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LD Score regression distinguishes confounding from polygenicity in genome-wide association studies

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LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. / Schizophrenia Working Group of the Psychiatric Genomics Consortium.
In: Nature Genetics, Vol. 47, No. 3, 03.2015, p. 291-295.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Schizophrenia Working Group of the Psychiatric Genomics Consortium 2015, 'LD Score regression distinguishes confounding from polygenicity in genome-wide association studies', Nature Genetics, vol. 47, no. 3, pp. 291-295. https://doi.org/10.1038/ng.3211

APA

Schizophrenia Working Group of the Psychiatric Genomics Consortium (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47(3), 291-295. https://doi.org/10.1038/ng.3211

Vancouver

Schizophrenia Working Group of the Psychiatric Genomics Consortium. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics. 2015 Mar;47(3):291-295. Epub 2015 Feb 2. doi: 10.1038/ng.3211

Author

Schizophrenia Working Group of the Psychiatric Genomics Consortium. / LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. In: Nature Genetics. 2015 ; Vol. 47, No. 3. pp. 291-295.

Bibtex

@article{bcec82f3d13448278226120568c91bc2,
title = "LD Score regression distinguishes confounding from polygenicity in genome-wide association studies",
abstract = "Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.",
keywords = "Computer Simulation, Genome, Human, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Regression Analysis, Sample Size",
author = "Bulik-Sullivan, {Brendan K.} and Po-Ru Loh and Finucane, {Hilary K.} and Stephan Ripke and Jian Yang and Nick Patterson and Daly, {Mark J.} and Price, {Alkes L.} and Neale, {Benjamin M.} and Jo Knight and {Schizophrenia Working Group of the Psychiatric Genomics Consortium}",
year = "2015",
month = mar,
doi = "10.1038/ng.3211",
language = "English",
volume = "47",
pages = "291--295",
journal = "Nature Genetics",
issn = "1061-4036",
publisher = "Nature Publishing Group",
number = "3",

}

RIS

TY - JOUR

T1 - LD Score regression distinguishes confounding from polygenicity in genome-wide association studies

AU - Bulik-Sullivan, Brendan K.

AU - Loh, Po-Ru

AU - Finucane, Hilary K.

AU - Ripke, Stephan

AU - Yang, Jian

AU - Patterson, Nick

AU - Daly, Mark J.

AU - Price, Alkes L.

AU - Neale, Benjamin M.

AU - Knight, Jo

AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium

PY - 2015/3

Y1 - 2015/3

N2 - Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

AB - Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.

KW - Computer Simulation

KW - Genome, Human

KW - Genome-Wide Association Study

KW - Humans

KW - Linkage Disequilibrium

KW - Polymorphism, Single Nucleotide

KW - Regression Analysis

KW - Sample Size

U2 - 10.1038/ng.3211

DO - 10.1038/ng.3211

M3 - Journal article

C2 - 25642630

VL - 47

SP - 291

EP - 295

JO - Nature Genetics

JF - Nature Genetics

SN - 1061-4036

IS - 3

ER -