Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein

Abstract

BackgroundPseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets.ResultsWe propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions.ConclusionsOur rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0383-0) contains supplementary material, which is available to authorized users.

Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model

“…Additionally, DRP obtained from the routine national genetic evaluation of millions of individuals, instead of EBV, were used as the response variable, which benefited from correcting confounding environmental factors and taking advantage of population pedigree information. Our results were similar to the outcome of Ning et al (2018b), who validated their novel longitudinal GWAS method using test-day records with the same chip data and conducted a GWAS of MY, FP, and PP in the first 3 parities.…”

“…In our study, a Chinese Holstein population of 6,675 cows were genotyped with the BovineSNP50v1 Bead-Chip (54,001 SNP; Illumina Inc.), the BovineSNP50v2 BeadChip (54,609 SNP; Illumina Inc.), and the Gene-Seek Genomic Profiler HD (76,879 SNP; Neogen Corp., Lansing, MI), which were described in our previous longitudinal GWAS (Ning et al, 2018b). The individuals genotyped with the GeneSeek Genomic Profiler HD chip were used as reference, and all others were imputed to the high density of 70k SNP by using FImputev2.2 (Sargolzaei et al, 2014).…”

Genome‐wide association for milk production and lactation curve parameters in Holstein dairy cows

“…The accumulated 305‐day milk yield is estimated by summing the test‐day milk yield (TDMY) recorded every day during the lactation period or combining the weekly or monthly TDMY by linear interpolation (Schaeffer, ). Since the additive genetic variance for milk yield changes during lactation (Bignardi, El Faro, Cardoso, Machado, & de Albuquerque, ; Singh et al, ), the genetic effects of QTL related to 305‐day milk yield are not constant during the lactation period; therefore, many QTL whose genetic effects change during lactation might not be detected in this approach (Lund, Sorensen, Madsen, & Jaffrézic, ; Ning et al, ). Strucken, Bortfeldt, De Koning, and Brockmann () showed that lactation curve parameters provide a higher power to screen the whole genome for region whose effect change during lactation.…”

Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops

“…However, their method was shown to be computationally inefficient due to the high dimensionality of the mixed model equations compared to other models. Subsequently, Ning et al (2018) proposed a rapid longitudinal GWAS method, transforming the covariance matrices to diagonal matrices using eigen-decomposition. This way the model can be solved by a weighted least squares model for each SNP test.…”