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Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

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Harvard

Ni, G, Moser, G, Ripke, S, Neale, BM, Corvin, A, Walters, JTR, Farh, KH, Holmans, PA, Lee, P, Bulik-Sullivan, B, Collier, DA, Huang, H, Pers, TH, Agartz, I, Agerbo, E, Albus, M, Alexander, M, Amin, F, Bacanu, SA, Begemann, M, Belliveau, RA, Bene, J, Bergen, SE, Bevilacqua, E, Bigdeli, TB, Black, DW, Bruggeman, R, Buccola, NG, Buckner, RL, Byerley, W, Cahn, W, Cai, G, Campion, D, Cantor, RM, Carr, VJ, Carrera, N, Catts, SV, Chambert, KD, Chan, RCK, Chen, RYL, Chen, EYH, Cheng, W, Cheung, EFC, Chong, SA, Hansen, M, Hansen, T, Meier, S, Olsen, L, Rasmussen, HB, Werge, T, Wellcome Trust Case-Control Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium & Psychosis Endophenotypes International Consortium 2018, 'Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood' American Journal of Human Genetics, vol. 102, no. 6, pp. 1185-1194. https://doi.org/10.1016/j.ajhg.2018.03.021

APA

Ni, G., Moser, G., Ripke, S., Neale, B. M., Corvin, A., Walters, J. T. R., ... Psychosis Endophenotypes International Consortium (2018). Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. American Journal of Human Genetics, 102(6), 1185-1194. https://doi.org/10.1016/j.ajhg.2018.03.021

CBE

Ni G, Moser G, Ripke S, Neale BM, Corvin A, Walters JTR, Farh KH, Holmans PA, Lee P, Bulik-Sullivan B, Collier DA, Huang H, Pers TH, Agartz I, Agerbo E, Albus M, Alexander M, Amin F, Bacanu SA, Begemann M, Belliveau RA, Bene J, Bergen SE, Bevilacqua E, Bigdeli TB, Black DW, Bruggeman R, Buccola NG, Buckner RL, Byerley W, Cahn W, Cai G, Campion D, Cantor RM, Carr VJ, Carrera N, Catts SV, Chambert KD, Chan RCK, Chen RYL, Chen EYH, Cheng W, Cheung EFC, Chong SA, Hansen M, Hansen T, Meier S, Olsen L, Rasmussen HB, Werge T, Wellcome Trust Case-Control Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Psychosis Endophenotypes International Consortium. 2018. Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. American Journal of Human Genetics. 102(6):1185-1194. https://doi.org/10.1016/j.ajhg.2018.03.021

MLA

Vancouver

Author

Ni, Guiyan ; Moser, Gerhard ; Ripke, Stephan ; Neale, Benjamin M. ; Corvin, Aiden ; Walters, James T.R. ; Farh, Kai How ; Holmans, Peter A. ; Lee, Phil ; Bulik-Sullivan, Brendan ; Collier, David A. ; Huang, Hailiang ; Pers, Tune H. ; Agartz, Ingrid ; Agerbo, Esben ; Albus, Margot ; Alexander, Madeline ; Amin, Farooq ; Bacanu, Silviu A. ; Begemann, Martin ; Belliveau, Richard A. ; Bene, Judit ; Bergen, Sarah E. ; Bevilacqua, Elizabeth ; Bigdeli, Tim B. ; Black, Donald W. ; Bruggeman, Richard ; Buccola, Nancy G. ; Buckner, Randy L. ; Byerley, William ; Cahn, Wiepke ; Cai, Guiqing ; Campion, Dominique ; Cantor, Rita M. ; Carr, Vaughan J. ; Carrera, Noa ; Catts, Stanley V. ; Chambert, Kimberly D. ; Chan, Raymond C.K. ; Chen, Ronald Y.L. ; Chen, Eric Y.H. ; Cheng, Wei ; Cheung, Eric F.C. ; Chong, Siow Ann ; Hansen, Mark ; Hansen, Thomas ; Meier, Sandra ; Olsen, Line ; Rasmussen, Henrik B. ; Werge, Thomas ; Wellcome Trust Case-Control Consortium ; Schizophrenia Working Group of the Psychiatric Genomics Consortium ; Psychosis Endophenotypes International Consortium. / Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. In: American Journal of Human Genetics. 2018 ; Vol. 102, No. 6. pp. 1185-1194.

