Alternative haplotype construction methods for genomic evaluation

Jonas, David; Ducrocq, Vincent; Fouilloux, Marie-Noëlle; Croiseau, Pascal

doi:10.3168/jds.2015-10433

Cited by 18 publications

(32 citation statements)

References 16 publications

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“…Some studies that tested other livestock populations reported that haplotypes can be advantageous for genomic prediction. Applying haplotype to genomic prediction has been studied in Montbeliarde bulls (Jónás et al, 2016), New Zealand dairy cattle (Hess et al, 2017), Nordic Holstein (Cuyabano et al, 2014;Cuyabano et al, 2015), and Danish Holstein bulls (Edriss et al, 2013). Although different haplotypes were used in these studies and the design of the studies may differ, their study still shows the benefits of using haplotype for genomic prediction.…”

Section: Discussionmentioning

confidence: 99%

Genomic Prediction Accuracy Using Haplotypes Defined by Size and Hierarchical Clustering Based on Linkage Disequilibrium

et al. 2020

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Genomic prediction is an effective way to estimate the genomic breeding values from genetic information based on statistical methods such as best linear unbiased prediction (BLUP). The used of haplotype, clusters of linked single nucleotide polymorphism (SNP) as markers instead of individual SNPs can improve the accuracy of genomic prediction. Since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with a cluster of markers is higher compared to an individual marker. To make haplotypes efficient in genomic prediction, finding optimal ways to define haplotypes is essential. In this study, 770K or 50K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 3,498 cattle. Using SNP chip data, haplotype was defined in three different ways based on 1) the number of SNPs included, 2) length of haplotypes (bp), and 3) agglomerative hierarchical clustering based on LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; 5, 10, 20 or 50 SNPs on average per haplotype. A linear mixed model using haplotype to calculated the covariance matrix was applied for testing the prediction accuracy of each haplotype size. Also, conventional SNP-based linear mixed model was tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight (CWT), eye muscle area (EMA) and backfat thickness (BFT) were used as the phenotypes. This study reveals that using haplotypes generally showed increased accuracy compared to conventional SNP-based model for CWT and EMA, but found to be small or no increase in accuracy for BFT. LD clustering-based haplotypes specifically the five SNPs size showed the highest prediction accuracy for CWT and EMA. Meanwhile, the highest accuracy was obtained when length-based haplotypes with five SNPs were used for BFT. The maximum gain in accuracy was 1.3% from cross-validation and 4.6% from forward validation for EMA, suggesting that genomic prediction accuracy can be increased by using haplotypes. However, the improvement from using haplotypes may depend on the trait of interest. In addition, when the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles, thereby reducing computational costs. Therefore, finding optimal ways to define haplotypes and using the haplotype alleles as markers can improve the accuracy of genomic prediction.

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Section: Discussionmentioning

confidence: 99%

Genomic Prediction Accuracy Using Haplotypes Defined by Size and Hierarchical Clustering Based on Linkage Disequilibrium

et al. 2020

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“…As expected, the lower average |D'| value in the breeding population reflects its recombination history. The synthetic and the biparental population If there is higher LD between QTLs and haplotypes than with individual SNPs, haplotype blocks can provide substantial statistical power in association studies [6] and increased accuracy of genomic prediction of complex traits [36]. Surprisingly, our results evidenced that the number and length of the haplotype blocks and the number of SNPs per haplotype block were proportional to the average r 2 .…”

