Farmer-preferred traits in smallholder dairy farming systems in Tanzania.

Decisions of breeding schemes in many countries in sub-Saharan Africa tend to be either government or project driven, with a focus on upgrading local breeds. However, there is scant information on the individual animal traits that smallholder farmers prefer. The aim of this study was to examine farmers' preferences of dairy cattle traits using a discrete choice experiment methodology. The study was conducted through visits to 555 randomly selected dairy farms in the sub-humid Eastern coast and temperate Southern highlands of Tanzania. Choices of animal traits were presented to farmers who were asked to evaluate choice alternatives based on attribute levels and finally select the alternative with the highest utility. The choice experiment data were analysed using a conditional logit model. Coefficients for milk yield, fertility, feed requirement, temperament and diseases resistance were overall statistically significant (p < 0.05). In order of perceived importance, farmers were willing to keep a cow with high milk yield (coefficient = 1.43 ± 0.059), good fertility (0.85 ± 0.050), easy temperament (0.76 ± 0.066), low feed requirement (- 0.56 ± 0.092) and enhanced tropical disease resistance (0.48 ± 0.048). The purchase price coefficient was negative (- 0.001 ± 0.0003), indicating that farmers would prefer improved dairy cattle at affordable prices. Farmers' preferred traits were influenced by agro-ecological zone and type of production system (extensive vs intensive). The study provides an opportunity for breeding programme designers to take farmers' preferred dairy traits into serious consideration.

Assessment of runs of homozygosity islands and estimates of genomic inbreeding in Gyr (Bos indicus) dairy cattle.

BACKGROUND: Runs of homozygosity (ROH) are continuous homozygous segments of the DNA sequence. They have been applied to quantify individual autozygosity and used as a potential inbreeding measure in livestock species. The aim of the present study was (i) to investigate genome-wide autozygosity to identify and characterize ROH patterns in Gyr dairy cattle genome; (ii) identify ROH islands for gene content and enrichment in segments shared by more than 50% of the samples, and (iii) compare estimates of molecular inbreeding calculated from ROH (FROH), genomic relationship matrix approach (FGRM) and based on the observed versus expected number of homozygous genotypes (FHOM), and from pedigree-based coefficient (FPED). RESULTS: ROH were identified in all animals, with an average number of 55.12 ± 10.37 segments and a mean length of 3.17 Mb. Short segments (ROH1-2 Mb) were abundant through the genomes, which accounted for 60% of all segments identified, even though the proportion of the genome covered by them was relatively small. The findings obtained in this study suggest that on average 7.01% (175.28 Mb) of the genome of this population is autozygous. Overlapping ROH were evident across the genomes and 14 regions were identified with ROH frequencies exceeding 50% of the whole population. Genes associated with lactation (TRAPPC9), milk yield and composition (IRS2 and ANG), and heat adaptation (HSF1, HSPB1, and HSPE1), were identified. Inbreeding coefficients were estimated through the application of FROH, FGRM, FHOM, and FPED approaches. FPED estimates ranged from 0.00 to 0.327 and FROH from 0.001 to 0.201. Low to moderate correlations were observed between FPED-FROH and FGRM-FROH, with values ranging from -0.11 to 0.51. Low to high correlations were observed between FROH-FHOM and moderate between FPED-FHOM and FGRM-FHOM. Correlations between FROH from different lengths and FPED gradually increased with ROH length. CONCLUSIONS: Genes inside ROH islands suggest a strong selection for dairy traits and enrichment for Gyr cattle environmental adaptation. Furthermore, low FPED-FROH correlations for small segments indicate that FPED estimates are not the most suitable method to capture ancient inbreeding. The existence of a moderate correlation between larger ROH indicates that FROH can be used as an alternative to inbreeding estimates in the absence of pedigree records.

Association of SNP and STR polymorphisms of insulin-like growth factor 2 receptor (IGF2R) gene with milk traits in Holstein-Friesian cows.

The objective of the study reported in this Research Communication was to investigate the association of polymorphisms in the insulin-like growth factor receptor 2 (IGF2R) gene with milk traits in 283 Polish Holstein-Friesian (PHF) cows from the IGAB PAS farm in Jastrzebiec. IGF2R regulates the availability of biologically active IGF2 which is considered as a genetic marker for milk or meat production in farm animals. Two novel genetic polymorphisms were identified in the bovine IGF2R gene: a polymorphic TG-repeat in intron 23 (g.72389 (TG)15-67), and a g.72479 G > A SNP RFLP-StyI in exon 24. The following milk traits were investigated: milk yield, protein and fat yield, SCC and lactose content. To determine the influence of the IGF2R STR and SNP genotypes on the milk traits, we used the AI-REML (average information restricted maximum likelihood) method with repeatability, multi-trait animal model based on test-day information using DMU package. Statistical analysis revealed that the G/A genotype (P ≤ 0·01) was associated with milk and protein yield, lactose content and somatic cell count (SCC) in Polish HF cows. TGn (29/22, 28/29, 28/22, 28/28) genotypes were associated with high values for milk, (28/22, 28/23) with protein and fat yield, (25/20) with lactose content, and (29/33, 28/28) with low SCC. We suggest that the IGF2R gene polymorphisms could be useful genetic markers for dairy production traits in cattle.

Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle.

