Genetic diversity, population structure and kinship relationships highlight the environmental influence on Uganda's indigenous goat populations.

Knowledge about genetic diversity and population structure among goat populations is essential for understanding environmental adaptation and fostering efficient utilization, development, and conservation of goat breeds. Uganda's indigenous goats exist in three phenotypic groups: Mubende, Kigezi, and Small East African. However, a limited understanding of their genetic attributes and population structure hinders the development and sustainable utilization of the goats. Using the Goat Illumina 60k chip International Goat Genome Consortium V2, the whole-genome data for 1,021 indigenous goats sourced from 10 agroecological zones in Uganda were analyzed for genetic diversity and population structure. A total of 49,337 (82.6%) single-nucleotide polymorphism markers were aligned to the ARS-1 goat genome and used to assess the genetic diversity, population structure, and kinship relationships of Uganda's indigenous goats. Moderate genetic diversity was observed. The observed and expected heterozygosities were 0.378 and 0.383, the average genetic distance was 0.390, and the average minor allele frequency was 0.30. The average inbreeding coefficient (Fis) was 0.014, and the average fixation index (Fst) was 0.016. Principal component analysis, admixture analysis, and discriminant analysis of principal components grouped the 1,021 goat genotypes into three genetically distinct populations that did not conform to the known phenotypic populations but varied across environmental conditions. Population 1, comprising Mubende (90%) and Kigezi (8.1%) goats, is located in southwest and central Uganda, a warm and humid environment. Population 2, which is 59% Mubende and 49% Small East African goats, is located along the Nile Delta in northwestern Uganda and around the Albertine region, a hot and humid savannah grassland. Population 3, comprising 78.4% Small East African and 21.1% Mubende goats, is found in northeastern to eastern Uganda, a hot and dry Commiphora woodlands. Genetic diversity and population structure information from this study will be a basis for future development, conservation, and sustainable utilization of Uganda's goat genetic resources.

Assessment of Genomic Diversity and Selective Pressures in Crossbred Dairy Cattle of Pakistan.

Improving the low productivity levels of native cattle breeds in smallholder farming systems is a pressing concern in Pakistan. Crossbreeding high milk-yielding holstein friesian (HF) breed with the adaptability and heat tolerance of Sahiwal cattle has resulted in offspring that are well-suited to local conditions and exhibit improved milk yield. The exploration of how desirable traits in crossbred dairy cattle are selected has not yet been investigated. This study aims to provide the first overview of the selective pressures on the genome of crossbred dairy cattle in Pakistan. A total of eighty-one crossbred, thirty-two HF and twenty-four Sahiwal cattle were genotyped, and additional SNP genotype data for HF and Sahiwal were collected from a public database to equate the sample size in each group. Within-breed selection signatures in crossbreds were investigated using the integrated haplotype score. Crossbreds were also compared to each of their parental breeds to discover between-population signatures of selection using two approaches: cross-population extended haplotype homozygosity and fixation index. We identified several overlapping genes associated with production, immunity, and adaptation traits, including U6, TMEM41B, B4GALT7, 5S_rRNA, RBM27, POU4F3, NSD1, PRELID1, RGS14, SLC34A1, TMED9, B4GALT7, OR2AK3, OR2T16, OR2T60, OR2L3, and CTNNA1. Our results suggest that regions responsible for milk traits have generally experienced stronger selective pressure than others.

Variability in body weight and morphology of Uganda's indigenous goat breeds across agroecological zones.

