Kathryn E. Kemper

Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle

The aim of this study was to assess the accuracy of genomic predictions for 19 traits including feed efficiency, growth, and carcass and meat quality traits in beef cattle. The 10,181 cattle in our study had real or imputed genotypes for 729,068 SNP although not all cattle were measured for all traits. Animals included Bos taurus, Brahman, composite, and crossbred animals. Genomic EBV (GEBV) were calculated using 2 methods of genomic prediction [BayesR and genomic BLUP (GBLUP)] either using a common training dataset for all breeds or using a training dataset comprising only animals of the same breed. Accuracies of GEBV were assessed using 5-fold cross-validation. The accuracy of genomic prediction varied by trait and by method. Traits with a large number of recorded and genotyped animals and with high heritability gave the greatest accuracy of GEBV. Using GBLUP, the average accuracy was 0.27 across traits and breeds, but the accuracies between breeds and between traits varied widely. When the training population was restricted to animals from the same breed as the validation population, GBLUP accuracies declined by an average of 0.04. The greatest decline in accuracy was found for the 4 composite breeds. The BayesR accuracies were greater by an average of 0.03 than GBLUP accuracies, particularly for traits with known genes of moderate to large effect mutations segregating. The accuracies of 0.43 to 0.48 for IGF-I traits were among the greatest in the study. Although accuracies are low compared with those observed in dairy cattle, genomic selection would still be beneficial for traits that are hard to improve by conventional selection, such as tenderness and residual feed intake. BayesR identified many of the same quantitative trait loci as a genomewide association study but appeared to map them more precisely. All traits appear to be highly polygenic with thousands of SNP independently associated with each trait.

Components of the accuracy of genomic prediction in a multi-breed sheep population

In genome-wide association studies, failure to remove variation due to population structure results in spurious associations. In contrast, for predictions of future phenotypes or estimated breeding values from dense SNP data, exploiting population structure arising from relatedness can actually increase the accuracy of prediction in some cases, for example, when the selection candidates are offspring of the reference population where the prediction equation was derived. In populations with large effective population size or with multiple breeds and strains, it has not been demonstrated whether and when accounting for or removing variation due to population structure will affect the accuracy of genomic prediction. Our aim in this study was to determine whether accounting for population structure would increase the accuracy of genomic predictions, both within and across breeds. First, we have attempted to decompose the accuracy of genomic prediction into contributions from population structure or linkage disequilibrium (LD) between markers and QTL using a diverse multi-breed sheep (Ovis aries) data set, genotyped for 48,640 SNP. We demonstrate that SNP from a single chromosome can achieve up to 86% of the accuracy for genomic predictions using all SNP. This result suggests that most of the prediction accuracy is due to population structure, because a single chromosome is expected to capture relationships but is unlikely to contain all QTL. We then explored principal component analysis (PCA) as an approach to disentangle the respective contributions of population structure and LD between SNP and QTL to the accuracy of genomic predictions. Results showed that fitting an increasing number of principle components (PC; as covariates) decreased within breed accuracy until a lower plateau was reached. We speculate that this plateau is a measure of the accuracy due to LD. In conclusion, a large proportion of the accuracy for genomic predictions in our data was due to variation associated with population structure. Surprisingly, accounting for this structure generally decreased the accuracy of across breed genomic predictions.

Selection for complex traits leaves little or no classic signatures of selection

BackgroundSelection signatures aim to identify genomic regions underlying recent adaptations in populations. However, the effects of selection in the genome are difficult to distinguish from random processes, such as genetic drift. Often associations between selection signatures and selected variants for complex traits is assumed even though this is rarely (if ever) tested. In this paper, we use 8 breeds of domestic cattle under strong artificial selection to investigate if selection signatures are co-located in genomic regions which are likely to be under selection.ResultsOur approaches to identify selection signatures (haplotype heterozygosity, integrated haplotype score and FST) identified strong and recent selection near many loci with mutations affecting simple traits under strong selection, such as coat colour. However, there was little evidence for a genome-wide association between strong selection signatures and regions affecting complex traits under selection, such as milk yield in dairy cattle. Even identifying selection signatures near some major loci was hindered by factors including allelic heterogeneity, selection for ancestral alleles and interactions with nearby selected loci.ConclusionsSelection signatures detect loci with large effects under strong selection. However, the methodology is often assumed to also detect loci affecting complex traits where the selection pressure at an individual locus is weak. We present empirical evidence to suggests little discernible ‘selection signature’ for complex traits in the genome of dairy cattle despite very strong and recent artificial selection.

