Use Of Whole-genome Sequence Data Research Articles

Efficient genomic prediction based on whole-genome sequence data using split-and-merge Bayesian variable selection.

BackgroundUse of whole-genome sequence data is expected to increase persistency of genomic prediction across generations and breeds but affects model performance and requires increased computing time. In this study, we investigated whether the split-and-merge Bayesian stochastic search variable selection (BSSVS) model could overcome these issues. BSSVS is performed first on subsets of sequence-based variants and then on a merged dataset containing variants selected in the first step.ResultsWe used a dataset that included 4,154,064 variants after editing and de-regressed proofs for 3415 reference and 2138 validation bulls for somatic cell score, protein yield and interval first to last insemination. In the first step, BSSVS was performed on 106 subsets each containing ~39,189 variants. In the second step, 1060 up to 472,492 variants, selected from the first step, were included to estimate the accuracy of genomic prediction. Accuracies were at best equal to those achieved with the commonly used Bovine 50k-SNP chip, although the number of variants within a few well-known quantitative trait loci regions was considerably enriched. When variant selection and the final genomic prediction were performed on the same data, predictions were biased. Predictions computed as the average of the predictions computed for each subset achieved the highest accuracies, i.e. 0.5 to 1.1 % higher than the accuracies obtained with the 50k-SNP chip, and yielded the least biased predictions. Finally, the accuracy of genomic predictions obtained when all sequence-based variants were included was similar or up to 1.4 % lower compared to that based on the average predictions across the subsets. By applying parallelization, the split-and-merge procedure was completed in 5 days, while the standard analysis including all sequence-based variants took more than three months.ConclusionsThe split-and-merge approach splits one large computational task into many much smaller ones, which allows the use of parallel processing and thus efficient genomic prediction based on whole-genome sequence data. The split-and-merge approach did not improve prediction accuracy, probably because we used data on a single breed for which relationships between individuals were high. Nevertheless, the split-and-merge approach may have potential for applications on data from multiple breeds.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0225-x) contains supplementary material, which is available to authorized users.

Within- and across-breed genomic prediction using whole-genome sequence and single nucleotide polymorphism panels.

BackgroundCurrently, genomic prediction in cattle is largely based on panels of about 54k single nucleotide polymorphisms (SNPs). However with the decreasing costs of and current advances in next-generation sequencing technologies, whole-genome sequence (WGS) data on large numbers of individuals is within reach. Availability of such data provides new opportunities for genomic selection, which need to be explored.MethodsThis simulation study investigated how much predictive ability is gained by using WGS data under scenarios with QTL (quantitative trait loci) densities ranging from 45 to 132 QTL/Morgan and heritabilities ranging from 0.07 to 0.30, compared to different SNP densities, with emphasis on divergent dairy cattle breeds with small populations. The relative performances of best linear unbiased prediction (SNP-BLUP) and of a variable selection method with a mixture of two normal distributions (MixP) were also evaluated. Genomic predictions were based on within-population, across-population, and multi-breed reference populations.ResultsThe use of WGS data for within-population predictions resulted in small to large increases in accuracy for low to moderately heritable traits. Depending on heritability of the trait, and on SNP and QTL densities, accuracy increased by up to 31 %. The advantage of WGS data was more pronounced (7 to 92 % increase in accuracy depending on trait heritability, SNP and QTL densities, and time of divergence between populations) with a combined reference population and when using MixP. While MixP outperformed SNP-BLUP at 45 QTL/Morgan, SNP-BLUP was as good as MixP when QTL density increased to 132 QTL/Morgan.ConclusionsOur results show that, genomic predictions in numerically small cattle populations would benefit from a combination of WGS data, a multi-breed reference population, and a variable selection method.

Genomic selection: A paradigm shift in animal breeding

• Traditional marker-assisted selection (MAS) did not result in a widespread use of DNA information in animal breeding. The main reason was that the traits of interest in livestock production were much more complex than expected: they were determined by thousands of genes with small effects on phenotype. These effects were usually too small to be statistically significant and so were ignored. • Genomic selection (GS) assumes that all markers might be linked to a gene affecting the trait and concentrates on estimating their effect rather than testing its significance. Three technological breakthroughs resulted in the current wide-spread use of DNA information in animal breeding: the development of the genomic selection technology, the discovery of massive numbers of genetic markers (single nucleotide polymorphisms; SNPs), and high-throughput technology to genotype animals for (hundreds of) thousands of SNPs in a cost-effective manner. • Here we review current methods for GS, including how they deal with practical data, where genotypes are missing on a large scale. The use of whole-genome sequence data is anticipated, and its advantages and disadvantages are depicted. Current and predicted future impacts of GS on dairy and beef cattle, pigs, and poultry breeding are described. Finally, future directions for GS are discussed. • It is anticipated that future GS applications will either be: within breed (wbGS), where accuracy is obtained by maintaining huge withinbreed reference populations; or across breed (abGS) where accuracy is obtained from across-breed reference populations and high-density GS methods that focus on causative genomic regions. We argue that future GS applications will increasingly turn toward abGS.

Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle.

BackgroundIn contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data.MethodsHighly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training.ResultsPrediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed.ConclusionsCompared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the putative advantage of genomic prediction using (imputed) sequence data, a training set with a larger number of individuals that are distantly related to each other and genomic prediction models that incorporate biological information on the SNPs or that apply stricter SNP pre-selection should be considered.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0149-x) contains supplementary material, which is available to authorized users.

Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle.

BackgroundThe use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle.MethodsWhole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated.ResultsMean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs.ConclusionsAccuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability.

Data Re-Identification: Prioritize Privacy

In our October 2012 Report, and progress in whole genome ([ 1 ][1]), the Presidential Commission for the Study of Bioethical Issues (Bioethics Commission) called for a consistent floor of privacy protections across state lines covering genomic data, regardless of how the data were obtained. The need for this consistent approach to privacy protections is now more urgent than ever in light of recent advances that demonstrate the limits of genomic deidentification (Identifying personal genomes by surname inference, M. Gymrek et al. , Reports, 18 January, p. [321][2]), which challenge obtaining the benefits of genomic data sharing (The complexities of genomic identifiability, L. L. Rodriguez et al. , Policy Forum, 18 January, p. [275][3]). The promise of genomic technologies will not be realized if individuals are unwilling to share their sensitive data with the research community because of privacy concerns, or if those who share their data later discover that their privacy was invaded without their informed consent. The decline in scientists' abilities to keep personal data deidentified threatens to further erode public confidence around the privacy of unique genomic data. A deidentified genome has become a spectrum of possibilities for re-identification, rather than an absolute protection against privacy invasion. It therefore is incumbent on clinicians and researchers to obtain informed consent before any whole-genome sequencing and to develop and enforce appropriate limits on access to and use of all genomic data. In addition to strengthening consent and security standards, we need to streamline and strengthen laws governing the collection and use of whole-genome sequence data. 1. [↵][4] Presidential Commission for the Study of Bioethical Issues, Privacy and progress in whole genome sequencing (2012); . [1]: #ref-1 [2]: /lookup/doi/10.1126/science.1229566 [3]: /lookup/doi/10.1126/science.1234593 [4]: #xref-ref-1-1 View reference 1 in text