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Background: Whole-genome sequence (WGS) data give access to more complete structural genetic information of individuals, including rare variants, not fully covered by single nucleotide polymorphism chips. We used WGS to investigate the amount of genetic diversity remaining after selection using optimal contribution (OC), considering different methods to estimate the relationships used in OC. OC was applied to minimise average relatedness of the selection candidates and thus miminise the loss of genetic diversity in a conservation strategy, e.g. for establishment of gene bank collections. Furthermore, OC was used to maximise average genetic merit of the selection candidates at a given level of relatedness, similar to a genetic improvement strategy. In this study, we used data from 277 bulls from the 1000 bull genomes project. We measured genetic diversity as the number of variants still segregating after selection using WGS data, and compared strategies that targeted conservation of rare (minor allele frequency < 5 %) versus common variants.Results: When OC without restriction on the number of selected individuals was applied, loss of variants was minimal and most individuals were selected, which is often unfeasible in practice. When 20 individuals were selected, the number of segregating rare variants was reduced by 29 % for the conservation strategy, and by 34 % for the genetic improvement strategy. The overall number of segregating variants was reduced by 30 % when OC was restricted to selecting five individuals, for both conservation and genetic improvement strategies. For common variants, this loss was about 15 %, while it was much higher, 72 %, for rare variants. Fewer rare variants were conserved with the genetic improvement strategy compared to the conservation strategy.Conclusions: The use of WGS for genetic diversity quantification revealed that selection results in considerable losses of genetic diversity for rare variants. Using WGS instead of SNP chip data to estimate relationships slightly reduced the loss of rare variants, while using 50 K SNP chip data was sufficient to conserve common variants. The loss of rare variants could be mitigated by a few percent (up to 8 %) depending on which method is chosen to estimate relationships from WGS data.

Muffs and beard (Mb) is a phenotype in chickens where groups of elongated feathers gather from both sides of the face (muffs) and below the beak (beard). It is an autosomal, incomplete dominant phenotype encoded by the Muffs and beard (Mb) locus. Here we use genome-wide association (GWA) analysis, linkage analysis, Identity-by-Descent (IBD) mapping, array-CGH, genome re-sequencing and expression analysis to show that the Mb allele causing the Mb phenotype is a derived allele where a complex structural variation (SV) on GGA27 leads to an altered expression of the gene HOXB8. This Mb allele was shown to be completely associated with the Mb phenotype in nine other independent Mb chicken breeds. The Mb allele differs from the wild-type mb allele by three duplications, one in tandem and two that are translocated to that of the tandem repeat around 1.70 Mb on GGA27. The duplications contain total seven annotated genes and their expression was tested during distinct stages of Mb morphogenesis. A continuous high ectopic expression of HOXB8 was found in the facial skin of Mb chickens, strongly suggesting that HOXB8 directs this regional feather-development. In conclusion, our results provide an interesting example of how genomic structural rearrangements alter the regulation of genes leading to novel phenotypes. Further, it again illustrates the value of utilizing derived phenotypes in domestic animals to dissect the genetic basis of developmental traits, herein providing novel insights into the likely role of HOXB8 in feather development and differentiation.

Stature is affected by many polymorphisms of small effect in humans(1). In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes(2,3). Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 x 10(-8)) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP-seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals

Background In crossbreeding programs, various genomic prediction models have been proposed for using phenotypic records of crossbred animals to increase the selection response for crossbred performance in purebred animals. A possible model is a model that assumes identical single nucleotide polymorphism (SNP) effects for the crossbred performance trait across breeds (ASGM). Another model is a genomic model that assumes breed-specific effects of SNP alleles (BSAM) for crossbred performance. The aim of this study was to derive and validate equations for predicting the reliability of estimated genomic breeding values for crossbred performance in both these models. Prediction equations were derived for situations when all (phenotyping and) genotyping data have already been collected, i.e. based on the genetic evaluation model, and for situations when all genotyping data are not yet available, i.e. when designing breeding programs. Results When all genotyping data are available, prediction equations are based on selection index theory. Without availability of all genotyping data, prediction equations are based on population parameters (e.g., heritability of the traits involved, genetic correlation between purebred and crossbred performance, effective number of chromosome segments). Validation of the equations for predicting the reliability of genomic breeding values without all genotyping data was performed based on simulated data of a two-way crossbreeding program, using either two closely-related breeds, or two unrelated breeds, to produce crossbred animals. The proposed equations can be used for an easy comparison of the reliability of genomic estimated breeding values across many scenarios, especially if all genotyping data are available. We show that BSAM outperforms ASGM for a specific breed, if the effective number of chromosome segments that originate from this breed and are shared by selection candidates of this breed and crossbred reference animals is less than half the effective number of all chromosome segments that are independently segregating in the same animals. Conclusions The derived equations can be used to predict the reliability of genomic estimated breeding values for crossbred performance using ASGM or BSAM in many scenarios, and are thus useful to optimize the design of breeding programs. Scenarios can vary in terms of the genetic correlation between purebred and crossbred performances, heritabilities, number of reference animals, or distance between breeds.

While susceptibility to hypersensitive reactions is a common problem amongst humans and animals alike, the population structure of certain animal species and breeds provides a more advantageous route to better understanding the biology underpinning these conditions. The current study uses Exmoor ponies, a highly inbred breed of horse known to frequently suffer from insect bite hypersensitivity, to identify genomic regions associated with a type I and type IV hypersensitive reaction. A total of 110 cases and 170 controls were genotyped on the 670K Axiom Equine Genotyping Array. Quality control resulted in 452,457 SNPs and 268 individuals being tested for association. Genome-wide association analyses were performed using the GenABEL package in R and resulted in the identification of two regions of interest on Chromosome 8. The first region contained the most significant SNP identified, which was located in an intron of the DCC netrin 1 receptor gene. The second region identified contained multiple top SNPs and encompassed the PIGN, KIAA1468, TNFRSF11A, ZCCHC2, and PHLPP1 genes. Although additional studies will be needed to validate the importance of these regions in horses and the relevance of these regions in other species, the knowledge gained from the current study has the potential to be a step forward in unraveling the complex nature of hypersensitive reactions.

