Gene mapping and genomic prediction of bull fertility using sex chromosome markers

Pacheco, Hendyel Aparecida; Rezende, Fernanda Marcondes de; Peñagaricano, Francisco

doi:10.3168/jds.2019-17767

Cited by 28 publications

(19 citation statements)

References 32 publications

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“…The contribution of the X-chromosome to phenotypic variation might be important for fertility or reproduction traits, e.g. [10][11][12]. Examples include variants that affect litter size in sheep, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Theoretical and empirical comparisons of expected and realized relationships for the X-chromosome

Druet

Legarra

2020

Genet Sel Evol

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Background: X-chromosomal loci present different inheritance patterns compared to autosomal loci and must be modeled accordingly. Sexual chromosomes are not systematically considered in whole-genome relationship matrices although rules based on genealogical or marker information have been derived. Loci on the X-chromosome could have a significant contribution to the additive genetic variance, in particular for some traits such as those related to reproduction. Thus, accounting for the X-chromosome relationship matrix might be informative to better understand the architecture of complex traits (e.g., by estimating the variance associated to this chromosome) and to improve their genomic prediction. For such applications, previous studies have shown the benefits of combining information from genotyped and ungenotyped individuals. Results: In this paper, we start by presenting rules to compute a genomic relationship matrix (GRM) for the X-chromosome (G X) without making any assumption on dosage compensation, and based on coding of gene content with 0/1 for males and 0/1/2 for females. This coding adjusts naturally to previously derived pedigree-based relationships (S) for the X-chromosome. When needed, we propose to accommodate and estimate dosage compensation and genetic heterogeneity across sexes via multiple trait models. Using a Holstein dairy cattle dataset, including males and females, we then empirically illustrate that realized relationships (G X) matches expectations (S). However, G X presents high deviations from S. G X has also a lower dimensionality compared to the autosomal GRM. In particular, individuals are frequently identical along the entire chromosome. Finally, we confirm that the heritability of gene content for markers on the X-chromosome that are estimated by using S is 1, further demonstrating that S and G X can be combined. For the pseudo-autosomal region, we demonstrate that the expected relationships vary according to position because of the sex-gradient. We end by presenting the rules to construct the 'H matrix' by combining both relationship matrices. Conclusions: This work shows theoretically and empirically that a pedigree-based relationship matrix built with rules specifically developed for the X-chromosome (S) matches the realized GRM for the X-chromosome. Therefore, applications that combine expected relationships and genotypes for markers on the X-chromosome should use S and G X .

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Section: Introductionmentioning

confidence: 99%

“…Examples include variants that affect litter size in sheep, e.g. [13], infertility in cattle [14], or bull fertility [11]. More generally, quantitative trait loci (QTL) on the X-chromosome have been reported in previous studies, e.g.…”

Section: Introductionmentioning

confidence: 99%

Theoretical and empirical comparisons of expected and realized relationships for the X-chromosome

Druet

Legarra

2020

Genet Sel Evol

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“…On the other hand, bull fertility has been generally overlooked. Recent studies have reported promising results related to the feasibility of predicting dairy bull fertility using genomic data (Abdollahi-Arpanahi et al, 2017;Nani et al, 2019;Rezende et al, 2019;Pacheco et al, 2020). It should be noted that national dairy bull fertility evaluations are implemented in a few countries, including sire conception rate (SCR) in the United States and semen fertility value (SFV) in Australia (Carrick et al, 2000;.…”

