The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

Abstract

SummaryGenetic resistance to gastrointestinal worms is a complex trait of great importance in both livestock and humans. In order to gain insights into the genetic architecture of this trait, a mixed breed population of sheep was artificially infected with Trichostrongylus colubriformis (n=3326) and then Haemonchus contortus (n=2669) to measure faecal worm egg count (WEC). The population was genotyped with the Illumina OvineSNP50 BeadChip and 48 640 single nucleotide polymorphism (SNP) markers passed the quality controls. An independent population of 316 sires of mixed breeds with accurate estimated breeding values for WEC were genotyped for the same SNP to assess the results obtained from the first population. We used principal components from the genomic relationship matrix among genotyped individuals to account for population stratification, and a novel approach to directly account for the sampling error associated with each SNP marker regression. The largest marker effects were estimated to explain an average of 0 . 48% (T. colubriformis) or 0 . 08 % (H. contortus) of the phenotypic variance in WEC. These effects are small but consistent with results from other complex traits. We also demonstrated that methods which use all markers simultaneously can successfully predict genetic merit for resistance to worms, despite the small effects of individual markers. Correlations of genomic predictions with breeding values of the industry sires reached a maximum of 0 . 32. We estimate that effective across-breed predictions of genetic merit with multi-breed populations will require an average marker spacing of approximately 10 kbp.

Genome-wide association and regional heritability mapping to identify loci underlying variation in nematode resistance and body weight in Scottish Blackface lambs

“…More recently, GWA studies using higher density SNP panels have become more common. To our knowledge, however, few studies have been published so far using SNP chip data in sheep (for example, Kemper et al, 2011;Sallé et al, 2012). Comparisons across studies are not easy, given the complexity of the trait and the differences among populations and approaches used.…”

“…However, in sheep, as well as in other species including humans, answers have not been so definitive for complex traits and GWA studies have generally failed to explain most of the known genetic variation influencing complex diseases (for example, Manolio et al, 2009;Kemper et al, 2011). These studies typically test each marker independently for an association with the trait.…”

“…Attempts to increase the power of association studies have focused either on increasing the number of markers or the number of observations for a trait. Few have attempted to formally estimate the distribution of marker effects with dense SNP markers (for example, Hayes et al, 2010;Kemper et al, 2011) and thus the required power of experiments for complex disease traits, such as nematode resistance, is still unknown. An alternate approach exploiting dense SNP chip data, known as Regional Genomic Relationship Mapping or Regional Heritability Mapping (RHM) (Nagamine et al, 2012), has been advanced as a better approach to capture more of the underlying genetic effects.…”

Accuracy of genomic prediction within and across populations for nematode resistance and body weight traits in sheep

“…However, for many quantitative traits, such as production and health traits, a large number of loci appear to affect the trait, with each of them individually explaining only a limited proportion of the total genetic variance (Hayes and Goddard, 2001;Sanna et al, 2008;Kemper et al, 2011). Genomic selection (GS) has the advantage of potentially capturing the effects of the majority of loci that contribute to genetic variation, even when the effects of the individual loci are very small (Hayes et al, 2009a).…”

“…Apart from the study by Kemper et al (2011), the use of high-density genomic information to select for nematode resistance in sheep has received less attention. Therefore, the aims of this study were to: (i) evaluate the potential of GS to predict GEBV for nematode resistance traits, as well as BW, both within and across populations; (ii) evaluate the accuracy of these predictions through within-population cross-validation; and (iii) explore the impact of population structure within population, by decomposing the accuracy of genomic prediction into component parts.…”