Inferring quantitative trait pathways associated with bull fertility from a genome-wide association study

A comparison of five sets of overlapping and non-overlapping sliding windows for semen production traits in the Thai multibreed dairy population

OBJECTIVE

This study compared five distinct sets of biological pathways and associated genes related to semen volume (VOL), number of sperm (NS), and sperm motility (MOT) in the Thai multibreed dairy population.

METHODS

The phenotypic data included 13,533 VOL records, 12,773 NS records, and 12,660 MOT records from 131 bulls. The genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNPs) from 72 animals. The SNP additive genetic variances for VOL, NS, and MOT were estimated for SNP windows of one SNP (SW1), ten SNP (SW10), 30 SNP (SW30), 50 SNP (SW50), and 100 SNP (SW100) using a single-step genomic best linear unbiased prediction approach. The fixed effects in the model were contemporary group, ejaculate order, bull age, ambient temperature, and heterosis. The random effects accounted for animal additive genetic effects, permanent environment effects, and residual. The SNPs explaining at least 0.001% of the additive genetic variance in SW1, 0.01% in SW10, 0.03% in SW30, 0.05% in SW50, and 0.1% in SW100 were selected for gene identification through the NCBI database. The pathway analysis utilized genes associated with the identified SNP windows.

RESULTS

Comparison of overlapping and non-overlapping SNP windows revealed notable differences among the identified pathways and genes associated with the studied traits. Overlapping windows consistently yielded a larger number of shared biological pathways and genes than non-overlapping windows. In particular, overlapping SW30 and SW50 identified the largest number of shared pathways and genes in the Thai multibreed dairy population.

CONCLUSION

This study yielded valuable insights into the genetic architecture of VOL, NS, and MOT. It also highlighted the importance of assessing overlapping and non-overlapping SNP windows of various sizes for their effectiveness to identify shared pathways and genes influencing multiple traits.

Genetic parameters and genome-wide association studies for mozzarella and milk production traits, lactation length, and lactation persistency in Murrah buffaloes

Genetic and genomic analyses of longitudinal traits related to milk production efficiency are paramount for optimizing water buffaloes breeding schemes. Therefore, this study aimed to (1) compare single-trait random regression models under a single-step genomic BLUP setting based on alternative covariance functions (i.e., Wood, Wilmink, and Ali and Schaeffer) to describe milk (MY), fat (FY), protein (PY), and mozzarella (MZY) yields, fat-to-protein ratio (FPR), somatic cell score (SCS), lactation length (LL), and lactation persistency (LP) in Murrah dairy buffaloes (Bubalus bubalis); (2) combine the best functions for each trait under a multiple-trait framework; (3) estimate time-dependent SNP effects for all the studied longitudinal traits; and (4) identify the most likely candidate genes associated with the traits. A total of 323,140 test-day records from the first lactation of 4,588 Murrah buffaloes were made available for the study. The model included the average curve of the population nested within herd-year-season of calving, systematic effects of number of milkings per day, and age at first calving as linear and quadratic covariates, and additive genetic, permanent environment, and residual as random effects. The Wood model had the best goodness of fit based on the deviance information criterion and posterior model probabilities for all traits. Moderate heritabilities were estimated over time for most traits (0.30 ± 0.02 for MY; 0.26 ± 0.03 for FY; 0.45 ± 0.04 for PY; 0.28 ± 0.05 for MZY; 0.13 ± 0.02 for FPR; and 0.15 ± 0.03 for SCS). The heritability estimates for LP ranged from 0.38 ± 0.02 to 0.65 ± 0.03 depending on the trait definition used. Similarly, heritabilities estimated for LL ranged from 0.10 ± 0.01 to 0.14 ± 0.03. The genetic correlation estimates across days in milk (DIM) for all traits ranged from -0.06 (186-215 DIM for MY-SCS) to 0.78 (66-95 DIM for PY-MZY). The SNP effects calculated for the random regression model coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d). Numerous relevant genomic regions and candidate genes were identified for all traits, confirming their polygenic nature. The candidate genes identified contribute to a better understanding of the genetic background of milk-related traits in Murrah buffaloes and reinforce the value of incorporating genomic information in their breeding programs.

Characteristics of miRNAs Present in Bovine Sperm and Associations With Differences in Fertility

Small non-coding RNAs have been linked to different phenotypes in bovine sperm, however attempts to identify sperm-borne molecular biomarkers of male fertility have thus far failed to identify a robust profile of expressed miRNAs related to fertility. We hypothesized that some differences in bull fertility may be reflected in the levels of different miRNAs in sperm. To explore such differences in fertility that are not due to differences in visible metrics of sperm quality, we employed Next Generation Sequencing to compare the miRNA populations in Bos taurus sperm from bulls with comparable motility and morphology but varying Sire Conception Rates. We identified the most abundant miRNAs in both populations (miRs -34b-3p; -100-5p; -191-5p; -30d-4p; -21-5p) and evaluated differences in the overall levels and specific patterns of isomiR expression. We also explored correlations between specific pairs of miRNAs in each population and identified 10 distinct pairs of miRNAs that were positively correlated in bulls with higher fertility and negatively correlated in comparatively less fertile individuals. Furthermore, 8 additional miRNA pairs demonstrated the opposite trend; negatively correlated in high fertility animals and positively correlated in less fertile bulls. Finally, we performed pathway analysis to identify potential roles of miRNAs present in bull sperm in the regulation of specific genes that impact spermatogenesis and embryo development. Together, these results present a comprehensive picture of the bovine sperm miRNAome that suggests multiple potential roles in fertility.

