Facilitating ACGG partnerships and institutional engagements for effective implementation: An update

<p>Asterisk – significantly above chance level (p<0.05, chance level is 50%).</p

The effects of severe myopia on the properties of sampling units in peripheral retina

Significance: Poor peripheral visual acuity in myopia may reflect, in part, photoreceptor misalignment with the exit pupil of the eye. We speculate that if such misalignment causes sufficient visual deprivation and/or disrupts retinal feedback processes, it may influence eye growth itself. Purpose: It is known that myopic eyes have a reduced peripheral resolution acuity relative to emmetropic eyes, though it remains unclear how mechanical stretching of the retina in myopia impacts on peripheral visual performance. Our aim was to determine how retinal stretching affects the properties of sampling units in peripheral vision. Methods: Three-dimensional magnetic resonance imaging provided a depiction in vivo of ocular shape, allowing the inter-eye ratio of retinal image surface areas and the relative alignment of surfaces to be determined in our observer, who was unique in having severe myopia in the right eye (~21D) but only modest myopia in the left (~3D). Visual performance was assessed for the detection and direction discrimination of drifting sinusoids positioned 40º in the temporal retina. Applying the sampling theorem to our measures, we estimated the density and cut-off frequency of the underlying sampling units. Results: The retinal image surface area of the right eye was 40% larger than that of the left, and was rotated 8.9º anticlockwise relative to the left eye’s image surface. In agreement with a linear stretch model of myopia, the sampling density of the right eye was reduced by approximately the same ratio as that predicted from the inter-eye MRI data, namely 1.18. However, the cut-off frequency (cycles/mm) of the right eye was approximately half that of the left, a reduction that cannot be explained solely by a linear areal expansion of retinal sampling units. Conclusions: Poor peripheral acuity in severe myopia may be caused, at least in part, by receptoral misalignment with the exit pupil

Use of High Density Single Nucleotide Polymorphism (SNP) Arrays to Assess Genetic Diversity and Population Structure of Dairy Cattle in Smallholder Dairy Systems: The Case of Girinka Programme in Rwanda

In most smallholder dairy programmes, farmers are not fully benefitting from the genetic potential of their dairy cows. This is in part due to the mismatch between the available genotypes and the environment, including management, in which the animals perform. With sparse performance and pedigree records in smallholder dairy farms, the true degree of baseline genetic variability and breed composition is not known and hence rendering any genetic improvement initiative difficult to implement. Using the Girinka programme of Rwanda as an exemplar, the current study was aimed at better understanding the genetic diversity and population structure of dairy cattle in the smallholder dairy farm set up. Further, the association between farmer self-reported cow genotypes and genetically determined genotypes was investigated. The average heterozygosity estimates were highest (0.38 ± 0.13) for Rwandan dairy cattle and lowest for Gir and N’Dama (0.18 ± 0.19 and 0.25 ± 0.20, respectively). Systematic characterization of the genetic variation and diversity available may inform the formulation of sustainable improvement strategies such as targeting and matching the genotype of cows to productivity goals and farmer profile and hence reducing the negative impact of genotype by environment interaction

Signatures of Selection in Admixed Dairy Cattle in Tanzania

Multiple studies have investigated selection signatures in domestic cattle and other species. However, there is a dearth of information about the response to selection in genomes of highly admixed crossbred cattle in relation to production and adaptation to tropical environments. In this study, we evaluated 839 admixed crossbred cows sampled from two major dairy regions in Tanzania namely Rungwe and Lushoto districts, in order to understand their genetic architecture and detect genomic regions showing preferential selection. Animals were genotyped at 150,000 SNP loci using the Geneseek Genomic Profiler (GGP) High Density (HD) SNP array. Population structure analysis showed a large within-population genetic diversity in the study animals with a high degree of variation in admixture ranging between 7 and 100% taurine genes (dairyness) of mostly Holstein and Friesian ancestry. We explored evidence of selection signatures using three statistical methods (iHS, XP-EHH, and pcadapt). Selection signature analysis identified 108 candidate selection regions in the study population. Annotation of these regions yielded interesting genes potentially under strong positive selection including ABCG2, ABCC2, XKR4, LYN, TGS1, TOX, HERC6, KIT, PLAG1, CHCHD7, NCAPG, and LCORL that are involved in multiple biological pathways underlying production and adaptation processes. Several candidate selection regions showed an excess of African taurine ancestral allele dosage. Our results provide further useful insight into potential selective sweeps in the genome of admixed cattle with possible adaptive and productive importance. Further investigations will be necessary to better characterize these candidate regions with respect to their functional significance to tropical adaptations for dairy cattle

