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A genetically unique Chinese cattle population shows evidence of common ancestry with wild species when analysed with a reduced ascertainment bias SNP panel.

ABSTRACT: In Hong Kong, there is a cattle population of ~1,200 individuals of uncertain origin and genetic diversity. This population shows heterogeneous morphology, both in body type and pigmentation. Once used as draught animals by the local farmers, they were abandoned around the 1970s due to changes in the economy, and since then have lived as feral populations. To explore the origins of these cattle, we analysed ~50k genotype data of 21 Hong Kong feral cattle, along with data from 703 individuals of 36 cattle populations of European, African taurine, and Asian origin, the wild x domestic hybrid gayal, plus two wild bovine species, gaur and banteng. To reduce the effect of ascertainment bias ~4k loci that are polymorphic in the two wild species were selected for further analysis. The stringent SNP selection we applied resulted in increased heterozygosity across all populations studies, compared with the full panel of SNP, thus reducing the impact of ascertainment bias and facilitating the comparison of divergent breeds of cattle. Our results showed that Hong Kong feral cattle have relatively high levels of genetic distinctiveness, possibly due to the low level of artificial selection, and a likely common ancestry with wild species. We found signs of a putative taurine introgression, probably dating to the import of north European breeds during the British colonialism of Hong Kong. We showed that Hong Kong feral cattle, are distinct from Bos taurus and Bos indicus breeds. Our results highlight the distinctiveness of Hong Kong feral cattle and stress the conservation value of this indigenous breed that is likely to harbour adaptive genetic variation, which is a fundamental livestock resource in the face of climate change and diversifying market demands.

SUBMITTER: Barbato M

PROVIDER: S-EPMC7145104 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A genetically unique Chinese cattle population shows evidence of common ancestry with wild species when analysed with a reduced ascertainment bias SNP panel.

Barbato Mario M Reichel Michael P MP Passamonti Matilde M Low Wai Yee WY Colli Licia L Tearle Rick R Williams John L JL Ajmone Marsan Paolo P

PloS one 20200409 4

In Hong Kong, there is a cattle population of ~1,200 individuals of uncertain origin and genetic diversity. This population shows heterogeneous morphology, both in body type and pigmentation. Once used as draught animals by the local farmers, they were abandoned around the 1970s due to changes in the economy, and since then have lived as feral populations. To explore the origins of these cattle, we analysed ~50k genotype data of 21 Hong Kong feral cattle, along with data from 703 individuals of ...[more]

PMID: 32271816

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Project description:BACKGROUND:Single nucleotide polymorphism (SNP) panels have been widely used to study genomic variations within and between populations. Methods of SNP discovery have been a matter of debate for their potential of introducing ascertainment bias, and genetic diversity results obtained from the SNP genotype data can be misleading. We used a total of 42 chicken populations where both individual genotyped array data and pool whole genome resequencing (WGS) data were available. We compared allele frequency distributions and genetic diversity measures (expected heterozygosity (H e ), fixation index (F ST ) values, genetic distances and principal components analysis (PCA)) between the two data types. With the array data, we applied different filtering options (SNPs polymorphic in samples of two Gallus gallus wild populations, linkage disequilibrium (LD) based pruning and minor allele frequency (MAF) filtering, and combinations thereof) to assess their potential to mitigate the ascertainment bias. RESULTS:Rare SNPs were underrepresented in the array data. Array data consistently overestimated H e compared to WGS data, however, with a similar ranking of the breeds, as demonstrated by Spearman's rank correlations ranging between 0.956 and 0.985. LD based pruning resulted in a reduced overestimation of H e compared to the other filters and slightly improved the relationship with the WGS results. The raw array data and those with polymorphic SNPs in the wild samples underestimated pairwise F ST values between breeds which had low F ST (<0.15) in the WGS, and overestimated this parameter for high WGS F ST (>0.15). LD based pruned data underestimated F ST in a consistent manner. The genetic distance matrix from LD pruned data was more closely related to that of WGS than the other array versions. PCA was rather robust in all array versions, since the population structure on the PCA plot was generally well captured in comparison to the WGS data. CONCLUSIONS:Among the tested filtering strategies, LD based pruning was found to account for the effects of ascertainment bias in the relatively best way, producing results which are most comparable to those obtained from WGS data and therefore is recommended for practical use.

