Recovery of trait heritability from whole genome sequence data
Open accessPreprint — published as 10.1038/s41588-021-00997-7
Abstract
Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2–5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be largely recovered from whole-genome sequence (WGS) data on 25,465 unrelated individuals of European ancestry. We assigned 33.7 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned genetic variance accordingly. The estimated heritability was 0.68 (SE 0.10) for height and 0.30 (SE 0.10) for BMI, with a range of ~0.60 – 0.71 for height and ~0.25 – 0.35 for BMI, depending on quality control and analysis strategies. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection thereon. Cumulatively variants with 0.0001 < MAF < 0.1 explained 0.47 (SE 0.07) and 0.30 (SE 0.10) of heritability for height and BMI, respectively. Our results imply that rare variants, in particular those in regions of low LD, is a major source of the still missing heritability of complex traits and disease.
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The collective contribution of low-frequency and rare risk alleles to overall trait variability appears modest compared with that attributable to common variants.
“Although the collective contribution of low-frequency and rare risk alleles to overall trait variability appears modest compared with that attributable to common variants45,48”
From A brief history of human disease genetics (2020) · cites this paper as [48]
This enables more rigorous evaluation of the contribution of rare and common variants to trait susceptibility and supports enumeration of allelic series critical for studies of disease mechanism and therapeutic target optimization.
“This enables more rigorous evaluation of the contribution of rare and common variants to trait susceptibility48 and supports the enumeration of ‘allelic series’ (sets of alleles of varying frequency, effect size and direction that disrupt the same gene) critical for studies of disease mechanism and therapeutic target optimization89,96.”
From A brief history of human disease genetics (2020) · cites this paper as [48]
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BackgroundHeritability estimates for 361 blood metabolites across 40 genome-wide association studies
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“The missing heritability gap will be narrowed with bigger and better genome-wide association studies and with whole-genome sequencing that assesses all DNA differences in the genome rather than several hundred thousand SNPs assessed in current studies 24 .”
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“Recent advances in DNA sequencing have also enabled assessing the contribution of rare genetic variants to heritable traits [109].”
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“%) per individual genome, 84 which can help explain the phenotypic variance of complex trait, 85 justifying the necessity to adopt whole genome sequencing technology in the field of sport and exercise genomics.”
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“While these trends have been observed in previous studies [29, 9, 30], the ability of RHE-mc to jointly fit multiple variance components allows us to estimate effect sizes at SNPs with MAF as low as 0.”
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“Increasing the GWA sample size and employing whole-genome sequencing approaches that can capture rare variants are among the approaches in use to narrow the missing heritability gap [34].”
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BackgroundMeasuring heritable contributions to Alzheimer’s disease: polygenic risk score analysis with twins
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“In studies of other complex traits such as BMI and height, whole genome sequencing was shown to recover the expected heritability, suggesting that standard GWASs miss a relevant part of the genetic contribution to polygenic traits (Wainschtein et al. , 2019).”
Correlations between complex human phenotypes vary by genetic background, gender, and environment
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“Additionally, recent large-scale sequencing studies have demonstrated that remaining unexplained heritability in complex traits and diseases can be accounted for by rare variants that are not in LD with any common variation (93,94).”
BackgroundRare genetic variants explain missing heritability in smoking
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Quantitative Human Paleogenetics: What can Ancient DNA Tell us About Complex Trait Evolution?
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- Authors
- Wainschtein, P., Jain, D., Zheng, Z., TOPMed Anthropometry Working Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Cupples, L. A., Shadyab, A. H., McKnight, B., Shoemaker, B. M., Mitchell, B. D., Psaty, B. M., Kooperberg, C., Liu, C.-T., Albert, C. M., Roden, D., Chasman, D. I., Darbar, D., Lloyd-Jones, D. M., Arnett, D. K., Regan, E. A., Boerwinkle, E., Rotter, J. I., O’Connell, J. R., Yanek, L. R., de Andrade, M., Allison, M. A., McDonald, M.-L. N., Chung, M. K., Fornage, M., Chami, N., Smith, N. L., Ellinor, P. T., Vasan, R. S., Mathias, R. A., Loos, R. J. F., Rich, S. S., Lubitz, S. A., Heckbert, S. R., Redline, S., Guo, X., Chen, Y.-D. I., Laurie, C. A., Hernandez, R. D., McGarvey, S. T., Goddard, M. E., Laurie, C. C., North, K. E., Lange, L. A., Weir, B. S., Yengo, L., Yang, J., & Visscher, P. M.
