Genetic parameter estimates for male and female fertility traits using genomic data to improve fertility in Australian beef cattle

Olasege, Babatunde Shittu; Tahir, Muhammad; Gouveia, Gabriela Canabrava; Kour, Jagish; Porto-Neto, Laércio R.; Hayes, Ben J.; Fortes, M. R. S.

doi:10.1071/an21097

Cited by 8 publications

(18 citation statements)

References 51 publications

(71 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number was 136 (BB) and 154 (TC) for IGF1c vs IGF1b. For AGECL vs IGF1b, the most enriched chromosome in BB were both BTA2 (12) and BTA17 (12) in BB, whereas in TC, the most enriched chromosome was BTA26 (29). For IGF1c vs IGF1b, on the other hand, the most enriched chromosome was BTA6 (26) and BTA26 (29) in BB and TC, respectively.…”

Section: Qtl Enrichment Analysismentioning

confidence: 93%

“…The traits used to demonstrate this methodology are a subset of traits from our previous study [29], where bivariate genetic correlations were estimated between 7 male and 6 female early-in-life reproductive phenotypes in two independent tropical beef cattle populations (BB TC). The two female traits selected for this study are age at detection of the first corpus luteum (AGECL, days) and cows' blood concentration of insulin growthfactor 1, measured at 18 months of age (IGF1c).…”

Section: Traits Genotypes and Estimated Genetic Correlationsmentioning

confidence: 99%

“…These traits are important in beef cattle fertility, especially during pubertal development. The estimated global genomic correlations between the traits listed above in each population using high-density SNPs have been reported in our previous study [29]. The estimated genomic correlations and their corresponding standard error (S.E), heritability of each trait, number of SNPs and number of animals in each population are provided in Additional file 6 (Table S20).…”

Section: Traits Genotypes and Estimated Genetic Correlationsmentioning

confidence: 99%

“…These phenotypes are from animals raised in a research station with precise measurement taken by regular ovarian scan (AGECL, [26]) and laboratory assay (IGF1c or IGF1b, [27,28]). These traits are highly heritable [28,29] and each pair serve as example of a positive and a negative correlation between phenotypes measured in males and females, during pubertal development. The populations used in the study are formed by either Brahman (BB) cattle or Tropical Composite (TC) cattle, as described in our previous study [29].…”

Section: Introductionmentioning

confidence: 99%

“…These traits are highly heritable [28,29] and each pair serve as example of a positive and a negative correlation between phenotypes measured in males and females, during pubertal development. The populations used in the study are formed by either Brahman (BB) cattle or Tropical Composite (TC) cattle, as described in our previous study [29]. For completeness, we compared the CS methodology with two other recently proposed methods (SUPERGNOVA and LAVA) that map local genomic correlations.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

et al. 2022

Self Cite

View full text Add to dashboard Cite

Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don’t fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher’s Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA’s in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.

show abstract

Section: Qtl Enrichment Analysismentioning

confidence: 93%

Section: Traits Genotypes and Estimated Genetic Correlationsmentioning

confidence: 99%

Section: Traits Genotypes and Estimated Genetic Correlationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Genomic regions and biological pathways associated with sex-limited reproductive traits in bovine species

Olasege,

Oh,

Tahir

et al. 2024

Journal of Animal Science

Self Cite

View full text Add to dashboard Cite

Many animal species exhibit sex-limited traits, where certain phenotypes are exclusively expressed in one sex. Yet, the genomic regions that contribute to these sex-limited traits in males and females remain a subject of debate. Reproductive traits are ideal phenotypes to study sexual differences since they are mostly expressed in a sex-limited way. Therefore, this study aims to use local correlation analyses to identify genomic regions and biological pathways significantly associated with male and female sex-limited traits in two distinct cattle breeds (Brahman (BB) and Tropical Composite (TC)). We used the Correlation Scan method to perform local correlation analysis on 42 trait pairs consisting of six female and seven male reproductive traits recorded on ~1000 animals for each sex in each breed. To pinpoint a specific region associated these sex-limited reproductive traits, we investigated the genomic region(s) consistently identified as significant across the 42 trait pairs in each breed. The genes found in the identified regions were subjected to Quantitative Trait Loci (QTL) colocalization, QTL enrichment analyses, and functional analyses to gain biological insight into sexual differences. We found that the genomic regions associated with the sex-limited reproductive phenotypes are widely distributed across all the chromosomes. However, no single region across the genome was associated all the 42 reproductive trait pairs in the two breeds. Nevertheless, we found a region on the X-chromosome to be most significant for 80-90% (BB; 33 and TC; 38) of the total 42 trait pairs. A considerable number of the genes in this region were regulatory genes. By considering only genomic regions that were significant for at least 50% of the 42 trait pairs, we observed more regions spread across the autosomes and the X-chromosome. All genomic regions identified were highly enriched for trait-specific QTL linked to sex-limited traits (percentage normal sperm, metabolic weight, average daily gain, carcass weight, age at puberty, etc.). The gene list created from these identified regions were enriched for biological pathways that contribute to the observed differences between sexes. Our results demonstrate that genomic regions associated with male and female sex-limited reproductive traits are distributed across the genome. Yet, chromosome X seems to exert a relatively larger effect on the phenotypic variation observed between the sexes.

show abstract

Genetic Correlations between Days to Calving across Joinings and Lactation Status in a Tropically Adapted Composite Beef Herd

et al. 2022

View full text Add to dashboard Cite

Female fertility is essential to any beef breeding program. However, little genetic gain has been made due to long generation intervals and low levels of phenotyping. Days to calving (DC) is a fertility trait that may provide genetic gain and lead to an increased weaning rate. Genetic parameters and correlations were estimated and compared for DC across multiple joinings (first, second and third+) and lactation status (lactating and non-lactating) for a tropical composite cattle population where cattle were first mated as yearlings. The genetic correlation between first joining DC and mature joining DC (third+) was moderate–high (0.55–0.83). DC was uncorrelated between multiparous lactating and non-lactating cows (rG = −0.10). Mature joining DC was more strongly correlated with second joining lactating DC (0.41–0.69) than with second joining non-lactating DC (−0.14 to −0.16). Thus, first joining DC, second joining DC and mature joining DC should be treated as different traits to maximise genetic gain. Further, for multi-parous cows, lactating and non-lactating DC should be treated as different traits. Three traits were developed to report back to the breeding programs to maximise genetic gain: the first joining days to calving, the second joining days to calving lactating and mature days to calving lactating.

show abstract

Genetic parameter estimates for male and female fertility traits using genomic data to improve fertility in Australian beef cattle

Cited by 8 publications

References 51 publications

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Genomic regions and biological pathways associated with sex-limited reproductive traits in bovine species

Genetic Correlations between Days to Calving across Joinings and Lactation Status in a Tropically Adapted Composite Beef Herd

Contact Info

Product

Resources

About