Indigenous goat breeds in Uganda are classified based on average body size parameters and coat color. However, variations in the body size of animals may be influenced by several factors, including management and the environment. To understand the effect of the agroecological zone on the physical characteristics and live weight of Uganda's indigenous goats, this study evaluated the body size characteristics of the three indigenous goat breeds of Uganda across ten agroecological zones. The cross-sectional survey was conducted in 323 households from the ten zones, where 1020 goats composed of three breeds (Mubende, Kigezi, and Small East African) were sampled and measured for body weight, linear body size, and age. We confirmed that Mubende and Kigezi goats from the original homeland had a higher mean body weight than reported in FAO reports. In addition, Mubende appeared to perform better in pastoral rangelands, with a higher mean body weight (38.1 kg) and body size being significantly higher (P < 0.0001) compared to other zones. The mean body weight for the Kigezi breed in the original homeland (34 kg) was comparable to those from Western Savannah grasslands and pastoral rangelands and less than that initially reported by FAO (30 kg). Similarly, there was no significant difference in the linear body size characteristics of Kigezi goats in the home zone of highland ranges relative to those found in other agroecological zones (P > 0.05). Although the Small East African goats were originally found in Northwestern Savannah grassland and Northeastern dryland zones, they performed poorly regarding mean body weight and body size characteristics in the former zone. In the Northwestern Savannah grasslands, the mean body weight (23.8 kg) was even less than that reported by FAO, which ranged between 25 and 30 kg. Finally, we confirmed that Mubende and Kigezi goats are significantly heavier than small East African goats (p ≤ 0.0001). The results of this study can be useful in designing precise management strategies to improve indigenous goat productivity in different environments in Uganda.

Genetic insights into crossbred dairy cattle of Pakistan: exploring allele frequency, linkage disequilibrium, and effective population size at a genome-wide scale.

Linkage disequilibrium (LD) affects genomic studies accuracy. High-density genotyping platforms identify SNPs across animal genomes, increasing LD evaluation resolution for accurate analysis. This study aimed to evaluate the decay and magnitude of LD in a cohort of 81 crossbred dairy cattle using the GGP_HDv3_C Bead Chip. After quality control, 116,710 Single Nucleotide Polymorphisms (SNPs) across 2520.241 Mb of autosomes were retained. LD extent was assessed between autosomal SNPs within a 10 Mb range using the r2 statistics. LD value declined as inter-marker distance increased. The average r2 value was 0.24 for SNP pairs < 10 kb apart, decreasing to 0.13 for 50-100 kb distances. Minor allele frequency (MAF) and sample size significantly impact LD. Lower MAF thresholds result in smaller r2 values, while higher thresholds show increased r2 values. Additionally, smaller sample sizes exhibit higher average r2 values, especially for larger physical distance intervals (> 50 kb) between SNP pairs. Effective population size and inbreeding coefficient were 150 and 0.028 for the present generation, indicating a decrease in genetic diversity over time. These findings imply that the utilization of high-density SNP panels and customized/breed-specific SNP panels represent a highly favorable approach for conducting genome-wide association studies (GWAS) and implementing genomic selection (GS) in the Bos indicus cattle breeds, whose genomes are still largely unexplored. Furthermore, it is imperative to devise a meticulous breeding strategy tailored to each herd, aiming to enhance desired traits while simultaneously preserving genetic diversity.

Transcriptome analysis reveals gene expression changes of pigs infected with non-lethal African swine fever virus.

African swine fever (ASF) is an important viral disease of swine caused by the African swine fever virus (ASFV), which threatens swine production profoundly. To better understand the gene expression changes when pig infected with ASFV, RNA sequencing was performed to characterize differentially expressed genes (DEGs) of six tissues from Kenya domestic pigs and Landrace × Yorkshire (L/Y) pigs infected with ASFV Kenya1033 in vivo. As results, a total of 209, 522, 34, 505, 634 and 138 DEGs (q-value < 0.05 and |Log2foldchange| values >2) were detected in the kidney, liver, mesenteric lymph node, peripheral blood mononuclear cell, submandibular lymph node and spleen, respectively. The expression profiles of DEGs shared in the multiple tissues illustrated variation in regulation function in the different tissues. Functional annotation analysis and interaction of proteins encoded by DEGs revealed that genes including IFIT1, IFITM1, MX1, OASL, ISG15, SAMHD1, IFINA1, S100A12 and S100A8 enriched in the immune and antivirus pathways were significantly changed when the hosts were infected with ASFV. The genes mentioned could play crucial roles in the process of the reaction to non-lethal ASF infection, which may will help to improve the ASF tolerance in the pig population through molecular breeding strategies.

