Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle

Brøndum, Rasmus Froberg; Guldbrandtsen, Bernt; Sahana, Goutam; Lund, Mogens Sandø; Su, Guosheng

doi:10.1186/1471-2164-15-728

Cited by 100 publications

(154 citation statements)

References 28 publications

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“…This multi-breed reference population included 1147 sequenced animals with on average an 11-fold coverage, among which 311 Holstein bulls. All the individuals were used as reference because earlier studies showed that a multi-breed sequenced reference population can be beneficial for imputation accuracy, especially for SNPs with a low minor allele frequency (MAF) [4, 18, 19]. Polymorphic sites, including SNPs and short insertions and deletions (InDels), were identified for the 1147 individuals simultaneously using the multi-sample approach implemented in SAMtools’ mpileup along with BCFtools as described in Daetwyler et al [4].…”

Section: Methodsmentioning

confidence: 99%

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

et al. 2016

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BackgroundWhole-genome sequence data is expected to capture genetic variation more completely than common genotyping panels. Our objective was to compare the proportion of variance explained and the accuracy of genomic prediction by using imputed sequence data or preselected SNPs from a genome-wide association study (GWAS) with imputed whole-genome sequence data.MethodsPhenotypes were available for 5503 Holstein–Friesian bulls. Genotypes were imputed up to whole-genome sequence (13,789,029 segregating DNA variants) by using run 4 of the 1000 bull genomes project. The program GCTA was used to perform GWAS for protein yield (PY), somatic cell score (SCS) and interval from first to last insemination (IFL). From the GWAS, subsets of variants were selected and genomic relationship matrices (GRM) were used to estimate the variance explained in 2087 validation animals and to evaluate the genomic prediction ability. Finally, two GRM were fitted together in several models to evaluate the effect of selected variants that were in competition with all the other variants.ResultsThe GRM based on full sequence data explained only marginally more genetic variation than that based on common SNP panels: for PY, SCS and IFL, genomic heritability improved from 0.81 to 0.83, 0.83 to 0.87 and 0.69 to 0.72, respectively. Sequence data also helped to identify more variants linked to quantitative trait loci and resulted in clearer GWAS peaks across the genome. The proportion of total variance explained by the selected variants combined in a GRM was considerably smaller than that explained by all variants (less than 0.31 for all traits). When selected variants were used, accuracy of genomic predictions decreased and bias increased.ConclusionsAlthough 35 to 42 variants were detected that together explained 13 to 19% of the total variance (18 to 23% of the genetic variance) when fitted alone, there was no advantage in using dense sequence information for genomic prediction in the Holstein data used in our study. Detection and selection of variants within a single breed are difficult due to long-range linkage disequilibrium. Stringent selection of variants resulted in more biased genomic predictions, although this might be due to the training population being the same dataset from which the selected variants were identified.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0274-1) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

et al. 2016

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“…We used a 15-fold cross-validation strategy described in several previous studies 33–35 . Ninety individuals were selected randomly from the sequenced reference population as a target population for each fold (i.e.…”

Section: Methodsmentioning

confidence: 99%

Imputation-Based Whole-Genome Sequence Association Study Rediscovered the Missing QTL for Lumbar Number in Sutai Pigs

Yan

Qiao

Zhang

et al. 2017

Sci Rep

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Resequencing a number of individuals of various breeds as reference population and imputing the whole-genome sequences of individuals that were genotyped with medium-density chips to perform an association study is a very efficient strategy. Previously, we performed a genome-wide association study (GWAS) of lumbar number using 60K SNPs from the porcine Illumina chips in 418 Sutai pigs and did not detect any significant signals. Therefore, we imputed the whole-genome sequences of 418 Sutai individuals from 403 deeply resequenced reference individuals and performed association tests. We identified a quantitative trait locus (QTL) for lumbar number in SSC1 with a P value of 9.01E-18 that was close to the potential causative gene of NR6A1. The result of conditioning on the top SNP association test indicated that only one QTL was responsible for this trait in SSC1. The linkage disequilibrium (LD) drop test result for the condition of the reported potential causative mutation (c.575T > C missense mutation of NR6A1) indicated that this mutation was probably not the underlying mutation that affected lumbar number in our study. As the first trial of imputed whole-genome sequence GWAS in swine, this approach can be also powerful to investigate complex traits in pig like in human and cattle.

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“…These imputed genotypes were subsequently imputed to the whole genome sequence level using a multi-breed reference of 1,228 animals from run4 of the 1,000 Bull Genomes project (Daetwyler et al, 2014) and private data from Aarhus University. Data sets at different densities were prephased with BEAGLE4 r1274 (Browning and Browning, 2013) and imputations were done using IM-PUTE2 v2.3.1 (Howie et al, 2011), as suggested by Brøndum et al (2014). Genotypes for the QTL markers were then extracted from the whole genome sequence data.…”

Section: Genomic Predictionmentioning

confidence: 99%

Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction

Brøndum

Janss

et al. 2015

Journal of Dairy Science

163

187

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This study investigated the effect on the reliability of genomic prediction when a small number of significant variants from single marker analysis based on whole genome sequence data were added to the regular 54k single nucleotide polymorphism (SNP) array data. The extra markers were selected with the aim of augmenting the custom low-density Illumina BovineLD SNP chip (San Diego, CA) used in the Nordic countries. The single-marker analysis was done breed-wise on all 16 index traits included in the breeding goals for Nordic Holstein, Danish Jersey, and Nordic Red cattle plus the total merit index itself. Depending on the trait's economic weight, 15, 10, or 5 quantitative trait loci (QTL) were selected per trait per breed and 3 to 5 markers were selected to tag each QTL. After removing duplicate markers (same marker selected for more than one trait or breed) and filtering for high pairwise linkage disequilibrium and assaying performance on the array, a total of 1,623 QTL markers were selected for inclusion on the custom chip. Genomic prediction analyses were performed for Nordic and French Holstein and Nordic Red animals using either a genomic BLUP or a Bayesian variable selection model. When using the genomic BLUP model including the QTL markers in the analysis, reliability was increased by up to 4 percentage points for production traits in Nordic Holstein animals, up to 3 percentage points for Nordic Reds, and up to 5 percentage points for French Holstein. Smaller gains of up to 1 percentage point was observed for mastitis, but only a 0.5 percentage point increase was seen for fertility. When using a Bayesian model accuracies were generally higher with only 54k data compared with the genomic BLUP approach, but increases in reliability were relatively smaller when QTL markers were included. Results from this study indicate that the reliability of genomic prediction can be increased by including markers significant in genome-wide association studies on whole genome sequence data alongside the 54k SNP set.

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Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle

Cited by 100 publications

References 28 publications

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein–Friesian cattle

Imputation-Based Whole-Genome Sequence Association Study Rediscovered the Missing QTL for Lumbar Number in Sutai Pigs

Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction

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