Mapping genomic adaptation to environmental heterogeneity in Indian native goat populations through landscape genomics.

The potential of landscape genomics approach in the characterization of adaptive genetic diversity in indigenous goat genetic resources: A South African perspective

The survey was conducted before the war (before October 2020) in Tigray Regional State, Ethiopia. Indigenous (102 Begait, 106 Hassan and 181 Arado) goat respondents and sheep respondents (126 Begait, 37 Rutanna and 90 Arado) were randomly involved in the head to head interview. Statistical Package for Social Sciences, index ranking and mathematical formulas were used for data analysis. The objective of this survey was to assess flock dynamics, production challenges and opportunities of indigenous goat and sheep populations. Very limited numbers of goat respondents (17% of Begait, 1% of Hassan and 6% of Arado) and sheep respondents (5% of Begait and 7% of Arado) were female headed households. Goat respondents of about 42% of Begait, 29% of Hassan and 55% of Arado were illiterate whereas 25% of Begait, 43% of Hassan and 26% of Arado goat respondents, and 48% of Begait and 57% of Rutanna sheep respondents attended lower primary school. About half (50%) of the Arado sheep respondents were illiterate. The indigenous goat (Begait, Hassan and Arado) and sheep (Begait, Rutanna and Arado) populations were kept under low input extensive production system. Economic contributions ranking of the respondents indicated that goat, cattle and sheep were ranked first, second and third in goat survey whilst sheep, goats and cattle were ranked first, second and third in sheep survey, respectively. Indigenous goat respondents reported that the first, second, and third ranked income sources of the respondents were crop production, goat production, and cattle production, whereas crop production, sheep production, and goat production were the first, second, and third ranked income sources of the indigenous sheep respondents. The major entry in all the indigenous goat and sheep in 2017 production year was additions of newborns to the flocks. Sale and death were the first and second ranked exits in indigenous goats and sheep. Except Hassan goats (+0.1%), flock dynamics (%) in entries and exits of indigenous goat and sheep flocks were at decreasing rates which greatly damaged the economy of the respondents. Begait flock (-16.5%) and Arado flock (-16.7%) goat populations were highly decreased in 2017 production year compared to indigenous sheep flock populations. Respondents of indigenous goat (92% of Begait, 99% of Hassan and 99% of Arado) and sheep (all respondents-100%) reported production challenges and negatively affected productivity. Respondents of indigenous goat (87% of Begait, 80% of Hassan and 38% of Arado) and sheep (76% of Begait, 100% of Rutanna and 53% of Arado respondents) reported production opportunities which could enhance small ruminant productivity. The first, second and third ranked challenges in the indigenous goat and sheep were rangeland scarcity, diseases and lack of veterinary services, respectively. Rangeland scarcity directly negatively affected annual reproductive rate of the indigenous goat and sheep of the study areas. The first, second and third ranked diseases in both goat and sheep were shipping fever, Coenuruses and Contagious Caprine Pleuropneumonia, respectively. The first and second ranked external parasites in both goat and sheep were ticks and fleas whilst the third ranked external parasites in goats was sore mouth and in sheep was jigger flea infestation. Therefore, the diseases, external parasites and challenges should be taken in to account in the designing of genetic improvement of the indigenous goat and sheep of the study areas. Veterinary service centers should also be accessible to all indigenous goat and sheep producers in the study areas.

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.

Landscape genomics identifies how spatial and environmental factors structure the amount and distribution of genetic variation among populations. Landscape genomic analyses have been applied across diverse taxonomic groups and ecological settings, and are increasingly used to analyse datasets composed of large numbers of genomic markers and multiple environmental predictors. It is in this context that multivariate methods show their strengths. Redundancy analysis (RDA) is a constrained ordination that, in a landscape genomics framework, models linear relationships among environment predictors and genomic variation, effectively identifying covarying allele frequencies associated with the multivariate environment. RDA can be used at both individual and population levels, can include covariates to account for confounding factors and can be used to directly infer genotype–environment associations on the landscape. The modelling of both multivariate response and explanatory variables allows RDA to accommodate the genomic and environmental complexity found in nature, producing a powerful and efficient tool for landscape genomics. In this review, we outline the diverse uses of RDA in landscape genomics, including variable selection, variance partitioning, genotype–environment associations, and the calculation of adaptive indices and genomic offset. To illustrate these applications, we use a published dataset for lodgepole pine that includes genomic, phenotypic and environmental data. We provide an introduction to the statistical basis of RDA, a tutorial on its use and interpretation in landscape genomics applications, discuss limitations and provide guidelines to avoid misuse. This review and associated tutorial provide a comprehensive resource to the landscape genomics community to improve understanding of RDA as a modelling framework, and encourage the appropriate use of RDA across diverse landscape genomics applications. RDA is truly a Swiss Army Knife for landscape genomics: a multipurpose, adaptable and versatile approach to identifying, evaluating and forecasting relationships between genetic and environmental variation.

