Genome scan of pigmentation traits in Friesian-Jersey crossbred cattle

The Hoof Color of Australian White Sheep Is Associated with Genetic Variation of the MITF Gene

Studying the characteristics of mammalian hoof colors is important for genetic improvements in animals. A deeper black hoof color is the standard for breeding purebred Australian White (AUW) sheep and this phenotype could be used as a phenotypic marker of purebred animals. We conducted a genome-wide association study (GWAS) analysis using restriction site associated DNA sequencing (RAD-seq) data from 577 Australian White sheep (black hoof color = 283, grey hoof color = 106, amber hoof color = 186) and performed association analysis utilizing the mixed linear model in EMMAX. The results of GWAS demonstrated that a specific single-nucleotide polymorphism (SNP; g. 33097911G>A) in intron 14 of the microphthalmia-associated transcription factor (MITF) gene was significantly associated with the hoof color in AUW sheep (p = 9.40 × 10-36). The MITF gene plays a key role in the development, differentiation, and functional regulation of melanocytes. Furthermore, the association between this locus and hoof color was validated in a cohort of 212 individuals (black hoof color = 122, grey hoof color = 38, amber hoof color = 52). The results indicated that the hoof color of AUW sheep with GG, AG, and AA genotypes tended to be black, grey, and amber, respectively. This study provided novel insights into hoof color genetics in AUW sheep, enhancing our comprehension of the genetic mechanisms underlying the diverse range of hoof colors. Our results agree with previous studies and provide molecular markers for marker-assisted selection for hoof color in sheep.

A Meta-Assembly of Selection Signatures in Cattle

Since domestication, significant genetic improvement has been achieved for many traits of commercial importance in cattle, including adaptation, appearance and production. In response to such intense selection pressures, the bovine genome has undergone changes at the underlying regions of functional genetic variants, which are termed “selection signatures”. This article reviews 64 recent (2009–2015) investigations testing genomic diversity for departure from neutrality in worldwide cattle populations. In particular, we constructed a meta-assembly of 16,158 selection signatures for individual breeds and their archetype groups (European, African, Zebu and composite) from 56 genome-wide scans representing 70,743 animals of 90 pure and crossbred cattle breeds. Meta-selection-scores (MSS) were computed by combining published results at every given locus, within a sliding window span. MSS were adjusted for common samples across studies and were weighted for significance thresholds across and within studies. Published selection signatures show extensive coverage across the bovine genome, however, the meta-assembly provides a consensus profile of 263 genomic regions of which 141 were unique (113 were breed-specific) and 122 were shared across cattle archetypes. The most prominent peaks of MSS represent regions under selection across multiple populations and harboured genes of known major effects (coat color, polledness and muscle hypertrophy) and genes known to influence polygenic traits (stature, adaptation, feed efficiency, immunity, behaviour, reproduction, beef and dairy production). As the first meta-assembly of selection signatures, it offers novel insights about the hotspots of selective sweeps in the bovine genome, and this method could equally be applied to other species.

Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep

Background

Discerning the traits evolving under neutral conditions from those traits evolving rapidly because of various selection pressures is a great challenge. We propose a new method, composite selection signals (CSS), which unifies the multiple pieces of selection evidence from the rank distribution of its diverse constituent tests. The extreme CSS scores capture highly differentiated loci and underlying common variants hauling excess haplotype homozygosity in the samples of a target population.

Results

The data on high-density genotypes were analyzed for evidence of an association with either polledness or double muscling in various cohorts of cattle and sheep. In cattle, extreme CSS scores were found in the candidate regions on autosome BTA-1 and BTA-2, flanking the POLL locus and MSTN gene, for polledness and double muscling, respectively. In sheep, the regions with extreme scores were localized on autosome OAR-2 harbouring the MSTN gene for double muscling and on OAR-10 harbouring the RXFP2 gene for polledness. In comparison to the constituent tests, there was a partial agreement between the signals at the four candidate loci; however, they consistently identified additional genomic regions harbouring no known genes. Persuasively, our list of all the additional significant CSS regions contains genes that have been successfully implicated to secondary phenotypic diversity among several subpopulations in our data. For example, the method identified a strong selection signature for stature in cattle capturing selective sweeps harbouring UQCC-GDF5 and PLAG1-CHCHD7 gene regions on BTA-13 and BTA-14, respectively. Both gene pairs have been previously associated with height in humans, while PLAG1-CHCHD7 has also been reported for stature in cattle. In the additional analysis, CSS identified significant regions harbouring multiple genes for various traits under selection in European cattle including polledness, adaptation, metabolism, growth rate, stature, immunity, reproduction traits and some other candidate genes for dairy and beef production.

Conclusions

CSS successfully localized the candidate regions in validation datasets as well as identified previously known and novel regions for various traits experiencing selection pressure. Together, the results demonstrate the utility of CSS by its improved power, reduced false positives and high-resolution of selection signals as compared to individual constituent tests.

QTL and association analysis for skin and fibre pigmentation in sheep provides evidence of a major causative mutation and epistatic effects

The pursuits of white features and white fleeces free of pigmented fibre have been important selection objectives for many sheep breeds. The cause and inheritance of non-white colour patterns in sheep has been studied since the early 19th century. Discovery of genetic causes, especially those which predispose pigmentation in white sheep, may lead to more accurate selection tools for improved apparel wool. This article describes an extended QTL study for 13 skin and fibre pigmentation traits in sheep. A total of 19 highly significant, 10 significant and seven suggestive QTL were identified in a QTL mapping experiment using an Awassi × Merino × Merino backcross sheep population. All QTL on chromosome 2 exceeded a LOD score of greater than 4 (range 4.4-30.1), giving very strong support for a major gene for pigmentation on this chromosome. Evidence of epistatic interactions was found for QTL for four traits on chromosomes 2 and 19. The ovine TYRP1 gene on OAR 2 was sequenced as a strong positional candidate gene. A highly significant association (P < 0.01) of grandparental haplotypes across nine segregating SNP/microsatellite markers including one non-synonymous SNP with pigmentation traits could be shown. Up to 47% of the observed variation in pigmentation was accounted for by models using TYRP1 haplotypes and 83% for models with interactions between two QTL probabilities, offering scope for marker-assisted selection for these traits.

Evaluation of confidence limit estimates of cluster analysis on molecular marker data

BACKGROUND

Diversity studies employ cluster analysis as a statistical tool, whereby relationships between individuals are shown in a dendrogram, mostly accompanied by bootstrap support for merging branches to indicate confidence limits. The objective of this study was to evaluate the reliability of the currently applied method of obtaining confidence limit estimates in cluster analysis and to propose an improved alternative bootstrap method.

RESULTS

It was illustrated via a simulation study that conventional bootstrap support for cluster analysis was affected by the sample size. The reliability of merging branches decreased with increasing number of individuals in the sample. Unlike the current bootstrap support for cluster analysis, the proposed method provides confidence intervals for the similarity coefficients between individuals. To facilitate the interpretation of similarity coefficients and confidence intervals, alternative graphical presentations are proposed for both 'similarity coefficients' and 'confidence interval range'.

CONCLUSION

The proposed bootstrap method is not affected by the number of individuals in the sample.