Revealing the genetic basis of eyelid pigmentation in Hereford cattle

Ocular squamous cell carcinoma and infectious keratoconjunctivitis are common ocular pathologies in Hereford cattle with considerable economic impact. Both pathologies have been associated with low eyelid pigmentation, and thus, genetic selection for higher eyelid pigmentation could reduce their incidence. The objective of the present study was to reveal the genetic basis of eyelid pigmentation in Hereford cattle. The analysis included a single-step genome-wide association study (ssGWAS) and a subsequent gene-set analysis in order to identify individual genes, genetic mechanisms and biological pathways implicated in this trait. Data consisted of eyelid pigmentation records in 1,165 Hereford bulls and steers, visually assessed in five categories between 0% and 100%. Genotypic data for 774,660 single nucleotide polymorphism markers were available for 886 animals with pigmentation records. Pedigree information of three generations of ancestors of animals with phenotype was considered in this study, with a total of 4,929 animals. Our analyses revealed that eyelid pigmentation is a moderately heritable trait, with heritability estimates around 0.41. The ssGWAS identified at least eight regions, located on BTA1, BTA3, BTA5, BTA14, BTA16, BTA18, BTA19 and BTA24, associated with eyelid pigmentation. These regions harbor genes that are directly implicated in melanocyte biology and skin pigmentation, such as ADCY8, PLD1, KITLG, and PRKCA. The gene-set analysis revealed several functional terms closely related to melanogenesis, such as positive regulation of melanocyte differentiation and regulation of ERK1 and ERK2 cascade. Overall, our findings provide evidence that eyelid pigmentation is a heritable trait influenced by many loci. Indeed, the ssGWAS detected several candidate genes that are directly implicated in melanocyte biology, including melanogenesis. This study contributes to a better understanding of the genetic and biological basis of eyelid pigmentation and presents novel information that could aid to design breeding strategies for reducing the incidence of ocular pathologies in cattle. Additional research on the genetic link between eyelid pigmentation and ocular pathologies is needed.

Root angle in maize influences nitrogen capture and is regulated by calcineurin B-like protein (CBL)-interacting serine/threonine-protein kinase 15 (ZmCIPK15)

Crops with reduced nutrient and water requirements are urgently needed in global agriculture. Root growth angle plays an important role in nutrient and water acquisition. A maize diversity panel of 481 genotypes was screened for variation in root angle employing a high-throughput field phenotyping platform. Genome-wide association mapping identified several single nucleotide polymorphisms (SNPs) associated with root angle, including one located in the root expressed CBL-interacting serine/threonine-protein kinase 15 (ZmCIPK15) gene (LOC100285495). Reverse genetic studies validated the functional importance of ZmCIPK15, causing a approximately 10° change in root angle in specific nodal positions. A steeper root growth angle improved nitrogen capture in silico and in the field. OpenSimRoot simulations predicted at 40 days of growth that this change in angle would improve nitrogen uptake by 11% and plant biomass by 4% in low nitrogen conditions. In field studies under suboptimal N availability, the cipk15 mutant with steeper growth angles had 18% greater shoot biomass and 29% greater shoot nitrogen accumulation compared to the wild type after 70 days of growth. We propose that a steeper root growth angle modulated by ZmCIPK15 will facilitate efforts to develop new crop varieties with optimal root architecture for improved performance under edaphic stress.

Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers

Simple Summary

Tropical pasture-based beef production systems play a vital role in global food security. The importance of promoting sustainable intensification of such systems has been debated worldwide. Demand for beef is growing together with concerns over the impact of its production on the environment. Implementing sustainable livestock intensification programs relies on animal genetic improvement. In tropical areas, the lack of sexual precocity is a bottleneck for cattle efficiency, directly impacting the sustainability of production systems. In the present review we present and discuss the state of the art of genetic evaluation for sexual precocity in Bos indicus beef cattle, covering the definition of measurable traits, genetic parameter estimates, genomic analyses, and a case study of selection for sexual precocity in Nellore breeding programs.

Abstract

Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.

Gene mapping, gene-set analysis, and genomic prediction of postpartum blood calcium in Holstein cows

The onset of lactation results in a sudden irreversible loss of Ca for colostrum and milk synthesis. Some cows are unable to quickly adapt to this demand and succumb to clinical hypocalcemia, whereas a larger proportion of cows develop subclinical hypocalcemia that predisposes them to other peripartum diseases. The objective of this study was to perform a comprehensive genomic analysis of blood total Ca concentration in periparturient Holstein cows. We first performed a genomic scan and a subsequent gene-set analysis to identify candidate genes, biological pathways, and molecular mechanisms affecting postpartum Ca concentration. Then, we assessed the prediction of postpartum Ca concentration using genomic information. Data consisted of 7,691 records of plasma or serum concentrations of Ca measured in the first, second, and third day after parturition of 959 primiparous and 1,615 multiparous cows that calved between December 2015 and June 2020 in 2 dairy herds. All cows were genotyped with 80k SNPs. The statistical model included lactation (1 to 5+), calf category (male, females, twins), and day as fixed effects, and season-treatment-experiment, animal, and permanent environmental as random effects. Model predictive ability was evaluated using 10-fold cross-validation. Heritability and repeatability estimates were 0.083 (standard error = 0.017) and 0.444 (standard error = 0.028). The association mapping identified 2 major regions located on Bos taurus autosome (BTA)6 and BTA16 that explained 1.2% and 0.7% of additive genetic variance of Ca concentration, respectively. Interestingly, the region on BTA6 harbors the GC gene, which encodes the vitamin D binding protein, and the region on BTA16 harbors LRRC38, which is actively involved in K transport. Other sizable peaks were identified on BTA5, BTA2, BTA7, BTA14, and BTA9. These regions harbor genes associated with Ca channels (CACNA1S, CRACR2A), K channels (KCNK9), bone remodeling (LRP6), and milk production (SOCS2). The gene-set analysis revealed terms related to vitamin transport, calcium ion transport, calcium ion binding, and calcium signaling. Genomic predictions of phenotypic and genomic estimated breeding values of Ca concentration yielded predictive correlations up to 0.50 and 0.15, respectively. Overall, the present study contributes to a better understanding of the genetic basis of postpartum blood Ca concentration in Holstein cows. In addition, the findings may contribute to the development of novel selection and management strategies for reducing periparturient hypocalcemia in dairy cattle.

Genes and pathways associated with pregnancy loss in dairy cattle

Pregnancy loss directly impairs reproductive performance in dairy cattle. Here, we investigated genetic factors associated with pregnancy loss following detection of a viable embryo around 42 days of gestation. The objectives of this study were to perform whole-genome scans and subsequent gene-set analyses for identifying candidate genes, functional gene-sets and gene signaling pathways implicated in pregnancy loss in US Holstein cows. Data consisted of about 58,000 pregnancy/abortion records distributed over nulliparous, primiparous, and multiparous cows. Threshold models were used to assess the binary response of pregnancy loss. Whole‐genome scans identified at least seven genomic regions on BTA2, BTA10, BTA14, BTA16, BTA21, BTA24 and BTA29 associated with pregnancy loss in heifers and lactating cows. These regions harbor several candidate genes that are directly implicated in pregnancy maintenance and fetal growth, such as CHST14, IGF1R, IGF2, PSEN2, SLC2A5 and WNT4. Moreover, the enrichment analysis revealed at least seven significantly enriched processes, containing genes associated with pregnancy loss, including calcium signaling, cell–cell attachment, cellular proliferation, fetal development, immunity, membrane permeability, and steroid metabolism. Additionally, the pathway analysis revealed a number of significant gene signaling pathways that regulate placental development and fetal growth, including Wnt, Hedgehog, Notch, MAPK, Hippo, mTOR and TGFβ pathways. Overall, our findings contribute to a better understanding of the genetic and biological basis of pregnancy loss in dairy cattle and points out novel strategies for improving pregnancy maintenance via marker‐assisted breeding.

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.

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.

Fine mapping of genomic regions associated with female fertility in Nellore beef cattle based on sequence variants from segregating sires

Background

Impaired fertility in cattle limits the efficiency of livestock production systems. Unraveling the genetic architecture of fertility traits would facilitate their improvement by selection. In this study, we characterized SNP chip haplotypes at QTL blocks then used whole-genome sequencing to fine map genomic regions associated with reproduction in a population of Nellore (Bos indicus) heifers.

Methods

The dataset comprised of 1337 heifers genotyped using a GeneSeek® Genomic Profiler panel (74677 SNPs), representing the daughters from 78 sires. After performing marker quality control, 64800 SNPs were retained. Haplotypes carried by each sire at six previously identified QTL on BTAs 5, 14 and 18 for heifer pregnancy and BTAs 8, 11 and 22 for antral follicle count were constructed using findhap software. The significance of the contrasts between the effects of every two paternally-inherited haplotype alleles were used to identify sires that were heterozygous at each QTL. Whole-genome sequencing data localized to the haplotypes from six sires and 20 other ancestors were used to identify sequence variants that were concordant with the haplotype contrasts. Enrichment analyses were applied to these variants using KEGG and MeSH libraries.

Results

A total of six (BTA 5), six (BTA 14) and five (BTA 18) sires were heterozygous for heifer pregnancy QTL whereas six (BTA 8), fourteen (BTA 11), and five (BTA 22) sires were heterozygous for number of antral follicles’ QTL. Due to inadequate representation of many haplotype alleles in the sequenced animals, fine mapping analysis could only be reliably performed for the QTL on BTA 5 and 14, which had 641 and 3733 concordant candidate sequence variants, respectively. The KEGG “Circadian rhythm” and “Neurotrophin signaling pathway” were significantly associated with the genes in the QTL on BTA 5 whereas 32 MeSH terms were associated with the QTL on BTA 14. Among the concordant sequence variants, 0.2% and 0.3% were classified as missense variants for BTAs 5 and 14, respectively, highlighting the genes MTERF2, RTMB, ENSBTAG00000037306 (miRNA), ENSBTAG00000040351, PRKDC, and RGS20. The potential causal mutations found in the present study were associated with biological processes such as oocyte maturation, embryo development, placenta development and response to reproductive hormones.

Conclusions

The identification of heterozygous sires by positionally phasing SNP chip data and contrasting haplotype effects for previously detected QTL can be used for fine mapping to identify potential causal mutations and candidate genes. Genomic variants on genes MTERF2, RTBC, miRNA ENSBTAG00000037306, ENSBTAG00000040351, PRKDC, and RGS20, which are known to have influence on reproductive biological processes, were detected.

Genome-wide association study applied to type traits related to milk yield in water buffaloes (Bubalus bubalis)

This research aimed to estimate genetic parameters for milk yield and type traits [withers height (WH), croup height (CH), body length (BL), croup length (CL), iliac width (ILW), ischial width (ISW), and thoracic circumference] in Murrah buffaloes and to identify genomic regions related to type traits by applying a single-step genome-wide association study. Data used to estimate the genetic parameters consisted of 601 records of milk yield in the first lactation and the aforementioned type traits. For the single-step genome-wide association study, 322 samples genotyped with a 90K Axiom Buffalo Genotyping array (Thermo Fisher Scientific, Santa Clara, CA) were used. Bivariate analysis revealed that heritability for milk yield (kg) at 305 d was 0.31 ± 0.11, whereas it ranged from 0.22 ± 0.07 to 0.34 ± 0.09 for the studied conformation traits. Based on the percentages of genetic variance explained by windows of 10 markers, there were 16 genomic regions explaining more than 0.5% of the variance for WH, CH, BL, CL, ILW, ISW, and thoracic circumference. Between those regions, 4 were associated with more than 1 trait, suggesting pleiotropic roles for some genes of Bos taurus autosome (BTA) 12 on CL and WH, BTA13 on ISW and ILW, BTA23 on CH and BL, and BTA28 on ISW and BL. Most of these regions coincide with known quantitative trait loci for milk traits. Thus, further studies based on sequence data will help to validate the association of this region with type traits and likely identify the causal mutations.

Whole Genome Mapping Reveals Novel Genes and Pathways Involved in Milk Production Under Heat Stress in US Holstein Cows

Heat stress represents a major environmental factor that negatively affects the health and performance of dairy cows, causing huge economic losses to the dairy industry. Identifying and selecting animals that are thermotolerant is an attractive alternative for reducing the negative effects of heat stress on dairy cattle performance. As such, the objectives of the present study were to estimate genetic components of milk yield, fat yield, and protein yield considering heat stress and to perform whole-genome scans and a subsequent gene-set analysis for identifying candidate genes and functional gene-sets implicated in milk production under heat stress conditions. Data consisted of about 254k test-day records from 17,522 Holstein cows. Multi-trait repeatability test day models with random regressions on a function of temperature-humidity index (THI) values were used for genetic analyses. The models included herd-test-day and DIM classes as fixed effects, and general and thermotolerance additive genetic and permanent environmental as random effects. Notably, thermotolerance additive genetic variances for all milk traits increased across parities suggesting that cows become more sensitive to heat stress as they age. In addition, our study revealed negative genetic correlations between general and thermotolerance additive effects, ranging between −0.18 to −0.68 indicating that high producing cows are more susceptible to heat stress. The association analysis identified at least three different genomic regions on BTA5, BTA14, and BTA15 strongly associated with milk production under heat stress conditions. These regions harbor candidate genes, such as HSF1, MAPK8IP1, and CDKN1B that are directly involved in the cellular response to heat stress. Moreover, the gene-set analysis revealed several functional terms related to heat shock proteins, apoptosis, immune response, and oxidative stress, among others. Overall, the genes and pathways identified in this study provide a better understanding of the genetic architecture underlying dairy cow performance under heat stress conditions. Our findings point out novel opportunities for improving thermotolerance in dairy cattle through marker-assisted breeding.

Genomic prediction of bull fertility in US Jersey dairy cattle

Service sire has a major effect on reproductive success in dairy cattle. Recent studies have reported accurate predictions for Holstein bull fertility using genomic data. The objective of this study was to assess the feasibility of genomic prediction of sire conception rate (SCR) in US Jersey cattle using alternative predictive models. Data set consisted of 1.5k Jersey bulls with SCR records and 95k SNP covering the entire genome. The analyses included the use of linear and Gaussian kernel-based models fitting either all the SNP or subsets of markers with presumed functional roles, such as SNP significantly associated with SCR or SNP located within or close to annotated genes. Model predictive ability was evaluated using 5-fold cross-validation with 10 replicates. The entire SNP set exhibited predictive correlations around 0.30. Interestingly, either SNP marginally associated with SCR or genic SNP achieved higher predictive abilities than their counterparts using random sets of SNP. Among alternative SNP subsets, Gaussian kernel models fitting significant SNP achieved the best performance with increases in predictive correlation up to 7% compared with the standard whole-genome approach. Notably, the use of a multi-breed reference population including the entire US Holstein SCR data set (11.5k bulls) allowed us to achieve predictive correlations up to 0.315, gaining 8% in accuracy compared with the standard model fitting a pure Jersey reference set. Overall, our findings indicate that genomic prediction of Jersey bull fertility is feasible. The use of Gaussian kernels fitting markers with relevant roles and the inclusion of Holstein records in the training set seem to be promising alternatives to the standard whole-genome approach. These results have the potential to help the dairy industry improve US Jersey sire fertility through accurate genome-guided decisions.

Gene Mapping and Gene-Set Analysis for Milk Fever Incidence in Holstein Dairy Cattle

Milk fever is an important metabolic disorder that affects dairy cows around parturition. It is associated with a breakdown in the mechanisms of calcium homeostasis, resulting in very low blood calcium levels (hypocalcemia). The main objective of this study was to dissect the genetic basis underlying milk fever incidence in Holstein cattle. Data consisted of 31.6 k producer-recorded lactation incidence records from 15.3 k cows. The analysis included a whole-genome scan and a subsequent gene-set analysis in order to reveal individual genes, genetic mechanisms and biological pathways implicated in the incidence of periparturient hypocalcemia. The association analysis identified at least eight different genomic regions that explain considerable amounts of additive genetic variance for milk fever incidence. Notably, some of these regions harbor genes, such as CYP27A1, CYP2J2, GC, SNAI2, and PIM1, that are directly involved in vitamin D metabolic pathway. Moreover, the gene-set analysis revealed several functional terms, such as calcium ion binding, calcium ion transportation, T cell differentiation, B cell activation, protein phosphorylation, apoptosis, and protein kinase activity, among others, that could be implicated in the development of periparturient hypocalcemia. Overall, this comprehensive study contributes to a better understanding of the genetic control of this complex disease. In addition, these findings may contribute to the development of novel breeding strategies for reducing the incidence of milk fever in dairy cattle.

Characterization of biological pathways associated with semen traits in the Thai multibreed dairy population

The objective of this research was to characterize biological pathways associated with semen volume (VOL), number of sperm (NS), and sperm motility (MOT) of dairy bulls in the Thai multibreed dairy population. Phenotypes for VOL (n = 13,535), NS (n = 12,773), and MOT (n = 12,660) came from 131 bulls of the Dairy Farming Promotion Organization of Thailand. Genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNP) from 72 animals. The SNP variances for VOL, NS, and MOT were estimated using a three-trait genomic-polygenic repeatability model. Fixed effects were contemporary group, ejaculate order, age of bull, ambient temperature, and heterosis. Random effects were animal additive genetic, permanent environmental, and residual. Individual SNP explaining at least 0.001% of the total genetic variance for each trait were selected to identify associated genes in the NCBI database (UMD Bos taurus 3.1 assembly) using the R package Map2NCBI. A set of 1,999 NCBI genes associated with all three semen traits was utilized for the pathway analysis conducted with the ClueGO plugin of Cytoscape using information from the Kyoto Encyclopedia of Genes and Genomes database. The pathway analysis revealed seven significant biological pathways involving 127 genes that explained 1.04% of the genetic variance for VOL, NS, and MOT. These genes were known to affect cell structure, motility, migration, proliferation, differentiation, survival, apoptosis, signal transduction, oxytocin release, calcium channel, neural development, and immune system functions related to sperm morphology and physiology during spermatogenesis.

