The African Goat Improvement Network: a scientific group empowering smallholder farmers.

The African Goat Improvement Network (AGIN) is a collaborative group of scientists focused on genetic improvement of goats in small holder communities across the African continent. The group emerged from a series of workshops focused on enhancing goat productivity and sustainability. Discussions began in 2011 at the inaugural workshop held in Nairobi, Kenya. The goals of this diverse group were to: improve indigenous goat production in Africa; characterize existing goat populations and to facilitate germplasm preservation where appropriate; and to genomic approaches to better understand adaptation. The long-term goal was to develop cost-effective strategies to apply genomics to improve productivity of small holder farmers without sacrificing adaptation. Genome-wide information on genetic variation enabled genetic diversity studies, facilitated improved germplasm preservation decisions, and provided information necessary to initiate large scale genetic improvement programs. These improvements were partially implemented through a series of community-based breeding programs that engaged and empowered local small farmers, especially women, to promote sustainability of the production system. As with many international collaborative efforts, the AGIN work serves as a platform for human capacity development. This paper chronicles the evolution of the collaborative approach leading to the current AGIN organization and describes how it builds capacity for sustained research and development long after the initial program funds are gone. It is unique in its effectiveness for simultaneous, multi-level capacity building for researchers, students, farmers and communities, and local and regional government officials. The positive impact of AGIN capacity building has been felt by participants from developing, as well as developed country partners.

Using the community-based breeding program (CBBP) model as a collaborative platform to develop the African Goat Improvement Network-Image collection protocol (AGIN-ICP) with mobile technology for data collection and management of livestock phenotypes.

Introduction: The African Goat Improvement Network Image Collection Protocol (AGIN-ICP) is an accessible, easy to use, low-cost procedure to collect phenotypic data via digital images. The AGIN-ICP collects images to extract several phenotype measures including health status indicators (anemia status, age, and weight), body measurements, shapes, and coat color and pattern, from digital images taken with standard digital cameras or mobile devices. This strategy is to quickly survey, record, assess, analyze, and store these data for use in a wide variety of production and sampling conditions. Methods: The work was accomplished as part of the multinational African Goat Improvement Network (AGIN) collaborative and is presented here as a case study in the AGIN collaboration model and working directly with community-based breeding programs (CBBP). It was iteratively developed and tested over 3 years, in 12 countries with over 12,000 images taken. Results and discussion: The AGIN-ICP development is described, and field implementation and the quality of the resulting images for use in image analysis and phenotypic data extraction are iteratively assessed. Digital body measures were validated using the PreciseEdge Image Segmentation Algorithm (PE-ISA) and software showing strong manual to digital body measure Pearson correlation coefficients of height, length, and girth measures (0.931, 0.943, 0.893) respectively. It is critical to note that while none of the very detailed tasks in the AGIN-ICP described here is difficult, every single one of them is even easier to accidentally omit, and the impact of such a mistake could render a sample image, a sampling day's images, or even an entire sampling trip's images difficult or unusable for extracting digital phenotypes. Coupled with tissue sampling and genomic testing, it may be useful in the effort to identify and conserve important animal genetic resources and in CBBP genetic improvement programs by providing reliably measured phenotypes with modest cost. Potential users include farmers, animal husbandry officials, veterinarians, regional government or other public health officials, researchers, and others. Based on these results, a final AGIN-ICP is presented, optimizing the costs, ease, and speed of field implementation of the collection method without compromising the quality of the image data collection.

How to succeed in implementing community-based breeding programs: Lessons from the field in Eastern and Southern Africa.