Heat stress effects on milk yield traits and metabolites and mitigation strategies for dairy cattle breeds reared in tropical and sub-tropical countries.

Heat stress is an important problem for dairy industry in many parts of the world owing to its adverse effects on productivity and profitability. Heat stress in dairy cattle is caused by an increase in core body temperature, which affects the fat production in the mammary gland. It reduces milk yield, dry matter intake, and alters the milk composition, such as fat, protein, lactose, and solids-not-fats percentages among others. Understanding the biological mechanisms of climatic adaptation, identifying and exploring signatures of selection, genomic diversity and identification of candidate genes for heat tolerance within indicine and taurine dairy breeds is an important progression toward breeding better dairy cattle adapted to changing climatic conditions of the tropics. Identifying breeds that are heat tolerant and their use in genetic improvement programs is crucial for improving dairy cattle productivity and profitability in the tropics. Genetic improvement for heat tolerance requires availability of genetic parameters, but these genetic parameters are currently missing in many tropical countries. In this article, we reviewed the HS effects on dairy cattle with regard to (1) physiological parameters; (2) milk yield and composition traits; and (3) milk and blood metabolites for dairy cattle reared in tropical countries. In addition, mitigation strategies such as physical modification of environment, nutritional, and genetic development of heat tolerant dairy cattle to prevent the adverse effects of HS on dairy cattle are discussed. In tropical climates, a more and cost-effective strategy to overcome HS effects is to genetically select more adaptable and heat tolerant breeds, use of crossbred animals for milk production, i.e., crosses between indicine breeds such as Gir, white fulani, N'Dama, Sahiwal or Boran to taurine breeds such as Holstein-Friesian, Jersey or Brown Swiss. The results of this review will contribute to policy formulations with regard to strategies for mitigating the effects of HS on dairy cattle in tropical countries.

Comparative Genomic Analysis of Warthog and Sus Scrofa Identifies Adaptive Genes Associated with African Swine Fever.

BACKGROUND: As warthogs (Phacochoerus africanus) have innate immunity against African swine fever (ASF), it is critical to understand the evolutionary novelty of warthogs to explain their specific ASF resistance. METHODS: Here, we present two completed new genomes of one warthog and one Kenyan domestic pig as fundamental genomic references to elucidate the genetic mechanisms of ASF tolerance. RESULTS: Multiple genomic variations, including gene losses, independent contraction, and the expansion of specific gene families, likely molded the warthog genome to adapt to the environment. Importantly, the analysis of the presence and absence of genomic sequences revealed that the DNA sequence of the warthog genome had an absence of the gene lactate dehydrogenase B (LDHB) on chromosome 2 compared with the reference genome. The overexpression and siRNA of LDHB inhibited the replication of the African swine fever virus. Combined with large-scale sequencing data from 42 pigs worldwide, the contraction and expansion of tripartite motif-containing (TRIM) gene families revealed that TRIM family genes in the warthog genome are potentially responsible for its tolerance to ASF. CONCLUSION: Our results will help improve the understanding of genetic resistance to ASF in pigs.

Livestock phenomics and genetic evaluation approaches in Africa: current state and future perspectives.

