Trans-Omics For Precision Medicine Research Articles

1722-P: Colocalization of TOPMed Whole Genome Sequencing Analysis and Tissue-Specific eQTL Signals Detects Target Genes for Type 2 Diabetes Risk

Type 2 diabetes (T2D) risk is highly heritable, and although many associated loci have been identified, analyses of regulatory regions in most whole-genome sequencing (WGS) studies have not been specific to relevant tissues. The availability of tissue-specific expression quantitative trait (eQTL) information can further elucidate the functional role of non-coding regulatory variants. A WGS (>38x sequencing depth) association study was performed for variants in 9,663 cases and 35,050 controls from the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program. eQTL data from 48 different tissues was obtained from the Genotype-Tissue Expression (GTEx) Portal. To generate loci of interest, 200-kb windows were considered upstream and downstream of each variant found to be genome-wide significant (p < 5x10^-8) in both the TOPMed and GTEx datasets. A total of 11 regions in different tissues were significantly associated with T2D status, centered around variants such as rs6585201 in the ascending aorta (TCF7L2, TOPMed p = 1.18x10^-9, GTEx p = 1.12x10^-10, but did not localize to the strongest signal in the region), rs76895963 in the cerebellum (CCND2, TOPMed p = 3.51x10^-9, GTEx p = 4.40x10^-17), and rs4898431 in ovarian tissue (DUSP9, TOPMed p = 1.88x10^-8, GTEx p = 4.39x10^-8). We plan to implement eCAVIAR to examine causal variant colocalization, with the advantages of leveraging summary statistics and accounting for the possibility of more than one causal variant per locus. These results confirm previously published studies, but provide validation of methods we will extend to larger samples and diabetes-related outcomes. Disclosure M.D. Szeto: None. H.M. Highland: None. A. Manning: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK078616, K01DK107836)

30-OR: Whole Genome Sequence Analysis of Type 2 Diabetes Risk in 44,713 Humans of Diverse Ancestry in the TOPMed Study

Risk of developing type 2 diabetes (T2D) is heritable, with over 403 independent genetic variants having been identified primarily through analysis of common variants in European ancestry populations. To further illuminate the genetic architecture of T2D, we conducted a whole genome sequence (WGS) association study (>38x depth) of common, low frequency, and rare variants in 9,663 individuals with T2D and 35,050 controls of diverse ancestry from NHLBI’s Trans-Omics for Precision Medicine (TOPMed) study. We conducted single variant analyses and SKAT tests of rare variants (MAF <1%) annotated with pancreatic islet specific regulatory annotation to define variant sets. In single variant analyses, a total of 15 regions were significantly associated (α=5e-08) with T2D, seven of which are novel associations (in or near ODF2L, LMAN2, KCNV1, CBR1, VLDLR-AS1, LINC01052 and NKX2-5). These new associations were either ancestry specific or rare variants. For instance, a variant not present in other ancestral groups, near KCNV1, was associated with increased risk of T2D (MAF 5.5%, P= 1.57e-08, OR=1.43) in African ancestry. Set based testing of rare variants identified seven regions associated with T2D in at least one ancestry. Six of these were noncoding: in African ancestry CBR1 gene centric variants (cumulative MAC[cMAC]=623, P = 5.85e-09) and an active promoter region in proximity to MRPL46 and MRPS11 (cMAC=3,218, P = 4.89e-07); and in Asian ancestry an enhancer hub containing NXPH3 (cMAC=3,317, P = 3.42e-05) and an active enhancer region linked to RP11-135A1.2 and HES1 (cMAC=57, P = 4.87e-07) were associated with T2D. KLRC4 predicted deleterious coding variants were associated with T2D in the European ancestry group (cMAC=25, P = 3.63e-06). In large scale WGS analyses, we extended our knowledge of the genetic basis of T2D through the inclusion of ancestrally diverse samples, variant typing without imputation, and use of tissue specific regulatory annotation to identify new T2D loci. Disclosure H.M. Highland: None. J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. A. Manning: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (5U01DK078616-10, K01DK107836)

Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data.

