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Am J Hum Genet DOI:10.1016/j.ajhg.2018.03.021

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

Publication TypeJournal Article
Year of Publication2018
AuthorsNi, G, Moser, G, Wray, NR, S Lee, H
Corporate AuthorsSchizophrenia Working Group of the Psychiatric Genomics Consortium
JournalAm J Hum Genet
Volume102
Issue6
Pages1185-1194
Date Published2018 06 07
ISSN1537-6605
KeywordsAdult, Body Height, Computer Simulation, Databases, Genetic, Genome, Human, Genotype, Haplotypes, Humans, Inheritance Patterns, Likelihood Functions, Linkage Disequilibrium, Phenotype, Polymorphism, Single Nucleotide, Regression Analysis, Schizophrenia
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.

DOI10.1016/j.ajhg.2018.03.021
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/29754766?dopt=Abstract

Alternate JournalAm. J. Hum. Genet.
PubMed ID29754766
PubMed Central IDPMC5993419
Grant List / / Wellcome Trust / United Kingdom
R00 MH101367 / MH / NIMH NIH HHS / United States
R01 AG033067 / AG / NIA NIH HHS / United States