Determination of quantitative trait nucleotides by concordance analysis between quantitative trait loci and marker genotypes of US Holsteins

Uncovering the genetic diversity and adaptability of Butuo Black Sheep through whole-genome re-sequencing

The Butuo Black Sheep (BBS) is well-known for its ability to thrive at high altitudes, resist diseases, and produce premium-quality meat. Nonetheless, there is insufficient data regarding its genetic diversity and population-specific Single nucleotide polymorphisms (SNPs). This paper centers on the genetic diversity of (BBS). The investigation conducted a whole-genome resequencing of 33 BBS individuals to recognize distinct SNPs exclusive to BBS. The inquiry utilized bioinformatic analysis to identify and explain SNPs and pinpoint crucial mutation sites. The findings reveal that reproductive-related genes (GHR, FSHR, PGR, BMPR1B, FST, ESR1), lipid-related genes (PPARGC1A, STAT6, DGAT1, ACACA, LPL), and protein-related genes (CSN2, LALBA, CSN1S1, CSN1S2) were identified as hub genes. Functional enrichment analysis showed that genes associated with reproduction, immunity, inflammation, hypoxia, PI3K-Akt, and AMPK signaling pathways were present. This research suggests that the unique ability of BBS to adapt to low oxygen levels in the plateau environment may be owing to mutations in a variety of genes. This study provides valuable insights into the genetic makeup of BBS and its potential implications for breeding and conservation efforts. The genes and SPNs identified in this study could serve as molecular markers for BBS.

Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data

The main objective of the current research was to locate, annotate, and highlight specific areas of the bovine genome that are undergoing intense positive selection. Here, we are analyzing selection signatures in crossbred (Bos taurus X Bos indicus), taurine (Bos taurus), and indicine (Bos indicus) cattle breeds. Indicine cattle breeds found throughout India are known for their higher heat tolerance and disease resilience. More breeds and more methods can provide a better understanding of the selection signature. So, we have worked on nine distinct cattle breeds utilizing seven different summary statistics, which is a fairly extensive approach. In this study, we carried out a thorough genome-wide investigation of selection signatures using bovine 50K SNP data. We have included the genotyped data of two taurine, two crossbreds, and five indicine cattle breeds, for a total of 320 animals. During the 1950s, these indicine (cebuine) cattle breeds were exported with the aim of enhancing the resilience of taurine breeds in Western countries. For this study, we employed seven summary statistics, including intra-population, i.e., Tajima's D, CLR, iHS, and ROH and inter-population statistics, i.e., FST, XP-EHH, and Rsb. The NCBI database, PANTHER 17.0, and CattleQTL database were used for annotation after finding the important areas under selection. Some genes, including EPHA6, CTNNA2, NPFFR2, HS6ST3, NPR3, KCNIP4, LIPK, SDCBP, CYP7A1, NSMAF, UBXN2B, UGDH, UBE2K, and DAB1, were shown to be shared by three or more different approaches. Therefore, it gives evidence of the most intense selection in these areas. These genes are mostly linked to milk production and adaptability traits. This study also reveals selection regions that contain genes which are crucial to numerous biological functions, including those associated with milk production, coat color, glucose metabolism, oxidative stress response, immunity and circadian rhythms.

Re-Evaluation of Genotyping Methodologies in Cattle: The Proficiency of Imputation

In dairy cattle, identifying polymorphisms that contribute to complex economical traits such as residual feed intake (RFI) is challenging and demands accurate genotyping. In this study, we compared imputed genotypes (n = 192 cows) to those obtained using the TaqMan and high-resolution melting (HRM) methods (n = 114 cows), for mutations in the FABP4 gene that had been suggested to have a large effect on RFI. Combining the whole genome sequence (n = 19 bulls) and the cows’ BovineHD BeadChip allowed imputing genotypes for these mutations that were verified by Sanger sequencing, whereas, an error rate of 11.6% and 10.7% were encountered for HRM and TaqMan, respectively. We show that this error rate seriously affected the linkage-disequilibrium analysis that supported this gene candidacy over other BTA14 gene candidates. Thus, imputation produced superior genotypes and should also be regarded as a method of choice to validate the reliability of the genotypes obtained by other methodologies that are prone to genotyping errors due to technical conditions. These results support the view that RFI is a complex trait and that searching for the causative sequence variation underlying cattle RFI should await the development of statistical methods suitable to handle additive and epistatic interactions.