Genetic dissection of bull fertility in US Jersey dairy cattle

The service sire has been recognized as an important factor affecting herd fertility in dairy cattle. Recent studies suggest that genetic factors explain part of the difference in fertility among Holstein sires. The main objective of this study was to dissect the genetic architecture of sire fertility in US Jersey cattle. The dataset included 1.5 K Jersey bulls with sire conception rate (SCR) records and 96 K single nucleotide polymorphism (SNP) markers spanning the whole genome. The analysis included whole‐genome scans for both additive and non‐additive effects and subsequent functional enrichment analyses using KEGG Pathway, Gene Ontology (GO) and Medical Subject Headings (MeSH) databases. Ten genomic regions located on eight different chromosomes explained more than 0.5% of the additive genetic variance for SCR. These regions harbor genes, such as PKDREJ,EPB41L2,PDGFD,STX2,SLC25A20 and IP6K1, that are directly implicated in testis development and spermatogenesis, sperm motility and the acrosome reaction. In addition, the genomic scan for non‐additive effects identified two regions on BTA11 and BTA25 with marked recessive effects. These regions harbor three genes—FER1L5,CNNM4 and DNAH3—with known roles in sperm biology. Moreover, the gene‐set analysis revealed terms associated with calcium regulation and signaling, membrane fusion, sperm cell energy metabolism, GTPase activity and MAPK signaling. These gene sets are directly implicated in sperm physiology and male fertility. Overall, this integrative genomic study unravels genetic variants and pathways affecting Jersey bull fertility. These findings may contribute to the development of novel genomic strategies for improving sire fertility in Jersey cattle.

Whole-genome scan reveals significant non-additive effects for sire conception rate in Holstein cattle

Background

Service sire has a considerable impact on reproductive success in dairy cattle. Most gene mapping studies for bull fertility have focused on additive effects, while non-additive effects have been largely ignored. The main goal of this study was to assess the relevance of non-additive effects on Sire Conception Rate (SCR) in Holstein dairy cattle. The analysis included 7.5 k Holstein bulls with both SCR records and 57.8 k single nucleotide polymorphism (SNP) markers spanning the entire genome.

Results

The importance of non-additive effects was evaluated using an efficient two-step mixed model-based approach. Four genomic regions located on chromosomes BTA8, BTA9, BTA13 and BTA17 showed marked dominance and/or recessive effects. Most of these regions harbor genes, such as ADAM28, DNAJA1, TBC1D20, SPO11, PIWIL3 and TMEM119, that are directly implicated in testis development, male germ line maintenance, and sperm maturation.

Conclusions

This study provides further evidence for the relevance of non-additive effects in fitness-related traits, such as male fertility. In addition, these findings may point out new strategies for improving service sire fertility in dairy cattle via marker-assisted selection.

Integration of GWAS, pathway and network analyses reveals novel mechanistic insights into the synthesis of milk proteins in dairy cows

The quantities and proportions of protein fractions have notable effects on the nutritional and technological value of milk. Although much is known about the effects of genetic variants on milk proteins, the complex relationships among the set of genes and pathways regulating the different protein fractions synthesis and secretion into milk in dairy cows are still not completely understood. We conducted genome-wide association studies (GWAS) for milk nitrogen fractions in a cohort of 1,011 Brown Swiss cows, which uncovered 170 significant single nucleotide polymorphism (SNPs), mostly located on BTA6 and BTA11. Gene-set analysis and the network-based Associated Weight Matrix approach revealed that the milk proteins associated genes were involved in several biological functions, particularly ion and cation transmembrane transporter activity and neuronal and hormone signalling, according to the structure and function of casein micelles. Deeper analysis of the transcription factors and their predicted target genes within the network revealed that GFI1B, ZNF407 and NR5A1 might act as master regulators of milk protein synthesis and secretion. The information acquired provides novel insight into the regulatory mechanisms controlling milk protein synthesis and secretion in bovine mammary gland and may be useful in breeding programmes aimed at improving milk nutritional and/or technological properties.

Genomewide association mapping and pathway analysis of meat tenderness in Polled Nellore cattle

Brazil is one of the world's largest beef exporters, although the product has a low price due to quality issues. The meat exported by Brazil is considered medium and low quality by international buyers, mainly due to lack of tenderness. The predominant Zebu breeds (80% Nellore) are known for producing tougher beef than taurine breeds. Nonetheless, some studies have shown that there is substantial genetic variability for tenderness within the Nellore breed, although it is a difficult trait to improve by conventional selection methods. Therefore, the aim of this study was to perform a genomewide association study (GWAS) and a gene set enrichment analysis to identify genomic regions and biologically relevant pathways associated with meat tenderness in Polled Nellore cattle. Data consisted of Warner-Bratzler shear force values of LM from 427 Polled Nellore animals divided into 3 experimental slaughters (years 2005, 2008, and 2010). The animals were genotyped with either the Illumina BovineHD BeadChip (777k, on 61 samples) or the GGP Indicus HD chip (77k, on 366 samples). Single nucleotide polymorphisms were excluded when the call rate was <90%, the Hardy-Weinberg proportions -value was <1% (Fisher exact test, Bonferroni adjusted), and the minor allele frequency was <1%. Imputation from the GGP Indicus HD chip to the Illumina BovineHD BeadChip was performed using the FImput program. Genomewide association analysis was performed using the Efficient Mixed Model Association eXpedited (EMMAx) and the population parameters previously determined (P3D) methods. The GWAS was complemented with a gene set enrichment analysis performed using the FatiGO procedure. Significant markers ( < 0.0001) explaining a larger proportion of variation than other significant SNPs were located on chromosomes 3, 13, 17, 20, 21, and 25, indicating QTL associated with meat tenderness throughout the genome. Additionally, gene set analysis identified 22 Gene Ontology functional terms and 2 InterPro entries that showed significant enrichment of genes associated with tenderness. The functional categories included protein tyrosine and serine/threonine kinase activity, calcium ion binding, lipid metabolic process, and growth factors, among others. These results help to elucidate the genetic architecture and metabolic pathways underlying this trait, which is of extreme economic and social importance to Brazil, because Nellore is the dominant beef cattle breed in the country.