Section: Introductionmentioning

confidence: 99%

Across-country genomic prediction of bull fertility in Jersey dairy cattle

Rezende

Haile‐Mariam

Pryce

et al. 2020

Journal of Dairy Science

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The use of information across populations is an attractive approach to increase the accuracy of genomic predictions for numerically small breeds and traits that are time-consuming and difficult to measure, such as male fertility in cattle. This study was conducted to evaluate genomic prediction of Jersey bull fertility using an across-country reference population combining records from the United States and Australia. The data set consisted of 1,570 US Jersey bulls with sire conception rate (SCR) records, 603 Australian Jersey bulls with semen fertility value (SFV) records and SNP genotypes for roughly 90,000 loci. Both SCR and SFV are evaluations of service sire fertility based on cow field data, and both are intended as phenotypic evaluations because the estimates include genetic and nongenetic effects. Within-and across-country genomic predictions were evaluated using univariate and bivariate genomic best linear unbiased prediction models. Predictive ability was assessed in 5-fold cross-validation using the correlation between observed and predicted fertility values and mean squared error of prediction. Within-country genomic predictions exhibited predictive correlations of around 0.28 and 0.02 for the United States and Australia, respectively. The Australian Jersey population is genetically diverse and small in size, so careful selection of the reference population by including only closely related animals (e.g., excluding New Zealand bulls, which is a less-related population) increased the predictive correlations up to 0.20. Notably, the use of bivariate models fitting all US Jersey records and the optimized Australian population resulted in predictive correlations around of 0.24 for SFV values, which is a relative increase in predictive ability of 20%. Conversely, for predicting SCR values, the use of an across-country reference population did not outperform the standard approach using pure US Jersey reference data set. Our findings indicate that genomic prediction of male fertility in dairy cattle is feasible, and the use of an acrosscountry reference population would be beneficial when local populations are small and genetically diverse.

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“…This might be a particularity of the trait and/or population, or which may also be related to the presence of variance heterogeneity for the two traits (SC and P16). Pacheco et al (2019), evaluating the effect of sex chromosomes on reproductive traits in males and females, found an increase of 7.5% in the prediction ability of a model that considered the X chromosome together with the autosomes.…”

Section: Discussionmentioning

confidence: 99%

“…Complementarily, the autosomes harbor genes with diverse functions and extremely heterogeneous expression patterns, while the sex chromosomes are rich in genes related to development and sexual reproduction (Liu, 2019). Within this context, some studies reported interesting results regarding the roles of the X chromosome in reproductive efficiency, including testicular development and spermatogenesis, female conception rate, and sexual differentiation, among others (Lynch and Walsh, 2007;Chang et al, 2013;Fortes et al, 2013;Lyons et al, 2014;De Camargo et al, 2015;Taylor et al, 2018;Pacheco et al, 2019). The inclusion of the effect of X chromosome markers also provided positive results by increasing the accuracy of genomic breeding value predictions (Su et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Important effect of the X chromosome in genomic evaluations of reproductive traits in beef cattle

Diaz

Camargo

Cruz

et al. 2020

Preprint

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AbstractThe aim of this study was to evaluate the efficiency of inclusion and the prediction ability of the X chromosome for reproductive (occurrence of early pregnancy – P16 and age at first calving - AFC) and andrological traits (scrotal circumference -SC) in a herd of Nellore beef cattle herd. 3,263 genotypes of females and males were used. Genomic prediction for SC, AFC and P16 was carried out considering two scenarios: 1) only autosomal markers or 2) autosomal + X chromosome markers. To evaluate the effect of inclusion of the X chromosome on selection, the responses to the selection performed were compared including or not the X chromosome in the evaluation of the traits. Higher heritability estimates were obtained for SC (0.40 and 0.31), AFC (0.11 and 0.09) and P16 (0.43 and 0.38) for the analyses including the X chromosome compared to those without. The percent reduction on mean genomic breeding values when selection was based on the results of analysis that did not include the X chromosome to 1, 5 and 10% of the top males, was for SC slightly more than 7% of the mean genomic breeding value of the selected animals. For P16, the loss can reach more than 4%, while this loss does not seem to be as important for AFC. Average predictive correlation of 0.79, 0.98 and 0.84 for SC, AFC and P16 was obtained, respectively. These estimates demonstrate that inclusion of the X chromosome in the analysis can improve the prediction of genomic breeding values, especially for SC.

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Gene mapping and genomic prediction of bull fertility using sex chromosome markers

Cited by 28 publications

References 32 publications

Theoretical and empirical comparisons of expected and realized relationships for the X-chromosome

Theoretical and empirical comparisons of expected and realized relationships for the X-chromosome

Across-country genomic prediction of bull fertility in Jersey dairy cattle

Important effect of the X chromosome in genomic evaluations of reproductive traits in beef cattle

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