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

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.

Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions

BackgroundGenomic selection is increasingly widely practised, particularly in dairy cattle. However, the accuracy of current predictions using GBLUP (genomic best linear unbiased prediction) decays rapidly across generations, and also as selection candidates become less related to the reference population. This is likely caused by the effects of causative mutations being dispersed across many SNPs (single nucleotide polymorphisms) that span large genomic intervals. In this paper, we hypothesise that the use of a nonlinear method (BayesR), combined with a multi-breed (Holstein/Jersey) reference population will map causative mutations with more precision than GBLUP and this, in turn, will increase the accuracy of genomic predictions for selection candidates that are less related to the reference animals.ResultsBayesR improved the across-breed prediction accuracy for Australian Red dairy cattle for five milk yield and composition traits by an average of 7% over the GBLUP approach (Australian Red animals were not included in the reference population). Using the multi-breed reference population with BayesR improved accuracy of prediction in Australian Red cattle by 2 – 5% compared to using BayesR with a single breed reference population. Inclusion of 8478 Holstein and 3917 Jersey cows in the reference population improved accuracy of predictions for these breeds by 4 and 5%. However, predictions for Holstein and Jersey cattle were similar using within-breed and multi-breed reference populations. We propose that the improvement in across-breed prediction achieved by BayesR with the multi-breed reference population is due to more precise mapping of quantitative trait loci (QTL), which was demonstrated for several regions. New candidate genes with functional links to milk synthesis were identified using differential gene expression in the mammary gland.ConclusionsQTL detection and genomic prediction are usually considered independently but persistence of genomic prediction accuracies across breeds requires accurate estimation of QTL effects. We show that accuracy of across-breed genomic predictions was higher with BayesR than with GBLUP and that BayesR mapped QTL more precisely. Further improvements of across-breed accuracy of genomic predictions and QTL mapping could be achieved by increasing the size of the reference population, including more breeds, and possibly by exploiting pleiotropic effects to improve mapping efficiency for QTL with small effects.

Haemonchus contortus and Trichostrongylus colubriformis did not adapt to long-term exposure to sheep that were genetically resistant or susceptible to nematode infections

We tested the hypothesis that Haemonchus contortus and Trichostrongylus colubriformis would adapt to long-term exposure to sheep that were either genetically resistant or susceptible to H. contortus. Sheep genotypes were from lines with 10 years prior selection for low (resistant, R) or high (susceptible, S) faecal worm egg count (WEC) following H. contortus infection. Long-term exposure of H. contortus and T.colubriformis to R or S genotypes was achieved using serial passage for up to 30 nematode generations. Thus, we generated four nematode strains; one strain of each species solely exposed to R sheep and one strain of each species solely exposed to S sheep. Considerable host genotype differences in mean WEC during serial passage confirmed adequate nematode selection pressure for both H. contortus (R 4900 eggs per gram (epg), S 19,900 epg) and T. colubriformis (R 5300 epg, S 13,500 epg). Adaptation of nematode strain to host genotype was tested using seven cross-classified tests for H. contortus, and two cross-classified and one outbred genotype test for T. colubriformis. In the cross-classified design, where each strain infects groups of R, S or randomly bred control sheep, parasite adaptation would be indicated by a significant host genotype by nematode strain interaction for traits indicating parasite reproductive success; specifically WEC and, for H. contortus strains, packed cell volume. We found no significant evidence of parasite adaptation to host genotype (P>0.05) for either the H. contortus or T. colubriformis strains. Therefore, we argue that nematodes will not adapt quickly to sheep bred for nematode resistance, where selection is based on low WEC, although selecting sheep using a subset of immune functions may increase adaptation risk. Our results support the hypothesis that nematode resistance is determined by many genes each with relatively small effect. In conclusion, selection of sheep for nematode resistance using WEC should be sustainable in the medium to long-term.