Single Nucleotide Polymorphism Effects on Lamb Fecal Egg Count Estimated Breeding Values in Progeny-Tested Katahdin Sires

Estimated breeding values (EBV) for fecal egg counts (FEC) at 42–90 days of age (WFEC) and 91–150 days of age (PFEC) for 84 progeny-tested Katahdin sires were used to identify associations of deregressed EBV with single-nucleotide polymorphisms (SNP) using 388,000 SNP with minor-allele frequencies ≥0.10 on an Illumina high-density ovine array. Associations between markers and FEC EBV were initially quantified by single-SNP linear regression. Effects of linkage disequilibrium (LD) were minimized by assigning SNP to 2,535 consecutive 1-Mb bins and focusing on the effect of the most significant SNP in each bin. Bonferroni correction was used to define bin-based (BB) genome- and chromosome-wide significance. Six bins on chromosome 5 achieved BB genome-wide significance for PFEC EBV, and three of those SNP achieved chromosome-wide significance after Bonferroni correction based on the 14,530 total SNP on chromosome 5. These bins were nested within 12 consecutive bins between 59 and 71 Mb on chromosome 5 that reached BB chromosome-wide significance. The largest SNP effects were at 63, 67, and 70 Mb, with LD among these SNP of r² ≤ 0.2. Regional heritability mapping (RHM) was then used to evaluate the ability of different genomic regions to account for additive variance in FEC EBV. Chromosome-level RHM indicated that one 500-SNP window between 65.9 and 69.9 Mb accounted for significant variation in PFEC EBV. Five additional 500-SNP windows between 59.3 and 71.6 Mb reached suggestive (p < 0.10) significance for PFEC EBV. Although previous studies rarely identified markers for parasite resistance on chromosome 5, the IL12B gene at 68.5 Mb codes for the p40 subunit of both interleukins 12 and 23. Other immunoregulatory genes are also located in this region of chromosome 5, providing opportunity for additive or associative effects.

A Large-Scale Genome-Wide Association Study of Epistasis Effects of Production Traits and Daughter Pregnancy Rate in U.S. Holstein Cattle

Epistasis is widely considered important, but epistasis studies lag those of SNP effects. Genome-wide association study (GWAS) using 76,109 SNPs and 294,079 first-lactation Holstein cows was conducted for testing pairwise epistasis effects of five production traits and three fertility traits: milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FPC), protein percentage (PPC), and daughter pregnancy rate (DPR). Among the top 50,000 pairwise epistasis effects of each trait, the five production traits had large chromosome regions with intra-chromosome epistasis. The percentage of inter-chromosome epistasis effects was 1.9% for FPC, 1.6% for PPC, 10.6% for MY, 29.9% for FY, 39.3% for PY, and 84.2% for DPR. Of the 50,000 epistasis effects, the number of significant effects with log10(1/p) ≥ 12 was 50,000 for FPC and PPC, and 10,508, 4763, 4637 and 1 for MY, FY, PY and DPR, respectively, and A × A effects were the most frequent epistasis effects for all traits. Majority of the inter-chromosome epistasis effects of FPC across all chromosomes involved a Chr14 region containing DGAT1, indicating a potential regulatory role of this Chr14 region affecting all chromosomes for FPC. The epistasis results provided new understanding about the genetic mechanism underlying quantitative traits in Holstein cattle.

Meta-Analysis of Heifer Traits Identified Reproductive Pathways in Bos indicus Cattle