In the past decade, biobanking has fuelled great scientific advances in the human medical sector. Well-established domesticated animal biobanks and integrated networks likewise harbour immense potential for great scientific advances with broad societal impacts, which are currently not being fully realised. Political and scientific leaders as well as journals and ethics committees should help to ensure that we are well equipped to meet future demands in livestock production, animal models, and veterinary care of companion animals.

Cattle and other ruminants produce large quantities of methane (~110 million metric tonnes per annum), which is a potent greenhouse gas affecting global climate change. Methane (CH4) is a natural by-product of gastro-enteric microbial fermentation of feedstuffs in the rumen and contributes to 6% of total CH4 emissions from anthropogenic-related sources. The extent to which the host genome and rumen microbiome influence CH4 emission is not yet well known. This study confirms individual variation in CH4 production was influenced by individual host (cow) genotype, as well as the host's rumen microbiome composition. Abundance of a small proportion of bacteria and archaea taxa were influenced to a limited extent by the host's genotype and certain taxa were associated with CH4 emissions. However, the cumulative effect of all bacteria and archaea on CH4 production was 13%, the host genetics (heritability) was 21% and the two are largely independent. This study demonstrates variation in CH4 emission is likely not modulated through cow genetic effects on the rumen microbiome. Therefore, the rumen microbiome and cow genome could be targeted independently, by breeding low methane-emitting cows and in parallel, by investigating possible strategies that target changes in the rumen microbiome to reduce CH4 emissions in the cattle industry.

Reproductive performance in Holstein dairy cattle managed during summer in southern Sonora is a challenge because of high ambient temperature and relative humidity. Both of these factors contribute to heat stress, which influences cow behavior. The physiological response of cows to heat stress is one component of a system-wide gene network. Within this environment, a superior cow's ability to get pregnant early during postpartum is favorable as to reduce the trait days open and to increase productive life. Recently, many reproductive specialists have recommended using pregnancy rate as a measure of reproductive success, after converting this trait into a quantitative value using a linear formula. In comparison to the traditional measure of days open, pregnancy rate calculation includes more easily cows that do not become pregnant; furthermore, the output variable indicates that larger values are more desirable, and therefore, more understandable by dairy producers. The objective herein was to predict pregnancy rate in lactating Holstein cows using molecular markers associated with fertility in Holstein cows under a heat-stressed environment. This study included 500 cows from three dairy herds located in the Yaqui Valley of Sonora. A blood sample was collected from every cow and spotted onto FTA cards. The DNA was extracted from each card and used to genotype 179 tag SNP within 43 genes in the prolactin and GH-IGF1 pathways. Five SNP within the genes IGFBP7, IGFBP2, PAPPA1, SSTR2, and STAT6 were associated with pregnancy rate using a mixed effects model. The genotype term was later included in this model to calculate allele substitution effects. Molecular breeding values of the individual cows were calculated by summing the additive genotype effect for each SNP that showed a significant independent association with pregnancy rate, and the average MBV was 0.46 ± 0.01%. Two statistical regression models were used to predict the variable pregnancy rate: a full model that included effects of days and number of lactations, contemporary group (e.g., farm management group), health status, and MBV and a reduced model that only included MBV. Coefficients of determination were 37.61% and 3.07% for full and reduced models, respectively (P < 0.01). These results indicate that five SNP explained only a small proportion of the additive genetic variance for pregnancy rate. Additional research is needed to understand if these results are due to low heritability/repeatability of a fertility and (or) if these results are also influenced by heat stress.

Respiratory disease is a complex phenotype and the diagnostic can be attributed to multiple causes including viral infection (e.g., respiratory coronavirus, bovine respiratory syncytial virus), bacterial infection (e.g., pneumonic spp.; lungworm), and vena caval thrombosis. Moreover, the impact of respiratory disease in cows varies with the stage of lactation when the disease is detected. In general, intense management practices facilitate the detection of respiratory disorders in dairy cattle herds relative to beef cattle herds. Thus, we propose that study of respiratory disease incidence in a large dairy cattle data set as paradigm to advance the knowledge on the factors influencing the incidence of this disease across cattle types. Respiratory disease information on 6,283 Holstein cows across four U.S. states and nine herds were evaluated. Two descriptors of respiratory disease were evaluated: days in milk to respiratory disease detection and the binary detection of respiratory disease. Survival analysis was used to study the days in milk-to-disease. The binary variable respiratory disease detection was analyzed using a binary logistic model. Lactation number, season, region, farm, body condition score, and milk yield level (3 levels) were included in the model as fixed explanatory effects whereas sire was considered a random effect. Incidence of respiratory disease was lower in summer relative to winter, and there was a nonsignificant trend on lactation number. Body condition score had a significant effect, with higher body condition score associated with lower incidence of respiratory disease. Farm, body condition score, and milk yield level had significant effect on the time when respiratory disease was identified. The heritability estimate for incidence of respiratory disease was 0.4, suggesting that despite the high number of potential causative agents, selection for less susceptible cattle can be an effective strategy to reduce the impact of this disease. The heritability estimate of the days in milk-to-disease was 0.13, showing that non-genetic components may play an important role on the stage of the lactation when the disease is detected. These findings contribute to an animal health project (USDA-NIFA-ILLU-538909) and a multistate project database (USDA-NIFA-AFRI-003542) for direct measures of health and fertility in cattle.