Project description:Genomic tools to better define breed composition in agriculturally important species have sparked scientific and commercial industry interest. Knowledge of breed composition can inform multiple scientifically important decisions of industry application including DNA marker-assisted selection, identification of signatures of selection, and inference of product provenance to improve supply chain integrity. Genomic tools are expensive but can be economized by deploying a relatively small number of highly informative single-nucleotide polymorphisms (SNP) scattered evenly across the genome. Using resources from the 1000 Bull Genomes Project we established calibration (more stringent quality criteria; N = 1,243 cattle) and validation (less stringent; N = 864) data sets representing 17 breeds derived from both taurine and indicine bovine subspecies. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied 2 criteria, high differential allele frequencies across the breeds as measured by average Euclidean distance (AED) and high uniformity (even spacing) across the physical genome. Those SNP awarded the highest AED were in or near genes previously identified as important signatures of selection in cattle such as LCORL, NCAPG, KITLG, and PLAG1. For each panel, the genomic breed composition (GBC) of each animal in the validation dataset was estimated using a linear regression model. A systematic exploration of the predictive accuracy of the various sized panels was then undertaken on the validation population using 3 benchmarking approaches: (1) % error (expressed relative to the estimated GBC made from over 1 million SNP), (2) % breed misassignment (expressed relative to each individual's breed recorded), and (3) Shannon's entropy of estimated GBC across the 17 target breeds. Our analyses suggest that a panel of just 250 SNP represents an adequate balance between accuracy and cost-only modest gains in accuracy are made as one increases panel density beyond this point.

Project description:The objective of this study was to develop and validate a customized cost-effective single nucleotide polymorphism (SNP) panel for genetic improvement of feed efficiency in beef cattle. The SNPs identified in previous association studies and through extensive analysis of candidate genomic regions and genes, were screened for their functional impact and allele frequency in Angus and Hereford breeds used as validation candidates for the panel. Association analyses were performed on genotypes of 159 SNPs from new samples of Angus (n = 160), Hereford (n = 329), and Angus-Hereford crossbred (n = 382) cattle using allele substitution and genotypic models in ASReml. Genomic heritabilities were estimated for feed efficiency traits using the full set of SNPs, SNPs associated with at least one of the traits (at P ? 0.05 and P < 0.10), as well as the Illumina bovine 50K representing a widely used commercial genotyping panel. A total of 63 SNPs within 43 genes showed association (P ? 0.05) with at least one trait. The minor alleles of SNPs located in the GHR and CAST genes were associated with decreasing effects on residual feed intake (RFI) and/or RFI adjusted for backfat (RFIf), whereas minor alleles of SNPs within MKI67 gene were associated with increasing effects on RFI and RFIf. Additionally, the minor allele of rs137400016 SNP within CNTFR was associated with increasing average daily gain (ADG). The SNPs genotypes within UMPS, SMARCAL, CCSER1, and LMCD1 genes showed significant over-dominance effects whereas other SNPs located in SMARCAL1, ANXA2, CACNA1G, and PHYHIPL genes showed additive effects on RFI and RFIf. Gene enrichment analysis indicated that gland development, as well as ion and cation transport are important physiological mechanisms contributing to variation in feed efficiency traits. The study revealed the effect of the Jak-STAT signaling pathway on feed efficiency through the CNTFR, OSMR, and GHR genes. Genomic heritability using the 63 significant (P ? 0.05) SNPs was 0.09, 0.09, 0.13, 0.05, 0.05, and 0.07 for ADG, dry matter intake, midpoint metabolic weight, RFI, RFIf, and backfat, respectively. These SNPs contributed to genetic variation in the studied traits and thus can potentially be used or tested to generate cost-effective molecular breeding values for feed efficiency in beef cattle.