The sustainability of goat farming in marginal areas of southern Africa depends on local breeds that are adapted to specific agro-ecological conditions. Unimproved non-descript goats are the main genetic resources used for the development of commercial meat-type breeds of South Africa. Little is known about genetic diversity and the genetics of adaptation of these indigenous goat populations. This study investigated the genetic diversity, population structure and breed relations, linkage disequilibrium, effective population size and persistence of gametic phase in goat populations of South Africa. Three locally developed meat-type breeds of the Boer (n=33), Savanna (n=31), Kalahari Red (n=40), a feral breed of Tankwa (n=25) and unimproved non-descript village ecotypes (n=110) from four goat-producing provinces of the Eastern Cape, KwaZulu-Natal, Limpopo and North West were assessed using the Illumina Goat 50K SNP Bead Chip assay. The proportion of SNPs with minor allele frequencies >0.05 ranged from 84.22% in the Tankwa to 97.58% in the Xhosa ecotype, with a mean of 0.32±0.13 across populations. Principal components analysis, admixture and pairwise FST identified Tankwa as a genetically distinct population and supported clustering of the populations according to their historical origins. Genome-wide FST identified 101 markers potentially under positive selection in the Tankwa. Average linkage disequilibrium was highest in the Tankwa (r(2) =0.25±0.26) and lowest in the village ecotypes (r(2) range=0.09±0.12 to 0.11±0.14). We observed an effective population size of <150 for all populations 13 generations ago. The estimated correlations for all breed pairs were lower than 0.80 at marker distances >100kb with the exception of those in Savanna and Tswana populations. This study highlights the high level of genetic diversity in South African indigenous goats as well as the utility of the genome-wide SNP marker panels in genetic studies of these populations.

The Small East African (SEA) goat are widely distributed in different agro‐ecological zones of Tanzania. We report the genetic diversity, maternal origin, and phylogenetic relationship among the 12 Tanzanian indigenous goat populations, namely Fipa, Songwe, Tanga, Pwani, Iringa, Newala, Lindi, Gogo, Pare, Maasai, Sukuma, and Ujiji, based on the mitochondrial DNA (mtDNA) D‐loop. High haplotype (Hd = 0.9619–0.9945) and nucleotide (π = 0.0120–0.0162) diversities were observed from a total of 389 haplotypes. The majority of the haplotypes (n = 334) belonged to Haplogroup A which was consistent with the global scenario on the genetic pattern of maternal origin of all goat breeds in the world. Haplogroup G comprised of 45 haplotypes drawn from all populations except the Ujiji goat population while Haplogroup B with 10 haplotypes was dominated by Ujiji goats (41%). Tanzanian goats shared four haplotypes with the Kenyan goats and two with goats from South Africa, Namibia, and Mozambique. There was no sharing of haplotypes observed between individuals from Tanzanian goat populations with individuals from North or West Africa. The indigenous goats in Tanzania have high genetic diversity defined by 389 haplotypes and multiple maternal origins of haplogroup A, B, and G. There is a lot of intermixing and high genetic variation within populations which represent an abundant resource for selective breeding in the different agro‐ecological regions of the country.