SNP co-association and network analyses identify E2F3, KDM5A and BACH2 as key regulators of the bovine milk fatty acid profile

The fatty acid (FA) profile has a considerable impact on the nutritional and technological quality of milk and dairy products. The molecular mechanism underlying the regulation of fat metabolism in bovine mammary gland have been not completely elucidated. We conducted genome-wide association studies (GWAS) across 65 milk FAs and fat percentage in 1,152 Brown Swiss cows. In total, we identified 175 significant single nucleotide polymorphism (SNPs) spanning all chromosomes. Pathway analyses revealed that 12:0 was associated with the greatest number of overrepresented categories/pathways (e.g. mitogen-activated protein kinase (MAPK) activity and protein phosphorylation), suggesting that it might play an important biological role in controlling milk fat composition. An Associated Weight Matrix approach based on SNP co-associations predicted a network of 791 genes related to the milk FA profile, which were involved in several connected molecular pathways (e.g., MAPK, lipid metabolism and hormone signalling) and undetectable through standard GWAS. Analysis of transcription factors and their putative target genes within the network identified BACH2, E2F3 and KDM5A as key regulators of milk FA metabolism. These findings contribute to increasing knowledge of FA metabolism and mammary gland functionality in dairy cows and may be useful in developing targeted breeding practices to improve milk quality.

Modeling correlated marker effects in genome-wide prediction via Gaussian concentration graph models

In genome-wide prediction, independence of marker allele substitution effects is typically assumed; however, since early stages in the evolution of this technology it has been known that nature points to correlated effects. In statistics, graphical models have been identified as a useful and powerful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In particular, Gaussian concentration graph models (GCGM) have been widely studied. These are models in which the distribution of a set of random variables, the marker effects in this case, is assumed to be Markov with respect to an undirected graph G. In this paper, Bayesian (Bayes G and Bayes G-D) and frequentist (GML-BLUP) methods adapting the theory of GCGM to genome-wide prediction were developed. Different approaches to define the graph G based on domain-specific knowledge were proposed, and two propositions and a corollary establishing conditions to find decomposable graphs were proven. These methods were implemented in small simulated and real datasets. In our simulations, scenarios where correlations among allelic substitution effects were expected to arise due to various causes were considered, and graphs were defined on the basis of physical marker positions. Results showed improvements in correlation between phenotypes and predicted additive genetic values and accuracies of predicted additive genetic values when accounting for partially correlated allele substitution effects. Extensions to the multiallelic loci case were described and some possible refinements incorporating more flexible priors in the Bayesian setting were discussed. Our models are promising because they allow incorporation of biological information in the prediction process, and because they are more flexible and general than other models accounting for correlated marker effects that have been proposed previously.

Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction

Several statistical models used in genome-wide prediction assume uncorrelated marker allele substitution effects, but it is known that these effects may be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high-dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated data sets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies, which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multi-allelic loci case is straightforward.

Multiple Trait Covariance Association Test Identifies Gene Ontology Categories Associated with Chill Coma Recovery Time in Drosophila melanogaster

The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants. We aimed at searching for patterns in GBLUP-derived single-marker statistics, by including them in genetic marker set tests, that could reveal associations between a set of genetic markers (genomic feature) and a complex trait. GBLUP-derived set tests proved to be powerful for detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affecting a quantitative trait in a population with low degree of relatedness. Different set test approaches were compared using simulated data illustrating the impact of trait- and genomic feature-specific factors on detection power. We extended the most powerful single trait set test, covariance association test (CVAT), to a multiple trait setting. The multiple trait CVAT (MT-CVAT) identified functionally relevant GO categories associated with the quantitative trait, chill coma recovery time, in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel.

Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle

It is becoming common to complement genome-wide association studies (GWAS) with gene-set enrichment analysis to deepen the understanding of the biological pathways affecting quantitative traits. Our objective was to conduct a gene ontology and pathway-based analysis to identify possible biological mechanisms involved in the regulation of bovine milk technological traits: coagulation properties, curd firmness modeling, individual cheese yield (CY), and milk nutrient recovery into the curd (REC) or whey loss traits. Results from 2 previous GWAS studies using 1,011 cows genotyped for 50k single nucleotide polymorphisms were used. Overall, the phenotypes analyzed consisted of 3 traditional milk coagulation property measures [RCT: rennet coagulation time defined as the time (min) from addition of enzyme to the beginning of coagulation; k₂₀: the interval (min) from RCT to the time at which a curd firmness of 20 mm is attained; a₃₀: a measure of the extent of curd firmness (mm) 30 min after coagulant addition], 6 curd firmness modeling traits [RCT_eq: RCT estimated through the CF equation (min); CF_P: potential asymptotic curd firmness (mm); k_CF: curd-firming rate constant (% × min^-1); k_SR: syneresis rate constant (% × min^-1); CF_max: maximum curd firmness (mm); and t_max: time to CF_max (min)], 3 individual CY-related traits expressing the weight of fresh curd (%CY_CURD), curd solids (%CY_SOLIDS), and curd moisture (%CY_WATER) as a percentage of weight of milk processed and 4 milk nutrient and energy recoveries in the curd (REC_FAT, REC_PROTEIN, REC_SOLIDS, and REC_ENERGY calculated as the % ratio between the nutrient in curd and the corresponding nutrient in processed milk), milk pH, and protein percentage. Each trait was analyzed separately. In total, 13,269 annotated genes were used in the analysis. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases were queried for enrichment analyses. Overall, 21 Gene Ontology and 17 Kyoto Encyclopedia of Genes and Genomes categories were significantly associated (false discovery rate at 5%) with 7 traits (RCT, RCT_eq, k_CF, %CY_SOLIDS, REC_FAT, REC_SOLIDS, and REC_ENERGY), with some being in common between traits. The significantly enriched categories included calcium signaling pathway, salivary secretion, metabolic pathways, carbohydrate digestion and absorption, the tight junction and the phosphatidylinositol pathways, as well as pathways related to the bovine mammary gland health status, and contained a total of 150 genes spanning all chromosomes but 9, 20, and 27. This study provided new insights into the regulation of bovine milk coagulation and cheese ability that were not captured by the GWAS.