- Affiliations
- The University of Queensland (AU)
- University of Washington (US)
- University of Alabama at Birmingham (US)
- Boston University (US)
- University of California San Diego (US)
- University of Maryland, Baltimore (US)
- Brigham and Women's Hospital (US)
- University of Illinois Chicago (US)
- Northwestern University (US)
- University of Kentucky (US)
- The Lundquist Institute (US)
- Johns Hopkins University (US)
- Cleveland Clinic (US)
- United States Department of Veterans Affairs (US)
- Massachusetts General Hospital (US)
- Harvard University (US)
- Broad Institute (US)
- The University of Melbourne (AU)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Wainschtein, Pierrick
- Yang, Jian
- Visscher, Peter M.
- ORCID
- Wainschtein, Pierrick
- Jain, Deepti
- Zheng, Zhili
- Cupples, L. Adrienne
- Shadyab, Aladdin H.
- Mitchell, Braxton D.
- Psaty, Bruce M.
- Kooperberg, Charles
- Liu, Ching-Ti
- Albert, Christine M.
- Roden, Dan
- Chasman, Daniel I.
- Arnett, Donna K.
- Regan, Elizabeth A.
- Boerwinkle, Eric
- Rotter, Jerome I.
- O’Connell, Jeffrey R.
- Yanek, Lisa R.
- Allison, Matthew A.
- McDonald, Merry-Lynn N.
- Chung, Mina K
- Fornage, Myriam
- Chami, Nathalie
- Smith, Nicholas L.
- Ellinor, Patrick T.
- Vasan, Ramachandran S.
- Mathias, Rasika A.
- Loos, Ruth J.F.
- Rich, Stephen S.
- Lubitz, Steven A.
- Heckbert, Susan R.
- Redline, Susan
- Chen, Y.-D Ida
- Laurie, Cecelia A.
- Hernandez, Ryan D.
- McGarvey, Stephen T.
- Goddard, Michael E.
- Laurie, Cathy C.
- North, Kari E.
- Weir, Bruce S.
- Yengo, Loic
- Yang, Jian
- Visscher, Peter M.
- Venue
- Nature Genetics
- Publisher
- openRxiv
- Published
- 2019-03-25
- Volume
- 54
- Issue
- 3
- Pages
- 263-273
- ISSN
- 1061-4036 (ISSN-L)
- PMC
- PMC9119698
- OpenAlex
- W4220827000
- Cited by
- 515
- References
- 47
- Citations by year
- 2026: 46 · 2025: 83 · 2024: 78 · 2023: 82 · 2022: 77 · 2021: 43 · 2020: 61 · 2019: 44 · 2016: 1
- FWCI
- 48.53
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion)
- NHRI Institutional Repository (submittedVersion)
- Funders
- U.S. Department of Health and Human Services (75N92020D00005, HHSN268201600002 C, UL1-TR-000040, N01-HC-95159, UL1TR001079, and HHSN268201600004C, N01-HC-95161, DK063491, 1R01HL139731, 75N92020D00003, N01-HC-95168, UL1TR001420, HL071025, 75N92020D00001, R01 MH100141, UL1-TR-001881, HHSN268201600018 C, N01-HC-95160, 75N92020D00002, 75N92020D00007, HHSN268201600003C, N01-HC-95164, N01-HC-95162, N01-HC-95167, HHSN268201500003I, N01-HC-95165, R01DK124097, 75N92020D00004, M01-RR000052, R01DK110113, R01DK107786, 3U54HG003067-13S1, 75N92020D00006, 3U54HG003273-12S2, HL087698, R01HL142302, HHSN268201600004 C, HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C, HL112064, NR0224103, N01-HC-95163, HL49762, N01HC95166, HHSN268201600001 C)
- U.S. Department of Veterans Affairs (IK6 BX006185)
- American Heart Association (N01-HC-95167, N01HC95161, UL1TR001881, 18SFRN34250007, N01-HC-95162, N01-HC-95160, UL1-TR-001420, UL1-TR-001079, N01-HC-95164, N01HC95163, UL1 TR000040, DK063491, N01-HC 95168, N01HC95165, N01HC95166, N01-HC-95159, HHSN268201500003I, N01-HC-)
- Women's Health Initiative (HHSN268201600018C, HHSN268201600002C, HHSN268201600003C, HHSN268201600001C, HHSN268201600004C)
- Cleveland Clinic
- University of Washington (N01-HC-95159, HHSN268201500015C)
- Johns Hopkins University (N01-HC-95162)
- Sylvia and Charles Viertel Charitable Foundation
- Broad Institute (3U54HG003067‐13S1, HHSN268201500014C)
- Department of Education and Training (DE200100425, FL180100072, DP160102400)
