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

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  • Guiyan Ni
  • Gerhard Moser
  • Stephan Ripke
  • Benjamin M. Neale
  • Aiden Corvin
  • James T.R. Walters
  • Kai How Farh
  • Peter A. Holmans
  • Phil Lee
  • Brendan Bulik-Sullivan
  • David A. Collier
  • Hailiang Huang
  • Tune H. Pers
  • Ingrid Agartz
  • Esben Agerbo
  • Margot Albus
  • Madeline Alexander
  • Farooq Amin
  • Silviu A. Bacanu
  • Martin Begemann
  • Richard A. Belliveau
  • Judit Bene
  • Sarah E. Bergen
  • Elizabeth Bevilacqua
  • Tim B. Bigdeli
  • Donald W. Black
  • Richard Bruggeman
  • Nancy G. Buccola
  • Randy L. Buckner
  • William Byerley
  • Wiepke Cahn
  • Guiqing Cai
  • Dominique Campion
  • Rita M. Cantor
  • Vaughan J. Carr
  • Noa Carrera
  • Stanley V. Catts
  • Kimberly D. Chambert
  • Raymond C.K. Chan
  • Ronald Y.L. Chen
  • Eric Y.H. Chen
  • Wei Cheng
  • Eric F.C. Cheung
  • Siow Ann Chong
  • Mark Hansen
  • Thomas Hansen
  • Sandra Meier
  • Line Olsen
  • Henrik B. Rasmussen
  • Thomas Werge
  • Wellcome Trust Case-Control Consortium
  • Schizophrenia Working Group of the Psychiatric Genomics Consortium
  • Psychosis Endophenotypes International Consortium
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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.

Original languageEnglish
JournalAmerican Journal of Human Genetics
Volume102
Issue number6
Pages (from-to)1185-1194
Number of pages10
ISSN0002-9297
DOIs
Publication statusPublished - 7 Jun 2018

    Research areas

  • accuracy, biasedness, body mass index, genetic correlation, genome-wide SNPs, genomic restricted maximum likelihood, height, linkage disequilibrium score regression, schizophrenia, SNP heritability

ID: 55287925