Dataset Information

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

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

SUBMITTER: Li X

PROVIDER: S-EPMC7483769 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Li Xihao X Li Zilin Z Zhou Hufeng H Gaynor Sheila M SM Liu Yaowu Y Chen Han H Sun Ryan R Dey Rounak R Arnett Donna K DK Aslibekyan Stella S Ballantyne Christie M CM Bielak Lawrence F LF Blangero John J Boerwinkle Eric E Bowden Donald W DW Broome Jai G JG Conomos Matthew P MP Correa Adolfo A Cupples L Adrienne LA Curran Joanne E JE Freedman Barry I BI Guo Xiuqing X Hindy George G Irvin Marguerite R MR Kardia Sharon L R SLR Kathiresan Sekar S Khan Alyna T AT Kooperberg Charles L CL Laurie Cathy C CC Liu X Shirley XS Mahaney Michael C MC Manichaikul Ani W AW Martin Lisa W LW Mathias Rasika A RA McGarvey Stephen T ST Mitchell Braxton D BD Montasser May E ME Moore Jill E JE Morrison Alanna C AC O'Connell Jeffrey R JR Palmer Nicholette D ND Pampana Akhil A Peralta Juan M JM Peyser Patricia A PA Psaty Bruce M BM Redline Susan S Rice Kenneth M KM Rich Stephen S SS Smith Jennifer A JA Tiwari Hemant K HK Tsai Michael Y MY Vasan Ramachandran S RS Wang Fei Fei FF Weeks Daniel E DE Weng Zhiping Z Wilson James G JG Yanek Lisa R LR Neale Benjamin M BM Sunyaev Shamil R SR Abecasis Gonçalo R GR Rotter Jerome I JI Willer Cristen J CJ Peloso Gina M GM Natarajan Pradeep P Lin Xihong X

Nature genetics 20200824 9

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introdu ...[more]

PMID: 32839606

Similar Datasets

Project description:BackgroundThe success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches.Methods & resultsWe conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with 'nonsynonymous' and 'loss of function' consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa.ConclusionIncorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure