Identification of genomic regions related to age at first calving and first calving interval in water buffalo using single-step GBLUP

Genome-wide association studies of dairy cattle resistance to digital dermatitis recorded at four distinct lactation stages

Digital dermatitis (DD) is an endemic infectious hoof disease causing lameness in dairy cattle. The aim of the present study was to investigate the genetic profile of DD development using phenotypic and genotypic data on 2192 Holstein cows. The feet of each cow were clinically examined four times: pre-calving, shortly after calving, near peak of milk production, and in late lactation. Presence or absence of disease and proportion of healthy feet per cow constituted two DD phenotypes of study. For each phenotype and timepoint of clinical examination, we conducted single-step genome-wide association analyses to identify individual markers and genomic regions linked to DD. We focused on the ten 1-Mb windows that explained the largest proportion of the total genetic variance as well as windows that enclosed significant markers. Functional enrichment analysis was also applied to determine functional candidate genes for DD. Significant (P < 0.05) genomic heritability estimates were derived ranging from 0.21 to 0.25. Results revealed two markers on chromosomes 7 and 15 that were related to both disease phenotypes. Furthermore, we identified three genomic windows on chromosome 14 and one window on chromosome 7 each explaining more than 1% of the trait additive genetic variance. Functional enrichment analysis revealed multiple promising candidate genes implicated in hoof health, wound healing, and inflammatory skin diseases. Collectively, our results provide novel insights into the biological mechanism of host resistance to DD development in dairy cattle and support genomic selection towards improving foot health.

Genomic analysis of genotype-environment interaction in age at first calving of Murrah buffaloes

Age at first calving (AFC) is a measure of sexual maturity associated with the start of productive life of dairy animals. Additionally, a lower AFC reduces the generation interval and early culling of females. However, AFC has low heritability, making it a trait highly influenced by environmental factors. In this scenario, one way to improve the reproductive performance of buffalo cows is to select robust animals according to estimated breeding value (EBV) using models that include genotype-environment interaction (GEI) with the application of reaction norm models (RNMs). This can be achieved by understanding the genomic basis related to GEI of AFC. Thus, in this study, we aimed to predict EBV considering GEI via the RNM and identify candidate genes related to this component in dairy buffaloes through genome-wide association studies (GWAS). We used 1795 AFC records from three Murrah buffalo herds and formed environmental gradients (EGs) from contemporary group solutions obtained from genetic analysis of 270-day cumulative milk yield. Heritability estimates ranged from 0.15 to 0.39 along the EG. GWAS of the RNM slope parameter identified important genomic regions. The genomic window that explained the highest percentage of genetic variance of the slope (0.67%) was located on BBU1. After functional analysis, five candidate genes were detected, involved in two biological processes. The results suggested the existence of a GEI for AFC in Murrah buffaloes, with reclassification of animals when different environmental conditions were considered. The inclusion of genomic information increased the accuracy of breeding values for the intercept and slope of the reaction norm. GWAS analysis suggested that important genes associated with the AFC reaction norm slope were possibly also involved in biological processes related to lipid metabolism and immunity.

Genetic parameters for reproductive traits in the Italian Mediterranean buffalo using milk yield as a correlated trait

Until now, the genetic evaluation of the Italian Mediterranean Buffalo has been mainly focused on production traits. However, female fertility affects the efficiency of the dairy industry as it is essential to maintain the profitability of dairy farms. Indeed, the estimation of its genetic component is crucial for its improvement. In this study, 3 measures of buffalo's fertility were analyzed: the age at first calving (AFC), the interval between first and second calving (CIV1), and the interval between second and successive calvings (CIV2_12). Milk yield at 270 d (MY270) was used as a correlated trait. First, genetic parameters were estimated using 7,915 buffalo cows with first calving from 1991 to 2018, then breeding values were calculated from 236,087 buffalo cows. Genetic parameters were estimated by Bayesian inference fitting a multiple-trait animal model using the GIBBS1F90 program, and BLUPF90 was used for estimation of breeding value. The heritability and repeatability estimates of fertility traits were low. The genetic correlations among fertility traits ranged from 0.10 (AFC-CIV1) to 0.92 (CIV1-CIV2_12). Genetic correlation between MY270 and fertility traits was unfavorable, ranging from 0.23 to 0.48. The results from this study can be used as a basis for the future genetic improvement of fertility traits in the Italian Mediterranean Buffaloes.

