A multiple-SNP approach for genome-wide association study of milk production traits in Chinese Holstein cattle

Abstract

The multiple-SNP analysis has been studied by many researchers, in which the effects of multiple SNPs are simultaneously estimated and tested in a multiple linear regression. The multiple-SNP association analysis usually has higher power and lower false-positive rate for detecting causative SNP(s) than single marker analysis (SMA). Several methods have been proposed to simultaneously estimate and test multiple SNP effects. In this research, a fast method called MEML (Mixed model based Expectation-Maximization Lasso algorithm) was developed for simultaneously estimate of multiple SNP effects. An improved Lasso prior was assigned to SNP effects which were estimated by searching the maximum joint posterior mode. The residual polygenic effect was included in the model to absorb many tiny SNP effects, which is treated as missing data in our EM algorithm. A series of simulation experiments were conducted to validate the proposed method, and the results showed that compared with SMMA, the new method can dramatically decrease the false-positive rate. The new method was also applied to the 50k SNP-panel dataset for genome-wide association study of milk production traits in Chinese Holstein cattle. Totally, 39 significant SNPs and their nearby 25 genes were found. The number of significant SNPs is remarkably fewer than that by SMMA which found 105 significant SNPs. Among 39 significant SNPs, 8 were also found by SMMA and several well-known QTLs or genes were confirmed again; furthermore, we also got some positional candidate gene with potential function of effecting milk production traits. These novel findings in our research should be valuable for further investigation.

Figure 1. The profiles of the true SNP parameters (the top panel), the estimated 500 SNP heritabilities with MEML (the middle panel) and −log₁₀ ^P with SMMA (the bottom panel), respectively.

The x-axis indicates the SNP numbers. In the top panel, the true heritabilities of small-effect SNPs are presented with diamonds on the top of their needles but not for large-effect SNPs. The dotted horizontal lines in the middle and the bottom panels present the thresholds with 1,000 permutations from the multiple-SNP and SMMA methods, respectively.

Figure 2. The profiles of the estimated heritabilities of 500 SNPs for five milk production traits against on the selected SNPs.

The panels from the top to the bottom are the estimated heritiabilities for milk yield, fat yield, protein yield, fat percentage and protein percentage traits, respectively. The x-axis indicates the chromosome number (chromosome are divided by vertical dotted lines). The dotted horizontal line presents the threshold from 1,000 permutations.