βz = -0.31, 95% CI = -0.41- -0.21). Mendelian randomization and mediation analyses revealed that this genetic effect on FEV1 was partially mediated by DNA methylation levels at this locus in airway epithelial cells, which were also associated with environmental tobacco smoke exposure (p = 0.015). Promoter-enhancer interactions in airway epithelial cells revealed chromatin interaction loops between FEV1-associated variants in TDRD9 and the promoter region of the PPP1R13B gene, a stimulator of p53-mediated apoptosis. Expression of PPP1R13B in airway epithelial cells was significantly associated the FEV1 risk alleles (p = 1.3x10-5; β = 0.12, 95% CI = 0.06-0.17). These combined results highlight a potential novel mechanism for reduced lung function in urban youth resulting from both genetics and smoking exposure.

Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

The protective effect of rs373863828 on type 2 diabetes does not operate through a body composition pathway in adult Samoans

OBJECTIVE

The aim of this study was to understand whether the paradoxical association of missense variant rs373863828 in CREB3 regulatory factor (CREBRF) with higher BMI but lower odds of diabetes is explained by either metabolically favorable body fat distribution or greater fat-free mass.

METHODS

This study explored the association of the minor allele with dual-energy x-ray absorptiometry-derived body composition in n = 421 Samoans and used path analysis to examine the mediating role of fat and fat-free mass on the relationship between rs373863828 and fasting glucose.

RESULTS

Among females, the rs373863828 minor A allele was associated with greater BMI. There was no association of genotype with percent body fat, visceral adiposity, or fat distribution in either sex. In both females and males, lean mass was greater with each A allele: 2.16 kg/copy (p = 0.0001) and 1.73 kg/copy (p = 0.02), respectively. Path analysis showed a direct negative effect of rs373863828 genotype on fasting glucose (p = 0.004) consistent with previous findings, but also an indirect positive effect on fasting glucose operating through fat-free mass (p = 0.027).

CONCLUSIONS

The protective effect of rs373863828 in CREBRF, common among Pacific Islanders, on type 2 diabetes does not operate through body composition. Rather, the variant's effects on body size/composition and fasting glucose likely operate via different, tissue-specific mechanisms.

Rare Variants in Genes Encoding Subunits of the Epithelial Na+ Channel Are Associated With Blood Pressure and Kidney Function

BACKGROUND

The epithelial Na+ channel (ENaC) is intrinsically linked to fluid volume homeostasis and blood pressure. Specific rare mutations in SCNN1A, SCNN1B, and SCNN1G, genes encoding the α, β, and γ subunits of ENaC, respectively, are associated with extreme blood pressure phenotypes. No associations between blood pressure and SCNN1D, which encodes the δ subunit of ENaC, have been reported. A small number of sequence variants in ENaC subunits have been reported to affect functional transport in vitro or blood pressure. The effects of the vast majority of rare and low-frequency ENaC variants on blood pressure are not known.

METHODS

We explored the association of low frequency and rare variants in the genes encoding ENaC subunits, with systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure. Using whole-genome sequencing data from 14 studies participating in the Trans-Omics in Precision Medicine Whole-Genome Sequencing Program, and sequence kernel association tests.

RESULTS

We found that variants in SCNN1A and SCNN1B were associated with diastolic blood pressure and mean arterial pressure (P<0.00625). Although SCNN1D is poorly expressed in human kidney tissue, SCNN1D variants were associated with systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure (P<0.00625). ENaC variants in 2 of the 4 subunits (SCNN1B and SCNN1D) were also associated with estimated glomerular filtration rate (P<0.00625), but not with stroke.

CONCLUSIONS

Our results suggest that variants in extrarenal ENaCs, in addition to ENaCs expressed in kidneys, influence blood pressure and kidney function.

Genetic determinants of cardiometabolic and pulmonary phenotypes and obstructive sleep apnoea in HCHS/SOL

BACKGROUND

Obstructive Sleep Apnoea (OSA) often co-occurs with cardiometabolic and pulmonary diseases. This study is to apply genetic analysis methods to explain the associations between OSA and related phenotypes.

METHODS

In the Hispanic Community Healthy Study/Study of Latinos, we estimated genetic correlations ρ_g between the respiratory event index (REI) and 54 anthropometric, glycemic, cardiometabolic, and pulmonary phenotypes. We used summary statistics from published genome-wide association studies to construct Polygenic Risk Scores (PRSs) representing the genetic basis of each correlated phenotype (ρ_g>0.2 and p-value<0.05), and of OSA. We studied the association of the PRSs of the correlated phenotypes with both REI and OSA (REI≥5), and the association of OSA PRS with the correlated phenotypes. Causal relationships were tested using Mendelian Randomization (MR) analysis.

