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

Danielle Posthuma, Schizophrenia Working Group of the Psychiatric Genomics Consortium

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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.

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

Funding

This research is supported by the Australian National Health and Medical Research Council ( 1080157 , 1087889 ) and the Australian Research Council ( DP160102126 , FT160100229 ). This research has been conducted using the UK Biobank Resource. UK Biobank Research Ethics Committee (REC) approval number is 11/NW/0382. Our reference number approved by UK Biobank is 14575. GERA data came from a grant, the Resource for Genetic Epidemiology Research in Adult Health and Aging ( RC2 AG033067 ; Schaefer and Risch, PIs) awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics. The RPGEH was supported by grants from the Robert Wood Johnson Foundation , the Wayne and Gladys Valley Foundation , the Ellison Medical Foundation , Kaiser Permanente Northern California , and the Kaiser Permanente National and Northern California Community Benefit Programs . The RPGEH and the Resource for Genetic Epidemiology Research in Adult Health and Aging are described in the GERA website (see Web Resources ). This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the WTCCC data is available online. Funding for the WTCCC project was provided by the Wellcome Trust under awards 076113 , 085475 , and 090355 . This research is supported by the Australian National Health and Medical Research Council (1080157, 1087889) and the Australian Research Council (DP160102126, FT160100229). This research has been conducted using the UK Biobank Resource. UK Biobank Research Ethics Committee (REC) approval number is 11/NW/0382. Our reference number approved by UK Biobank is 14575. GERA data came from a grant, the Resource for Genetic Epidemiology Research in Adult Health and Aging (RC2 AG033067; Schaefer and Risch, PIs) awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics. The RPGEH was supported by grants from the Robert Wood Johnson Foundation, the Wayne and Gladys Valley Foundation, the Ellison Medical Foundation, Kaiser Permanente Northern California, and the Kaiser Permanente National and Northern California Community Benefit Programs. The RPGEH and the Resource for Genetic Epidemiology Research in Adult Health and Aging are described in the GERA website (see Web Resources). This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the WTCCC data is available online. Funding for the WTCCC project was provided by the Wellcome Trust under awards 076113, 085475, and 090355.

FundersFunder number
Kaiser Permanente National and Northern California Community Benefit Programs
Kaiser Permanente Northern California
PIs
UCSF Institute for Human Genetics
Wellcome Trust Case-Control Consortium
National Institute on AgingR01AG033067
Ellison Medical Foundation
Robert Wood Johnson Foundation
Wayne and Gladys Valley Foundation
Division of Research, Evaluation, and Communication11/NW/0382, RC2 AG033067
Kaiser Permanente
Wellcome Trust085475, 076113, 090355
Australian Research CouncilFT160100229, DP160102126
National Health and Medical Research Council1087889, 1080157

    Keywords

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

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