Summary statistic analyses can mistake confounding bias for heritability

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Summary statistic analyses can mistake confounding bias for heritability. / Holmes, John B; Speed, Doug; Balding, David J.

In: Genetic Epidemiology, Vol. 43, No. 8, 12.2019, p. 930-940.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Holmes, JB, Speed, D & Balding, DJ 2019, 'Summary statistic analyses can mistake confounding bias for heritability', Genetic Epidemiology, vol. 43, no. 8, pp. 930-940. https://doi.org/10.1002/gepi.22259

APA

Holmes, J. B., Speed, D., & Balding, D. J. (2019). Summary statistic analyses can mistake confounding bias for heritability. Genetic Epidemiology, 43(8), 930-940. https://doi.org/10.1002/gepi.22259

MLA

Holmes, John B, Doug Speed and David J Balding. "Summary statistic analyses can mistake confounding bias for heritability". Genetic Epidemiology. 2019, 43(8). 930-940. https://doi.org/10.1002/gepi.22259

Vancouver

Holmes JB, Speed D, Balding DJ. Summary statistic analyses can mistake confounding bias for heritability. Genetic Epidemiology. 2019 Dec;43(8):930-940. doi: 10.1002/gepi.22259

Author

Holmes, John B ; Speed, Doug ; Balding, David J. / Summary statistic analyses can mistake confounding bias for heritability. In: Genetic Epidemiology. 2019 ; Vol. 43, No. 8. pp. 930-940.

Bibtex

title = "Summary statistic analyses can mistake confounding bias for heritability",
abstract = "Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.",
keywords = "GWAS, heritability estimation, misspecified models",
author = "Holmes, {John B} and Doug Speed and Balding, {David J}",
note = "{\textcopyright} 2019 Wiley Periodicals, Inc.",
year = "2019",
month = dec,
doi = "10.1002/gepi.22259",
language = "English",
volume = "43",
pages = "930--940",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "JohnWiley & Sons, Inc.",
number = "8",

}

RIS

TY - JOUR

T1 - Summary statistic analyses can mistake confounding bias for heritability

AU - Holmes, John B

AU - Speed, Doug

AU - Balding, David J

N1 - © 2019 Wiley Periodicals, Inc.

PY - 2019/12

Y1 - 2019/12

N2 - Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.

AB - Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.

KW - GWAS

KW - heritability estimation

KW - misspecified models

U2 - 10.1002/gepi.22259

DO - 10.1002/gepi.22259

M3 - Journal article

C2 - 31541496

VL - 43

SP - 930

EP - 940

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 8

ER -