Search

By their paternal transmission, Y-chromosomal haplotypes are sensitive markers of population history and male-mediated introgression. Previous studies identified biallelic single-nucleotide variants in the SRY, ZFY and DDX3Y genes, which in domestic goats identified four major Y-chromosomal haplotypes, Y1A, Y1B, Y2A and Y2B, with a marked geographical partitioning. Here, we extracted goat Y-chromosomal variants from whole-genome sequences of 386 domestic goats (75 breeds) and seven wild goat species, which were generated by the VarGoats goat genome project. Phylogenetic analyses indicated domestic haplogroups corresponding to Y1B, Y2A and Y2B, respectively, whereas Y1A is split into Y1AA and Y1AB. All five haplogroups were detected in 26 ancient DNA samples from southeast Europe or Asia. Haplotypes from present-day bezoars are not shared with domestic goats and are attached to deep nodes of the trees and networks. Haplogroup distributions for 186 domestic breeds indicate ancient paternal population bottlenecks and expansions during migrations into northern Europe, eastern and southern Asia, and Africa south of the Sahara. In addition, sharing of haplogroups indicates male-mediated introgressions, most notably an early gene flow from Asian goats into Madagascar and the crossbreeding that in the 19th century resulted in the popular Boer and Anglo-Nubian breeds. More recent introgressions are those from European goats into the native Korean goat population and from Boer goat into Uganda, Kenya, Tanzania, Malawi and Zimbabwe. This study illustrates the power of the Y-chromosomal variants for reconstructing the history of domestic species with a wide geographical range.

[Background]: Since their domestication 10,500 years ago, goat populations with distinctive genetic backgrounds have adapted to a broad variety of environments and breeding conditions. The VarGoats project is an international 1000-genome resequencing program designed to understand the consequences of domestication and breeding on the genetic diversity of domestic goats and to elucidate how speciation and hybridization have modeled the genomes of a set of species representative of the genus Capra., [Findings]: A dataset comprising 652 sequenced goats and 507 public goat sequences, including 35 animals representing eight wild species, has been collected worldwide. We identified 74,274,427 single nucleotide polymorphisms (SNPs) and 13,607,850 insertion-deletions (InDels) by aligning these sequences to the latest version of the goat reference genome (ARS1). A Neighbor-joining tree based on Reynolds genetic distances showed that goats from Africa, Asia and Europe tend to group into independent clusters. Because goat breeds from Oceania and Caribbean (Creole) all derive from imported animals, they are distributed along the tree according to their ancestral geographic origin., [Conclusions]: We report on an unprecedented international effort to characterize the genome-wide diversity of domestic goats. This large range of sequenced individuals represents a unique opportunity to ascertain how the demographic and selection processes associated with post-domestication history have shaped the diversity of this species. Data generated for the project will also be extremely useful to identify deleterious mutations and polymorphisms with causal effects on complex traits, and thus will contribute to new knowledge that could be used in genomic prediction and genome-wide association studies., We are grateful to France Génomique “Call for high impact projects” (ANR‐10‐INBS‐09‐08) for selecting our project and providing us the resources to sequence 400 goats. We would like to mention that APIS-GENE funded some WGS sequences through ACTIVEGOAT & CAPRISNP projects. We thank the Occitanie region and the Animal Genetics Division of the French National Institute for Agriculture, Food and Environment (INRAE-GA) for financing the PhD of ET. We thank the Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation for financing LD. We thank André Eggen (Illumina) for providing chips to genotype 192 animals. We thank the Animal Genetics Division of the French National Institute for Agriculture, Food and Environment (INRAE-GA) for funding VarGoats2 grant, which allowed DNA extraction and genotyping of 384 animals and CRB-Anim, Grant Agreement ANR-11-INBS-0003, (https://crb-anim.fr/) for funding French local breeds sampling. We thank the Italian Goat and Sheep Breeders Association (AssoNaPa) for supporting in sampling. Whole-genome sequencing libraries for the African goats were prepared and sequenced by Edinburgh Genomics and funded via Biotechnology and Biological Sciences Research Council research grant (BBS/OS/GC/000012F) ‘Reference genome and population sequencing of African goats’ awarded to The Roslin Institute. USDA-ARS with funding from USAID funded the collection of samples from Uganda, Tanzania, Malawi, Mozambique and Zimbabwe. EC and MS were partially supported by the Bill & Melinda Gates Foundation and with UK aid from the UK Government’s Department for International Development (Grant Agreement OPP1127286) under the auspices of the Centre for Tropical Livestock Genetics and Health (CTLGH), established jointly by the University of Edinburgh, SRUC (Scotland’s Rural College), and the International Livestock Research Institute. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation nor the UK Government.