Genome-wide screening for selection signatures in native and cosmopolitan cattle breeds reared in Türkiye

Via long-term natural and artificial selection pressure, homozygosity may extend across the genome, leaving genomic patterns called selection signatures. This study is the first attempt to assess genome-wide selection signatures in six native Turkish and two cosmopolitan cattle breeds by 211.119 bi-allelic SNPs recovered using the double digest restriction associated DNA sequencing method. The integrated haplotype score (iHS) statistic was utilised to reveal selection signatures within populations, whereas the cross-population extended haplotype homozygosity (XP-EHH) and fixation index (FST ) approaches were preferred to reveal differently fixed genomic regions between native Turkish and cosmopolitan cattle breeds. Selection signatures in 142 genomic regions containing 305 genes were detected within eight cattle breeds by iHS statistics. The XP-EHH and FST approaches revealed that 197 and 114 SNPs were under selection pressure, respectively, which overlapped with 144 and 190 genes, respectively. A total of 18 genes were detected by at least two approaches. Six genes related to disease resistance (TTP2), meat yield (DIAPH3 and METTL21C), meat quality (ZNF24 and ZNF397) and first calving interval (ZSCAN30) turned out to be differently fixed between native Turkish and cosmopolitan cattle breeds, as they were identified by both XP-EHH and FST approaches. In addition, the iHS approach revealed that eight genes associated with visual modality (LSGN), olfaction (MOXD2, OR4C1F and OR4C1F), and immune response (TRBV3-1 and CLDN10) were under selection pressure in both native and cosmopolitan cattle breeds. Owing to their being significantly related to survival traits, these regions may have played a key role in cattle genome evolution. Future studies utilising denser genetic data are required to obtain deeper knowledge on effects of natural and artificial selection in Anatolian cattle breeds. © 2023 Stichting International Foundation for Animal Genetics.

Genetic improvement of economic traits in Murrah buffalo using significant SNPs from genome-wide association study

GWAS helps to identify QTL and candidate genes of specific traits. Buffalo breeding has primarily focused on milk production, but its negative correlation with reproduction traits resulted in unfavorable decline of reproductive performance among buffaloes. A genome wide scan was performed on a total of 120 Murrah buffaloes genotyped by ddRAD sequencing for 13 traits related to female fertility, production, and growth. The identified 25 significant single nucleotide polymorphisms (SNPs) (P <1×10⁶) are associated with age at first calving (AFC), age at first service (AFS), period from calving to 1^st Artifical Insemination (AI), service period (SP) and 6 month body weight (6M). Fifteen genetic variants overlapped with different QTL regions of reported studies. Among the associated loci, outstanding candidate genes for fertility, including AQP1, TRNAE-CUC, NRIP1, CPNE4, and VOPP1, have effect in different fertility traits. AQP1 gene is expressed in ovulatory phase and various stages of pregnancy. TRNAE-CUC gene is associated with AFC and number . of calvings after 4 years of age. Glycogen content-associated gene CPNE4 regulates muscle glycogen and is upregulated during early pregnancy. NRIP1 generegulates ovulation, corpus luteum at pregnancy, and mammary gland development. The objective is to identify potential genomic regions and genetic variants associated with economic traits and to select the most significant SNP which have positive effect on all the traits.

Identifying pleiotropic variants and candidate genes for fertility and reproduction traits in Holstein cattle via association studies based on imputed whole-genome sequence genotypes

Background

Genetic progress for fertility and reproduction traits in dairy cattle has been limited due to the low heritability of most indicator traits. Moreover, most of the quantitative trait loci (QTL) and candidate genes associated with these traits remain unknown. In this study, we used 5.6 million imputed DNA sequence variants (single nucleotide polymorphisms, SNPs) for genome-wide association studies (GWAS) of 18 fertility and reproduction traits in Holstein cattle. Aiming to identify pleiotropic variants and increase detection power, multiple-trait analyses were performed using a method to efficiently combine the estimated SNP effects of single-trait GWAS based on a chi-square statistic.

Results

There were 87, 72, and 84 significant SNPs identified for heifer, cow, and sire traits, respectively, which showed a wide and distinct distribution across the genome, suggesting that they have relatively distinct polygenic nature. The biological functions of immune response and fatty acid metabolism were significantly enriched for the 184 and 124 positional candidate genes identified for heifer and cow traits, respectively. No known biological function was significantly enriched for the 147 positional candidate genes found for sire traits. The most important chromosomes that had three or more significant QTL identified are BTA22 and BTA23 for heifer traits, BTA8 and BTA17 for cow traits, and BTA4, BTA7, BTA17, BTA22, BTA25, and BTA28 for sire traits. Several novel and biologically important positional candidate genes were strongly suggested for heifer (SOD2, WTAP, DLEC1, PFKFB4, TRIM27, HECW1, DNAH17, and ADAM3A), cow (ANXA1, PCSK5, SPESP1, and JMJD1C), and sire (ELMO1, CFAP70, SOX30, DGCR8, SEPTIN14, PAPOLB, JMJD1C, and NELL2) traits.

Conclusions

These findings contribute to better understand the underlying biological mechanisms of fertility and reproduction traits measured in heifers, cows, and sires, which may contribute to improve genomic evaluation for these traits in dairy cattle.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08555-z.

Cross-population selection signatures in Canchim composite beef cattle

Analyses of livestock genomes have been used to detect selection signatures, which are genomic regions associated with traits under selection leading to a change in allele frequency. The objective of the present study was to characterize selection signatures in Canchim composite beef cattle using cross-population analyses with the founder Nelore and Charolais breeds. High-density single nucleotide polymorphism genotypes were available on 395 Canchim representing the target population, along with genotypes from 809 Nelore and 897 Charolais animals representing the reference populations. Most of the selection signatures were co-located with genes whose functions agree with the expectations of the breeding programs; these genes have previously been reported to associate with meat quality, as well as reproductive traits. Identified genes were related to immunity, adaptation, morphology, as well as behavior, could give new perspectives for understanding the genetic architecture of Canchim. Some selection signatures identified genes that were recently introduced in Canchim, such as the loci related to the polled trait.

Comparative Signatures of Selection Analyses Identify Loci Under Positive Selection in the Murrah Buffalo of India

India is home to a large and diverse buffalo population. The Murrah breed of North India is known for its milk production, and it has been used in breeding programs in several countries. Selection signature analysis yield valuable information about how the natural and artificial selective pressures have shaped the genomic landscape of modern-day livestock species. Genotype information was generated on six buffalo breeds of India, namely, Murrah, Bhadawari, Mehsana, Pandharpuri, Surti, and Toda using ddRAD sequencing protocol. Initially, the genotypes were used to carry out population diversity and structure analysis among the six breeds, followed by pair-wise comparisons of Murrah with the other five breeds through XP-EHH and F _ST methodologies to identify regions under selection in Murrah. Admixture results showed significant levels of Murrah inheritance in all the breeds except Pandharpuri. The selection signature analysis revealed six regions in Murrah, which were identified in more than one pair-wise comparison through both XP-EHH and F _ST analyses. The significant regions overlapped with QTLs for milk production, immunity, and body development traits. Genes present in these regions included SLC37A1, PDE9A, PPBP, CXCL6, RASSF6, AFM, AFP, ALB, ANKRD17, CNTNAP2, GPC5, MYLK3, and GPT2. These genes emerged as candidates for future polymorphism studies of adaptability and performance traits in buffaloes. The results also suggested ddRAD sequencing as a useful cost-effective alternative for whole-genome sequencing to carry out diversity analysis and discover selection signatures in Indian buffalo breeds.

Genetic Features of Reproductive Traits in Bovine and Buffalo: Lessons From Bovine to Buffalo

Bovine and buffalo are important livestock species that have contributed to human lives for more than 1000 years. Improving fertility is very important to reduce the cost of production. In the current review, we classified reproductive traits into three categories: ovulation, breeding, and calving related traits. We systematically summarized the heritability estimates, molecular markers, and genomic selection (GS) for reproductive traits of bovine and buffalo. This review aimed to compile the heritability and genome-wide association studies (GWASs) related to reproductive traits in both bovine and buffalos and tried to highlight the possible disciplines which should benefit buffalo breeding. The estimates of heritability of reproductive traits ranged were from 0 to 0.57 and there were wide differences between the populations. For some specific traits, such as age of puberty (AOP) and calving difficulty (CD), the majority beef population presents relatively higher heritability than dairy cattle. Compared to bovine, genetic studies for buffalo reproductive traits are limited for age at first calving and calving interval traits. Several quantitative trait loci (QTLs), candidate genes, and SNPs associated with bovine reproductive traits were screened and identified by candidate gene methods and/or GWASs. The IGF1 and LEP pathways in addition to non-coding RNAs are highlighted due to their crucial relevance with reproductive traits. The distribution of QTLs related to various traits showed a great differences. Few GWAS have been performed so far on buffalo age at first calving, calving interval, and days open traits. In addition, we summarized the GS studies on bovine and buffalo reproductive traits and compared the accuracy between different reports. Taken together, GWAS and candidate gene approaches can help to understand the molecular genetic mechanisms of complex traits. Recently, GS has been used extensively and can be performed on multiple traits to improve the accuracy of prediction even for traits with low heritability, and can be combined with multi-omics for further analysis.

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from -0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.