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

G Ni; G Moser; S Ripke; BM Neale; A Corvin; JTR Walters; KH Farh; PA Holmans; P Lee; B Bulik-Sullivan; DA Collier; H Huang; TH Pers; I Agartz; E Agerbo; M Albus; M Alexander; F Amin; SA Bacanu; M Begemann; RA Belliveau; J Bene; SE Bergen; E Bevilacqua; TB Bigdeli; DW Black; R Bruggeman; NG Buccola; RL Buckner; W Byerley; W Cahn; G Cai; D Campion; RM Cantor; VJ Carr; N Carrera; SV Catts; KD Chambert; RCK Chan; RYL Chen; EYH Chen; W Cheng; EFC Cheung; SA Chong; CR Cloninger; D Cohen; N Cohen; P Cormican; N Craddock; JJ Crowley; D Curtis; M Davidson; KL Davis; F Degenhardt; J Del Favero; D Demontis; D Dikeos; T Dinan; S Djurovic; G Donohoe; E Drapeau; J Duan; F Dudbridge; N Durmishi; P Eichhammer; J Eriksson; V Escott-Price; L Essioux; AH Fanous; MS Farrell; J Frank; L Franke; R Freedman; NB Freimer; M Friedl; JI Friedman; M Fromer; G Genovese; L Georgieva; I Giegling; P Giusti-Rodríguez; S Godard; JI Goldstein; V Golimbet; S Gopal; J Gratten; L de Haan; C Hammer; ML Hamshere; M Hansen; T Hansen; V Haroutunian; AM Hartmann; FA Henskens; S Herms; JN Hirschhorn; P Hoffmann; A Hofman; MV Hollegaard; DM Hougaard

Report

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

G Ni, G Moser, S Ripke, BM Neale, A Corvin, JTR Walters, KH Farh, PA Holmans, P Lee, B Bulik-Sullivan, DA Collier, H Huang, TH Pers, I Agartz, E Agerbo, M Albus, M Alexander, F Amin, SA Bacanu, M Begemann Show all

American Journal of Human Genetics | CELL PRESS | Published : 2018

DOI: 10.1016/j.ajhg.2018.03.021

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 ..

View full abstract

University of Melbourne Researchers

Christos Pantelis Author

Grants

Awarded by National Institute on Aging

Funding Acknowledgements

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.