Heritability estimation using a regularized regression approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome

Malka Gorfine, Sonja I. Berndt, Jenny Chang-Claude, Michael Hoffmeister, Loic Le Marchand, John Potter, Martha L. Slattery, Nir Keret, Ulrike Peters, Li Hsu

Research output: Contribution to journalArticlepeer-review

Abstract

The popular Genome-wide Complex Trait Analysis (GCTA) software uses the randomeffects models for estimating the narrow-sense heritability based on GWAS data of unrelated individuals without knowing and identifying the causal loci. Many methods have since extended this approach to various situations. However, since the proportion of causal loci among the variants is typically very small and GCTA uses all variants to calculate the similarities among individuals, the estimation of heritability may be unstable, resulting in a large variance of the estimates. Moreover, if the causal SNPs are not genotyped, GCTA sometimes greatly underestimates the true heritability. We present a novel narrow-sense heritability estimator, named HERRA, using well-developed ultra-high dimensional machinelearning methods, applicable to continuous or dichotomous outcomes, as other existing methods. Additionally, HERRA is applicable to time-to-event or age-at-onset outcome, which, to our knowledge, no existing method can handle. Compared to GCTA and LDAK for continuous and binary outcomes, HERRA often has a smaller variance, and when causal SNPs are not genotyped, HERRA has a much smaller empirical bias. We applied GCTA, LDAK and HERRA to a large colorectal cancer dataset using dichotomous outcome (4,312 cases, 4,356 controls, genotyped using Illumina 300K), the respective heritability estimates of GCTA, LDAK and HERRA are 0.068 (SE = 0.017), 0.072 (SE = 0.021) and 0.110 (SE = 5.19 x 10-3). HERRA yields over 50% increase in heritability estimate compared to GCTA or LDAK.

Original languageEnglish
Article numbere0181269
JournalPLoS ONE
Volume12
Issue number8
DOIs
StatePublished - Aug 2017

Funding

FundersFunder number
GECCO
National CancerU01 CA164930, U01 CA185094, Institute-U01 CA137088
National Institutes of HealthR01 CA60987, R01 CA189532
National Heart, Lung, and Blood InstituteHHSN268201100004C, HHSN271201100004C
NIH Office of the DirectorS10OD020069
Deutsche ForschungsgemeinschaftBR 1704/6-3, BR 1704/6-1, CH 117/1-1, BR 1704/6-4
Bundesministerium für Bildung und ForschungR01 CA48998, Z01 CP 010200, 01KH0404, U01 HG004446, K05 CA154337, R01 CA63464, 01ER0814, GEI U01 HG 004438, P01 CA033619

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