The African livestock sector plays a key role in improving the livelihoods of people through the supply of food, improved nutrition and consequently health. However, its impact on the economy of the people and contribution to national GDP is highly variable and generally below its potential. This study was conducted to assess the current state of livestock phenomics and genetic evaluation methods being used across the continent, the main challenges, and to demonstrate the effects of various genetic models on the accuracy and rate of genetic gain that could be achieved. An online survey of livestock experts, academics, scientists, national focal points for animal genetic resources, policymakers, extension agents and animal breeding industry was conducted in 38 African countries. The results revealed 1) limited national livestock identification and data recording systems, 2) limited data on livestock production and health traits and genomic information, 3) mass selection was the common method used for genetic improvement with very limited application of genetic and genomic-based selection and evaluation, 4) limited human capacity, infrastructure, and funding for livestock genetic improvement programmes, as well as enabling animal breeding policies. A joint genetic evaluation of Holstein-Friesian using pooled data from Kenya and South Africa was piloted. The pilot analysis yielded higher accuracy of prediction of breeding values, pointing to possibility of higher genetic gains that could be achieved and demonstrating the potential power of multi-country evaluations: Kenya benefited on the 305-days milk yield and the age at first calving and South Africa on the age at first calving and the first calving interval. The findings from this study will help in developing harmonized protocols for animal identification, livestock data recording, and genetic evaluations (both national and across-countries) as well as in designing subsequent capacity building and training programmes for animal breeders and livestock farmers in Africa. National governments need to put in place enabling policies, the necessary infrastructure and funding for national and across country collaborations for a joint genetic evaluation which will revolutionize the livestock genetic improvement in Africa.

Genes and models for estimating genetic parameters for heat tolerance in dairy cattle.

Dairy cattle are highly susceptible to heat stress. Heat stress causes a decline in milk yield, reduced dry matter intake, reduced fertility rates, and alteration of physiological traits (e.g., respiration rate, rectal temperature, heart rates, pulse rates, panting score, sweating rates, and drooling score) and other biomarkers (oxidative heat stress biomarkers and stress response genes). Considering the significant effect of global warming on dairy cattle farming, coupled with the aim to reduce income losses of dairy cattle farmers and improve production under hot environment, there is a need to develop heat tolerant dairy cattle that can grow, reproduce and produce milk reasonably under the changing global climate and increasing temperature. The identification of heat tolerant dairy cattle is an alternative strategy for breeding thermotolerant dairy cattle for changing climatic conditions. This review synthesizes information pertaining to quantitative genetic models that have been applied to estimate genetic parameters for heat tolerance and relationship between measures of heat tolerance and production and reproductive performance traits in dairy cattle. Moreover, the review identified the genes that have been shown to influence heat tolerance in dairy cattle and evaluated the possibility of using them in genomic selection programmes. Combining genomics information with environmental, physiological, and production parameters information is a crucial strategy to understand the mechanisms of heat tolerance while breeding heat tolerant dairy cattle adapted to future climatic conditions. Thus, selection for thermotolerant dairy cattle is feasible.

Potential for quantifying general environmental resilience of dairy cattle in sub-Saharan Africa using deviations in milk yield.

Introduction: Genetic improvement of general resilience of dairy cattle is deemed as a part of the solution to low dairy productivity and poor cattle adaptability in sub-Saharan Africa (SSA). While indicators of general resilience have been proposed and evaluated in other regions, their applicability in SSA remains unexplored. This study sought to test the viability of utilizing log-transformed variance (LnVar), autocorrelation (rauto), and skewness (Skew) of deviations in milk yield as indicators of general resilience of dairy cows performing in the tropical environment of Kenya. Methods: Test-day milk yield records of 2,670 first-parity cows performing in three distinct agroecological zones of Kenya were used. To predict expected milk yield, quantile regression was used to model lactation curve for each cow. Subsequently, resilience indicators were defined based on actual and standardized deviations of observed milk yield from the expected milk yield. The genetic parameters of these indicators were estimated, and their associations with longevity and average test-day milk yield were examined. Results: All indicators were heritable except skewness of actual and standardized deviation. The log-transformed variance of actual (LnVar1) and standardized (LnVar2) deviations had the highest heritabilities of 0.19 ± 0.04 and 0.17 ± 0.04, respectively. Auto-correlation of actual (rauto1) and standardized (rauto2) deviations had heritabilities of 0.05 ± 0.03 and 0.07 ± 0.03, respectively. Weak to moderate genetic correlations were observed among resilience indicators. Both rauto and Skew indicators had negligible genetic correlations with both longevity and average test-day milk yield. LnVar1 and LnVar2 were genetically associated with better longevity (rg = -0.47 ± 0.26 and -0.49 ± 0.26, respectively). Whereas LnVar1 suggested that resilient animals produce lower average test-day milk yield, LnVar2 revealed a genetic association between resilience and higher average test-day milk yield. Discussion: Log transformed variance of deviations in milk yield holds a significant potential as a robust resilience indicator for dairy animals performing in SSA. Moreover, standardized as opposed to actual deviations should be employed in defining resilience indicators because the resultant indicator does not inaccurately infer that low-producing animals are inherently resilient. This study offers an opportunity for enhancing the productivity of dairy cattle performing in SSA through selective breeding for resilience to environmental stressors.