Genetic variants associated with disease outcomes can be used to develop personalized treatment. To reach this precision medicine goal, hundreds of large-scale genome-wide association studies (GWAS) have been conducted in the past decade to search for promising genetic variants associated with various traits. They have successfully identified tens of thousands of disease-related variants. However, in total these identified variants explain only part of the variation for most complex traits. There remain many genetic variants with small effect sizes to be discovered, which calls for the development of (a) GWAS with more samples and more comprehensively genotyped variants, for example, the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program is planning to conduct whole genome sequencing on over 100 000 individuals; and (b) novel and more powerful statistical analysis methods. The current dominating GWAS analysis approach is the "single trait" association test, despite the fact that many GWAS are conducted in deeply phenotyped cohorts including many correlated and well-characterized outcomes, which can help improve the power to detect novel variants if properly analyzed, as suggested by increasing evidence that pleiotropy, where a genetic variant affects multiple traits, is the norm in genome-phenome associations. We aim to develop pleiotropy informed powerful association test methods across multiple traits for GWAS. Since it is generally very hard to access individual-level GWAS phenotype and genotype data for those existing GWAS, due to privacy concerns and various logistical considerations, we develop rigorous statistical methods for pleiotropy informed adaptive multitrait association test methods that need only summary association statistics publicly available from most GWAS. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical P-value for the proposed adaptive test without the need of resampling or permutation. We illustrate the performance of proposed methods through application to joint association test of GWAS meta-analysis summary data for several glycemic traits. Our proposed adaptive test identified several novel loci missed by individual trait based GWAS meta-analysis. All the proposed methods are implemented in a publicly available R package.

Populations from under‐represented backgrounds are not adequately represented in clinical databases

Personalized medicine (PM) is a field in medicine that leverages information about a patient's genes, environment and behavior to prevent, diagnose, and treat disease. Health care providers rely on publicly available genetic databases for recommendations for implementing PM. For example, the Clinical Pharmacogenetics Implementation Consortium (CPIC) makes recommendations for clinical implementation of pharmacogenetics tests by creating, curating, and sharing peer‐reviewed, evidence‐based and updated drug‐gene pair clinical practice guidelines. While most drugs with CPIC guidelines have at least some evidence based off of non‐European data, none have been exclusively developed for a specific ancestral group. Similarly, ClinVar, an NIH supported, freely accessible archive, reports relationships between genetic variation and disease phenotypes. However, these databases reflect a measurable bias toward genetic data based on European ancestry, owed in large part to the underrepresentation of non‐white populations in federally funded and published research programs, including genome wide association (GWAS) and whole genome sequencing studies, which in turn limits the ability to identify variants associated with disease and response to certain therapeutics. Inadequate representation further exacerbates existing health disparities. After over a decade of GWAS, non‐European, non‐Asian groups combined account for <4% of individuals represented in the international GWAS catalog. Previously the role that ancestry plays in variant prioritization approaches often implemented in genomic medicine (e.g., Human Gene Mutation Database, ClinVar) was explored and it was determined these databases are missing considerable African‐specific pathogenicity data. Recent developments through national and international initiatives have provided an opportunity to classify new genetic variants and reassess prior variant classifications and deposit this information into public databases. Approximately half of the NHLBI supported Trans‐Omics for Precision Medicine (TOPMed) program's samples represent non‐European samples. Similarly, the NHGRI Population Architecture using Genomics and Epidemiology (PAGE) study aims to better characterize how genetic factors influence susceptibility to disease in a multi‐ethnic dataset. Researchers representing both TOPMed and PAGE previously designed a commercially available multi‐ethnic genotyping array that was enriched with genetic variants representing underrepresented minority populations, which is being widely used, including institutional biobanks. Together, these developments promise to improve genomic diversity and subsequently benefit underrepresented minorities through personalized medicine.Support or Funding InformationNIH grant #HL104608This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Leveraging linkage evidence to identify low-frequency and rare variants on 16p13 associated with blood pressure using TOPMed whole genome sequencing data.

In this study, we investigated low-frequency and rare variants associated with blood pressure (BP) by focusing on a linkage region on chromosome 16p13. We used whole genome sequencing (WGS) data obtained through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program on 395 Cleveland Family Study (CFS) European Americans (CFS-EA). By analyzing functional coding variants and non-coding rare variants with CADD score > 10 residing within the chromosomal region in families with linkage evidence, we observed 25 genes with nominal statistical evidence (burden or SKAT p < 0.05). One of the genes is RBFOX1, an evolutionarily conserved RNA-binding protein that regulates tissue-specific alternative splicing that we previously reported to be associated with BP using exome array data in CFS. After follow-up analysis of the 25 genes in ten independent TOPMed studies with individuals of European, African, and East Asian ancestry, and Hispanics (N = 29,988), we identified variants in SLX4 (p = 2.19 × 10-4) to be significantly associated with BP traits when accounting for multiple testing. We also replicated the associations previously reported for RBFOX1 (p = 0.007). Follow-up analysis with GTEx eQTL data shows SLX4 variants are associated with gene expression in coronary artery, multiple brain tissues, and right atrial appendage of the heart. Our study demonstrates that linkage analysis of family data can provide an efficient approach for detecting rare variants associated with complex traits in WGS data.