Unravelling the genomic architecture of bull fertility in Holstein cattle

BACKGROUND

Fertility is considered an important economic trait in dairy cattle. Most studies have investigated cow fertility while bull fertility has received much less consideration. The main objective of this study was to perform a comprehensive genomic analysis in order to unravel the genomic architecture underlying sire fertility in Holstein dairy cattle. The analysis included the application of alternative genome-wide association mapping approaches and the subsequent use of diverse gene set enrichment tools.

RESULTS

The association analyses identified at least eight genomic regions strongly associated with bull fertility. Most of these regions harbor genes, such as KAT8, CKB, TDRD9 and IGF1R, with functions related to sperm biology, including sperm development, motility and sperm-egg interaction. Moreover, the gene set analyses revealed many significant functional terms, including fertilization, sperm motility, calcium channel regulation, and SNARE proteins. Most of these terms are directly implicated in sperm physiology and male fertility.

CONCLUSIONS

This study contributes to the identification of genetic variants and biological processes underlying sire fertility. These findings can provide opportunities for improving bull fertility via marker-assisted selection.

MeSH-Informed Enrichment Analysis and MeSH-Guided Semantic Similarity Among Functional Terms and Gene Products in Chicken

Biomedical vocabularies and ontologies aid in recapitulating biological knowledge. The annotation of gene products is mainly accelerated by Gene Ontology (GO), and more recently by Medical Subject Headings (MeSH). Here, we report a suite of MeSH packages for chicken in Bioconductor, and illustrate some features of different MeSH-based analyses, including MeSH-informed enrichment analysis and MeSH-guided semantic similarity among terms and gene products, using two lists of chicken genes available in public repositories. The two published datasets that were employed represent (i) differentially expressed genes, and (ii) candidate genes under selective sweep or epistatic selection. The comparison of MeSH with GO overrepresentation analyses suggested not only that MeSH supports the findings obtained from GO analysis, but also that MeSH is able to further enrich the representation of biological knowledge and often provide more interpretable results. Based on the hierarchical structures of MeSH and GO, we computed semantic similarities among vocabularies, as well as semantic similarities among selected genes. These yielded the similarity levels between significant functional terms, and the annotation of each gene yielded the measures of gene similarity. Our findings show the benefits of using MeSH as an alternative choice of annotation in order to draw biological inferences from a list of genes of interest. We argue that the use of MeSH in conjunction with GO will be instrumental in facilitating the understanding of the genetic basis of complex traits.

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster

Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unrelated individuals when the number of causal variants is low relative to the total number of polymorphisms and causal variants individually have small effects on the traits. We hypothesized that mapping molecular polymorphisms to genomic features such as genes and their gene ontology categories could increase the accuracy of genomic prediction models. We developed a genomic feature best linear unbiased prediction (GFBLUP) model that implements this strategy and applied it to three quantitative traits (startle response, starvation resistance, and chill coma recovery) in the unrelated, sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel. Our results indicate that subsetting markers based on genomic features increases the predictive ability relative to the standard genomic best linear unbiased prediction (GBLUP) model. Both models use all markers, but GFBLUP allows differential weighting of the individual genetic marker relationships, whereas GBLUP weighs the genetic marker relationships equally. Simulation studies show that it is possible to further increase the accuracy of genomic prediction for complex traits using this model, provided the genomic features are enriched for causal variants. Our GFBLUP model using prior information on genomic features enriched for causal variants can increase the accuracy of genomic predictions in populations of unrelated individuals and provides a formal statistical framework for leveraging and evaluating information across multiple experimental studies to provide novel insights into the genetic architecture of complex traits.

Bovine Genome Database: new tools for gleaning function from the Bos taurus genome

We report an update of the Bovine Genome Database (BGD) (http://BovineGenome.org). The goal of BGD is to support bovine genomics research by providing genome annotation and data mining tools. We have developed new genome and annotation browsers using JBrowse and WebApollo for two Bos taurus genome assemblies, the reference genome assembly (UMD3.1.1) and the alternate genome assembly (Btau_4.6.1). Annotation tools have been customized to highlight priority genes for annotation, and to aid annotators in selecting gene evidence tracks from 91 tissue specific RNAseq datasets. We have also developed BovineMine, based on the InterMine data warehousing system, to integrate the bovine genome, annotation, QTL, SNP and expression data with external sources of orthology, gene ontology, gene interaction and pathway information. BovineMine provides powerful query building tools, as well as customized query templates, and allows users to analyze and download genome-wide datasets. With BovineMine, bovine researchers can use orthology to leverage the curated gene pathways of model organisms, such as human, mouse and rat. BovineMine will be especially useful for gene ontology and pathway analyses in conjunction with GWAS and QTL studies.

Transcriptomic Analysis of the Mouse Mammary Gland Reveals New Insights for the Role of Serotonin in Lactation

Serotonin regulates numerous processes in the mammary gland. Our objective was to discover novel genes, pathways and functions which serotonin modulates during lactation. The rate limiting enzyme in the synthesis of non-neuronal serotonin is tryptophan-hydroxylase (TPH1). Therefore, we used TPH1 deficient dams (KO; serotonin deficient, n = 4) and compared them to wild-type (WT; n = 4) and rescue (RC; KO + 100 mg/kg 5-hydroxytryptophan injected daily, n = 4) dams. Mammary tissues were collected on day 10 of lactation. Total RNA extraction, amplification, library preparation and sequencing were performed following the Illumina mRNA-Seq. Overall, 97 and 204 genes (false discovery rate, FDR ≤ 0.01) exhibited a minimum of a 2-fold expression difference between WT vs. KO and WT vs. RC dams, respectively. Most differentially expressed genes were related to calcium homeostasis, apoptosis regulation, cell cycle, cell differentiation and proliferation, and the immune response. Additionally, gene set enrichment analysis using Gene Ontology and Medical Subject Headings databases revealed the alteration of several biological processes (FDR ≤ 0.01) including fat cell differentiation and lipid metabolism, regulation of extracellular signal-related kinase and mitogen-activated kinase cascades, insulin resistance, nuclear transport, membrane potential regulation, and calcium release from the endoplasmic reticulum into the cytosol. The majority of the biological processes and pathways altered in the KO dams are central for mammary gland homeostasis. Increasing peripheral serotonin in the RC dams affects specific pathways that favor lactation. Our data confirms the importance of serotonin during lactation in the mammary gland.

Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle

Background

We have used a linear mixed model (LMM) approach to examine the joint contribution of genetic markers associated with a biological pathway. However, with these markers being scattered throughout the genome, we are faced with the challenge of modelling the contribution from several, sometimes even all, chromosomes at once. Due to linkage disequilibrium (LD), all markers may be assumed to account for some genomic variance; but the question is whether random sets of markers account for the same genomic variance as markers associated with a biological pathway?

Results

We applied the LMM approach to identify biological pathways associated with udder health and milk production traits in dairy cattle. A random gene sampling procedure was applied to assess the biological pathways in a dataset that has an inherently complex genetic correlation pattern due to the population structure of dairy cattle, and to linkage disequilibrium within the bovine genome and within the genes associated to the biological pathway.

Conclusions

Several biological pathways that were significantly associated with health and production traits were identified in dairy cattle; i.e. the markers linked to these pathways explained more of the genomic variance and provided a better model fit than 95 % of the randomly sampled gene groups. Our results show that immune related pathways are associated with production traits, and that pathways that include a causal marker for production traits are identified with our procedure.

We are confident that the LMM approach provides a general framework to exploit and integrate prior biological information and could potentially lead to improved understanding of the genetic architecture of complex traits and diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0132-6) contains supplementary material, which is available to authorized users.

Maternal nutrition induces gene expression changes in fetal muscle and adipose tissues in sheep

Background

Maternal nutrition during different stages of pregnancy can induce significant changes in the structure, physiology, and metabolism of the offspring. These changes could have important implications on food animal production especially if these perturbations impact muscle and adipose tissue development. Here, we evaluated the impact of different maternal isoenergetic diets, alfalfa haylage (HY; fiber), corn (CN; starch), and dried corn distillers grains (DG; fiber plus protein plus fat), on the transcriptome of fetal muscle and adipose tissues in sheep.

Results

Prepartum diets were associated with notable gene expression changes in fetal tissues. In longissimus dorsi muscle, a total of 224 and 823 genes showed differential expression (FDR ≤0.05) in fetuses derived from DG vs. CN and HY vs. CN maternal diets, respectively. Several of these significant genes affected myogenesis and muscle differentiation. In subcutaneous and perirenal adipose tissues, 745 and 208 genes were differentially expressed (FDR ≤0.05), respectively, between CN and DG diets. Many of these genes are involved in adipogenesis, lipogenesis, and adipose tissue development. Pathway analysis revealed that several GO terms and KEGG pathways were enriched (FDR ≤0.05) with differentially expressed genes associated with tissue and organ development, chromatin biology, and different metabolic processes.

Conclusions

These findings provide evidence that maternal nutrition during pregnancy can alter the programming of fetal muscle and fat tissues in sheep. The ramifications of the observed gene expression changes, in terms of postnatal growth, body composition, and meat quality of the offspring, warrant future investigation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1034) contains supplementary material, which is available to authorized users.

Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake

Residual feed intake (RFI) is a complex trait that is economically important for livestock production; however, the genetic and biological mechanisms regulating RFI are largely unknown in pigs. Therefore, the study aimed to identify single nucleotide polymorphisms (SNPs), candidate genes and biological pathways involved in regulating RFI using Genome-wide association (GWA) and pathway analyses. A total of 596 Yorkshire boars with phenotypes for two different measures of RFI (RFI1 and 2) and 60k genotypic data was used. GWA analysis was performed using a univariate mixed model and 12 and 7 SNPs were found to be significantly associated with RFI1 and RFI2, respectively. Several genes such as xin actin-binding repeat-containing protein 2 (XIRP2),tetratricopeptide repeat domain 29 (TTC29),suppressor of glucose, autophagy associated 1 (SOGA1),MAS1,G-protein-coupled receptor (GPCR) kinase 5 (GRK5),prospero-homeobox protein 1 (PROX1),GPCR 155 (GPR155), and FYVE domain containing the 26 (ZFYVE26) were identified as putative candidates for RFI based on their genomic location in the vicinity of these SNPs. Genes located within 50 kbp of SNPs significantly associated with RFI and RFI2 (q-value ≤ 0.2) were subsequently used for pathway analyses. These analyses were performed by assigning genes to biological pathways and then testing the association of individual pathways with RFI using a Fisher's exact test. Metabolic pathway was significantly associated with both RFIs. Other biological pathways regulating phagosome, tight junctions, olfactory transduction, and insulin secretion were significantly associated with both RFI traits when relaxed threshold for cut-off p-value was used (p ≤ 0.05). These results implied porcine RFI is regulated by multiple biological mechanisms, although the metabolic processes might be the most important. Olfactory transduction pathway controlling the perception of feed via smell, insulin pathway controlling food intake might be important pathways for RFI. Furthermore, our study revealed key genes and genetic variants that control feed efficiency that could potentially be useful for genetic selection of more feed efficient pigs.

Genetic variants related to gap junctions and hormone secretion influence conception rates in cows

The recent decline in fertility is a serious problem in the dairy industry. To overcome this problem, we performed a genome-wide association study using 384 Holsteins and identified four loci associated with conception rates. Two of them contained gap junction-related genes: PKP2 and CTTNBP2NL. Further analysis confirmed that PKP2 increased connexin 43, a gap junction protein, whereas CTTNBP2NL dephosphorylated connexin 43. Knockdown of PKP2 or overexpression of CTTNBP2NL inhibited embryo implantation in mice. The other two loci contained neuroendocrine-related genes: SETD6 and CACNB2. Additional experiments indicated that SETD6 is involved in the transcriptional regulation of gonadotropin-releasing hormone, whereas CACNB2 controlled the secretion of follicle-stimulating hormone in cattle. The total allele substitution effect of these genes on conception rate was 3.5%. Our findings reveal important roles for gap junction communication and the neuroendocrine system in conception and suggest unique selection methods to improve reproductive performance in the livestock industry.