Model Comparison of Heritability Enrichment Analysis in Livestock Population

Abstract

Heritability enrichment analysis is an important means of exploring the genetic architecture of complex traits in human genetics. Heritability enrichment is typically defined as the proportion of an SNP subset explained heritability, divided by the proportion of SNPs. Heritability enrichment enables better study of underlying complex traits, such as functional variant/gene subsets, biological networks and metabolic pathways detected through integrating explosively increased omics data. This would be beneficial for genomic prediction of disease risk in humans and genetic values estimation of important economical traits in livestock and plant species. However, in livestock, factors affecting the heritability enrichment estimation of complex traits have not been examined. Previous studies on humans reported that the frequencies, effect sizes, and levels of linkage disequilibrium (LD) of underlying causal variants (CVs) would affect the heritability enrichment estimation. Therefore, the distribution of heritability across the genome should be fully considered to obtain the unbiased estimation of heritability enrichment. To explore the performance of different heritability enrichment models in livestock populations, we used the VanRaden, GCTA and α models, assuming different α values, and the LDAK model, considering LD weight. We simulated three types of phenotypes, with CVs from various minor allele frequency (MAF) ranges: genome-wide (0.005 ≤ MAF ≤ 0.5), common (0.05 ≤ MAF ≤ 0.5), and uncommon (0.01 ≤ MAF < 0.05). The performances of the models with two different subsets (one of which contained known CVs and the other consisting of randomly selected markers) were compared to verify the accuracy of heritability enrichment estimation of functional variant sets. Our results showed that models with known CV subsets provided more robust enrichment estimation. Models with different α values tended to provide stable and accurate estimates for common and genome-wide CVs (relative deviation 0.5–2.2%), while tending to underestimate the enrichment of uncommon CVs. As the α value increased, enrichments from 15.73% higher than true value (i.e., 3.00) to 48.93% lower than true value for uncommon CVs were observed. In addition, the long-range LD windows (e.g., 5000 kb) led to large bias of the enrichment estimations for both common and uncommon CVs. Overall, heritability enrichment estimations were sensitive for the α value assumption and LD weight consideration of different models. Accuracy would be greatly improved by using a suitable model. This study would be helpful in understanding the genetic architecture of complex traits and provides a reference for genetic analysis in the livestock population.

Evaluation of heritability partitioning approaches in livestock populations

“…Although this two-component strategy reduces computational costs and allows fitting a LDMS model, it is therefore not recommended. This is an important observation as this is a common strategy [ 10 , 11 , 32 , 33 , 35 – 37 ].…”

“…In livestock, a large fraction of pairs of individuals would have levels above such a threshold. Recently, Cai et al [ 35 ] conducted a study to evaluate different GREML approaches for estimating heritability enrichment in a cattle population. They used data from 2,000 Holstein bulls imputed for about 700,000 markers, and mainly evaluated the accuracy of the estimators for three different MAF categories.…”

“…They used data from 2,000 Holstein bulls imputed for about 700,000 markers, and mainly evaluated the accuracy of the estimators for three different MAF categories. Although some models gave unbiased results, biased estimators were observed when parameters from the simulated and fitted models did not match [ 35 ]. In particular, they found that estimated enrichment values were biased when CVs were enriched in rare alleles and that using LD scores calculated in too large windows resulted in biased estimates.…”

“…Importantly, our data were imputed at the whole-genome sequence level, providing a finer resolution for annotation. Compared to the study by Cai et al [ 35 ], we included more functional annotation groups, including information from a recently published ATAC-SEQ peak catalogue [ 5 ], and we explored more scenarios (CVs could be enriched as a function of MAF, LD score and functional annotation). Using this approach, we first observed that in relatively simple scenarios (no stratification by MAF or LD, with CVs enriched for a single functional category), the methods were unbiased, but that the estimates showed high levels of variation.…”

“…Importantly, the LD or MS groups fitted in the partitioning methods should match those that are truly enriched in CVs, an information that is rarely known. Other elements could further bias the results, such as the relationship between the MAF or LD scores of CVs and the magnitude of their effects, as previously highlighted by Speed et al [ 31 ] or Cai et al [ 35 ]. For example, the fitted GRMs could assume equal SNP contribution to the genetic variance (rare alleles having then larger effects) or comparable effect sizes for all SNPs regardless of MAF (common SNPs having higher contribution to the genetic variance), whereas the true relationship between CVs and MAF could be different.…”