Dataset Information

Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in white Leghorn crosses.

ABSTRACT: Prediction of heterosis has a long history with mixed success, partly due to low numbers of genetic markers and/or small data sets. We investigated the prediction of heterosis for egg number, egg weight and survival days in domestic white Leghorns, using ?400?000 individuals from 47 crosses and allele frequencies on ?53?000 genome-wide single nucleotide polymorphisms (SNPs). When heterosis is due to dominance, and dominance effects are independent of allele frequencies, heterosis is proportional to the squared difference in allele frequency (SDAF) between parental pure lines (not necessarily homozygous). Under these assumptions, a linear model including regression on SDAF partitions crossbred phenotypes into pure-line values and heterosis, even without pure-line phenotypes. We therefore used models where phenotypes of crossbreds were regressed on the SDAF between parental lines. Accuracy of prediction was determined using leave-one-out cross-validation. SDAF predicted heterosis for egg number and weight with an accuracy of ?0.5, but did not predict heterosis for survival days. Heterosis predictions allowed preselection of pure lines before field-testing, saving ?50% of field-testing cost with only 4% loss in heterosis. Accuracies from cross-validation were lower than from the model-fit, suggesting that accuracies previously reported in literature are overestimated. Cross-validation also indicated that dominance cannot fully explain heterosis. Nevertheless, the dominance model had considerable accuracy, clearly greater than that of a general/specific combining ability model. This work also showed that heterosis can be modelled even when pure-line phenotypes are unavailable. We concluded that SDAF is a useful predictor of heterosis in commercial layer breeding.

SUBMITTER: Amuzu-Aweh EN

PROVIDER: S-EPMC3833690 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in white Leghorn crosses.

Amuzu-Aweh E N EN Bijma P P Kinghorn B P BP Vereijken A A Visscher J J van Arendonk J Am JA Bovenhuis H H

Heredity 20131009 6

Prediction of heterosis has a long history with mixed success, partly due to low numbers of genetic markers and/or small data sets. We investigated the prediction of heterosis for egg number, egg weight and survival days in domestic white Leghorns, using ∼400 000 individuals from 47 crosses and allele frequencies on ∼53 000 genome-wide single nucleotide polymorphisms (SNPs). When heterosis is due to dominance, and dominance effects are independent of allele frequencies, heterosis is proportional ...[more]

PMID: 24105438

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Project description:Crossbreeding advantage in hybrids compared with their parents, termed heterosis, has been exhaustively exploited in chicken breeding over the last century. Reports for crossbreeding of elite laying chickens covering rearing and laying period remain infrequent. In this study, resource populations of Rhode Island Red (RIR) and White Leghorn (WL) pure-bred chickens were reciprocally crossed to generate 4 distinct groups that were evaluated for prelaying growth, egg production, and egg quality. Birds monitored for prelaying growth consists of 105 (RIR), 131 (WL), 207 (RIR × WL) and 229 (WL × RIR), and 30 pullets from each group were evaluated. Egg laying records were collected from 102, 89, 147, and 191 hens in the 4 populations, respectively. In addition, expression of 5 candidate genes for egg production in the ovarian follicles was measured by RT-qPCR. Results showed that BW of hatched chicks in the WL line was higher than the other populations. However, the 2 crossbreds grew faster than WL purebred throughout the prelaying period. Low to medium heterosis was observed for BW and body length before the onset of lay. White Leghorn and the hybrids commenced laying earlier than RIR pullets and egg production traits were favorable in the crossbreds compared with purebreds. Heterosis for egg number and clutch size was moderate in WL × RIR but low in RIR × WL hens. Expression of antimullerian hormone gene was high in WL and RIR × WL hybrids, suggesting WL parent-specific enhancing dominant expression. Shell weight was higher in the crossbreds than purebreds at 52 wk of age, but RIR hens laid eggs with higher shell ratio than the other populations (P < 0.05). Conversely, WL and the hybrids had higher eggshell strength than RIR birds (P < 0.05). Eggshell strength was the only egg quality trait that showed heterosis above 10% in WL × RIR hybrids at 32 and 52 wk of age. White Leghorn × RIR hens demonstrated higher percent heterosis for economic traits than birds of the reciprocal hybrid. This means that RIR breed is a better dam than a sire line for growth, egg laying, and egg quality traits.

Project description:In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.

Dataset Information

Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in white Leghorn crosses.

Publications

Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in white Leghorn crosses.

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