FINDINGS

The dataset included 11,155 participants, 31.03% with OSA. 22 phenotypes were genetically correlated with REI. 10 PRSs covering obesity and fat distribution (BMI, WHR, WHRadjBMI), blood pressure (DBP, PP, MAP), glycaemic control (fasting insulin, HbA1c, HOMA-B) and insomnia were associated with REI and/or OSA. OSA PRS was associated with BMI, WHR, DBP and glycaemic traits (fasting insulin, HbA1c, HOMA-B and HOMA-IR). MR analysis identified robust causal effects of BMI and WHR on OSA, and probable causal effects of DBP, PP, and HbA1c on OSA/REI.

INTERPRETATION

There are shared genetic underpinnings of anthropometric, blood pressure, and glycaemic phenotypes with OSA, with evidence for causal relationships between some phenotypes.

FUNDING

Described in Acknowledgments.

Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods

High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.

transferGWAS: GWAS of images using deep transfer learning

MOTIVATION

Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations.

RESULTS

We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases.

AVAILABILITY AND IMPLEMENTATION

Our method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV₁] and its ratio to forced vital capacity [FEV₁/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV₁ and FEV₁/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10^-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

Rare coding variants in RCN3 are associated with blood pressure

BACKGROUND

While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.

RESULTS

Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7).

CONCLUSIONS

Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease

BACKGROUND

Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants.

METHODS

Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics).

RESULTS

We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10-11. These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P=3.27×10-30) and MMP-3 (β=-0.60±0.05, P=1.67×10-32), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P=1.34×10-17) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure.

CONCLUSIONS

Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Upregulated heme biosynthesis increases obstructive sleep apnea severity: a pathway-based Mendelian randomization study

Obstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Iron and heme metabolism, implicated in ventilatory control and OSA comorbidities, was associated with OSA phenotypes in recent admixture mapping and gene enrichment analyses. However, its causal contribution was unclear. In this study, we performed pathway-level transcriptional Mendelian randomization (MR) analysis to investigate the causal relationships between iron and heme related pathways and OSA. In primary analysis, we examined the expression level of four iron/heme Reactome pathways as exposures and four OSA traits as outcomes using cross-tissue cis-eQTLs from the Genotype-Tissue Expression portal and published genome-wide summary statistics of OSA. We identify a significant putative causal association between up-regulated heme biosynthesis pathway with higher sleep time percentage of hypoxemia (p = 6.14 × 10-3). This association is supported by consistency of point estimates in one-sample MR in the Multi-Ethnic Study of Atherosclerosis using high coverage DNA and RNA sequencing data generated by the Trans-Omics for Precision Medicine project. Secondary analysis for 37 additional iron/heme Gene Ontology pathways did not reveal any significant causal associations. This study suggests a causal association between increased heme biosynthesis and OSA severity.

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value <5×10-9) in 36 loci associated with telomere length, including 20 newly associated loci (13 were replicated in external datasets). There was little evidence of effect size heterogeneity across populations. Fine-mapping at OBFC1 indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes.

Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program

Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.

A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

Multi-Trait Genomic Risk Stratification for Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40-70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50-94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

Whole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program

BACKGROUND

Sleep-disordered breathing is a common disorder associated with significant morbidity. The genetic architecture of sleep-disordered breathing remains poorly understood. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, we performed the first whole-genome sequence analysis of sleep-disordered breathing.

METHODS

The study sample was comprised of 7988 individuals of diverse ancestry. Common-variant and pathway analyses included an additional 13,257 individuals. We examined five complementary traits describing different aspects of sleep-disordered breathing: the apnea-hypopnea index, average oxyhemoglobin desaturation per event, average and minimum oxyhemoglobin saturation across the sleep episode, and the percentage of sleep with oxyhemoglobin saturation < 90%. We adjusted for age, sex, BMI, study, and family structure using MMSKAT and EMMAX mixed linear model approaches. Additional bioinformatics analyses were performed with MetaXcan, GIGSEA, and ReMap.