Made available in DSpace on 2019-10-06T15:24:30Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-11-19 Background: Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds. Results: Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments. Conclusions: These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide. Department of Animal Science Iowa State University National Institute of Aquatic Resources Technical University of Denmark (DTU) GenPhySE INRA Université de Toulouse INPT ENVT Dipartimento di Medicina Veterinaria Università Degli Studi di Milano Laboratory of Geographic Information Systems (LASIG) School of Architecture Civil and Environmental Engineering (ENAC) Ecole Polytechnique Fédérale de Lausanne (EPFL) Recombinetics Inc Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA) Research Centre for Animal Production and Acquaculture Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB Campus Universitat Autonoma de Barcelona DIANA Dipartimento di Scienze Animali della Nutrizione e Degli Alimenti Università Cattolica Del S. Cuore BioDNA Centro di Ricerca sulla Biodiversità e sul DNA Antico Università Cattolica Del S. Cuore Fondazione Parco Tecnologico Padano (PTP) Department of Support Production and Animal Health School of Veterinary Medicine São Paulo State University (UNESP) Animal Genomics and Improvement Laboratory ARS USDA Center for Quantitative Genetics and Genomics Aarhus University Department of Support Production and Animal Health School of Veterinary Medicine São Paulo State University (UNESP)

The aim of this study was to investigate the effect of different strategies for handling low-quality or missing data on prediction accuracy for direct genomic values of protein yield, mastitis and fertility using a Bayesian variable model and a GBLUP model in the Danish Jersey population. The data contained 1,071 Jersey bulls that were genotyped with the Illumina Bovine 50K chip. After preliminary editing, 39,227 SNP remained in the dataset. Four methods to handle missing genotypes were: 1) BEAGLE: missing markers were imputed using Beagle 3.3 software, 2) COMMON: missing genotypes at a locus were replaced by the most common genotype at this locus observed in the marker data, 3) EX-ALLELE: missing marker genotypes at a locus were treated as an extra allele, and 4) POP-EXP: missing genotypes at a locus were replaced with population expectation at this locus. It was shown that among the methods used in this study, imputation with Beagle was the best approach to handle missing genotypes. Treating missing markers as a pseudo-allele, replacing missing markers with a population average or substituting the most common alleles each reduced the accuracy of genomic predictions. The results from this study suggest that missing genotypes should be imputed in order to improve genomic prediction. Editing the marker data with stringent threshold on GenCall (GC) scores and then imputing the discarded genotypes did not lead to higher accuracy. All marker genotypes with a GC score over 0.15 should be retained for genomic prediction.

Genomic prediction of breeding values is now used as the basis for selection of dairy cattle, and in some cases beef cattle, in a number of countries. When genomic prediction was introduced most of the information was to thought to be derived from linkage disequilibrium between markers and causative variants. It has become clear that much of the predictive power, based on 50,000 DNA markers, in fact derives from prediction of the effect of large chromosome segments that segregate within fairly closely related animals. This has lead to problems with across breed prediction, rapid decay of predictive power over generations and insufficient accuracy in some situations. Using full genome sequence data in genomic prediction should overcome these problems. If linkage disequilibrium between SNP on standard arrays and causative mutations affecting the quantitative trait is incomplete, accuracy of prediction should be improved as a result of including the actual causative mutations affecting the trait of interest in the data set. Secondly, persistence of accuracy of genomic predictions across generations will be improved with full sequence data, as the genomic predictions no longer depend on associations between SNP and causative mutations which currently erode quite rapidly with recombination. Thirdly, if genomic predictions are made across breeds, using full sequence data is likely to be particularly advantageous, as there is no longer a need to rely on marker- associations which may not persist across breeds. However, the cost of sequencing is such that the very large numbers of animals required for genomic prediction will not be sequenced An alternative strategy is to sequence key ancestors of the population, then impute the genotypes for the sequence variants into much larger reference sets with phenotypes and SNP panel genotypes. The 1000 Bull Genomes Project aims at building such a resource of sequenced key ancestor bulls for the bovine research community. The most recent run of the project included 238 full genome sequences of 130 Holstein, 43 Fleckvieh, 48 Angus and 15 Jersey bulls, sequenced at an average of 10.5 fold coverage. There were 25.2 million filtered sequence variants detected in the sequences, including 23.5 million SNP and 1.7 million insertion-deletions. Agreement of sequence genotypes to genotypes from an 800K SNP array in the sequenced Holstein bulls, where there was most data, was excellent at 98.8%. This increased to 99.7% when the genotypes were imputed based on all sequences. Concordance was slightly lower in other breeds. This project will provide an excellent opportunity to identify the most important causative variants, leading to greater understanding of biology underlying quantitative traits. Examples are given of genomic predictions for fertility, health and production traits using imputed sequence data.

