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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #382731
Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Partitioning SNP heritability using related individuals

Author
item JIANG, JICAI - University Of Maryland
item Vanraden, Paul
item MA, LI - University Of Maryland
item O'CONNELL, JEFFREY - University Of Maryland School Of Medicine

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 4/8/2021
Publication Date: 6/28/2021
Citation: Jiang, J., Van Raden, P.M., Ma, L., O'Connell, J.R. 2021. Partitioning SNP heritability using related individuals [abstract]. Journal of Dairy Science. 104(Suppl. 1):79(abstr. 203).

Interpretive Summary:

Technical Abstract: Partitioning SNP heritability by many functional annotations has been a successful tool for understanding the genetic architecture of complex traits in human genetic studies. It is interesting to do similar analysis for animal productivity traits, as (imputed) whole-genome sequence data of many individuals and various functional annotations have become available in domestic animals. Though many approaches have been developed for that purpose (e.g., LDSC and HE-reg), they are mostly based on approximations tailored to human populations and few can produce statistically efficient estimates for animal genomic studies. To tackle this issue, we present a stochastic MINQUE (Minimum Norm Quadratic Unbiased Estimation) approach for partitioning SNP heritability, which we refer to as MPH. We provide a theoretical analysis comparing LDSC and HE-reg with REML and MPH and demonstrate what LDSC and HE-reg (and similar methods) take advantage of in their approximations: sparse relationships between individuals and relatively weak linkage disequilibrium. We also show that our method is mathematically equivalent to the MC-REML approach implemented in BOLT. MPH has three key features. First, it is comparable to genomic REML in terms of accuracy, while being at least one order of magnitude faster than GCTA and BOLT and using only ~1/4 of memory as much as GCTA, when applied to sequence data and many variance components (or functional annotation categories). Second, it can do weighted analyses if residual variances are unequal (such as DYD). Third, it works for many overlapping functional annotations. Using simulations based on a human pedigree and a dairy cattle pedigree, we illustrate the benefits of our method for partitioning SNP heritability in pedigree studies. We also demonstrate that it is feasible to efficiently partition SNP heritability for animal genomes where there is very strong, long-span LD. MPH is freely available at https://jiang18.github.io/mph.