RESULTS

We identified a multi-ethnic set-based rare-variant association (p = 3.48 × 10-8) on chromosome X with ARMCX3. Additional rare-variant associations include ARMCX3-AS1, MRPS33, and C16orf90. Novel common-variant loci were identified in the NRG1 and SLC45A2 regions, and previously associated loci in the IL18RAP and ATP2B4 regions were associated with novel phenotypes. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Additional analyses identified significantly associated pathways.

CONCLUSIONS

We have identified the first gene-based rare-variant associations with objectively measured sleep-disordered breathing traits. Our results increase the understanding of the genetic architecture of sleep-disordered breathing and highlight associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis, and HIF1A-mediated hypoxic response.

Genome sequencing unveils a regulatory landscape of platelet reactivity

Platelet aggregation at the site of atherosclerotic vascular injury is the underlying pathophysiology of myocardial infarction and stroke. To build upon prior GWAS, here we report on 16 loci identified through a whole genome sequencing (WGS) approach in 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) participants deeply phenotyped for platelet aggregation. We identify the RGS18 locus, which encodes a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with expression quantitative trait loci (eQTL) signatures for RGS18 expression in platelets. Gene-based approaches implicate the SVEP1 gene, a known contributor of coronary artery disease risk. Sentinel variants at RGS18 and PEAR1 are associated with thrombosis risk and increased gastrointestinal bleeding risk, respectively. Our WGS findings add to previously identified GWAS loci, provide insights regarding the mechanism(s) by which genetics may influence cardiovascular disease risk, and underscore the importance of rare variant and regulatory approaches to identifying loci contributing to complex phenotypes.

Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term 'variance stratification'. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.

Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program

Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease

BACKGROUND

Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations.

METHODS

A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center's (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank.

RESULTS

Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB.

CONCLUSIONS

Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research

Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further focusing on the environmental and genetic etiology of sleep disorders, and by development of risk stratification algorithms, to identify people who are at risk or are affected by, sleep disorders. These studies rely on comprehensive sleep-related data which often contains complex multi-dimensional physiological and molecular measurements across multiple timepoints. Thus, sleep research is well-suited for the application of computational approaches that can handle high-dimensional data. Here, we survey recent advances in machine and deep learning together with the availability of large human cohort studies with sleep data that can jointly drive the next breakthroughs in the sleep-research field. We describe sleep-related data types and datasets, and present some of the tasks in the field that can be targets for algorithmic approaches, as well as the challenges and opportunities in pursuing them.

A Genome-wide Association Study Discovers 46 Loci of the Human Metabolome in the Hispanic Community Health Study/Study of Latinos

Variation in levels of the human metabolome reflect changes in homeostasis, providing a window into health and disease. The genetic impact on circulating metabolites in Hispanics, a population with high cardiometabolic disease burden, is largely unknown. We conducted genome-wide association analyses on 640 circulating metabolites in 3,926 Hispanic Community Health Study/Study of Latinos participants. The estimated heritability for 640 metabolites ranged between 0%-54% with a median at 2.5%. We discovered 46 variant-metabolite pairs (p value < 1.2 × 10-10, minor allele frequency ≥ 1%, proportion of variance explained [PEV] mean = 3.4%, PEVrange = 1%-22%) with generalized effects in two population-based studies and confirmed 301 known locus-metabolite associations. Half of the identified variants with generalized effect were located in genes, including five nonsynonymous variants. We identified co-localization with the expression quantitative trait loci at 105 discovered and 151 known loci-metabolites sets. rs5855544, upstream of SLC51A, was associated with higher levels of three steroid sulfates and co-localized with expression levels of SLC51A in several tissues. Mendelian randomization (MR) analysis identified several metabolites associated with coronary heart disease (CHD) and type 2 diabetes. For example, two variants located in or near CYP4F2 (rs2108622 and rs79400241, respectively), involved in vitamin E metabolism, were associated with the levels of octadecanedioate and vitamin E metabolites (gamma-CEHC and gamma-CEHC glucuronide); MR analysis showed that genetically high levels of these metabolites were associated with lower odds of CHD. Our findings document the genetic architecture of circulating metabolites in an underrepresented Hispanic/Latino community, shedding light on disease etiology.