Fertility traits measured early in life define the reproductive potential of heifers. Knowledge of genetics and biology can help devise genomic selection methods to improve heifer fertility. In this study, we used ~2400 Brahman cattle to perform GWAS and multi-trait meta-analysis to determine genomic regions associated with heifer fertility. Heifer traits measured were pregnancy at first mating opportunity (PREG1, a binary trait), first conception score (FCS, score 1 to 3) and rebreeding score (REB, score 1 to 3.5). The heritability estimates were 0.17 (0.03) for PREG1, 0.11 (0.05) for FCS and 0.28 (0.05) for REB. The three traits were highly genetically correlated (0.75-0.83) as expected. Meta-analysis was performed using SNP effects estimated for each of the three traits, adjusted for standard error. We identified 1359 significant SNPs (p-value < 9.9 × 10-6 at FDR < 0.0001) in the multi-trait meta-analysis. Genomic regions of 0.5 Mb around each significant SNP from the meta-analysis were annotated to create a list of 2560 positional candidate genes. The most significant SNP was in the vicinity of a genomic region on chromosome 8, encompassing the genes SLC44A1, FSD1L, FKTN, TAL2 and TMEM38B. The genomic region in humans that contains homologs of these genes is associated with age at puberty in girls. Top significant SNPs pointed to additional fertility-related genes, again within a 0.5 Mb region, including ESR2, ITPR1, GNG2, RGS9BP, ANKRD27, TDRD12, GRM1, MTHFD1, PTGDR and NTNG1. Functional pathway enrichment analysis resulted in many positional candidate genes relating to known fertility pathways, including GnRH signaling, estrogen signaling, progesterone mediated oocyte maturation, cAMP signaling, calcium signaling, glutamatergic signaling, focal adhesion, PI3K-AKT signaling and ovarian steroidogenesis pathway. The comparison of results from this study with previous transcriptomics and proteomics studies on puberty of the same cattle breed (Brahman) but in a different population identified 392 genes in common from which some genes-BRAF, GABRA2, GABR1B, GAD1, FSHR, CNGA3, PDE10A, SNAP25, ESR2, GRIA2, ORAI1, EGFR, CHRNA5, VDAC2, ACVR2B, ORAI3, CYP11A1, GRIN2A, ATP2B3, CAMK2A, PLA2G, CAMK2D and MAPK3-are also part of the above-mentioned pathways. The biological functions of the positional candidate genes and their annotation to known pathways allowed integrating the results into a bigger picture of molecular mechanisms related to puberty in the hypothalamus-pituitary-ovarian axis. A reasonable number of genes, common between previous puberty studies and this study on early reproductive traits, corroborates the proposed molecular mechanisms. This study identified the polymorphism associated with early reproductive traits, and candidate genes that provided a visualization of the proposed mechanisms, coordinating the hypothalamic, pituitary, and ovarian functions for reproductive performance in Brahman cattle.

Genetic and Genome-Wide Association Analysis of Yearling Weight Gain in Israel Holstein Dairy Calves

Yearling weight gain in male and female Israeli Holstein calves, defined as 365 × ((weight - 35)/age at weight) + 35, was analyzed from 814,729 records on 368,255 animals from 740 herds recorded between 1994 and 2021. The variance components were calculated based on valid records from 2008 through 2017 for each sex separately and both sexes jointly by a single-trait individual animal model analysis, which accounted for repeat records on animals. The analysis model also included the square root, linear, and quadratic effects of age at weight. Heritability and repeatability were 0.35 and 0.71 in the analysis of both sexes and similar in the single sex analyses. The regression of yearling weight gain on birth date in the complete data set was -0.96 kg/year. The complete data set was also analyzed by the same model as the variance component analysis, including both sexes and accounting for differing variance components for each sex. The genetic trend for yearling weight gain, including both sexes, was 1.02 kg/year. Genetic evaluations for yearling weight gain was positively correlated with genetic evaluations for milk, fat, protein production, and cow survival but negatively correlated with female fertility. Yearling weight gain was also correlated with the direct effect on dystocia, and increased yearling weight gain resulted in greater frequency of dystocia. Of the 1749 Israeli Holstein bulls genotyped with reliabilities >50%, 1445 had genetic evaluations. As genotyping of these bulls was performed using several single nucleotide polymorhphism (SNP) chip platforms, we included only those markers that were genotyped in >90% of the tested cohort. A total of 40,498 SNPs were retained. More than 400 markers had significant effects after permutation and correction for multiple testing (p_nominal < 1 × 10^-8). Considering all SNPs simultaneously, 0.69 of variance among the sires' transmitting ability was explained. There were 24 markers with coefficients of determination for yearling weight gain >0.04. One marker, BTA-75458-no-rs on chromosome 5, explained ≈6% of the variance among the estimated breeding values for yearling weight gain. ARS-BFGL-NGS-39379 had the fifth largest coefficient of determination in the current study and was also found to have a significant effect on weight at an age of 13-14 months in a previous study on Holsteins. Significant genomic effects on yearling weight gain were mainly associated with milk production quantitative trait loci, specifically with kappa casein metabolism.