Breeding programs involving either centralized nucleus schemes and/or importation of exotic germplasm for crossbreeding were not successful and sustainable in most Africa countries. Community-based breeding programs (CBBPs) are now suggested as alternatives that aim to improve local breeds and concurrently conserve them. Community-based breeding program is unique in that it involves the different actors from the initial phase of design up until implementation of the programs, gives farmers the knowledge, skills and support they need to continue making improvements long into the future and is suitable for low input systems. In Ethiopia, we piloted CBBPs in sheep and goats, and the results show that they are technically feasible to implement, generate genetic gains in breeding goal traits and result in socio-economic impact. In Malawi, CBBPs were piloted in local goats, and results showed substantial gain in production traits of growth and carcass yields. CBBPs are currently being integrated into goat pass-on programs in few NGOs and is out-scaled to local pig production. Impressive results have also been generated from pilot CBBPs in Tanzania. From experiential monitoring and learning, their success depends on the following: 1) identification of the right beneficiaries; 2) clear framework for dissemination of improved genetics and an up/out scaling strategy; 3) institutional arrangements including establishment of breeders' cooperatives to support functionality and sustainability; 4) capacity development of the different actors on animal husbandry, breeding practices, breeding value estimation and sound financial management; 5) easy to use mobile applications for data collection and management; 6) long-term technical support mainly in data management, analysis and feedback of estimated breeding values from committed and accessible technical staff; 7) complementary services including disease prevention and control, proper feeding, and market linkages for improved genotypes and non-selected counterparts; 8) a system for certification of breeding rams/bucks to ensure quality control; 9) periodic program evaluation and impact assessment; and 10) flexibility in the implementation of the programs. Lessons relating to technical, institutional, community dynamics and the innovative approaches followed are discussed.

Detection of copy number variants in African goats using whole genome sequence data.

BACKGROUND: Copy number variations (CNV) are a significant source of variation in the genome and are therefore essential to the understanding of genetic characterization. The aim of this study was to develop a fine-scaled copy number variation map for African goats. We used sequence data from multiple breeds and from multiple African countries. RESULTS: A total of 253,553 CNV (244,876 deletions and 8677 duplications) were identified, corresponding to an overall average of 1393 CNV per animal. The mean CNV length was 3.3 kb, with a median of 1.3 kb. There was substantial differentiation between the populations for some CNV, suggestive of the effect of population-specific selective pressures. A total of 6231 global CNV regions (CNVR) were found across all animals, representing 59.2 Mb (2.4%) of the goat genome. About 1.6% of the CNVR were present in all 34 breeds and 28.7% were present in all 5 geographical areas across Africa, where animals had been sampled. The CNVR had genes that were highly enriched in important biological functions, molecular functions, and cellular components including retrograde endocannabinoid signaling, glutamatergic synapse and circadian entrainment. CONCLUSIONS: This study presents the first fine CNV map of African goat based on WGS data and adds to the growing body of knowledge on the genetic characterization of goats.

Timing and Extent of Inbreeding in African Goats.

Genetic characterization of African goats is one of the current priorities in the improvement of goats in the continent. This study contributes to the characterization effort by determining the levels and number of generations to common ancestors ("age") associated with inbreeding in African goat breeds and identifies regions that contain copy number variation mistyped as being homozygous. Illumina 50k single nucleotide polymorphism genotype data for 608 goats from 31 breeds were used to compute the level and age of inbreeding at both local (marker) and global levels (FG) using a model-based approach based on a hidden Markov model. Runs of homozygosity (ROH) segments detected using the Viterbi algorithm led to ROH-based inbreeding coefficients for all ROH (FROH) and for ROH longer than 2 Mb (FROH > 2Mb). Some of the genomic regions identified as having ROH are likely to be hemizygous regions (copy number deletions) mistyped as homozygous regions. Although the proportion of these miscalled ROH is small and does not substantially affect estimates of levels of inbreeding for individual animals, the inbreeding metrics were adjusted by removing these regions from the ROH. All the inbreeding metrics varied widely across breeds, with overall means of 0.0408, 0.0370, and 0.0691 and medians of 0.0125, 0.0098, and 0.0366 for FROH, FROH > 2Mb, and FG, respectively. Several breeds (including Menabe and Sofia from Madagascar) had high proportions of recent inbreeding, while Small East African, Ethiopian, and most of the West African breeds (including West African Dwarf) had more ancient inbreeding.

Misidentification of runs of homozygosity islands in cattle caused by interference with copy number variation or large intermarker distances.

