A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle

Abstract

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V-1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.

Figure 1. The Manhattan plot showing the −log₁₀(P-values) of SNPs of the multi-trait test of the whole genome except the X chromosome.

Figure 2. A: The −log₁₀(P-values) of single SNP regressions for 4 traits and multi-trait chi-squared statistic on a region of BTA 5; B: The −log₁₀(P-values) of single SNP regressions for 4 traits when SNP_i along with 28 lead SNPs were simultaneously fitted in the GWAS model.

Figure 3. Proportion of significant (P<10⁻⁵) SNPs in 100 kb steps from gene start and stop positions.

Position = 0 indicates SNPs between start and stop positions.

Figure 4. Correlations between pairs of the SNP effects on 32 traits.

A: Correlations on BTA7 from 93 Mb to 99 Mb. Three blocks of SNPs with high correlations within a block and low correlation between blocks are shown in blue. B: Correlations on BTA 14 near 25 Mb. The blue line shows the SNPs closest to the PLAG1 gene.

Figure 5. The −log₁₀(P-values) of the multi-trait test calculated using SNP effects from the single-trait GWAS for 32 traits on BTA 14 before (A) and after (B) fitting 28 lead SNPs in the model.

In (B) the significance of the lead SNP is also given after fitting the other 27 lead SNPs.

Figure 6. The −log₁₀(P-values) of the multi-trait test calculated using SNP effects from the single-trait GWAS for 32 traits on BTA 7 before (A) and after (B) fitting 28 lead SNPs in the model.

In (B) the significance of the lead SNP is also given after fitting the other 27 lead SNPs.

Figure 7. Correlation matrix between the 28 lead SNPs calculated from SNP effects on 32 traits (reordered for constructing a dendrogram).

Figure 8. The positions of the best SNPs (5×10⁻⁷) that are highly correlated with each group of linear index.