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

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...

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Vydáno v:American journal of human genetics Ročník 102; číslo 6; s. 1185
Hlavní autoři: Ni, Guiyan, Moser, Gerhard, Wray, Naomi R, Lee, S Hong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 07.06.2018
Témata:
Adult
Body Height - genetics
Computer Simulation
Databases, Genetic
Genome, Human
Genotype
Haplotypes - genetics
Humans
Inheritance Patterns - genetics
Likelihood Functions
Linkage Disequilibrium - genetics
Phenotype
Polymorphism, Single Nucleotide - genetics
Regression Analysis
Schizophrenia - genetics
biasedness
genome-wide SNPs
body mass index
linkage disequilibrium score regression
accuracy
genetic correlation
schizophrenia
genomic restricted maximum likelihood
SNP heritability
height
ISSN:1537-6605, 1537-6605
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Shrnutí: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.
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ISSN:1537-6605
1537-6605
DOI:10.1016/j.ajhg.2018.03.021