Genetic and environmental analysis of female calf survival in the Israel Holstein cattle population

The objectives were to estimate the effects of various environmental factors on female calf survival of Israeli Holsteins, to estimate the economic value of calf survival under Israeli conditions, to estimate the genetic and environmental variance components for calf and cow survival using the individual animal model, to perform GWAS analyses of survival to first calving and herd life after first calving, to estimate the genetic and environmental trends for calf survival since 1985, to estimate genetic correlations of calf survival with the traits included in the current Israeli breeding index, and to estimate the consequences of inclusion of calf survival in the national selection index. Mean calf survival rate of Israeli Holsteins from 2001 through 2008 was 0.85, and the mean economic value of survival to first calving was $526. Birth month, gestation length, dystocia, and twin birth significantly affected calf survival rate. Dystocia and twin birth each reduced survival rate by 0.034. Survival rate was highest for calves born in October and lowest for calves born in February. The difference between these months was 3.4%. Maximum survival was at a gestation length of 276 d, the mean gestation length for this population. Survival rate was reduced to 0.76 for calves born after a gestation length of 260 d. The individual animal model was applied for all the genetic analyses. Heritability for calf survival to first calving, as estimated by REML, was 0.009, whereas heritability of herd life from first calving was 0.15. The complete data set for genetic analysis of survival to first calving included 1,235,815 calves born between 1985 and 2017. Annual genetic and phenotypic trends for calf survival were 0.019 and 0.015%, respectively. Correlations of transmitting abilities of 226 sires born since 2010 for calf survival with the traits included in the Israeli breeding index were significant only for the maternal effects of dystocia and stillbirth. The GWAS analysis was based on the transmitting abilities of 1,493 bulls with genotypes and reliabilities >0.5 for calf survival and cow herd life. There were 7 single nucleotide polymorphisms with coefficients of determination >0.03 for calf survival and 12 single nucleotide polymorphisms with coefficients of determination >0.05 for cow survival. There was no overlap between the genome-wide significant markers for the GWAS analyses of calf survival and cow herd life. This corresponds to the conclusion from the REML results and the low correlations between the sire evaluations that the genetic control of the 2 traits are not similar. Inclusion of calf survival in the Israeli breeding would result in a 0.5% increase in calf survival over 10 yr but reduce progress for the other traits by 8%.

Proofs for genotype by environment interactions considering pedigree and genomic data from organic and conventional cow reference populations

The aim of the present study was to prove genotype by environment interactions (G × E) for production, longevity, and health traits considering conventional and organic German Holstein dairy cattle subpopulations. The full data set included 141,778 Holstein cows from 57 conventional herds and 7,915 cows from 9 organic herds. The analyzed traits were first-lactation milk yield and fat percentage (FP), the length of productive life (LPL) and the health traits mastitis, ovarian cycle disorders, and digital dermatitis in first lactation. A subset of phenotyped cows was genotyped and used for the implementation of separate cow reference populations. After SNP quality controls, the cow reference sets considered 40,830 SNP from 19,700 conventional cows and the same 40,830 SNP from 1,282 organic cows. The proof of possible G × E was made via multiple-trait model applications, considering same traits from the conventional and organic population as different traits. In this regard, pedigree (A), genomic (G) and combined relationship (H) matrices were constructed. For the production traits, heritabilities were very similar in both organic and conventional populations (i.e., close to 0.70 for FP and close to 0.40 for milk yield). For low heritability health traits and LPL, stronger heritability fluctuations were observed, especially for digital dermatitis with 0.05 ± 0.01 (organic, A matrix) to 0.33 ± 0.04 (conventional, G matrix). Quite large genetic correlations between same traits from the 2 environments were estimated for production traits, especially for high heritability FP. For LPL, the genetic correlation was 0.67 (A matrix) and 0.66 (H matrix). The genetic correlation between LPL organic with LPL conventional was 0.94 when considering the G matrix, but only 213 genotyped cows were included. For health traits, genetic correlations were throughout lower than 0.80, indicating possible G × E. Genetic correlations from the different matrices A, G, and H for health and production traits followed the same pattern, but the estimates from G for health traits were associated with quite large standard errors. In genome-wide association studies, significantly associated SNP for production traits overlapped in the conventional and organic population. In contrast, for low heritability LPL and health traits, significantly associated SNP and annotated potential candidate genes differed in both populations. In this regard, significantly associated SNP for mastitis from conventional cows were located on Bos taurus autosomes 6 and 19, but on Bos taurus autosomes 1, 10, and 22 in the organic population. For the remaining health traits and LPL, different potential candidate genes were annotated, but the different genes reflect similar physiological pathways. We found evidence of G × E for low heritability functional traits, suggesting different breeding approaches in organic and conventional populations. Nevertheless, for a verification of results and implementation of alternative breeding strategies, it is imperative to increase the organic cow reference population.

Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis

Native cattle breeds can carry specific signatures of selection reflecting their adaptation to the local environmental conditions and response to the breeding strategy used. In this study, we comprehensively analysed high-density single nucleotide polymorphism (SNP) genotypes to characterise the population structure and detect the selection signatures in Russian native Yaroslavl and Kholmogor dairy cattle breeds, which have been little influenced by introgression with transboundary breeds. Fifty-six samples of pedigree-recorded purebred animals, originating from different breeding farms and representing different sire lines, of the two studied breeds were genotyped using a genome-wide bovine genotyping array (Bovine HD BeadChip). Three statistical analyses—calculation of fixation index (F_ST) for each SNP for the comparison of the pairs of breeds, hapFLK analysis, and estimation of the runs of homozygosity (ROH) islands shared in more than 50% of animals—were combined for detecting the selection signatures in the genome of the studied cattle breeds. We confirmed nine and six known regions under putative selection in the genomes of Yaroslavl and Kholmogor cattle, respectively; the flanking positions of most of these regions were elucidated. Only two of the selected regions (localised on BTA 14 at 24.4–25.1 Mbp and on BTA 16 at 42.5–43.5 Mb) overlapped in Yaroslavl, Kholmogor and Holstein breeds. In addition, we detected three novel selection sweeps in the genome of Yaroslavl (BTA 4 at 4.74–5.36 Mbp, BTA 15 at 17.80–18.77 Mbp, and BTA 17 at 45.59–45.61 Mbp) and Kholmogor breeds (BTA 12 at 82.40–81.69 Mbp, BTA 15 at 16.04–16.62 Mbp, and BTA 18 at 0.19–1.46 Mbp) by using at least two of the above-mentioned methods. We expanded the list of candidate genes associated with the selected genomic regions and performed their functional annotation. We discussed the possible involvement of the identified candidate genes in artificial selection in connection with the origin and development of the breeds. Our findings on the Yaroslavl and Kholmogor breeds obtained using high-density SNP genotyping and three different statistical methods allowed the detection of novel putative genomic regions and candidate genes that might be under selection. These results might be useful for the sustainable development and conservation of these two oldest Russian native cattle breeds.

Genetic and genomic analysis of long insemination interval in Israeli dairy cattle as an indicator of early abortions

One of the causes of observed low fertility is embryo loss after fertilization. Previous findings suggested that more than half of fertilizations result in embryo loss before pregnancy is detected. We proposed reinsemination between 49 and 100 d after the first insemination as an indicator trait for early abortion (EA) in dairy cattle based on the mean estrus interval of 21 d. This trait was compared with conception rate from first insemination and conception status, computed as the inverse of the number of inseminations to conception. Animal model variance components were estimated by REML, including parents and grandparents of cows with records. First-parity heritability for first insemination conception rate was 3%. In the multitrait analysis of parities 1 to 3 for putative EA, heritabilities ranged from 8.9% for first parity to 10.4% for second parity. All genetic correlations were >0.9, whereas all environmental correlations were <0.12. The variance component for the service sire effect for putative EA rate was less than half the variance component for conception rate. Thus, genetic control of the 2 traits is clearly different, and analysis of EA rate by a single-trait animal model is justified. Genetic evaluation for putative EA was computed using this model, including all first- through third-parity cows with freshening dates from January 1, 1985, through December 31, 2016, that either became pregnant on first insemination or were reinseminated between 49 and 100 d after the first insemination. All known parents and grandparents of cows with records were included in the analysis. The regression of the breeding value for non-abortion rate on the cows' birth year was 0.083%/yr. The genetic correlation between first-parity EA and conception status was 0.995. The genetic correlations between first-parity EA and milk, fat, and protein production were all negative, whereas the genetic correlation between EA and herd life was 0.33. Inclusion of putative EA in the selection index instead of conception status resulted in 10 to 20% greater genetic gain for both fertility traits. In a genome-wide association study based on 1,200 dairy bulls with reliabilities >50% for abortion rate genotyped for 41,000 markers, 6 markers were found with nominal probabilities of <10^-12 to reject the null hypothesis of no effect on EA rate. The markers with the lowest probabilities for EA rate were also included among the markers with the lowest probabilities for female fertility, but not vice versa. The marker explaining the most variance for abortion rate is located within the ABCA9 gene, which is found within an ATP-binding cassette (ABC) genes cluster. The ABC family is the major class of primary active transporters in the placenta.

