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SumHer better estimates the SNP heritability of complex traits from summary statistics

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Speed, Doug, Balding, David J.·2019·Nature Genetics

DOI 10.1038/s41588-018-0279-5PMC PMC6485398

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Abstract

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
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Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577
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Genetic studies of body mass index yield new insights for obesity biology
Study, T. L. C., Locke, A. E., The ADIPOGen Consortium, The AGEN-BMI Working Group, The CARDIOGRAMplusC4D Consortium, The CKDGen Consortium, The, G. L. G. C., The, I. C. B. P., The MAGIC Investigators, The MuTHER Consortium, et al. (2015) · Nature · doi:10.1038/nature14177 · Cited by 4,534
Common SNPs explain a large proportion of the heritability for human height
Yang, J., Benyamin, B., McEvoy, B. P., Gordon, S., Henders, A. K., Nyholt, D. R., Madden, P. A., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2010) · Nature Genetics · doi:10.1038/ng.608 · Cited by 4,446
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(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336
An atlas of genetic correlations across human diseases and traits
ReproGen Consortium, Bulik-Sullivan, B., Psychiatric Genomics Consortium, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3, Finucane, H. K., Anttila, V., Gusev, A., Day, F. R., Loh, P.-R., Duncan, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3406 · Cited by 4,255
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Authors: Speed, D., & Balding, D. J.
Affiliations: Aarhus University (DK)
The University of Melbourne (AU)
Corresponding author: Speed, Doug
ORCID: Speed, Doug
Balding, David J.
Venue: Nature Genetics
Publisher: Springer Science and Business Media LLC
Published: 2018-12-03
Volume: 51
Issue: 2
Pages: 277-284
ISSN: 1061-4036 (ISSN-L)
1546-1718
DOI: 10.1038/s41588-018-0279-5
PMC: PMC6485398
OpenAlex: W2901957554
Cited by: 254
References: 67
Citations by year: 2026: 30 · 2025: 31 · 2024: 37 · 2023: 33 · 2022: 24 · 2021: 37 · 2020: 33 · 2019: 26 · 2018: 3
FWCI: 15.18
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by-nc
www.springer.com/tdm (tdm)
Locations: Nature Genetics (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Minerva Access (University of Melbourne) (submittedVersion)
Funders: Vanderbilt University (U01HG006378)
Northwestern University (U01HG006378, U01HG006388)
University of Michigan (U01 AG009740, RC2AG036495, RC4AG039029)
Aarhus Universitet
Wellcome Trust (754513)
Vanderbilt University Medical Center (U01HG006378, U01HG006385)
Aarhus Universitets Forskningsfond (7025- 00094B, 754513)
Medical Research Council (MR/L012561/1)
National Institute on Aging (U01AG009740, RC2AG036495, and RC4AG039029, RC2AG036495, and RC4AG039029, RC4AG039029, U01AG009740)
National Human Genome Research Institute (U01HG006385, U01HG006379, U01HG006830, U01HG006375, U01HG006380, U01HG006382, U01HG006828, U01HG006378, U01HG006388, U01HG006389)
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 › Genetic Mapping and Diversity in Plants and Animals
Keywords: SNP heritability, confounding, genetic correlation, complex traits, genome-wide association study, summary statistics, genetic architecture, LD score regression
MeSH terms: Humans, Models, Genetic, Phenotype, Software, Quantitative Trait, Heritable, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Inheritance Patterns, Genome-Wide Association Study

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SumHer better estimates the SNP heritability of complex traits from summary statistics

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