Project description:The objective of this study was to compare accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle. The dataset was composed of 2505 records reporting the occurrence of retained fetal membranes (RFM), metritis (MET), mastitis (MAST), displaced abomasum (DA), lameness (LS), clinical endometritis (CE), respiratory disease (RD), dystocia (DYST) and subclinical ketosis (SCK) in Holstein cows, collected between 2012 and 2014 in 16 dairies located across the US. Cows were genotyped with the Illumina BovineHD (HD, 777K). The quality controls for SNP genotypes were HWE P-value of at least 1 × 10-10 ; MAF greater than 0.01 and call rate greater than 0.95. The FImpute program was used for imputation of missing SNP markers. The effect of each SNP was estimated using the Bayesian Ridge Regression (BRR), Bayes A, Bayes B and Bayes Cπ methods. The prediction quality was assessed by the area under the curve, the prediction mean square error and the correlation between genomic breeding value and the observed phenotype, using a leave-one-out cross-validation technique that avoids iterative cross-validation. The highest accuracies of predictions achieved were: RFM [Bayes B (0.34)], MET [BRR (0.36)], MAST [Bayes B (0.55), DA [Bayes Cπ (0.26)], LS [Bayes A (0.12)], CE [Bayes A (0.32)], RD [Bayes Cπ (0.23)], DYST [Bayes A (0.35)] and SCK [Bayes Cπ (0.38)] models. Except for DA, LS and RD, the predictive abilities were similar between the methods. A strong relationship between the predictive ability and the heritability of the trait was observed, where traits with higher heritability achieved higher accuracy and lower bias when compared with those with low heritability. Overall, it has been shown that a high-density SNP panel can be used successfully to predict genomic breeding values of health traits in Holstein cattle and that the model of choice will depend mostly on the genetic architecture of the trait.

Project description:BACKGROUND:Accurate determination of genetic ancestry is of high interest for many areas such as biomedical research, personal genomics and forensics. It remains an important topic in genetic association studies, as it has been shown that population stratification, if not appropriately considered, can lead to false-positive and -negative results. While large association studies typically extract ancestry information from available genome-wide SNP genotypes, many important clinical data sets on rare phenotypes and historical collections assembled before the GWAS area are in need of a feasible method (i.e., ease of genotyping, small number of markers) to infer the geographic origin and potential admixture of the study subjects. Here we report on the development, application and limitations of a small, multiplexable ancestry informative marker (AIM) panel of SNPs (or AISNP) developed specifically for this purpose. RESULTS:Based on worldwide populations from the HGDP, a 41-AIM AISNP panel for multiplex application with the ABI SNPlex and a subset with 31 AIMs for the Sequenome iPLEX system were selected and found to be highly informative for inferring ancestry among the seven continental regions Africa, the Middle East, Europe, Central/South Asia, East Asia, the Americas and Oceania. The panel was found to be least informative for Eurasian populations, and additional AIMs for a higher resolution are suggested. A large reference set including over 4,000 subjects collected from 120 global populations was assembled to facilitate accurate ancestry determination. We show practical applications of this AIM panel, discuss its limitations for admixed individuals and suggest ways to incorporate ancestry information into genetic association studies. CONCLUSION:We demonstrated the utility of a small AISNP panel specifically developed to discern global ancestry. We believe that it will find wide application because of its feasibility and potential for a wide range of applications.

Dataset Information

A genetically unique Chinese cattle population shows evidence of common ancestry with wild species when analysed with a reduced ascertainment bias SNP panel.

Publications

A genetically unique Chinese cattle population shows evidence of common ancestry with wild species when analysed with a reduced ascertainment bias SNP panel.

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