From a documented past of the Jersey breed in Africa to a profit index linked future.

The paper reports on the prevalence and performance of the Jersey cattle breed in Africa, highlighting its geographic distribution and describing the reported performance and other related characteristics from the early 1900s to the present day. The review examines the contribution of Jersey cattle in increasing the volume and efficiency of milk production across the continent. Data relating to the Jersey cattle breed has been reported in more than 30 African countries based on available material published between 1964 and 2020. A key encompassing parameter of any reference was a well-described consideration of the Jersey cattle breed (as pure or crossbred with other exotic and/or indigenous breeds) with reported performance within a variety of production systems and agro-ecologies in Africa. The main focus was on breed and performance parameters, breed types, percentage of different breed types in specific environments, reproduction method and fertility; survival and longevity; disease incidence; and production efficiency metrics such as: feed efficiency (milk unit per dry matter intake, DMI) and milk yield (MY) per unit of body weight (BW). The main performance descriptors identified were based on observations on resilience under both abiotic (heat, nutrition) and biotic (incidences of pests and diseases) stressors, milk production, BW, nutrition and utilisation of feed resources. From the literature consulted, we grouped key dairy cattle performance characteristics reported in each country under the following areas to aid comparisons; a. Milk production (Milk nutrient value, daily MY, lifetime MY and annual MY); b. Fertility traits and AFC; c. Survival and longevity, d. Production efficiency (Feed efficiency, milk per unit BW and milk per unit DMI and e. Disease incidences. Results of the review showed that the smaller stature and lower maintenance nutrient requirements of the Jersey breed means that it is better suited to tolerate the tropical production conditions in the African small-scale dairy farming sector. Detailed analyses on MY and survival showed that Jersey crosses with exotic and African indigenous breeds performed better than purebred cattle with strong evidence to support the suitability of the Jersey breed in crossbreeding with indigenous breeds for use in smallholder production systems.

Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance.

BACKGROUND: Heat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock production, reproduction, health, and well-being. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were combined with a standard 50k single nucleotide polymorphism (SNP) panel used by the dairy industry. METHODS: Over 40,000 dairy cattle with genotype and phenotype data were analysed. The phenotypes used to measure an individual's heat tolerance were defined as the rate of decline in milk production traits with rising temperature and humidity. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance. The prioritised sequence variants were the most significant SNPs passing a GWAS p-value threshold selected based on sliding 100-kb windows along each chromosome. We used a bull reference set to develop the genomic prediction equations, which were then validated in an independent set of Holstein, Jersey, and crossbred cows. Prediction analyses were performed using the BayesR, BayesRC, and GBLUP methods. RESULTS: The accuracy of genomic prediction for heat tolerance improved by up to 0.07, 0.05, and 0.10 units in Holstein, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holstein cows were added to the 50k SNP panel. However, in some scenarios, the prediction accuracy decreased unexpectedly with the largest drop of - 0.10 units for the heat tolerance fat yield trait observed in Jersey cows when 50k plus pre-selected SNPs from Holstein cows were used. Using pre-selected SNPs discovered on a combined set of Holstein and Jersey cows generally improved the accuracy, especially in the Jersey validation. In addition, combining Holstein and Jersey bulls in the reference set generally improved prediction accuracy in most scenarios compared to using only Holstein bulls as the reference set. CONCLUSIONS: Informative sequence markers can be prioritised to improve the genomic prediction of heat tolerance in different breeds. In addition to providing biological insight, these variants could also have a direct application for developing customized SNP arrays or can be used via imputation in current industry SNP panels.