Goats adapt to extreme temperatures, undernourishment, high altitudes, long walking distances, and long periods of drought [1],[2].Ethiopian indigenous goat populations have developed certain valuable genetic traits such as ability to perform better under low input condition and climatic stress, tolerance to infectious diseases and parasites as well as heat stresses [3],[4].The largest number of goats are hosted in Asia (55.45%) followed by Africa (38.66%) of the global goat population [5].Large numbers of goat populations dwell in the diverse agro-ecology and climatic zones of Ethiopia.The rural sedentary areas of Ethiopia were hosts for about 30.2 million [6] and 32.74 million heads of goats [7].Analysis of 15 microsatellite loci identified eight separate goat genetic entities which comprised of Arsi-Bale, Gumez, Keffa, Woyto-Guji, Abergalle, Afar, Highland goats, and eastern and south eastern goats [8].Approximately 27% of goat population size of Ethiopia were found in crop-livestock mixed farms in the highlands whilst 73% usually inhabit in arid and semi-arid lowland areas.Ethiopian indigenous goats are genetically less productive as compared to temperate breeds [9].Goats are valuable for nutrition and income, and major farming activity on vast areas of natural grasslands in regions where crop production is impracticable [10].Ethiopia ranked highest in goat population both in the African continent and the global level [11].Most indigenous goats are kept in small-scale traditional production systems in communal areas [12].Almost all of the goat populations in Ethiopia are managed by resource poor smallholder A B S T R A C TThe survey was conducted before the ignition (before October 2020) of the war in Tigray Regional State, Ethiopia.Goats are concentrated in the lowland areas of Ethiopia.Goats are multifunctional for the resource-poor-farmers of Ethiopia.The objective of the survey was to characterize the husbandry practices of indigenous goat populations.Statistical Package for Social Sciences (SPSS) software was used for data analysis.Questionnaire based interview was made in randomly selected (n=389) respondents of Begait, Hassan and Arado goats.Illiterate (45%) whilst 30% of the respondents attended lower primary school.Major livestock species holdings (TLU) were cattle (7.169.6),goats (3.633.9)and sheep (2.595.9).However, population trends of cattle (61%), sheep (29%) and goats (62%) were at decreasing state in the last ten years.Seasonal feed scarcity (78%) was critical problem because communal browsing area trend was at decreasing state (96%) in the last five years.Hence, dry season supplementation was practiced in 60% of the respondents.Water delivery method, water source, watering point distance and watering frequency per day in dry season were highly significant different (P<0.05).Goats go to water source (85%), and river (61%) was main water source.Goats travel a distance of 1 up to 5 Kilometers to water sources (54%), and goats drink water once a day during the dry season in 65% of the respondents.Respondents reported production opportunities (62%) and challenges (3%).Diseases (83%) and external parasites (87%) were major challenges, and external parasites affected (54%) goats in both dry and wet seasons.There was no veterinary clinic service in 74% of the respondents.Own buck-doe mating used (68%), and own flock born bucks were in 59% of the respondents.Uncontrolled mating practiced (77%) due to communal browsing, and neighbor bucks used in 83% of the respondents.There was highly significant difference (P<0.05) in ratio of buck to does used.Unknown buck to does ratio used in 25% of the respondents, and one buck to all does in the flock used in 32% of the respondents.No crossbreeding was practiced in 82% of the respondents.Buck castration was practiced (57%) to improve carcass quality (39%).Feed improvement strategy options, access to water and veterinary clinic services, goat fattening, and controlled mating options should be the focus of the farmers and stakeholders.

Opportunities for unlocking the potential of genomics for African trees.

Indigenous goat populations in Ethiopia contain a number of significant genetic features, including the ability to function better under limited input and climatic stress, resistance to viral illnesses and parasites, as well as heat stress. Molecular or phenotypic characterisation is required to offer comprehensive database information of variance among goat populations for sensible utilization of this crucial trait and goat resources. Even though genetic characterization for Arsi-Bale and Woyito-Guji breeds have been done, which are distributed in southern part of Ethiopia, due to overlapping of the distribution of these two breeds in the study area the present phenotypic characterization of indigenous goat was initiated. Despite the studies done, information on phenotypic characteristics and production systems of some indigenous goat populations in study area is still scanty. Besides, there was little intervention works so far on the improvement of production and productivity of local goat breeds in the area. A study was conducted at Abaya and Yirgachafe districts to characterize indigenous goat types phenotypically. Data were collected through field measurements and visual observation of qualitative traits. Totally 540 goats were used for metric and morphometric measurement. Results of the study revealed that the goat populations found in Abaya and Yirgachafe district were different characteristics which are physically Abaya goats were closest with Arsi-Bale whereas yirgachafee with Woyto-Guji which are mostly distributed goat breeds in southern Ethiopia. The dominant coat color pattern in study area was plain, patchy, and spotted with proportions of 55.19, 37.04, and 7.78% and 46.67, 38.89, and 14.44% in Abaya and Yirgachafee district respectively. A strong and positive correlation (r = 0.83, 0.76) was observed between heart girth and body weight for male and female goat populations respectively. Generally, the indigenous goat population has its own difference in its morphological and morphometric traits. Traits have their own economic contribution. Therefore, identifying these important traits for further genetic improvements, conservation and sustainable utilization of the genetic resources of the diversified goat population is important.