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations

Powerful and efficient SNP-set association tests across multiple phenotypes using GWAS summary data.

Many GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data. We build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis. We have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT. Supplementary data are available at Bioinformatics online.

Whole Genome Sequence Association Analysis and Diabetes-centric Functional Annotation of Type 2 Diabetes

Our understanding of the genetic basis of T2D has been significantly advanced by identification of T2D loci through analysis of common variation; however, evidence suggests a role for rare regulatory variation influencing T2D. We leveraged the largest available whole genome sequence (WGS) data from NHLBI’s Trans-Omics for Precision Medicine (TOPMed) initiative and diabetes-centric genomic function data to the interpretation of results. We performed single-variant pooled-ancestry association analyses with (&amp;gt;30x deep sequence) WGS in 13,200 individuals (2,601 T2D and 10,599 controls) from 6 studies. The association analyses were performed in the Analysis Commons hosted on DNANexus. We demonstrated the utility of the OASIS (Omics Analysis, Search and Information System) platform and integrating diabetes-specific functional annotation in a recently discovered locus (GIP-IGF2BP1) to fine-map to the likely causal variants. Our results identified common (minor allele frequency [MAF] &amp;gt; 0.05) variant associations (P &amp;lt; 5 × 10-8) at a known locus with T2D: TCF7L2 (rs7903146, P = 3.8 × 10-12). We also identified a potentially novel association on Chr15q21 with 27 rare variants (MAF = 0.2%, P =1.3 × 10-8, r2=1) spanning a 7.5 MB region. In the Old Order Amish study, 53 individuals carry the haplotype tagged by these 27 variants; 17% of carriers are diagnosed with T2D compared to 3% of the non-carriers. Interrogation of the GIP-IGF2BP1 locus, utilizing diabetes-specific annotation and association evidence of both common and rare non-coding variants, within the OASIS browser implicated nearby genes in the region as being the likely biologic candidates. In conclusion, preliminary results suggest WGS analysis can discover novel loci for complex traits. Work is ongoing to refine annotation for rare variant association tests and perform functional fine-mapping across the genome. We soon will extend our analysis into the next release of WGS data from TOPMed (N∼69,000 individuals). Disclosure J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. J.A. Brody: None. A. Manning: None.

Expert Panel Discusses the Importance of Systems Medicine

Expert Panel Discusses the Importance of Systems Medicine

The National Sleep Research Resource: towards a sleep data commons.

ObjectiveThe gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.ApproachWe used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor.ResultsThe authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data.ConclusionsThe NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.

Abstract P212: Combined Analysis of Longitudinal Cohorts With Case-only Sample Sets for Detecting Genetic Effects on Venous Thromboembolism in Topmed

Background: The Trans-Omics for Precision Medicine (TOPMed) program is performing whole-genome sequencing across multiple studies, including longitudinal cohorts, case-control, and case-only sample sets. Detecting the effects of low frequency variants requires large sample sizes, which can only be achieved by combining data across diverse study designs, including matching of case-only sample sets with controls from other studies. Here we present a strategy for combined analysis of venous thromboembolism (VTE) case status in 3 longitudinal cohorts and 3 case-only sample sets, in the context of whole-genome association studies. Methods: For each cohort study, we sampled ‘pure’ controls (without replacement) from the risk set of each incident case, within strata defined by sex, ancestry and birth-cohort. Pure controls had no event throughout their period of observation. A case’s risk set was defined as controls with no prior VTE history and under observation through an age at least as old as the case. For case-only sample sets, controls for each case were sampled from a cohort study, using the same risk set definition. Because of limited overlap in birth years between the cohort studies and the case-only sample sets, this matching was done within strata defined only by sex and ancestry group. Mixed model logistic regression will be used to account for relatedness as a random effect. Although conditional logistic regression is not practical for whole-genome association studies, case-control matching is implicitly recognized by a fixed effect for age-at-event for each matched set (1 case and &gt;=1 matched controls). Additional fixed effects will include sample set and sex. We will also adjust for variations in case-control ratio among the matched sets. Results: In two cohort studies, we matched 1,231 cases to 4,820 controls (overall ratio = 1:3.9); only 500 controls and 5 cases could not be matched. For the case-only sample sets we matched 2,141 cases to 2,780 controls from one cohort study (overall ratio = 1:1.3); zero controls and only 14 cases could not be matched. Conclusions: We were successfully able to match nearly all cases to controls, and more than 90% of controls were also matched. By matching controls to cases based on age at event, we can account for the different risk of VTE by age clusters. Although this strategy does not provide an asymptotically unbiased estimated of the hazard ratio, compared to classical risk set sampling, it uses a large portion of the available data, it provides odds ratio estimates yielding the correct sign of association, and it reduces the potential influence of resampling subjects with rare variants. This strategy also enables combined analysis of multiple studies with different designs.