Modeling genetic components of hatch of fertile in broiler breeders.

Reproductive efficiency such as fertility and hatch of fertile (HoF) are of economic importance and concern to breeding companies becaue of their effects on chick output. Similar to other traits of economic importance in poultry breeding, the rate of response for HoF is largely dependent on the use of an appropriate model for evaluating the trait. Therefore, the objectives of this study were to estimate genetic parameters from cumulative, repeatability, fixed regression, random regression, and multitrait models for HoF from a pure-line broiler breeder. The data available for this study consisted of weekly HoF records from 11,729 hens with a total pedigree record of 38,260. Estimates of heritability from the various models ranged from 0.04 to 0.22 with the highest estimate obtained from the cumulative model and the lowest from the repeatability model. Responses to selection estimated for the different models ranged from 0.03 to 0.08% gain per year of the phenotypic mean. In general, the cumulative and the repeatability models underestimated response to selection. The multitrait and random regression models gave similar results for response to selection at 0.08 percentage change in phenotypic mean. In conclusion, the cumulative model is not optimal for modeling HoF, and likewise, the repeatability model. The random regression and multitrait models should be considered instead as they offered a higher response to selection. However, if a multitrait analysis is to be considered, it is recommended to split up the production period in such a way as to avoid computational constraints due to overparameterization.

A Random Regression Model Based on a Single-Step Method for Improving the Genomic Prediction Accuracy of Residual Feed Intake in Pigs.

Residual feed intake (RFI) is considered as a measurement of feed efficiency, which is greatly related to the growth performance in pigs. Daily feeding records can be obtained from automatic feeders. In general, RFI is usually calculated from the total measurement records during the whole test period. This measurement cannot reflect genetic changes in different growth periods during the test. A random regression model (RRM) provides a method to model such type of longitudinal data. To improve the accuracy of genetic prediction for RFI, the RRM and regular animal models were applied in this study, and their prediction performances were compared. Both traditional pedigree-based relationship matrix (A matrix) and pedigree and genomic information-based relationship matrix (H matrix) were applied for these two models. The results showed that, the prediction accuracy of the RRM was higher than that of the animal model, increasing 24.2% with A matrix and 40.9% with H matrix. Furthermore, genomic information constantly improved the accuracy of evaluation under each evaluation model. In conclusion, longitudinal traits such as RFI can describe feed efficiency better, and the RRM with both pedigree and genetic information was superior to the animal model. These results provide a feasible method of genomic prediction using longitudinal data in animal breeding.

Genomic evaluations using data recorded on smallholder dairy farms in low- to middle-income countries.

Breeding has increased genetic gain for dairy cattle in advanced economies but has had limited success in improving dairy cattle in low- to middle-income countries (LMIC). Genetic evaluations are a central component of delivering genetic gain, because they separate the genetic and environmental effects of animals' phenotypes. Genetic evaluations have been successful in advanced economies because of large data sets and strong genetic connectedness, provided by the widespread use of artificial insemination (AI) and accurate recording of pedigree information. In smallholder dairy production systems of many LMICs, the limited use of AI and small herd sizes results in a data structure with insufficient genetic connectedness between herds to facilitate genetic evaluations based on pedigree. Genomic information keeps track of shared haplotypes rather than shared relatives captured by pedigree records. Therefore, genomic information could capture "hidden" genetic relationships, that are not captured by pedigree information, to strengthen genetic connectedness in LMIC smallholder dairy data sets. This study's objective was to use simulation to quantify the power of genomic information to enable genetic evaluation using LMIC smallholder dairy data sets. The results from this study show that (1) genetic evaluations using genomic information were more accurate than those using pedigree information in populations with a high effective population size and weak genetic connectedness; and (2) genetic evaluations modeling herd as a random effect had higher or equal accuracy than those modeling herd as a fixed effect. This demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in situ records collected from herds of ≤4 cows. The establishment of routine genomic evaluations could allow the development of LMIC breeding programs comprising an informal set of nucleus animals distributed across many small herds within the target environment. These nucleus animals could be used for genetic evaluation, and the best animals could be disseminated to participating smallholder dairy farms. Together, this could increase the productivity, profitability, and sustainability of LMIC smallholder dairy production systems.

