scholarly journals FunSPU: a versatile and adaptive multiple functional annotation-based association test of whole-genome sequencing data

2018 ◽  
Author(s):  
Yiding Ma ◽  
Peng Wei
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Complex Traits ◽  
Functional Annotation ◽  
Rare Variants ◽  
Biological Information ◽  
Whole Genome Sequencing Data ◽  
Biological Knowledge ◽  
Whole Genome ◽  
Functional Annotations

AbstractDespite ongoing large-scale population-based whole-genome sequencing (WGS) projects such as the NIH NHLBI TOPMed program and the NHGRI Genome Sequencing Program, WGS-based association analysis of complex traits remains a tremendous challenge due to the large number of rare variants, many of which are non-trait-associated neutral variants. External biological knowledge, such as functional annotations based on ENCODE, may be helpful in distinguishing causal rare variants from neutral ones; however, each functional annotation can only provide certain aspects of the biological functions. Our knowledge for selecting informative annotations a priori is limited, and incorporating non-informative annotations will introduce noise and lose power. We propose FunSPU, a versatile and adaptive test that incorporates multiple biological annotations and is adaptive at both the annotation and variant levels and thus maintains high power even in the presence of noninformative annotations. In addition to extensive simulations, we illustrate our proposed test using the TWINSUK cohort (n=1,752) of UK10K WGS data based on six functional annotations: CADD, RegulomeDB, FunSeq, Funseq2, GERP++, and GenoSkyline. We identified genome-wide significant genetic loci on chromosome 19 near gene TOMM40 and APOC4-APOC2 associated with low-density lipoprotein (LDL), which are replicated in the UK10K ALSPAC cohort (n=1,497). These replicated LDL-associated loci were missed by existing rare variant association tests that either ignore external biological information or rely on a single source of biological knowledge. We have implemented the proposed test in an R package “FunSPU”.

scholarly journals High definition analyses of single cohort, whole genome sequencing data provides a direct route to defining sub-phenotypes and personalising medicine

2021 ◽  
Author(s):  
KE Joyce ◽  
E Onabanjo ◽  
S Brownlow ◽  
F Nur ◽  
KO Olupona ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variants ◽  
Phenotypic Variability ◽  
Clinical Impact ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
High Definition ◽  
Genomic Damage

ABSTRACTPossession of a clinical or molecular disease label alters the context in which life-course events operate, but rarely explains the phenotypic variability observed by clinicians. Whole genome sequencing of unselected endothelial vasculopathy patients demonstrated more than a third had rare, likely deleterious variants in clinically-relevant genes unrelated to their vasculopathy (1 in 10 within platelet genes; 1 in 8 within coagulation genes; and 1 in 4 within erythrocyte hemolytic genes). High erythrocyte membrane variant rates paralleled genomic damage and prevalence indices in the general population. In blinded analyses, patients with greater hemorrhagic severity that had been attributed solely to their vasculopathy had more deleterious variants in platelet (Spearman ρ=0.25, p=0.008) and coagulation (Spearman ρ=0.21, p=0.024) genes. We conclude that rare diseases can provide insights for medicine beyond their primary pathophysiology, and propose a framework based on rare variants to inform interpretative approaches to accelerate clinical impact from whole genome sequencing.

scholarly journals Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zihuai He ◽  
Linxi Liu ◽  
Chen Wang ◽  
Yann Le Guen ◽  
Justin Lee ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variants ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Association Tests ◽  
Sequencing Project ◽  
Risk Variants ◽  
Sequencing Studies

AbstractThe analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

scholarly journals Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

2021 ◽  
Author(s):  
Zihuai He ◽  
Linxi Liu ◽  
Chen Wang ◽  
Yann Le Guen ◽  
Justin Lee ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variants ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Association Tests ◽  
Sequencing Project ◽  
Risk Variants ◽  
Sequencing Studies

AbstractThe analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

scholarly journals The contribution of rare whole genome sequencing variants to plasma protein levels and to the missing heritability

2021 ◽  
Author(s):  
Marcin Kierczak ◽  
Nima Rafati ◽  
Julia Höglund ◽  
Hadrien Gourle ◽  
Daniel Schmitz ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Complex Traits ◽  
Rare Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Missing Heritability ◽  
Common Genetic Variants

Abstract Despite the success in identifying effects of common genetic variants, using genome-wide association studies (GWAS), much of the genetic contribution to complex traits remains unexplained. Here, we analysed high coverage whole-genome sequencing (WGS) data, to evaluate the contribution of rare genetic variants to 414 plasma proteins. The frequency distribution of genetic variants was skewed towards the rare spectrum, and damaging variants were more often rare. However, only 2.24% of the heritability was estimated to be explained by rare variants. A gene-based approach, developed to also capture the effect of rare variants, identified associations for 249 of the proteins, which was 25% more as compared to a GWAS. Out of those, 24 associations were driven by rare variants, clearly highlighting the capacity of aggregated tests and WGS data. We conclude that, while many rare variants have considerable phenotypic effects, their contribution to the missing heritability is limited by their low frequencies.

scholarly journals Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits

2018 ◽  
Author(s):  
Arthur Gilly ◽  
Daniel Suveges ◽  
Karoline Kuchenbaecker ◽  
Martin Pollard ◽  
Lorraine Southam ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Complex Traits ◽  
Rare Variants ◽  
Whole Genome ◽  
Isolated Population ◽  
Gamma Glutamyltransferase ◽  
Cardiometabolic Traits

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1,457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens overlapping with, and mostly independent of established common variant signals (ADIPOQ and adiponectin, P=4.2×10−8; APOC3 and triglyceride levels, P=1.58×10−26; GGT1 and gamma-glutamyltransferase, P=2.3×10−6; UGT1A9 and bilirubin, P=1.9×10−8), and identify replicating evidence for a burden associated with triglyceride levels in FAM189A (P=2.26×10−8), indicating a role for this gene in lipid metabolism.

scholarly journals STAAR Workflow: A cloud-based workflow for scalable and reproducible rare variant analysis

2021 ◽  
Author(s):  
Sheila M. Gaynor ◽  
Kenneth E. Westerman ◽  
Lea L. Ackovic ◽  
Xihao Li ◽  
Zilin Li ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Rare Variant ◽  
Rare Variants ◽  
Association Studies ◽  
Whole Genome ◽  
Sequencing Analysis ◽  
Functional Annotations ◽  
Rare Variant Analysis ◽  
Variant Analysis

AbstractSummaryWe developed the STAAR WDL workflow to facilitate the analysis of rare variants in whole genome sequencing association studies. The open-access STAAR workflow written in the workflow description language (WDL) allows a user to perform rare variant testing for both gene-centric and genetic region approaches, enabling genome-wide, candidate, and conditional analyses. It incorporates functional annotations into the workflow as introduced in the STAAR method in order to boost the rare variant analysis power. This tool was specifically developed and optimized to be implemented on cloud-based platforms such as BioData Catalyst Powered by Terra. It provides easy-to-use functionality for rare variant analysis that can be incorporated into an exhaustive whole genome sequencing analysis pipeline.Availability and implementationThe workflow is freely available from https://dockstore.org/workflows/github.com/sheilagaynor/STAAR_workflow.

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