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.

Sequence-based GWAS, network and pathway analyses reveal genes co-associated with milk cheese-making properties and milk composition in Montbéliarde cows

BACKGROUND

Milk quality in dairy cattle is routinely assessed via analysis of mid-infrared (MIR) spectra; this approach can also be used to predict the milk's cheese-making properties (CMP) and composition. When this method of high-throughput phenotyping is combined with efficient imputations of whole-genome sequence data from cows' genotyping data, it provides a unique and powerful framework with which to carry out genomic analyses. The goal of this study was to use this approach to identify genes and gene networks associated with milk CMP and composition in the Montbéliarde breed.

RESULTS

Milk cheese yields, coagulation traits, milk pH and contents of proteins, fatty acids, minerals, citrate, and lactose were predicted from MIR spectra. Thirty-six phenotypes from primiparous Montbéliarde cows (1,442,371 test-day records from 189,817 cows) were adjusted for non-genetic effects and averaged per cow. 50 K genotypes, which were available for a subset of 19,586 cows, were imputed at the sequence level using Run6 of the 1000 Bull Genomes Project (comprising 2333 animals). The individual effects of 8.5 million variants were evaluated in a genome-wide association study (GWAS) which led to the detection of 59 QTL regions, most of which had highly significant effects on CMP and milk composition. The results of the GWAS were further subjected to an association weight matrix and the partial correlation and information theory approach and we identified a set of 736 co-associated genes. Among these, the well-known caseins, PAEP and DGAT1, together with dozens of other genes such as SLC37A1, ALPL, MGST1, SEL1L3, GPT, BRI3BP, SCD, GPAT4, FASN, and ANKH, explained from 12 to 30% of the phenotypic variance of CMP traits. We were further able to identify metabolic pathways (e.g., phosphate and phospholipid metabolism and inorganic anion transport) and key regulator genes, such as PPARA, ASXL3, and bta-mir-200c that are functionally linked to milk composition.

CONCLUSIONS

By using an approach that integrated GWAS with network and pathway analyses at the whole-genome sequence level, we propose candidate variants that explain a substantial proportion of the phenotypic variance of CMP traits and could thus be included in genomic evaluation models to improve milk CMP in Montbéliarde cows.

Genome to Phenome: Improving Animal Health, Production, and Well-Being - A New USDA Blueprint for Animal Genome Research 2018-2027

In 2008, a consortium led by the Agricultural Research Service (ARS) and the National Institute for Food and Agriculture (NIFA) published the "Blueprint for USDA Efforts in Agricultural Animal Genomics 2008-2017," which served as a guiding document for research and funding in animal genomics. In the decade that followed, many of the goals set forth in the blueprint were accomplished. However, several other goals require further research. In addition, new topics not covered in the original blueprint, which are the result of emerging technologies, require exploration. To develop a new, updated blueprint, ARS and NIFA, along with scientists in the animal genomics field, convened a workshop titled "Genome to Phenome: A USDA Blueprint for Improving Animal Production" in November 2017, and these discussions were used to develop new goals for the next decade. Like the previous blueprint, these goals are grouped into the broad categories "Science to Practice," "Discovery Science," and "Infrastructure." New goals for characterizing the microbiome, enhancing the use of gene editing and other biotechnologies, and preserving genetic diversity are included in the new blueprint, along with updated goals within many genome research topics described in the previous blueprint. The updated blueprint that follows describes the vision, current state of the art, the research needed to advance the field, expected deliverables, and partnerships needed for each animal genomics research topic. Accomplishment of the goals described in the blueprint will significantly increase the ability to meet the demands for animal products by an increasing world population within the next decade.