Ancestral Haplotype Mapping for GWAS and Detection of Signatures of Selection in Admixed Dairy Cattle of Kenya.

Understanding the genetic structure of adaptation and productivity in challenging environments is necessary for designing breeding programs that suit such conditions. Crossbred dairy cattle in East Africa resulting from over 60 years of crossing exotic dairy breeds with indigenous cattle plus inter se matings form a highly variable admixed population. This population has been subject to natural selection in response to environmental stresses, such as harsh climate, low-quality feeds, poor management, and strong disease challenge. Here, we combine two complementary sets of analyses, genome-wide association (GWA) and signatures of selection (SoS), to identify genomic regions that contribute to variation in milk yield and/or contribute to adaptation in admixed dairy cattle of Kenya. Our GWA separates SNP effects due to ancestral origin of alleles from effects due to within-population linkage disequilibrium. The results indicate that many genomic regions contributed to the high milk production potential of modern dairy breeds with no region having an exceptional effect. For SoS, we used two haplotype-based tests to compare haplotype length variation within admixed and between admixed and East African Shorthorn Zebu cattle populations. The integrated haplotype score (iHS) analysis identified 16 candidate regions for positive selection in the admixed cattle while the between population Rsb test detected 24 divergently selected regions in the admixed cattle compared to East African Shorthorn Zebu. We compare the results from GWA and SoS in an attempt to validate the most significant SoS results. Only four candidate regions for SoS intersect with GWA regions using a low stringency test. The identified SoS candidate regions harbored genes in several enriched annotation clusters and overlapped with previously found QTLs and associations for different traits in cattle. If validated, the GWA and SoS results indicate potential for SNP-based genomic selection for genetic improvement of smallholder crossbred cattle.

Livestock Genomics for Developing Countries - African Examples in Practice.

African livestock breeds are numerous and diverse, and typically well adapted to the harsh environment conditions under which they perform. They have been used over centuries to provide livelihoods as well as food and nutritional security. However, African livestock systems are dynamic, with many small- and medium-scale systems transforming, to varying degrees, to become more profitable. In these systems the women and men livestock keepers are often seeking new livestock breeds or genotypes - typically those that increase household income through having enhanced productivity in comparison to traditional breeds while maintaining adaptedness. In recent years genomic approaches have started to be utilized in the identification and development of such breeds, and in this article we describe a number of examples to this end from sub-Saharan Africa. These comprise case studies on: (a) dairy cattle in Kenya and Senegal, as well as sheep in Ethiopia, where genomic approaches aided the identification of the most appropriate breed-type for the local productions systems; (b) a cross-breeding program for dairy cattle in East Africa incorporating genomic selection as well as other applications of genomics; (c) ongoing work toward creating a new cattle breed for East Africa that is both productive and resistant to trypanosomiasis; and (d) the use of African cattle as resource populations to identify genomic variants of economic or ecological significance, including a specific case where the discovery data was from a community based breeding program for small ruminants in Ethiopia. Lessons learnt from the various case studies are highlighted, and the concluding section of the paper gives recommendations for African livestock systems to increasingly capitalize on genomic technologies.

Performance Evaluation of Highly Admixed Tanzanian Smallholder Dairy Cattle Using SNP Derived Kinship Matrix.