BACKGROUND: Although genome-wide association and genomic selection studies have primarily focused on additive effects, dominance and imprinting effects play an important role in mammalian biology and development. The degree to which these non-additive genetic effects contribute to phenotypic variation and whether QTL acting in a non-additive manner can be detected in genetic association studies remain controversial. RESULTS: To empirically answer these questions, we analyzed a large cattle dataset that consisted of 42,701 genotyped Holstein cows with genotyped parents and phenotypic records for eight production and reproduction traits. SNP genotypes were phased in pedigree to determine the parent-of-origin of alleles, and a three-component GREML was applied to obtain variance decomposition for additive, dominance, and imprinting effects. The results showed a significant non-zero contribution from dominance to production traits but not to reproduction traits. Imprinting effects significantly contributed to both production and reproduction traits. Interestingly, imprinting effects contributed more to reproduction traits than to production traits. Using GWAS and imputation-based fine-mapping analyses, we identified and validated a dominance association signal with milk yield near RUNX2, a candidate gene that has been associated with milk production in mice. When adding non-additive effects into the prediction models, however, we observed little or no increase in prediction accuracy for the eight traits analyzed. CONCLUSIONS: Collectively, our results suggested that non-additive effects contributed a non-negligible amount (more for reproduction traits) to the total genetic variance of complex traits in cattle, and detection of QTLs with non-additive effect is possible in GWAS using a large dataset.

Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution.

BACKGROUND: Genotyping and whole-genome sequencing data have been generated for hundreds of thousands of cattle. International consortia used these data to compile imputation reference panels that facilitate the imputation of sequence variant genotypes for animals that have been genotyped using dense microarrays. Association studies with imputed sequence variant genotypes allow for the characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations. RESULTS: We imputed genotypes for 28 million sequence variants in 17,229 cattle of the Braunvieh, Fleckvieh and Holstein breeds in order to compile large mapping populations that provide high power to identify QTL for milk production traits. Association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P < 1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the results across breeds using meta-analysis and identified a total of 25 QTL including six that were not significant in the within-breed association studies. Two missense mutations in the ABCG2 (p.Y581S, rs43702337, P = 4.3e-34) and GHR (p.F279Y, rs385640152, P = 1.6e-74) genes were the top variants at QTL on chromosomes 6 and 20. Another known causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P = 8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were inconsistent across breeds. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation. CONCLUSIONS: Many QTL for milk production traits segregate across breeds and across-breed meta-analysis has greater power to detect such QTL than within-breed association testing. Association testing between imputed sequence variant genotypes and phenotypes of interest facilitates identifying causal mutations provided the accuracy of imputation is high. However, true causal mutations may remain undetected when the imputed sequence variant genotypes contain flaws. It is highly recommended to validate the effect of known causal variants in order to assess the ability to detect true causal mutations in association studies with imputed sequence variants.

Analyzing the genomic and transcriptomic architecture of milk traits in Murciano-Granadina goats.

BACKGROUND: In this study, we aimed to investigate the molecular basis of lactation as well as to identify the genetic factors that influence milk yield and composition in goats. To achieve these two goals, we have analyzed how the mRNA profile of the mammary gland changes in seven Murciano-Granadina goats at each of three different time points, i.e. 78 d (T1, early lactation), 216 d (T2, late lactation) and 285 d (T3, dry period) after parturition. Moreover, we have performed a genome-wide association study (GWAS) for seven dairy traits recorded in the 1st lactation of 822 Murciano-Granadina goats. RESULTS: The expression profiles of the mammary gland in the early (T1) and late (T2) lactation were quite similar (42 differentially expressed genes), while strong transcriptomic differences (more than one thousand differentially expressed genes) were observed between the lactating (T1/T2) and non-lactating (T3) mammary glands. A large number of differentially expressed genes were involved in pathways related with the biosynthesis of amino acids, cholesterol, triglycerides and steroids as well as with glycerophospholipid metabolism, adipocytokine signaling, lipid binding, regulation of ion transmembrane transport, calcium ion binding, metalloendopeptidase activity and complement and coagulation cascades. With regard to the second goal of the study, the performance of the GWAS allowed us to detect 24 quantitative trait loci (QTLs), including three genome-wide significant associations: QTL1 (chromosome 2, 130.72-131.01 Mb) for lactose percentage, QTL6 (chromosome 6, 78.90-93.48 Mb) for protein percentage and QTL17 (chromosome 17, 11.20 Mb) for both protein and dry matter percentages. Interestingly, QTL6 shows positional coincidence with the casein genes, which encode 80% of milk proteins. CONCLUSIONS: The abrogation of lactation involves dramatic changes in the expression of genes participating in a broad array of physiological processes such as protein, lipid and carbohydrate metabolism, calcium homeostasis, cell death and tissue remodeling, as well as immunity. We also conclude that genetic variation at the casein genes has a major impact on the milk protein content of Murciano-Granadina goats.

Targeted Analysis Reveals an Important Role of JAK-STAT-SOCS Genes for Milk Production Traits in Australian Dairy Cattle.

The Janus kinase and signal transducer and activator of transcription (JAK-STAT) pathway genes along with suppressors of cytokine signalling (SOCS) family genes play a crucial role in controlling cytokine signals in the mammary gland and thus mammary gland development. Mammary gene expression studies showed differential expression patterns for all the JAK-STAT pathway genes. Gene expression studies using qRT-PCR revealed differential expression of SOCS2, SOCS4, and SOCS5 genes across the lactation cycle in dairy cows. Using genotypes from 1,546 Australian Holstein-Friesian bulls, a statistical model for an association analysis based on SNPs within 500 kb of JAK-STAT pathway genes, and SOCS genes alone was constructed. The analysis suggested that these genes and pathways make a significant contribution to the Australian milk production traits. There were 24 SNPs close to SOCS1, SOCS3, SOCS5, SOCS7, and CISH genes that were significantly associated with Australian Profit Ranking (APR), Australian Selection Index (ASI), and protein yield (PY). This study supports the view that there may be some merit in choosing SNPs around functionally relevant genes for the selection and genetic improvement schemes for dairy production traits.