BACKGROUND: Runs of homozygosity (ROH) islands are stretches of homozygous sequence in the genome of a large proportion of individuals in a population. Algorithms for the detection of ROH depend on the similarity of haplotypes. Coverage gaps and copy number variants (CNV) may result in incorrect identification of such similarity, leading to the detection of ROH islands where none exists. Misidentified hemizygous regions will also appear as homozygous based on sequence variation alone. Our aim was to identify ROH islands influenced by marker coverage gaps or CNV, using Illumina BovineHD BeadChip (777 K) single nucleotide polymorphism (SNP) data for Austrian Brown Swiss, Tyrol Grey and Pinzgauer cattle. METHODS: ROH were detected using clustering, and ROH islands were determined from population inbreeding levels for each marker. CNV were detected using a multivariate copy number analysis method and a hidden Markov model. SNP coverage gaps were defined as genomic regions with intermarker distances on average longer than 9.24 kb. ROH islands that overlapped CNV regions (CNVR) or SNP coverage gaps were considered as potential artefacts. Permutation tests were used to determine if overlaps between CNVR with copy losses and ROH islands were due to chance. Diversity of the haplotypes in the ROH islands was assessed by haplotype analyses. RESULTS: In Brown Swiss, Tyrol Grey and Pinzgauer, we identified 13, 22, and 24 ROH islands covering 26.6, 389.0 and 35.8 Mb, respectively, and we detected 30, 50 and 71 CNVR derived from CNV by using both algorithms, respectively. Overlaps between ROH islands, CNVR or coverage gaps occurred for 7, 14 and 16 ROH islands, respectively. About 37, 44 and 52% of the ROH islands coverage in Brown Swiss, Tyrol Grey and Pinzgauer, respectively, were affected by copy loss. Intersections between ROH islands and CNVR were small, but significantly larger compared to ROH islands at random locations across the genome, implying an association between ROH islands and CNVR. Haplotype diversity for reliable ROH islands was lower than for ROH islands that intersected with copy loss CNVR. CONCLUSIONS: Our findings show that a significant proportion of the ROH islands in the bovine genome are artefacts due to CNV or SNP coverage gaps.

Milk production performance of Friesian-Holstein cows fed diets containing Medicago sativa, Centrosema pubescens, or groundnut haulms (Arachis hypogaea).

To investigate the effect of feeding dairy cows diets containing lucerne hay, centrosema hay, and groundnut haulms (crop residue) on dry matter intake (DMI), milk yield, and milk composition, nine multiparous Friesian-Holstein cows in their mid-lactation stage were used in a 3 × 3 crossover design replicated three times. Dairy cows fed lucerne hay had significantly (p < 0.001) higher DMI than dairy cows fed centrosema hay. DMI for cows fed groundnut haulms and lucerne hay was not significantly different. Daily milk yield for dairy cows fed diet containing lucerne hay was significantly (p < 0.01) higher than that for dairy cows fed diets containing groundnut haulms or centrosema hay. Milk composition and body condition scores of the cows were not significantly affected by either lucerne hay, groundnut haulms, or centrosema hay. Overall, the results in this study indicated that feeding dairy cows diets containing lucerne hay increased milk yield.

Characterisation of smallholder dairy production systems using animal welfare and milk quality.

The aim of the current study was to characterise and evaluate production system of smallholder dairy farmers using an index based on combined score of animal welfare and milk quality. Farms were grouped into three categories, tier 1, tier 2 and tier 3. To test the robustness of the characterisation, milk yield (MY), calving interval (CI) and body condition scores (BCS) were used. In the study area, the majority (66.3%) of smallholder dairy farmers practiced cut-and-carry as compared to 15.3% who grazed their cows. The rest combined cut-and-carry and grazing. Cows of farmers in tier 1 had the lowest mean MY (5.4 kg/day, SE = 0.4), lowest mean BCS (2.1 kg/day, SE = 0.09) and longest mean CI (603 days, SE = 27) than farmers in tier 3, mean MY (10.8 kg/day, SE = 0.6), mean BCS (2.6, SE = 0.06) and mean CI (404 days, SE = 17). The study demonstrated that a simple and yet novel method based on farm level indicators can be developed and could assist to timely identify specific problems on the farm.