A Large-Scale Genome-Wide Association Study in U.S. Holstein Cattle

Genome-wide association study (GWAS) is a powerful approach to identify genomic regions and genetic variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. We conducted a large-scale GWAS using 294,079 first-lactation Holstein cows and identified new additive and dominance effects on five production traits, three fertility traits, and somatic cell score. Four chromosomes had the most significant SNP effects on the five production traits, a Chr14 region containing DGAT1 mostly had positive effects on fat yield and negative effects on milk and protein yields, the 88.07-89.60 Mb region of Chr06 with SLC4A4, GC, NPFFR2, and ADAMTS3 for milk and protein yields, the 30.03-36.67 Mb region of Chr20 with C6 and GHR for milk yield, and the 88.19-88.88 Mb region with ABCC9 as well as the 91.13-94.62 Mb region of Chr05 with PLEKHA5, MGST1, SLC15A5, and EPS8 for fat yield. For fertility traits, the SNP in GC of Chr06, and the SNPs in the 65.02-69.43 Mb region of Chr01 with COX17, ILDR1, and KALRN had the most significant effects for daughter pregnancy rate and cow conception rate, whereas SNPs in AFF1 of Chr06, the 47.54-52.79 Mb region of Chr07, TSPAN4 of Chr29, and NPAS1 of Chr18 had the most significant effects for heifer conception rate. For somatic cell score, GC of Chr06 and PRLR of Chr20 had the most significant effects. A small number of dominance effects were detected for the production traits with far lower statistical significance than the additive effects and for fertility traits with similar statistical significance as the additive effects. Analysis of allelic effects revealed the presence of uni-allelic, asymmetric, and symmetric SNP effects and found the previously reported DGAT1 antagonism was an extreme antagonistic pleiotropy between fat yield and milk and protein yields among all SNPs in this study.

A QTL for Number of Teats Shows Breed Specific Effects on Number of Vertebrae in Pigs: Bridging the Gap Between Molecular and Quantitative Genetics

Modern breeding schemes for livestock species accumulate a large amount of genotype and phenotype data which can be used for genome-wide association studies (GWAS). Many chromosomal regions harboring effects on quantitative traits have been reported from these studies, but the underlying causative mutations remain mostly undetected. In this study, we combine large genotype and phenotype data available from a commercial pig breeding scheme for three different breeds (Duroc, Landrace, and Large White) to pinpoint functional variation for a region on porcine chromosome 7 affecting number of teats (NTE). Our results show that refining trait definition by counting number of vertebrae (NVE) and ribs (RIB) helps to reduce noise from other genetic variation and increases heritability from 0.28 up to 0.62 NVE and 0.78 RIB in Duroc. However, in Landrace, the effect of the same QTL on NTE mainly affects NVE and not RIB, which is reflected in reduced heritability for RIB (0.24) compared to NVE (0.59). Further, differences in allele frequencies and accuracy of rib counting influence genetic parameters. Correction for the top SNP does not detect any other QTL effect on NTE, NVE, or RIB in Landrace or Duroc. At the molecular level, haplotypes derived from 660K SNP data detects a core haplotype of seven SNPs in Duroc. Sequence analysis of 16 Duroc animals shows that two functional mutations of the Vertnin (VRTN) gene known to increase number of thoracic vertebrae (ribs) reside on this haplotype. In Landrace, the linkage disequilibrium (LD) extends over a region of more than 3 Mb also containing both VRTN mutations. Here, other modifying loci are expected to cause the breed-specific effect. Additional variants found on the wildtype haplotype surrounding the VRTN region in all sequenced Landrace animals point toward breed specific differences which are expected to be present also across the whole genome. This Landrace specific haplotype contains two missense mutations in the ABCD4 gene, one of which is expected to have a negative effect on the protein function. Together, the integration of largescale genotype, phenotype and sequence data shows exemplarily how population parameters are influenced by underlying variation at the molecular level.