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

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Psychiat Genomics Consortium , Lönnqvist , J & Paunio , T 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

Title: Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood
Author: Psychiat Genomics Consortium; Lönnqvist, Jouko; Paunio, Tiina
Contributor: University of Helsinki, Clinicum
University of Helsinki, Department of Psychiatry
Date: 2018-06-07
Language: eng
Number of pages: 10
Belongs to series: American Journal of Human Genetics
ISSN: 0002-9297
URI: http://hdl.handle.net/10138/305741
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 similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-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.
Subject: COMPLEX HUMAN TRAITS
BODY-MASS INDEX
WIDE ASSOCIATION
PARTITIONING HERITABILITY
SUSCEPTIBILITY LOCI
SNP HERITABILITY
HUMAN HEIGHT
SCHIZOPHRENIA
INFORMATION
STATISTICS
3111 Biomedicine
3124 Neurology and psychiatry
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