Identifying adaptive loci is important to understand the evolutionary potential of species undergoing range expansion. However, in expanding populations, spatial demographic processes such as allele surfing can create spatial patterns of neutral genetic variation that appear similar to those generated through adaptive processes. As a result, the false discovery rate of adaptive loci may be inflated in landscape genomic analyses. Here, we take a simulation modelling approach to investigate how range expansion affects our ability to correctly distinguish between neutral and adaptive genetic variation, using the mountain pine beetle outbreak system as a motivating example. We simulated the demographic and population genetic dynamics of populations undergoing range expansion using an individual-based genetic model CDMetaPOP. We investigated how the false discovery rate of adaptive loci is affected by (i) dispersal capacity, (ii) timing of sampling, and (iii) the strength of selection on an adaptive reference locus. We found that a combination of weak dispersal, weak selection, and early sampling presents the greatest risk of misidentifying loci under selection. Expanding populations present unique challenges to the reliable identification of adaptive loci. We demonstrate that there is a need for further methodological development to account for directional demographic processes in landscape genomics.

Landscape genomic approaches for detecting genotype-environment associations (GEA), isolation by distance (IBD) and isolation by environment (IBE) have seen a dramatic increase in use, but there have been few thorough analyses of the influence of sampling strategy on their performance under realistic genomic and environmental conditions. We simulated 24,000 datasets across a range of scenarios with complex population dynamics and realistic landscape structure to evaluate the effects of the spatial distribution and number of samples on common landscape genomics methods. Our results show that common analyses are relatively robust to sampling scheme as long as sampling covers enough environmental and geographic space. We found that for detecting adaptive loci and estimating IBE, sampling schemes that were explicitly designed to increase coverage of available environmental space matched or outperformed sampling schemes that only considered geographic space. When sampling does not cover adequate geographic and environmental space, such as with transect-based sampling, we detected fewer adaptive loci and had higher error when estimating IBD and IBE. We found that IBD could be detected with as few as nine sampling sites, while large sample sizes (e.g., greater than 100 individuals) were crucial for detecting adaptive loci and IBE. We also demonstrate that, even with optimal sampling strategies, landscape genomic analyses are highly sensitive to landscape structure and migration-when spatial autocorrelation and migration are weak, common GEA methods fail to detect adaptive loci.

Landscape genomics is a rapidly advancing research field that combines population genomics, landscape ecology, and spatial analytical techniques to explicitly quantify the effects of environmental heterogeneity on neutral and adaptive genetic variation and underlying processes. Landscape genomics has tremendous potential for addressing fundamental and applied research questions in various research fields, including ecology, evolution, and conservation biology. However, the unique combination of different scientific disciplines and analytical approaches also constitute a challenge to most researchers wishing to apply landscape genomics. Here, we present an introductory overview of important concepts and methods used in current landscape genomics. For this, we first define the field and explain basic concepts and methods to capture different hypotheses of landscape influences on neutral genetic variation. Next, we highlight established and emerging genomic tools for quantifying adaptive genetic variation in landscape genomic studies. To illustrate the covered topics and to demonstrate the potential of landscape genomics, we provide empirical examples addressing a variety of research question, i.e., the investigation of evolutionary processes driving population differentiation, the landscape genomics of range expanding species, and landscape genomic patterns in organisms of special interest, including species inhabiting aquatic and terrestrial environments. We conclude by outlining remaining challenges and future research avenues in landscape genomics.