Genetic architecture of body size in mammals

Much of the heritability for human stature is caused by mutations of small-to-medium effect. This is because detrimental pleiotropy restricts large-effect mutations to very low frequencies.

Reduction of faecal worm egg count, worm numbers and worm fecundity in sheep selected for worm resistance following artificial infection with Teladorsagia circumcincta and Trichostrongylus colubriformis

We examined the changes to populations of Trichostrongylus colubriformis and Teledorsagia circumcincta in mature sheep selected for reduced faecal worm egg count (WEC). Worm resistant (n=19) and control (n=10) genotype sheep were penned and dosed with a total of 10,000 T. colubriformis and 10,000 T. circumcincta per week for 18-weeks. Sheep genotypes were from lines previously bred over 15 years for either low WEC (resistant) or non-selected (control). Weekly WEC and the proportion of larvae from each species in faecal culture were measured during weeks 3-17. Egg hatchability was assessed on a pooled faecal sample from worm resistant or control genotype sheep at weeks 7, 9, 10, 13, 14 and 17. At week 18 the number of adult and immature worms (early and late 4th, and 5th stage), and indicators of worm fecundity (female worm length and number of eggs in utero) were assessed at necropsy. Results show that resistant sheep had reduced mean WEC to only 18% of the control (P<0.05) and increased the proportion of T. circumcincta larvae in faecal culture during weeks 8-17 (P<0.10). Egg hatch assays indicated a slight reduction in the viability of eggs from worm resistant genotypes at weeks 14 (P<0.05) and 17 (P<0.10). At necropsy, resistant animals had 93% fewer adult T. colubriformis, 44% fewer adult T. circumcincta and had reduced indicators of fecundity in T. circumcincta by up to 40% (P<0.05). We observed no change in the number T. circumcincta worms but an 11% increase in the proportion of early 4th stage T. circumcincta larvae in resistant animals (P<0.05). There were different temporal patterns in WEC and different prediction equations for WEC from necropsy traits for the two sheep genotypes (P<0.05). Thus, our results suggest a changed host-parasite relationship in sheep selected for low WEC. We conclude that lower WEC is achieved through reduced number of adult worms for both species and reduced fecundity for T. circumcincta. These results support the hypotheses that worm resistant sheep with a strong immune function can regulate T. colubriformis by rejecting adult worms but that T. circumcincta is regulated through a combination of suppressed development and reduced female fecundity.

Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies

BackgroundThe apparent effect of a single nucleotide polymorphism (SNP) on phenotype depends on the linkage disequilibrium (LD) between the SNP and a quantitative trait locus (QTL). However, the phase of LD between a SNP and a QTL may differ between Bos indicus and Bos taurus because they diverged at least one hundred thousand years ago. Here, we test the hypothesis that the apparent effect of a SNP on a quantitative trait depends on whether the SNP allele is inherited from a Bos taurus or Bos indicus ancestor.MethodsPhenotype data on one or more traits and SNP genotype data for 10 181 cattle from Bos taurus, Bos indicus and composite breeds were used. All animals had genotypes for 729 068 SNPs (real or imputed). Chromosome segments were classified as originating from B. indicus or B. taurus on the basis of the haplotype of SNP alleles they contained. Consequently, SNP alleles were classified according to their sub-species origin. Three models were used for the association study: (1) conventional GWAS (genome-wide association study), fitting a single SNP effect regardless of subspecies origin, (2) interaction GWAS, fitting an interaction between SNP and subspecies-origin, and (3) best variable GWAS, fitting the most significant combination of SNP and sub-species origin.ResultsFitting an interaction between SNP and subspecies origin resulted in more significant SNPs (i.e. more power) than a conventional GWAS. Thus, the effect of a SNP depends on the subspecies that the allele originates from. Also, most QTL segregated in only one subspecies, suggesting that many mutations that affect the traits studied occurred after divergence of the subspecies or the mutation became fixed or was lost in one of the subspecies.ConclusionsThe results imply that GWAS and genomic selection could gain power by distinguishing SNP alleles based on their subspecies origin, and that only few QTL segregate in both B. indicus and B. taurus cattle. Thus, the QTL that segregate in current populations likely resulted from mutations that occurred in one of the subspecies and can have both positive and negative effects on the traits. There was no evidence that selection has increased the frequency of alleles that increase body weight.

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.