Genetic diversity, breed composition and admixture of Kenyan domestic pigs

<div><p>The genetic diversity of African pigs, whether domestic or wild has not been widely studied and there is very limited published information available. Available data suggests that African domestic pigs originate from different domestication centers as opposed to international commercial breeds. We evaluated two domestic pig populations in Western Kenya, in order to characterize the genetic diversity, breed composition and admixture of the pigs in an area known to be endemic for African swine fever (ASF). One of the reasons for characterizing these specific populations is the fact that a proportion of indigenous pigs have tested ASF virus (ASFv) positive but do not present with clinical symptoms of disease indicating some form of tolerance to infection. Pigs were genotyped using either the porcine SNP60 or SNP80 chip. Village pigs were sourced from Busia and Homabay counties in Kenya. Because bush pigs (<i>Potamochoerus larvatus</i>) and warthogs (<i>Phacochoerus spp</i>.) are known to be tolerant to ASFv infection (exhibiting no clinical symptoms despite infection), they were included in the study to assess whether domestic pigs have similar genomic signatures. Additionally, samples representing European wild boar and international commercial breeds were included as references, given their potential contribution to the genetic make-up of the target domestic populations. The data indicate that village pigs in Busia are a non-homogenous admixed population with significant introgression of genes from international commercial breeds. Pigs from Homabay by contrast, represent a homogenous population with a “local indigenous’ composition that is distinct from the international breeds, and clusters more closely with the European wild boar than African wild pigs. Interestingly, village pigs from Busia that tested negative by PCR for ASFv genotype IX, had significantly higher local ancestry (>54%) compared to those testing positive, which contained more commercial breed gene introgression. This may have implication for breed selection and utilization in ASF endemic areas. A genome wide scan detected several regions under preferential selection with signatures for pigs from Busia and Homabay being very distinct. Additionally, there was no similarity in specific genes under selection between the wild pigs and domestic pigs despite having some broad areas under similar selection signatures. These results provide a basis to explore possible genetic determinants underlying tolerance to infection by ASFv genotypes and suggests multiple pathways for genetically mediated ASFv tolerance given the diversity of selection signatures observed among the populations studied.</p></div

A novel use of high density SNP assays to optimize choice of different crossbred dairy cattle genotypes in smallholder systems in East Africa

We present use of SNP technologies to obtain for the first time, rapid, large-scale, in situ estimates of performance of crossbred cows in smallholder herds. Compared to historical approaches, our approach allowed optimum crossbreed choices to be determined more rapidly and without question about the relevance of the environment. High-density SNP assays were used to estimate breed composition for a monitored population comprising 1292 cows from 610 smallholder farms in Kenya. The achieved milk yields are much lower than generally assumed and lactation curves are very flat out to 400 days. In poor production environments, lower grade crosses (containing <60% exotic dairy alleles) had the same yield as high grade crosses. Allowing for the larger size and maintenance requirements of high grade exotics, lower grade exotics will be the most economically productive animals in these environments

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

Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.

This research article published by Springer Nature Switzerland AG., 2020This study is concerned with developing predictive models using machine learning techniques to be used in identifying factors that influence farmers' decisions, predict farmers' decisions, and forecast farmers' demands relating to breeding service. The data used to develop the models comes from a survey of small-scale dairy farmers from Tanzania (n = 3500 farmers), Kenya (n = 6190 farmers), Ethiopia (n = 4920 farmers), and Uganda (n = 5390 farmers) and more than 120 variables were identified to influence breeding decisions. Feature engineering process was used to reduce the number of variables to a practical level and to identify the most influential ones. Three algorithms were used for feature selection, namely: logistic regression, random forest, and Boruta. Subsequently, six predictive models, using features selected by feature selection method, were tested for each country-neural network, logistic regression, K-nearest neighbor, decision tree, random forest, and Gaussian mixture model. A combination of decision tree and random forest algorithms was used to develop the final models. Each country model showed high predictive power (up to 93%) and are ready for practical use. The use of ML techniques assisted in identifying the key factors that influence the adoption of breeding method that can be taken and prioritized to improve the dairy sector among countries. Moreover, it provided various alternatives for policymakers to compare the consequences of different courses of action which can assist in determining which alternative at any particular choice point had a high probability to succeed, given the information and alternatives pertinent to the breeding decision. Also, through the use of ML, results to the identification of different clusters of farmers, who were classified based on their farm, and farmers' characteristics, i.e., farm location, feeding system, animal husbandry practices, etc. This information had significant value to decision-makers in finding the appropriate intervention for a particular cluster of farmers. In the future, such predictive models will assist decision-makers in planning and managing resources by allocating breeding services and capabilities where they would be most in demand