Molecular Markers for Harnessing Heterosis

Krishnan, Gopala S.; Singh, Atul K.; Waters, Daniel Le; Henry, Robert J.

doi:10.1002/9781118473023.ch8

Cited by 8 publications

(9 citation statements)

References 59 publications

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“…hybrid vigour) to describe a phenomenon where crossbred organisms have increased growth, productivity, fertility, and vigour over their purebred parents. Heterosis has immense economic value in plant and animal agriculture and has been exploited in many production systems (Lippman and Zamir, 2007;Krishnan et al, 2013). Although, the genetic basis of heterosis is still a subject of investigation, the hypotheses are based on the evidence of increased heterozygosity in crossbreds relative to straightbreds and is maximized in F 1 hybrids (Falconer and Mackay, 1996).…”

Section: Introductionmentioning

confidence: 99%

Modeling heterotic effects in beef cattle using genome-wide SNP-marker genotypes1

Akanno

Abo-Ismail

Chen

et al. 2018

Journal of Animal Science

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An objective of commercial beef cattle crossbreeding programs is to simultaneously optimize use of additive (breed differences) and non-additive (heterosis) effects. A total of 6,794 multibreed and crossbred beef cattle with phenotype and Illumina BovineSNP50 genotype data were used to predict genomic heterosis for growth and carcass traits by applying two methods assumed to be linearly proportional to heterosis. The methods were as follows: 1) retained heterozygosity predicted from genomic breed fractions (HET1) and 2) deviation of adjusted crossbred phenotype from midparent value (HET2). Comparison of methods was based on prediction accuracy from cross-validation. Here, a mutually exclusive random sampling of all crossbred animals (n = 5,327) was performed to form five groups replicated five times with approximately 1,065 animals per group. In each run within a replicate, one group was assigned as a validation set, while the remaining four groups were combined to form the reference set. The phenotype of the animals in the validation set was assumed to be unknown; thus, it resulted in every animal having heterosis values that were predicted without using its own phenotype, allowing their adjusted phenotype to be used for validation. The same approach was used to test the impact of predicted heterosis on accuracy of genomic breeding values (GBV). The results showed positive heterotic effects for growth traits but not for carcass traits that reflect the importance of heterosis for growth traits in beef cattle. Heterosis predicted by HET1 method resulted in less variable estimates that were mostly within the range of estimates generated by HET2. Prediction accuracy was greater for HET2 (0.37-0.98) than HET1 (0.34-0.43). Proper consideration of heterosis in genomic evaluation models has debatable effects on accuracy of EBV predictions. However, opportunity exists for predicting heterosis, improving accuracy of genomic selection, and consequently optimizing crossbreeding programs in beef cattle.

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Section: Introductionmentioning

confidence: 99%

Modeling heterotic effects in beef cattle using genome-wide SNP-marker genotypes1

Akanno

Abo-Ismail

Chen

et al. 2018

Journal of Animal Science

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“…Also, many prediction studies have been carried out on commercial crops such as maize, rapeseed, sunflower, chick pea and carrot. Some of these studies reported correlations between genetic distances (GD) and heterosis (Reif et al, 2003;Balestre et al, 2009), but others concluded that GD is not a reliable predictor of heterosis (Dias et al, 2004;Krishnan et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Because of inconsistencies in the results from previous studies, one cannot conclude whether the prediction of heterosis based on molecular marker information has been a success or not, as pointed out in reviews by Dias et al (2004) and Krishnan et al (2013). The former authors reviewed several studies in plants and suggested that the number of molecular markers (averages of 160 random amplified polymorphic DNAs, 281 restriction fragment length polymorphisms and 25 simple sequence repeats) should be increased for accurate predictions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2013

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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 B400 000 individuals from 47 crosses and allele frequencies on B53 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 crossvalidation. SDAF predicted heterosis for egg number and weight with an accuracy of B0.5, but did not predict heterosis for survival days. Heterosis predictions allowed preselection of pure lines before field-testing, saving B50% 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.

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“…modified Rogers' distance (Wright, 1984) and Nei's genetic distance (Nei, 1972), and estimated correlations between these variables and crossbred performance or heterosis. Results were inconclusive -both in plants and animalsand the general agreement was that a higher number of molecular markers with genome-wide coverage would be needed for further studies Haberfeld et al, 1996;Minvielle et al, 2000;Reif et al, 2003 andreviews by Dias et al, 2004;Krishnan et al, 2013).…”

Section: Motivationmentioning

confidence: 99%

“…Another perspective with more bearing on the prediction of heterosis is that with a larger number of markers, one gets a more accurate estimate of the true SDAF or genetic distance between parental pure lines, and that this genome-wide value also reflects the SDAF at the causative loci affecting the trait(s) of interest. These two lines of reasoning must be behind the conclusion by several authors Krishnan et al, 2013;Rajendrakumar et al, 2015) that one reason past studies on marker-based prediction of heterosis were inconclusive is that the number of markers used was too small. Therefore, to test the effect of the number of markers on the accuracy of predicting heterosis, I investigated how the number of markers affects the estimate of the predictor variable, SDAF.…”

Section: Effect Of the Number Of Markersmentioning

confidence: 99%

Genomics of heterosis and egg production in White Leghorns

Amuzu‐Aweh¹

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Chapter 1-General introduction Chapter 2-Prediction of heterosis using genome-wide SNP-marker data: application to egg production traits in White Leghorn crosses Chapter 3-Predicting heterosis for egg production traits in crossbred offspring of individual White Leghorn sires using genome-wide SNP data Chapter 4-Genomic estimation of variance components and dominance SNP effects for egg number and egg weight in White Leghorn pure lines Chapter 5-A genome-wide association study for egg number and egg weight in a large crossbred population of White Leghorns

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Molecular Markers for Harnessing Heterosis

Cited by 8 publications

References 59 publications

Modeling heterotic effects in beef cattle using genome-wide SNP-marker genotypes1

Modeling heterotic effects in beef cattle using genome-wide SNP-marker genotypes1

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

Genomics of heterosis and egg production in White Leghorns

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