Initial genomic characterization of Italian, Egyptian and Pakistani goat breeds

Selection and breeding practices in goats have differed greatly among countries and populations. These processes, together with natural selection and regional drift, have shaped the phenotypic variability of goat breeds (Kim et al., 2015). The availability of improved genomic analysis tools for this species may provide useful information on the history of selection, adaptation and differentiation of goats from different areas of the world, that can be evaluated by the study of gene frequencies and length of the Runs of Homozigosity (contiguous length of homozygous genotypes, ROH; Purfield et al., 2012). In current study, we examined using a goat medium density SNP chip animals from three different countries: Egypt (with lack of selection scheme), Italy (with several standardized breeds; Nicoloso et al., 2015) and Pakistan (with several breeds showing peculiar phenotypes) to produce a genomic landscape of goats breeds in these countries. A total of 1,123 animals of 39 different populations, and 48,895 SNP markers were analyzed. Genotypes were imputed on a country-based approach, and markers without known position in the genome were excluded from the analysis. MDS and ADMIXTURE plots confirmed the good differentiation among populations from the three countries. Runs of Homozygosity (ROH) were performed for each country and population allowed the detection of genomic regions with high homozygosity levels, common in at least two out of three sampling areas. These results provide new insights into goat genome structure within and among breeds and countries. The detection of conserved regions with different lengths may explain recent selection strategies or adaptation to different, extreme environmental conditions. The research was funded by INNOVAGEN project. Support by Iowa State University and the Ensminger funds for AE and AT as well as support by the Fulbright Foundation for AE are gratefully acknowledged. Sampling from Pakistan was funded by PAK-USAID project

Use of SNP genotyping to determine pedigree and breed composition of dairy cattle in Kenya

High levels of inbreeding in East African dairy cattle are a potential concern because of use of a limited range of imported germplasm coupled with strong selection, especially by disease, and sparse performance recording. To address this, genetic relationships and breed composition in an admixed population of Kenyan dairy cattle were estimated by means of a 50K SNP scan. Genomic DNA from 3 worldwide Holstein and 20 Kenyan bulls, 71 putative cow-calf pairs, 25 cows from a large ranch and 5 other Kenyan animals were genotyped for 37 238 informative SNPs. Sires were predicted and 89% of putative dam-calf relationships were supported by genotype data. Animals were clustered with the HapMap population using Structure software to assess breed composition. Cows from a large ranch primarily clustered with Holsteins, while animals from smaller farms were generally crosses between Holstein and Guernsey. Coefficients of relatedness were estimated and showed evidence of heavy use of one AI bull. We conclude that little native germplasm exists within the genotyped populations and mostly European ancestry remains

1000 Bull Genomes Consortium Project

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

Biodiversity among Buffalo genomes.

Archaeozoological data indicate that the water buffalo was domesticated between 4000 and 6000 years ago in the Indus and Yangtze valleys. Historically domestic water buffalo were divided into swamp and river subspecies that differ in morphology, behaviour, geography and chromosome number. The river buffalo has 2n=50 chromosomes and the swamp buffalo has 2n=48 . The swamp buffalo resembles more closely the ancestral wild Bubalus arnee; than the river buffalo. The two subspecies mate only if reared together from calf-hood. River buffalo are mainly found in India, Southwest Asia and the Mediterranean, while swamp buffalo are predominantly in Southeast Asia and China. The International Buffalo Genome Consortium recently sequenced the buffalo genome to create a reference buffalo genome, and has produced low pass whole genome sequences of 48 river buffaloes from 4 breeds: Mediterranean buffalo sampled in Italy, Jaffarabardi and Murrah breeds sampled in Brazil and Nili-Ravi sampled in Pakistan. Analysis of these data identified over 13M SNPs with MAF≥0.05. We used these data to estimate basic population genetics parameters and the genetic structure of the breeds. Expected heterozygosity varied significantly among breeds. The Fst index revealed that a remarkable portion of the total variability is explained by the between breed component. Principal component analysis of individual animals based on a subset of 50K randomly selected SNPs clustered animals from the same breed and showed a clear differentiation among breeds

1000 Bull Genomes Consortium Project

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

Ten simple rules to ruin a collaborative environment

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