Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

Berg, I. van den; Bowman, P.J.; MacLeod, Iona M.; Hayes, Ben J.; Wang, Tingting; Bolormaa, Sunduimijid; Goddard, Michael E.

doi:10.1186/s12711-017-0347-9

Cited by 35 publications

(49 citation statements)

References 24 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies have shown that inconsistent LD between SNPs and causative variants across populations causes disadvantages compared with using a combined population in genomic prediction [ 5 , 6 , 27 , 28 ]. In this study, the correlation of r LD values of adjacent SNPs with a mean of 0.55 indicated the insufficient consistency in LD between the Beijing and Fujian populations.…”

Section: Discussionmentioning

confidence: 99%

The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs

Song¹,

Zhang²,

Ding³

2020

J Animal Sci Biotechnol

View full text Add to dashboard Cite

Background Different production systems and climates could lead to genotype-by-environment (G × E) interactions between populations, and the inclusion of G × E interactions is becoming essential in breeding decisions. The objective of this study was to investigate the performance of multi-trait models in genomic prediction in a limited number of environments with G × E interactions. Results In total, 2,688 and 1,384 individuals with growth and reproduction phenotypes, respectively, from two Yorkshire pig populations with similar genetic backgrounds were genotyped with the PorcineSNP80 panel. Single- and multi-trait models with genomic best linear unbiased prediction (GBLUP) and BayesC π were implemented to investigate their genomic prediction abilities with 20 replicates of five-fold cross-validation. Our results regarding between-environment genetic correlations of growth and reproductive traits (ranging from 0.618 to 0.723) indicated the existence of G × E interactions between these two Yorkshire pig populations. For single-trait models, genomic prediction with GBLUP was only 1.1% more accurate on average in the combined population than in single populations, and no significant improvements were obtained by BayesC π for most traits. In addition, single-trait models with either GBLUP or BayesC π produced greater bias for the combined population than for single populations. However, multi-trait models with GBLUP and BayesC π better accommodated G × E interactions, yielding 2.2% – 3.8% and 1.0% – 2.5% higher prediction accuracies for growth and reproductive traits, respectively, compared to those for single-trait models of single populations and the combined population. The multi-trait models also yielded lower bias and larger gains in the case of a small reference population. The smaller improvement in prediction accuracy and larger bias obtained by the single-trait models in the combined population was mainly due to the low consistency of linkage disequilibrium between the two populations, which also caused the BayesC π method to always produce the largest standard error in marker effect estimation for the combined population. Conclusions In conclusion, our findings confirmed that directly combining populations to enlarge the reference population is not efficient in improving the accuracy of genomic prediction in the presence of G × E interactions, while multi-trait models perform better in a limited number of environments with G × E interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs

Song¹,

Zhang²,

Ding³

2020

J Animal Sci Biotechnol

View full text Add to dashboard Cite

show abstract

“…Therefore, we used the local GEBV variance, that combines the effects of variants in a region. Estimating variants effects for full sequence data using a Bayesian model is, however, computationally challenging [15, 16]. Therefore, we used the local GEBV intervals based on HD effects estimated with Bayes R to detect QTL regions, and used the GWAS for the colocalisation analysis and to zoom into sequence level.…”

Section: Discussionmentioning

confidence: 99%

Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production traits or fertility and with gene expression, in dairy cattle. We used three different approaches to discover QTL that are also expression QTL (eQTL): 1) estimate the correlation between local genomic estimated breeding values (GEBV) and gene expression, 2) investigate whether the 300 intervals explaining most genetic variance for a trait contain more eQTL than 300 randomly selected intervals, and 3) a colocalisation analysis. Phenotypes and genotypes up to sequence level of 35,775 dairy bulls and cows were used for QTL mapping, and gene expression and genotypes of 131 cows were used to identify eQTL. Results With all three approaches, we identified some overlap between eQTL and QTL, though the majority of QTL in our dataset did not seem to be eQTL. The most significant associations between QTL and eQTL were found for intervals on chromosome 18, where local GEBV for all traits showed a strong association with the expression of the FUK and DDX19B. Intervals whose local GEBV for a trait correlated highly significantly with the expression of a nearby gene explained only a very small part of the genetic variance for that trait. It is likely that part of these correlations were due to linkage disequilibrium (LD) in the interval. While the 300 intervals explaining most genetic variance explained most of the GEBV variance, they contained only slightly more eQTL than 300 randomly selected intervals that explained a minimal portion of the GEBV variance. Furthermore, some variants showed a high colocalisation probability, but this was only the case for few variants. Conclusions Several reasons may have contributed to the low level of overlap between QTL and eQTL detected in our study, including a lack of power in the eQTL study and long-range LD making it difficult to separate QTL and eQTL. Furthermore, it may be that eQTL explain only a small fraction of QTL. Electronic supplementary material The online version of this article (10.1186/s12864-019-5656-7) contains supplementary material, which is available to authorized users.

show abstract

“…However, as the number of common variants to consider increases from tens of thousands to millions without a concomitant increase in phenotypic data points, so too does the curse of dimensionality; that is, it becomes more difficult to accurately estimate the effects of the growing number of SNPs. Although numerical methods have been developed to handle millions of variants 101,[142][143][144][145] , indiscriminate use of whole sequence information has only modestly increased (≤5%) the accuracy of genomic predictions [146][147][148] . Strategies to further improve the accuracy of whole-genome-sequence-based GS currently involve either selecting or assigning more weight to a subset of imputed variants that are more likely to be causative.…”

Section: Increasing the Accuracy Of Gs Using Whole-genome Sequence Immentioning

confidence: 99%

Harnessing genomic information for livestock improvement

2018

View full text Add to dashboard Cite

The world demand for animal-based food products is anticipated to increase by 70% by 2050. Meeting this demand in a way that has a minimal impact on the environment will require the implementation of advanced technologies, and methods to improve the genetic quality of livestock are expected to play a large part. Over the past 10 years, genomic selection has been introduced in several major livestock species and has more than doubled genetic progress in some. However, additional improvements are required. Genomic information of increasing complexity (including genomic, epigenomic, transcriptomic and microbiome data), combined with technological advances for its cost-effective collection and use, will make a major contribution.

show abstract

Multi-breed genomic prediction using Bayes R with sequence data and dropping variants with a small effect

Cited by 35 publications

References 24 publications

The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs

The superiority of multi-trait models with genotype-by-environment interactions in a limited number of environments for genomic prediction in pigs

Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle

Harnessing genomic information for livestock improvement

Contact Info

Product

Resources

About