BackgroundClimate change is expected to alter the factors that drive changes in adaptive variation. This is especially true for species with long life spans and limited dispersal capabilities. Rapid climate changes may disrupt the migration of beneficial genetic variations, making it challenging for them to keep up with changing environments. Understanding adaptive genetic variations in tree species is crucial for conservation and effective forest management. Our study used landscape genomic analyses and phenotypic traits from a thorough sampling across the entire range of Quercus longinux, an oak species native to Taiwan, to investigate the signals of adaptation within this species.ResultsUsing ecological data, phenotypic traits, and 1,933 single-nucleotide polymorphisms (SNPs) from 205 individuals, we classified three genetic groups, which were also phenotypically and ecologically divergent. Thirty-five genes related to drought and freeze resistance displayed signatures of natural selection. The adaptive variation was driven by diverse environmental pressures such as low spring precipitation, low annual temperature, and soil grid sizes. Using linear-regression-based methods, we identified isolation by environment (IBE) as the optimal model for adaptive SNPs. Redundancy analysis (RDA) further revealed a substantial joint influence of demography, geology, and environments, suggesting a covariation between environmental gradients and colonization history. Lastly, we utilized adaptive signals to estimate the genetic offset for each individual under diverse climate change scenarios. The required genetic changes and migration distance are larger in severe climates. Our prediction also reveals potential threats to edge populations in northern and southeastern Taiwan due to escalating temperatures and precipitation reallocation.ConclusionsWe demonstrate the intricate influence of ecological heterogeneity on genetic and phenotypic adaptation of an oak species. The adaptation is also driven by some rarely studied environmental factors, including wind speed and soil features. Furthermore, the genetic offset analysis predicted that the edge populations of Q. longinux in lower elevations might face higher risks of local extinctions under climate change.

Italy is home to several indigenous goat breeds with great economic and cultural implications, but their genetic makeup remains largely unexplored. This study used the Illumina Goat SNP chip to genotype 142 samples from four Italian goat breeds: Capestrina, Fulva, Grigia Ciociara, and Bianca Monticellana. The genotypes were combined with genome-wide SNP data from 31 Italian breeds to evaluate the genetic profile of Lazio’s indigenous goats. After filtering, the dataset included 51,423 SNPs. Genetic structure and diversity were assessed using MDS, ADMIXTURE analysis and inbreeding coefficients. Focusing on the four Lazio breeds, we examined inbreeding levels (FROH), effective population size (Ne), gene exchange, and runs of homozygosity islands (ROHi). The Bianca appeared highly inbred, whereas Fulva, Capestrina, and Grigia showed heterozygosity levels comparable to other breeds. The increased sample sizes highlighted changes in genetic composition, indeed, ADMIXTURE analysis revealed genetic substructures within Bianca and Fulva. The Ne was found to be declining, with Bianca having the lowest Ne, indicating high susceptibility to inbreeding and genetic drift. The NetView analysis identified two interconnected genetic groups corresponding to: Bianca and Fulva. Key contributors to genetic structure, representing ancestral genotypes, were identified, providing targets for conservation. Moreover, ROH analysis pinpointed individuals with excessive ROH and long homozygosity stretches, particularly in Capestrina and Bianca, which could be limited from breeding to preserve genetic diversity. Finally, ROHi revealed genes linked to coat colour, disease resistance, and heat stress tolerance. These conclusions emphasise the importance of local breeds in maintaining biodiversity and enhancing breeding strategies.

The objective of this study was to assess the genetic diversity, phylogenetic relationship and population structure of five goat breeds in Shanxi, China. High genetic diversities were found in the five populations, among which, Licheng big green goat (LCBG) has the highest genetic diversity, while Jinlan cashmere goat (JLCG) population has the lowest genetic diversity. Bottleneck analysis showed the absence of recent genetic bottlenecks in the five goat populations. Genetic differentiation analysis shows that the closest genetic relationship between LCBG and LLBG (Lvliang black goat) was found, and the genetic distance between JLCG and the other four populations is the largest. The population structure of JLCG is different from the other four populations with K = 2, while LCBG and LLBG have high similarity population structure as the K value changes. Knowledge about genetic diversity and population structure of indigenous goats is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of goat breeds.