The main purpose of this study was to understand the type of dairy cattle that can be optimally used by smallholder farmers in various production environments such that they will maximize their yields without increasing the level of inputs. Anecdotal evidence and previous research suggests that the optimal level of taurine inheritance in crossbred animals lies between 50 and 75% when considering total productivity in tropical management clusters. We set out to assess the relationship between breed composition and productivity for various smallholder production systems in Tanzania. We surveyed 654 smallholder dairy households over a 1-year period and grouped them into production clusters. Based on supplementary feeding, milk productivity and sale as well as household wealth status four clusters were described: low-feed-low-output subsistence, medium-feed-low-output subsistence, maize germ intensive semi-commercial and feed intensive commercial management clusters. About 839 crossbred cows were genotyped at approximately 150,000 single nucleotide polymorphism (SNP) loci and their breed composition determined. Percentage dairyness (proportion of genes from international dairy breeds) was estimated through admixture analysis with Holstein, Friesian, Norwegian Red, Jersey, Guernsey, N'Dama, Gir, and Zebu as references. Four breed types were defined as RED-GUE (Norwegian Red/Friesian-Guernsey; Norwegian Red/Friesian-Jersey), RED-HOL (Norwegian Red/Friesian-Holstein), RED-Zebu (Norwegian Red/Friesian-Zebu), Zebu-RED (Zebu-Norwegian Red/Friesian) based on the combination of breeds that make up the top 76% breed composition. A fixed regression model using a genomic kinship matrix was used to analyze milk yield records. The fitted model accounted for year-month-test-date, parity, age, breed type and the production clusters as fixed effects in the model in addition to random effects of animal and permanent environment effect. Results suggested that RED-Zebu breed type with dairyness between 75 and 85% is the most appropriate for a majority of smallholder management clusters. Additionally, for farmers in the feed intensive management group, animals with a Holstein genetic background with at least 75% dairy composition were the best performing. These results indicate that matching breed type to production management group is central to maximizing productivity in smallholder systems. The findings from this study can serve as a basis to inform the development of the dairy sector in Tanzania and beyond.

Genome-wide diversity and demographic dynamics of Cameroon goats and their divergence from east African, north African, and Asian conspecifics.

Indigenous goats make significant contributions to Cameroon's national and local economy, but little effort has been devoted to identifying the populations. Here, we assessed the genetic diversity and demographic dynamics of Cameroon goat populations using mitochondrial DNA (two populations) and autosomal markers (four populations) generated with the Caprine 50K SNP chip. To infer genetic relationships at continental and global level, genotype data on six goat populations from Ethiopia and one population each from Egypt, Morocco, Iran, and China were included in the analysis. The mtDNA analysis revealed 83 haplotypes, all belonging to haplogroup A, in Cameroon goats. Four haplotypes were shared between goats found in Cameroon, Mozambique, Namibia, Zimbabwe, Kenya, and Ethiopia. Analysis of autosomal SNPs in Cameroon goats revealed the lowest HO (0.335±0.13) and HE (0.352±0.15) in the North-west Highland and Central Highland populations, respectively. Overall, the highest HO (0.401±0.12) and HE (0.422±0.12) were found for Barki and Iranian goats, respectively. Barki goats had the highest average MAF, while Central Highland Cameroon goats had the lowest. Overall, Cameroon goats demonstrated high FIS. AMOVA revealed that 13.29% of the variation was explained by genetic differences between the six population groups. Low average FST (0.01) suggests intermixing among Cameroon goats. All measures indicated that Cameroon goats are closer to Moroccan goats than to other goat populations. PCA and STRUCTURE analyses poorly differentiated the Cameroon goats, as did genetic distance, Neighbor-Net network, and neighbor-joining tree analyses. The haplotype analysis of mtDNA showed the initial dispersion of goats to Cameroon and central Africa from north-east Africa following the Nile Delta. Whereas, the approximate Bayesian computation indicated Cameroon goats were separated from Moroccan goats after 506 generations in later times (~1518 YA), as supported by the phylogenetic net-work and admixture outputs. Overall, indigenous goats in Cameroon show weak phylogenetic structure, suggesting either extensive intermixing.