Heritability Estimation Approaches Utilizing Genome-Wide Data

Abstract

Prior to the development of genome-wide arrays and whole genome sequencing technologies, heritability estimation mainly relied on the study of related individuals. Over the past decade, various approaches have been developed to estimate SNP-based narrow-sense heritability ( $h_{\mathrm{SNP}}^2$ ) in unrelated individuals. These latter approaches use either individual-level genetic variations or summary results from genome-wide association studies (GWAS). Recently, several studies compared these approaches using extensive simulations and empirical datasets. However, sparse information on hands-on training necessitates revisiting these approaches from the perspective of a stepwise guide for practical applications. Here, we provide an overview of the commonly used SNP-heritability estimation approaches utilizing genome-wide array, imputed or whole genome data from unrelated individuals, or summary results. We not only discuss these approaches based on their statistical concepts, utility, advantages, and limitations, but also provide step-by-step protocols to apply these approaches. For illustration purposes, we estimate $h_{\mathrm{SNP}}^2$ of height and BMI utilizing individual-level data from The Northern Finland Birth Cohort (NFBC) and summary results from the Genetic Investigation of ANthropometric Traits (GIANT;) consortium. We present this review as a template for the researchers who estimate and use heritability in their studies and as a reference for geneticists who develop or extend heritability estimation approaches. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: GREML (GCTA) Alternate Protocol 1: Stratified GREML Basic Protocol 2: LDAK Alternate Protocol 2: Stratified LDAK Basic Protocol 3: Threshold GREML Basic Protocol 4: LD score (LDSC) regression Basic Protocol 5: SumHer.

Keywords: SNP-heritability; individual-level data; summary results.

Figure 1.

A summary of SNP-heritability estimation approaches utilizing individual-level genome-wide SNPs or summary results from previous GWAS. Such data could be generated through array, imputation and whole genome sequencing. REML - Restricted Maximum Likelihood Method; PCGC - Phenotype Correlation-Genotype Correlation; HE – Hasemann Elston Regression; GREML – Genomic Restricted Maximum Likelihood; LDAK – Linkage Disequilibrium adjusted Kinship LDSC – LD Score Regression.

Figure 2.

Estimation of SNP-heritability of height and BMI using various approaches utilizing individual-level genetic data and summary results from previous GWAS. Threshold GREML shows variance attributable to the first GRM. Stratified GREML and LDAK approaches show sum of variances attributable to all genetic components.

Figure 3.

Partitioning the SNP-heritability using MAF and LD bins. For GREML-MS, LDAK-Thin-MS and LDAK-MS, MAF bins were created as 0.01 < MAF ≤ 0.1, 0.1 < MAF ≤ 0.2, 0.2 < MAF ≤ 0.3, 0.3 < MAF ≤ 0.4 and 0.4 < MAF ≤ 0.5. For GREML-LDMS, each MAF bin was further divided into quartiles of average regional LD score or SNP LD score.