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Pathan N, Deng WQ, Di Scipio M, Khan M, Mao S, Morton RW, Lali R, Pigeyre M, Chong MR, Paré G. A method to estimate the contribution of rare coding variants to complex trait heritability. Nat Commun 2024; 15:1245. [PMID: 38336875 PMCID: PMC10858280 DOI: 10.1038/s41467-024-45407-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
It has been postulated that rare coding variants (RVs; MAF < 0.01) contribute to the "missing" heritability of complex traits. We developed a framework, the Rare variant heritability (RARity) estimator, to assess RV heritability (h2RV) without assuming a particular genetic architecture. We applied RARity to 31 complex traits in the UK Biobank (n = 167,348) and showed that gene-level RV aggregation suffers from 79% (95% CI: 68-93%) loss of h2RV. Using unaggregated variants, 27 traits had h2RV > 5%, with height having the highest h2RV at 21.9% (95% CI: 19.0-24.8%). The total heritability, including common and rare variants, recovered pedigree-based estimates for 11 traits. RARity can estimate gene-level h2RV, enabling the assessment of gene-level characteristics and revealing 11, previously unreported, gene-phenotype relationships. Finally, we demonstrated that in silico pathogenicity prediction (variant-level) and gene-level annotations do not generally enrich for RVs that over-contribute to complex trait variance, and thus, innovative methods are needed to predict RV functionality.
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Affiliation(s)
- Nazia Pathan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
| | - Wei Q Deng
- Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, Canada
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada
| | - Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Mohammad Khan
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Shihong Mao
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
| | - Robert W Morton
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
| | - Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
| | - Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | - Michael R Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada.
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, Canada.
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Lee D, Kim Y, Chung Y, Lee D, Seo D, Choi TJ, Lim D, Yoon D, Lee SH. Accuracy of genotype imputation based on reference population size and marker density in Hanwoo cattle. JOURNAL OF ANIMAL SCIENCE AND TECHNOLOGY 2021; 63:1232-1246. [PMID: 34957440 PMCID: PMC8672260 DOI: 10.5187/jast.2021.e117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 11/20/2022]
Abstract
Recently, the cattle genome sequence has been completed, followed by developing a
commercial single nucleotide polymorphism (SNP) chip panel in the animal genome
industry. In order to increase statistical power for detecting quantitative
trait locus (QTL), a number of animals should be genotyped. However, a
high-density chip for many animals would be increasing the genotyping cost.
Therefore, statistical inference of genotype imputation (low-density chip to
high-density) will be useful in the animal industry. The purpose of this study
is to investigate the effect of the reference population size and marker density
on the imputation accuracy and to suggest the appropriate number of reference
population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo
cattle were divided into reference and validation populations. The reference
sets consisted of 50k (38,916) marker data and different population sizes (500,
1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four
validation sets (Total 889) and the different marker density (5k [5,000], 10k
[10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct
comparison of the true genotype and the imputed genotype. In conclusion, when
the lowest marker density (5k) was used in the validation set, according to the
reference population size, the imputation accuracy was 0.793 to 0.929. On the
other hand, when the highest marker density (15k), according to the reference
population size, the imputation accuracy was 0.904 to 0.967. Moreover, the
reference population size should be more than 1,000 to obtain at least 88%
imputation accuracy in Hanwoo cattle.
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Affiliation(s)
- DooHo Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea
| | - Yeongkuk Kim
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea
| | - Yoonji Chung
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea
| | - Dongjae Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea
| | - Dongwon Seo
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea
| | - Tae Jeong Choi
- National Institute of Animal Science, Cheonan 31000, Korea
| | - Dajeong Lim
- Animal Genomics and Bioinformatics Division, National Institute of Animal Science, Wanju 55365, Korea
| | - Duhak Yoon
- Department of Animal Science & Biotechnology, Kyungpook National University, Sangju 37224, Korea
| | - Seung Hwan Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea
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