bioRxivGeneticsPreprint
Whole genome association testing in 333,100 individuals across three biobanks identifies rare non-coding single variant and genomic aggregate associations with height
Gareth HawkesiD, Robin N. BeaumontiD, Zilin LiiD, Ravi MandlaiD, Xihao LiiD, Christine M. AlbertiD, Donna K. ArnettiD, Allison E. Ashley‐KochiD, Aneel A. AshraniiD, Kathleen C. BarnesiD, Eric BoerwinkleiD, Jennifer A. BrodyiD, April P. CarsoniD, Nathalie ChamiiD, Yii-Der Ida CheniD, Mina K. ChungiD, Joanne E. CurraniD, Dawood DarbariD, Patrick T. EllinoriD, Myriam FornageiD, Victor R. GordeukiD, Xiuqing GuoiD, Jiang HeiD, Chii‐Min HwuiD, Rita Rastogi KalyaniiD, , Michael N. WeedoniD
Abstract
The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N=200,003), TOPMed (N=87,652) and All of Us (N=45,445). We performed rare (<0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal, intergenic and deep-intronic annotation. We observed 29 independent variants associated with height at P < 6 x 10-10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed a novel approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.
- organism: human
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 9
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