Section: Discussionmentioning

confidence: 67%

Linkage disequilibrium and haplotype block patterns in popcorn populations

Andrade

Viana

Pereira

et al. 2019

Preprint

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Linkage disequilibrium (LD) analysis provides information on evolutionary aspects of the 9 populations and allows selecting populations and single nucleotide polymorphisms (SNPs) for 10 association studies. Recently, haplotype blocks have been used to increase the power of quantitative 11 trait loci detection in genome-wide association studies and the prediction accuracy with genomic 12 selection. The objectives of this study were to compare the degree of LD, the LD decay, the LD 13 decay extent, and the number and length of haplotype blocks in the populations and to elaborate the 14 first LD map for maize, for elucidating if the maize chromosomes also had a pattern of interspaced 15 regions of high and low rates of recombination. We used a biparental temperate population, a 16 tropical synthetic, and a tropical breeding population, genotyped for approximately 75,000 SNPs. 17The level of LD expressed by the r 2 values is surprisingly low (0.02, 0.04, and 0.04), but 18 comparable to some non-isolated human populations. The general evidence is that the synthetic is 19 the population with higher LD. It is not expected a significant advantage of haplotype-based 20 association study and along generations genomic selection due to the reduced number of SNPs in 21 the haplotype blocks (2 to 3). The results concerning LD decay (rapid decay after 5-10 kb) and LD 22 decay extent (along up to 300 kb) are in the range observed with maize inbred line panels. Our most 23 important result is that maize chromosomes had a pattern of regions of extensive LD interspaced 24 with regions of low LD. However, our simple simulated LD map provides evidence that this pattern 25 can reflect regions with differences of allele frequencies and LD level (expressed by D') and not 26 regions with high and low rates of recombination. 27 Keywords: gametic phase disequilibrium; LD map; haplotype blocks; intragenic LD. Introduction Linkage disequilibrium (LD) analysis is important to humans, other animal species, and plants because the results can be used for positional cloning, provide information on rate of recombination, gene conversion, and evolutionary aspects of the populations, including recombination history, mutation, selection, genetic drift, and admixture, and allows selecting populations and single nucleotide polymorphisms (SNPs) for association studies [1]. The most common LD measures are D' and r 2 . The statistic D' is the ratio between D (the difference between products of haplotypes, D = P(AB).P(ab)  P(Ab).P(aB)) and the deviation of the actual gametic frequency from linkage equilibrium [2]. The statistic r 2 is the square of the correlation between the values of alleles at two loci in the same gamete, where D is the covariance [3]. Additional information on historical recombination is provided by the analysis of the haplotype blocks pattern in populations. A haplotype block is a chromosome region in which there are few haplotypes (combinations of alleles of multiple SNPs within a haplotype block) (2-4 per block), and for which the ...

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“…The construction of haplotypes at a particular SNP position by merging this SNP with the flanking markers is straightforward. However, due to the short distance between the markers, most of the resulting haplotypes included a small number of over-represented alleles together with a large number of alleles with low frequencies within the population (Jónás et al, 2016). It is expected that there is an optimal haplotype length, which depends on the distance between the markers and extent of LD in the population.…”

Section: Discussionmentioning

confidence: 99%

A haplotype regression approach for genetic evaluation using sequences from the 1000 bull genomes Project

Lakhssassi¹,

González-Recio²

2018

Span. j. agric. res.

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Haplotypes from sequencing data may improve the prediction accuracy in genomic evaluations as haplotypes are in stronger linkage disequilibrium with quantitative trait loci than markers from SNP chips. This study focuses first, on the creation of haplotypes in a population sample of 450 Holstein animals, with full-sequence data from the 1000 bull genomes project; and second, on incorporating them into the whole genome prediction model. In total, 38,319,258 SNPs (and indels) from Next Generation Sequencing were included in the analysis. After filtering variants with minor allele frequency (MAF< 0.025) 13,912,326 SNPs were available for the haplotypes extraction with findhap.f90. The number of SNPs in the haploblocks was on average 924 SNP (166,552 bp). Unique haplotypes were around 97% in all chromosomes and were ignored leaving 153,428 haplotypes. Estimated haplotypes had a large contribution to the total variance of genomic estimated breeding values for kilogram of protein, Global Type Index, Somatic Cell Score and Days Open (between 32 and 99.9%). Haploblocks containing haplotypes with large effects were selected by filtering for each trait, haplotypes whose effect was larger/lower than the mean plus/minus 3 times the standard deviation (SD) and 1 SD above the mean of the haplotypes effect distribution. Results showed that filtering by 3 SD would not be enough to capture a large proportion of genetic variance, whereas filtering by 1 SD could be useful but model convergence should be considered. Additionally, sequence haplotypes were able to capture additional genetic variance to the polygenic effect for traits undergoing lower selection intensity like fertility and health traits.

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Alternative haplotype construction methods for genomic evaluation

Cited by 18 publications

References 16 publications

Genomic Prediction Accuracy Using Haplotypes Defined by Size and Hierarchical Clustering Based on Linkage Disequilibrium

Genomic Prediction Accuracy Using Haplotypes Defined by Size and Hierarchical Clustering Based on Linkage Disequilibrium

Linkage disequilibrium and haplotype block patterns in popcorn populations

A haplotype regression approach for genetic evaluation using sequences from the 1000 bull genomes Project

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