- National Institutes of Health (75N92020D00005, n01hc085081-021, 75N92020D00003, HHSN268201600018C, n01hc085079-024, UL1TR001420, n01hc085080-026, 75N92020D00006, HHSN268201600003C, 75N92020D00001 ,, HHSN268201600004C, 75N92021D00006, UL1TR001079, HHSN268201600001C, HHSN268201500003I, 75N92020D00004, 75N92020D00007, HHSN268201600002C, UL1TR000040, 75N92020D00002, 18SFRN34250007, UL1-TR-, UL1TR001881)
- Medical Research Council (1113400, 1078037)
- Australian Research Council (FL180100072, DP160102400, 1113400, FT180100186, DE200100425, FT180100186 and FL180100072)
- National Health and Medical Research Council (N01-HC-95159, N01-HC-95164, 75N92020D00005, N01-HC-95163, N01-HC-95166, 1113400, N01-HC-95160, N01-HC-95162, UL1-TR-001079, N01-HC-95168, 75N92020D00003, 75N92020D00002, 75N92020D00001, 1078037, HHSN268201500003I, N01-HC-95165, 75N92020D00006, N01-HC-95161, N01-HC-95167, 75N92020D00004, UL1-TR-000040, 75N92020D00007)
- National Institute on Aging (K01AG059898, R01AG023629)
- National Heart, Lung, and Blood Institute (DK-063491, R01HL111314, HHSN268201500014C, HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C and HHSN268201600004C, 75N 92021D00006, N01 HC-95159, N01-HC-95160, and UL1-TR-001420, 75N92020D00001, R01HL098433, N01 HC-95168, HL071025, and HHSN268201600004C, 75N92020D00006, HHSN268201600018C, HL092165, N01 HC95167, N01 HC085079, X01HL134588, (N01-HC-95163, 75N92020D00007, contracts HHSN268201600018C, N01 HC-95161, UL1-TR-000040, UL1-TR001079, N01 HC95165, N01HC95164, K23HL105897, N01 HC085080, HHSN268201600004C, HHSN 268200800007C, R35HL135818, HHSN268201800001C, U01HL130114, 75N92020D00002, UL1-TR-, HHSN268201600001C, N01-HC, phs001211, HC-95168, 75N92020D00005, HL112064, UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420, R01HL158071, HHSN26820150 0003I, 75N92020D00003, HHSN268201200036C, R01HL059367, U01HL080295, HHSN268201600038I, U01HL72518, HHSN268201500015C, N01‐HC‐95166, contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C, N01 HC085081, HHSN268201600003C, HL099747, HL-49762, N01 HC-95162, HC95159, K08HL141601, HL087698, HHSN268201600002C, HHSN268201600033I, 75N92020D00004, R01HL092577, R01HL142302, R01HL139731)
- National Institute of Mental Health (R01MH100141)
- National Human Genome Research Institute (U54HG003067, U54HG003273)
- National Cancer Institute (T32CA154274)
- National Institute of Nursing Research (NR-0224103, N01-HC-95162, HHSN268201600018C, N01-HC-95167, HHSN268201600002C, N01-HC-95165, N01-HC-95160, N01-HC-95164, N01-HC-95161, HHSN268201600003C, N01-HC-95159, N01-HC-95168, N01-HC-95163, HHSN268201600004C, HHSN268201600001C, N01-HC-95166)
- National Institute of General Medical Sciences (R01GM075091, U54GM115428)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK110113, R01DK124097)
- National Institute of Environmental Health Sciences (P30ES010126)
- National Center for Research Resources (N01-HC-95166, N01-HC-95164, N01-HC-95168, N01-HC-95160, UL1RR025005, N01-HC-95162, N01-HC-95163, N01 HC 95161, RR025005, M01RR000052, RR000052, N01-HC-95159, N01 HC 95165, N01-HC-95167)
- National Center for Advancing Translational Sciences (N01-HC-95161, 75N92020D00006, N01-HC-95168, UL1-TR-000040, UL1-TR-001079 and UL1-TR-001420, UL1-TR-000040, UL1-TR-001079, UL1-TR, N01-HC-95162, and UL1‐TR‐001420, 75N92020D00005, N01-HC-95159, DK063491, N01-HC-95160, 75N92020D00002, 75N92020D00001, HHSN268201500003I, N01-HC-95166, N01-HC-95163, 75N92020D00007, 75N92020D00003, N01-HC-95167, N01-HC-95165, N01-HC-95164, RR 025005, 75N92020D00004)
- Westlake Education Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- missing heritability, rare variants, height, body mass index, whole genome sequencing, protein-altering variants, negative selection, common SNPs, European ancestry
- MeSH terms
- Alleles, Humans, Linkage Disequilibrium, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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