Whole genome sequence analysis of pulmonary function and COPD in 19,996 multi-ethnic participants

Chronic obstructive pulmonary disease (COPD), diagnosed by reduced lung function, is a leading cause of morbidity and mortality. We performed whole genome sequence (WGS) analysis of lung function and COPD in a multi-ethnic sample of 11,497 participants from population- and family-based studies, and 8499 individuals from COPD-enriched studies in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We identify at genome-wide significance 10 known GWAS loci and 22 distinct, previously unreported loci, including two common variant signals from stratified analysis of African Americans. Four novel common variants within the regions of PIAS1, RGN (two variants) and FTO show evidence of replication in the UK Biobank (European ancestry n ~ 320,000), while colocalization analyses leveraging multi-omic data from GTEx and TOPMed identify potential molecular mechanisms underlying four of the 22 novel loci. Our study demonstrates the value of performing WGS analyses and multi-omic follow-up in cohorts of diverse ancestry.

Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand KITLG/SCF and Gene-By-Air-Pollution Interaction

Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV1), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV1 on chromosome 12 in 867 African American children with asthma (P = 1.26 × 10-8, β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV1 Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with KITLG (KIT ligand, also known as SCF), and their minor alleles were associated with increased expression of the KITLG gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure (P = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV1, possibly mediated through KITLG, in African American children with asthma. This is the first study that has identified a genetic association between lung function and KITLG, which has established a role in orchestrating allergic inflammation in asthma.

A rank-based normalization method with the fully adjusted full-stage procedure in genetic association studies

In the area of genetic epidemiology, studies of the genotype-phenotype associations have made significant contributions to human complicated trait genetics. These studies depend on specialized statistical methods for uncover the association between traits and genetic variants, both common and rare variants. Often, in analyzing such studies, potentially confounding factors, such as social and environmental conditions, are required to be involved. Multiple linear regression is the most widely used type of regression analysis when the outcome of interest is quantitative traits. Many statistical tests for identifying genotype-phenotype associations using linear regression rely on the assumption that the traits (or the residuals) of the regression follow a normal distribution. In genomic research, the rank-based inverse normal transformation (INT) is one of the most popular approaches to reach normally distributed traits (or normally distributed residuals). Many researchers believe that applying the INT to the non-normality of the traits (or the non-normality of the residuals) is required for valid inference, because the phenotypic (or residual) outliers and non-normality have the significant influence on both the type I error rate control and statistical power, especially under the situation in rare-variant association testing procedures. Here we propose a test for exploring the association of the rare variant with the quantitative trait by using a fully adjusted full-stage INT. Using simulations we show that the fully adjusted full-stage INT is more appropriate than the existing INT methods, such as the fully adjusted two-stage INT and the INT-based omnibus test, in testing genotype-phenotype associations with rare variants, especially when genotypes are uncorrelated with covariates. The fully adjusted full-stage INT retains the advantages of the fully adjusted two-stage INT and ameliorates the problems of the fully adjusted two-stage INT for analysis of rare variants under non-normality of the trait. We also present theoretical results on these desirable properties. In addition, the two available methods with non-normal traits, the quantile/median regression method and the Yeo-Johnson power transformation, are also included in simulations for comparison with these desirable properties.

Power loss due to testing association between covariate-adjusted traits and genetic variants

Multiple linear regression is commonly used to test for association between genetic variants and continuous traits and estimate genetic effect sizes. Confounding variables are controlled for by including them as additional covariates. An alternative technique that is increasingly used is to regress out covariates from the raw trait and then perform regression analysis with only the genetic variants included as predictors. In the case of single-variant analysis, this adjusted trait regression (ATR) technique is known to be less powerful than the traditional technique when the genetic variant is correlated with the covariates We extend previous results for single-variant tests by deriving exact relationships between the single-variant score, Wald, likelihood-ratio, and F test statistics and their ATR analogs. We also derive the asymptotic power of ATR analogs of the multiple-variant score and burden tests. We show that the maximum power loss of the ATR analog of the multiple-variant score test is completely characterized by the canonical correlations between the set of genetic variants and the set of covariates. Further, we show that for both single- and multiple-variant tests, the power loss for ATR analogs increases with increasing stringency of Type 1 error control ( α ) and increasing correlation (or canonical correlations) between the genetic variant (or multiple variants) and covariates. We recommend using ATR only when maximum canonical correlation between variants and covariates is low, as is typically true.

Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies

Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN.

Association between sleep disordered breathing and epigenetic age acceleration: Evidence from the Multi-Ethnic Study of Atherosclerosis

BACKGROUND

Sleep disordered breathing (SDB) is a common disorder that results in oxidative stress and inflammation and is associated with multiple age-related health outcomes. Epigenetic age acceleration is a DNA methylation (DNAm)-based marker of fast biological aging. We examined the associations of SDB traits with epigenetic age acceleration.

