Targeted sequencing reveals candidate causal variants for dairy bull subfertility.

Bull fertility is a key factor for successful reproductive performance in dairy cattle. Since the semen from a single bull can be used to inseminate hundreds of cows, one subfertile bull could have a major impact on herd reproductive efficiency. We have previously identified five genomic regions, located on BTA8 (72.2 Mb), BTA9 (43.7 Mb), BTA13 (60.2 Mb), BTA17 (63.3 Mb), and BTA27 (34.7 Mb), that show large dominance effects on bull fertility. Each of these regions explained about 5-8% of the observed differences in sire conception rate between Holstein bulls. Here, we aimed to identify candidate causal variants responsible for this variation using targeted sequencing (10 Mb per region). For each genomic region, two DNA pools were constructed from n ≈ 20 high-fertility and n ≈ 20 low-fertility Holstein bulls. The DNA-sequencing analysis included reads quality control (using FastQC), genome alignment (using BWA and ARS-UCD1.2), variant calling (using GATK) and variant annotation (using Ensembl). The sequencing depth per pool varied from 39× to 51×. We identified a set of nonsense mutations, missense mutations, and frameshift variants carried by low-fertility bulls. Notably, some of these variants were classified as strong candidate causal variants, i.e., mutations with deleterious effects located on genes exclusively/highly expressed in testis. Genes affected by these candidate causal variants include AK9, TTLL9, TCHP, and FOXN4. These results could aid in the development of novel genomic tools that allow early detection and culling of subfertile bull calves.

Genetic dissection of reproductive performance of dairy cows under heat stress.

Heat stress negatively impacts the reproductive performance of dairy cows. The main objective of this study was to dissect the genetic basis underlying dairy cow fertility under heat stress conditions. Our first goal was to estimate genetic components of cow conception across lactations considering heat stress. Our second goal was to reveal individual genes and functional gene-sets that explain a cow's ability to conceive under thermal stress. Data consisted of 74 221 insemination records on 13 704 Holstein cows. Multitrait linear repeatability test-day models with random regressions on a function of temperature-humidity index values were used for the analyses. Heritability estimates for cow conception under heat stress were around 2-3%, whereas genetic correlations between general and thermotolerance additive genetic effects were negative and ranged between -0.35 and -0.82, indicating an unfavorable relationship between cows' ability to conceive under thermo-neutral vs. thermo-stress conditions. Whole-genome scans identified at least six genomic regions on BTA1, BTA10, BTA11, BTA17, BTA21 and BTA23 associated with conception under thermal stress. These regions harbor candidate genes such as BRWD1, EXD2, ADAM20, EPAS1, TAOK3, and NOS1, which are directly implicated in reproductive functions and cellular response to heat stress. The gene-set enrichment analysis revealed functional terms related to fertilization, developmental biology, heat shock proteins and oxidative stress, among others. Overall, our findings contribute to a better understanding of the genetics underlying the reproductive performance of dairy cattle under heat stress conditions and point out novel genomic strategies for improving thermotolerance and fertility via marker-assisted breeding.

Case-control approach application for finding a relationship between candidate genes and clinical mastitis in Holstein dairy cattle.

Mastitis is a major source of economic loss in dairy herds. The objective of this research was to evaluate the association between genotypes within SLC11A1 and CXCR1 candidate genes and clinical mastitis in Holstein dairy cattle using the selective genotyping method. The data set contained clinical mastitis records of 3,823 Holstein cows from two Holstein dairy herds located in two different regions in Iran. Data included the number of cases of clinical mastitis per lactation. Selective genotyping was based on extreme values for clinical mastitis residuals (CMR) from mixed model analyses. Two extreme groups consisting of 135 cows were formed (as cases and controls), and genotyped for the two candidate genes, namely, SLC11A1 and CXCR1, using polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), respectively. Associations between single nucleotide polymorphism (SNP) genotypes with CMR and breeding values for milk and protein yield were carried out by applying logistic regression analyses, i.e. estimating the probability of the heterogeneous genotype in the dependency of values for CMR and breeding values (BVs). The sequencing results revealed a novel mutation in 1139 bp of exon 11 of the SLC11A1 gene and this SNP had a significant association with CMR (P < 0.05). PCR-RFLP analysis leads to three banding patterns for CXCR1c.735C>G and these genotypes had significant relationships with CMR. Overall, the results showed that SLC11A1 and CXCR1 are valuable candidate genes for the improvement of mastitis resistance as well as production traits in dairy cattle populations.

Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits.

Bootstrap aggregation (bagging) is a resampling method known to produce more accurate predictions when predictors are unstable or when the number of markers is much larger than sample size, because of variance reduction capabilities. The purpose of this study was to compare genomic best linear unbiased prediction (GBLUP) with bootstrap aggregated sampling GBLUP (Bagged GBLUP, or BGBLUP) in terms of prediction accuracy. We used a 600 K Affymetrix platform with 1351 birds genotyped and phenotyped for three traits in broiler chickens; body weight, ultrasound measurement of breast muscle and hen house egg production. The predictive performance of GBLUP versus BGBLUP was evaluated in different scenarios consisting of including or excluding the TOP 20 markers from a standard genome-wide association study (GWAS) as fixed effects in the GBLUP model, and varying training sample sizes and allelic frequency bins. Predictive performance was assessed via five replications of a threefold cross-validation using the correlation between observed and predicted values, and prediction mean-squared error. GBLUP overfitted the training set data, and BGBLUP delivered a better predictive ability in testing sets. Treating the TOP 20 markers from the GWAS into the model as fixed effects improved prediction accuracy and added advantages to BGBLUP over GBLUP. The performance of GBLUP and BGBLUP at different allele frequency bins and training sample sizes was similar. In general, results of this study confirm that BGBLUP can be valuable for enhancing genome-enabled prediction of complex traits.

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers.

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10-0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R(2) = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R(2) = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.

Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens.

The objective was to assess goodness of fit and predictive ability of subsets of single nucleotide polymorphism (SNP) markers constructed based on minor allele frequency (MAF), effect sizes and varying marker density. Target traits were body weight (BW), ultrasound measurement of breast muscle (BM) and hen house egg production (HHP) in broiler chickens. We used a 600 K Affymetrix platform with 1352 birds genotyped. The prediction method was genomic best linear unbiased prediction (GBLUP) with 354 564 single nucleotide polymorphisms (SNPs) used to derive a genomic relationship matrix (G). Predictive ability was assessed as the correlation between predicted genomic values and corrected phenotypes from a threefold cross-validation. Predictive ability was 0.27 ± 0.002 for BW, 0.33 ± 0.001 for BM and 0.20 ± 0.002 for HHP. For the three traits studied, predictive ability decreased when SNPs with a higher MAF were used to construct G. Selection of the 20% SNPs with the largest absolute effect sizes induced a predictive ability equal to that from fitting all markers together. When density of markers increased from 5 K to 20 K, predictive ability enhanced slightly. These results provide evidence that designing a low-density chip using low-frequency markers with large effect sizes may be useful for commercial usage.