Genomic selection, where selection decisions are based on estimates of breeding value from genome wide-marker effects, has enormous potential to improve genetic gain in dairy and beef cattle. Although successful in dairy cattle, some major challenges remain 1) only a proportion of the genetic variance is captured, particularly for some traits 2) marker effects are rarely consistent across breeds, 3) accuracy of genomic predictions decays rapidly over time. Using full genome sequences rather than DNA markers in genomic selection could address these challenges. However, sequencing all individuals in the very large resource populations required to estimate the typically small effects of mutations on target traits would be prohibitively expensive. An alternative is to sequence key ancestors contributing most of the genetic material of the current population, and to use this reference for imputation of sequence from SNP chip data. The reference set must still be large, in order to capture for example, rare variants which are likely to explain some of the variation in our target traits. Recognising the need for a comprehensive “reference set” of key ancestors by many groups undertaking cattle research and cattle breeding programs, we have initiated the 1000 bull genomes project. The project will assemble whole genome sequences of cattle from institutions around the world, to provide an extended data base for imputation of genetic variants. This will enable the bovine genomics community to impute full genome sequence from SNP genotypes, and then use this data for genomic selection, and rapid discovery of causal mutations. Some preliminary results from the variant detection pipeline will be reported.

absent

Quantitative trait loci (QTL) in Danish Jersey and Danish Red cattle were independently mapped by least squares regression analysis. For Jersey breed, five grandsire families were genotyped for 186 markers on 16 chromosomes (BTAs). Eight traits analysed were milk yield (MY), fat percentage (FP), protein percentage (PP), clinical mastitis (CM), somatic cell score (SCS), maternal stillbirth, maternal calf size (MCS) and maternal calving difficulty. For Red breed, nine grandsire families were genotyped for 166 markers on 18 BTAs. Six traits analysed were MY, FP, PP, CM, SCS and female fertility. Nine and five QTL were detected in Jersey and Red breed, respectively, in across family tests. In Jersey breed, the results indicate QTL for CM and MCS on BTA 3. Additionally, there is an indication of QTL for MCS and FP on BTA 1 and a tentative evidence for a QTL for MY on BTA 26. There is a high risk of detected QTL being false positives. The detected QTL in Jersey breed indicate interesting results from a breeding perspective, but a practical application should await genome-wide association studies Quantitative trait loci (QTL) in Danish Jersey and Danish Red cattle were independently mapped by least squares regression analysis. For Jersey breed, five grandsire families were genotyped for 186 markers on 16 chromosomes (BTAs). Eight traits analysed were milk yield (MY), fat percentage (FP), protein percentage (PP), clinical mastitis (CM), somatic cell score (SCS), maternal stillbirth, maternal calf size (MCS) and maternal calving difficulty. For Red breed, nine grandsire families were genotyped for 166 markers on 18 BTAs. Six traits analysed were MY, FP, PP, CM, SCS and female fertility. Nine and five QTL were detected in Jersey and Red breed, respectively, in across family tests. In Jersey breed, the results indicate QTL for CM and MCS on BTA 3. Additionally, there is an indication of QTL for MCS and FP on BTA 1 and a tentative evidence for a QTL for MY on BTA 26. There is a high risk of detected QTL being false positives. The detected QTL in Jersey breed indicate interesting results from a breeding perspective, but a practical application should await genome-wide association studies