METHODS

A sample of 622 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) had blood DNAm measured and underwent Type 2 in-home polysomnography that assessed apnea-hypopnea index (AHI), percentage of sleep time with oxygen saturation lower than 90% (Per90), and arousal index. DNAm data provided measures of DNAm-Age acceleration and DNAm-PhenoAge acceleration. The association of each SDB trait with age acceleration was estimated using linear regression, controlling for covariates. In secondary analyses, we studied the associations of SDB traits with epigenetic age acceleration 2-10 years after sleep study in 530 individuals from the Framingham Heart Study (FHS).

FINDINGS

In MESA, AHI was associated with greater DNAm-PhenoAge acceleration (β = 0.03; 95% CI [0.001, 0.06]). Arousal index was associated with greater DNAm-Age acceleration (β = 0.04; 95% CI [0.01, 0.07]). Both associations were stronger in women than men. In the secondary FHS analyses, Per90 was associated with greater DNAm-Age acceleration and this association was stronger in men.

INTERPRETATION

More severe SDB was associated with epigenetic age acceleration in both cohorts. Future work should prospectively study short- and long-term effects of SDB, and whether treatment reduces epigenetic age acceleration among those individuals with SBD.

FUNDING

National Institutes of Health.

Variants Associated with the Ankle Brachial Index Differ by Hispanic/Latino Ethnic Group: a genome-wide association study in the Hispanic Community Health Study/Study of Latinos

Lower extremity peripheral artery disease (PAD) burden differs by race/ethnicity. Although familial aggregation and heritability studies suggest a genetic basis, little is known about the genetic susceptibility to PAD, especially in non-European descent populations. Genome-wide association studies (GWAS) of the ankle brachial index (ABI) and PAD (defined as an ABI < 0.90) have not been conducted in Hispanics/Latinos. We performed a GWAS of PAD and the ABI in 7,589 participants aged >45 years from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We also performed GWAS for ABI stratified by Hispanic/Latino ethnic subgroups: Central American, Mexican, and South American (Mainland group), and Cuban, Dominican, and Puerto Rican (Caribbean group). We detected two genome-wide significant associations for the ABI in COMMD10 in Puerto Ricans, and at SYBU in the Caribbean group. The lead SNP rs4466200 in the COMMD10 gene had a replication p = 0.02 for the ABI in Multi-Ethnic Study of Atherosclerosis (MESA) African Americans, but it did not replicate in African Americans from the Cardiovascular Health Study (CHS). In a regional look-up, a nearby SNP rs12520838 had Bonferroni adjusted p = 0.05 (unadjusted p = 7.5 × 10⁻⁵) for PAD in MESA Hispanics. Among three suggestive associations (p < 10⁻⁷) in subgroup-specific analyses, DMD on chromosome X, identified in Central Americans, replicated in MESA Hispanics (p = 2.2 × 10⁻⁴). None of the previously reported ABI and PAD associations in whites generalized to Hispanics/Latinos.

Genome-wide Significance Thresholds for Admixture Mapping Studies

Admixture mapping studies have become more common in recent years, due in part to technological advances and growing international efforts to increase the diversity of genetic studies. However, many open questions remain about appropriate implementation of admixture mapping studies, including how best to control for multiple testing, particularly in the presence of population structure. In this study, we develop a theoretical framework to characterize the correlation of local ancestry and admixture mapping test statistics in admixed populations with contributions from any number of ancestral populations and arbitrary population structure. Based on this framework, we develop an analytical approach for obtaining genome-wide significance thresholds for admixture mapping studies. We validate our approach via analysis of simulated traits with real genotype data for 8,064 unrelated African American and 3,425 Hispanic/Latina women from the Women's Health Initiative SNP Health Association Resource (WHI SHARe). In an application to these WHI SHARe data, our approach yields genome-wide significant p value thresholds of 2.1 × 10-5 and 4.5 × 10-6 for admixture mapping studies in the African American and Hispanic/Latina cohorts, respectively. Compared to other commonly used multiple testing correction procedures, our method is fast, easy to implement (using our publicly available R package), and controls the family-wise error rate even in structured populations. Importantly, we note that the appropriate admixture mapping significance threshold depends on the number of ancestral populations, generations since admixture, and population structure of the sample; as a result, significance thresholds are not, in general, transferable across studies.