Whole genome sequence analyses of brain imaging measures in the Framingham Study

ObjectiveWe sought to identify rare variants influencing brain imaging phenotypes in the Framingham Heart Study by performing whole genome sequence association analyses within the Trans-Omics for Precision Medicine Program.MethodsWe performed association analyses of cerebral and hippocampal volumes and white matter hyperintensity (WMH) in up to 2,180 individuals by testing the association of rank-normalized residuals from mixed-effect linear regression models adjusted for sex, age, and total intracranial volume with individual variants while accounting for familial relatedness. We conducted gene-based tests for rare variants using (1) a sliding-window approach, (2) a selection of functional exonic variants, or (3) all variants.ResultsWe detected new loci in 1p21 for cerebral volume (minor allele frequency [MAF] 0.005, p = 10−8) and in 16q23 for hippocampal volume (MAF 0.05, p = 2.7 × 10−8). Previously identified associations in 12q24 for hippocampal volume (rs7294919, p = 4.4 × 10−4) and in 17q25 for WMH (rs7214628, p = 2.0 × 10−3) were confirmed. Gene-based tests detected associations (p ≤ 2.3 × 10−6) in new loci for cerebral (5q13, 8p12, 9q31, 13q12-q13, 15q24, 17q12, 19q13) and hippocampal volumes (2p12) and WMH (3q13, 4p15) including Alzheimer disease– (UNC5D) and Parkinson disease–associated genes (GBA). Pathway analyses evidenced enrichment of associated genes in immunity, inflammation, and Alzheimer disease and Parkinson disease pathways.ConclusionsWhole genome sequence–wide search reveals intriguing new loci associated with brain measures. Replication of novel loci is needed to confirm these findings.

Whole genome sequence association analysis of sleep-disordered breathing traits in trans-omics for precision medicine (topmed)

Whole genome sequence association analysis of sleep-disordered breathing traits in trans-omics for precision medicine (topmed)

Abstract P093: Analysis Commons: Team Science in a Big-data Environment for Genetic Epidemiology

The growing volume and complexity of whole-genome sequence (WGS) and multi-omic data requires new analytic approaches beyond those developed for the GWAS era. In response to this challenge, we present an Analysis Commons, which brings together genotype and phenotype data from multiple studies along with a suite of powerful and validated analysis tools into a secure cloud-computing framework that is equitably accessible by associated investigators. This framework is designed to address the emerging challenges of multi-center WGS analyses—data sharing mechanisms, phenotype harmonization, -omics integration, annotation—and the need for flexible, secure, efficient, high-performance computing for numerous users. The Analysis Commons is built on the DNAnexus cloud platform, which provides large parallel compute resources and robust security protocols. To permit multi-center data sharing, we implemented two parallel data sharing approaches: (1) a multi-lateral consortium agreement that enables data sharing across multiple studies, and (2) coordinated dbGaP applications among groups of institutions. Investigators with detailed knowledge of the phenotypes and contributing studies harmonize data from multiple sources for maintenance in a central database. The Analysis Commons supports multiple association-analysis software packages, as well as tools for annotation and visualization. Importantly, approved investigators have full access to the combined data sets, facilitating the rapid development and deployment of new methods. We demonstrate the Analysis Commons model with an analysis of fibrinogen in 3999 participants from the Old Order Amish Study and the Framingham Heart Study with WGS from the Trans-Omics for Precision Medicine (TOPMed) Program. We performed and validated single-variant and SKAT analyses using GENESIS and MMAP pipelines, accounting for relatedness with linear mixed models. We confirmed a known association of a nonsynonymous variant in FGG (p=2.5e-9, MAF=0.34%, rs148685782) . No other single variant or SKAT association was significant after correcting for the number of tests. Analyses were run in parallel across 1408 cores and took less than one hour of wall-clock time. The Analysis Commons offers the necessary infrastructure support for analysis of WGS and multi-omic data in a setting that empowers phenotype, analytic, and computational experts to transform raw data into knowledge of the determinants of cardiovascular health.