Bibtex

@article{6c0155d53f3548bebbf007335239c4ef,
title = "Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood",
abstract = "Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.",
keywords = "accuracy, biasedness, body mass index, genetic correlation, genome-wide SNPs, genomic restricted maximum likelihood, height, linkage disequilibrium score regression, schizophrenia, SNP heritability",
author = "Guiyan Ni and Gerhard Moser and Stephan Ripke and Neale, {Benjamin M.} and Aiden Corvin and Walters, {James T.R.} and Farh, {Kai How} and Holmans, {Peter A.} and Phil Lee and Brendan Bulik-Sullivan and Collier, {David A.} and Hailiang Huang and Pers, {Tune H.} and Ingrid Agartz and Esben Agerbo and Margot Albus and Madeline Alexander and Farooq Amin and Bacanu, {Silviu A.} and Martin Begemann and Belliveau, {Richard A.} and Judit Bene and Bergen, {Sarah E.} and Elizabeth Bevilacqua and Bigdeli, {Tim B.} and Black, {Donald W.} and Richard Bruggeman and Buccola, {Nancy G.} and Buckner, {Randy L.} and William Byerley and Wiepke Cahn and Guiqing Cai and Dominique Campion and Cantor, {Rita M.} and Carr, {Vaughan J.} and Noa Carrera and Catts, {Stanley V.} and Chambert, {Kimberly D.} and Chan, {Raymond C.K.} and Chen, {Ronald Y.L.} and Chen, {Eric Y.H.} and Wei Cheng and Cheung, {Eric F.C.} and Chong, {Siow Ann} and Mark Hansen and Thomas Hansen and Sandra Meier and Line Olsen and Rasmussen, {Henrik B.} and Thomas Werge and {Wellcome Trust Case-Control Consortium} and {Schizophrenia Working Group of the Psychiatric Genomics Consortium} and {Psychosis Endophenotypes International Consortium}",
year = "2018",
month = "6",
day = "7",
doi = "10.1016/j.ajhg.2018.03.021",
language = "English",
volume = "102",
pages = "1185--1194",
journal = "American Journal of Human Genetics",
issn = "0002-9297",
publisher = "Cell Press",
number = "6",

}

RIS

TY - JOUR

T1 - Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

AU - Ni, Guiyan

AU - Moser, Gerhard

AU - Ripke, Stephan

AU - Neale, Benjamin M.

AU - Corvin, Aiden

AU - Walters, James T.R.

AU - Farh, Kai How

AU - Holmans, Peter A.

AU - Lee, Phil

AU - Bulik-Sullivan, Brendan

AU - Collier, David A.

AU - Huang, Hailiang

AU - Pers, Tune H.

AU - Agartz, Ingrid

AU - Agerbo, Esben

AU - Albus, Margot

AU - Alexander, Madeline

AU - Amin, Farooq

AU - Bacanu, Silviu A.

AU - Begemann, Martin

AU - Belliveau, Richard A.

AU - Bene, Judit

AU - Bergen, Sarah E.

AU - Bevilacqua, Elizabeth

AU - Bigdeli, Tim B.

AU - Black, Donald W.

AU - Bruggeman, Richard

AU - Buccola, Nancy G.

AU - Buckner, Randy L.

AU - Byerley, William

AU - Cahn, Wiepke

AU - Cai, Guiqing

AU - Campion, Dominique

AU - Cantor, Rita M.

AU - Carr, Vaughan J.

AU - Carrera, Noa

AU - Catts, Stanley V.

AU - Chambert, Kimberly D.

AU - Chan, Raymond C.K.

AU - Chen, Ronald Y.L.

AU - Chen, Eric Y.H.

AU - Cheng, Wei

AU - Cheung, Eric F.C.

AU - Chong, Siow Ann

AU - Hansen, Mark

AU - Hansen, Thomas

AU - Meier, Sandra

AU - Olsen, Line

AU - Rasmussen, Henrik B.

AU - Werge, Thomas

AU - Wellcome Trust Case-Control Consortium

AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium

AU - Psychosis Endophenotypes International Consortium

PY - 2018/6/7

Y1 - 2018/6/7

N2 - Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

AB - Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

KW - accuracy

KW - biasedness

KW - body mass index

KW - genetic correlation

KW - genome-wide SNPs

KW - genomic restricted maximum likelihood

KW - height

KW - linkage disequilibrium score regression

KW - schizophrenia

KW - SNP heritability

UR - http://www.scopus.com/inward/record.url?scp=85046126170&partnerID=8YFLogxK

U2 - 10.1016/j.ajhg.2018.03.021

DO - 10.1016/j.ajhg.2018.03.021

M3 - Journal article

VL - 102

SP - 1185

EP - 1194

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

SN - 0002-9297

IS - 6

ER -

ID: 55287925