A homozygosity-based investigation of the South African feral Tankwa goat population

The Tankwa goat is a known feral goat landrace that originated in the Karoo region of South Africa. These goats are able to thrive with no managerial intervention, and prosper in the harsh, arid conditions that characterizes their natural habitat. This study aimed to use a ROH-approach to describe the Tankwa goat in terms of autozygosity and to identify possible signatures of selection. Genome-wide SNP data for 360 Tankwa goats were used to calculate diversity statistics, detect runs of homozygosity and estimate individual genetic inbreeding coefficients. SNP genotypes of 48 Angora and 40 Dairy individuals were compared using the FST approach to detect signatures of selection. Relatively low minor allele frequency (0.249), and high linkage disequilibrium (r2 = 0.469) levels were estimated for the Tankwa population, with moderate levels of heterozygosity (HE = 0.368; HO = 0.367). The results for both the detected runs of homozygosity and inbreeding estimate, indicates an ancient origin of inbreeding for the Tankwa goats with low levels of autozygosity. Signatures of selection were identified in 50 SNPs, of which 0.1% was considered significant. A total of 49 genes were identified that may possibly be significant in various biological pathways. Three of these genes, namely GJB2, GJB6 and GJA3 on CHI12, were previously associated with adaptation to heat and drought resistance in other breeds. Genes GJB2 and GJB6 are known to be linked to the sensory perception of sound, while GJA3 and OPA3 are linked to visual perception. These genes could play an important role in the survival of an individual existing in a harsh environment in terms of foraging and evading predators. Understanding the genetic background of these genes, as well as the metabolic pathways that they control, could assist in further investigating production efficiency of domesticated species in a climate change environment.The Department of Agriculture, Environmental Affairs, Rural Development and Land Reform.https://www.elsevier.com/locate/smallrumresAnimal and Wildlife SciencesSDG-02:Zero HungerSDG-15:Life on lan

Population genetics of a lethally managed medium-sized predator

Globally, levels of human–wildlife conflict are increasing as a direct consequence of the expansion of people into natural areas resulting in competition with wildlife for food and other resources. By being forced into increasingly smaller pockets of suitable habitat, many animal species are at risk of becoming susceptible to loss of genetic diversity, inbreeding depression and the associated inability to adapt to environmental changes. Predators are often lethally controlled due to their threat to livestock. Predators such as jackals (black backed, golden and side striped; Canis mesomelas, C. aureus and C. adustus, respectively), red foxes (Vulpes vulpes) and coyotes (C. latrans) are highly adaptable and may respond to ongoing persecution through compensatory reproduction such as reproducing at a younger age, producing larger litters and/or compensatory immigration including dispersal into vacant territories. Despite decades of lethal management, jackals are problematic predators of livestock in South Africa and, although considered a temporary measure, culling of jackals is still common. Culling may affect social groups, kinship structure, reproductive strategies and sex-biased dispersal in this species. Here, we investigated genetic structure, variation and relatedness of 178 culled jackals on private small-livestock farms in the central Karoo of South Africa using 13 microsatellites. Genetic variation was moderate to high and was similar per year and per farm. An absence of genetic differentiation was observed based on STRUCTURE, principal component analysis and AMOVA. Relatedness was significantly higher within farms (r = 0.189) than between farms (r = 0.077), a result corroborated by spatial autocorrelation analysis. We documented 18 occurrences of dispersal events where full siblings were detected on different farms (range: 0.78–42.93 km). Distance between identified parent–offspring varied from 0 to 36.49 km. No evidence for sex-biased dispersal was found. Our results suggest that in response to ongoing lethal management, this population is most likely able to maintain genetic diversity through physiological and behavioural compensation mechanisms.APPENDIX S1. Supplementary methods.SUPPLEMENTARY TABLES. TABLE S1. Primer details for microsatellite loci used to genotype black-backed jackals (Canis Mesomelas). TABLE S2. Per-locus summary statistics as calculated in Cervus v3.0.7. The non-exclusion probabilities and combined non-exclusion probabilities (final row, italics) are relevant indicators of the power of the loci for parentage and sibship analyses. TABLE S3. Summary statistics for 20 sampling localities (farms) with >1 sample and for all farms pooled. Produced using the basicStats command of the diveRsity package v1.9.90 in R v3.6.2 and RStudio v1.2.5033. Standard deviation was calculated across loci in Microsoft Excel (stdev.s). Sampling localities with only one sample are not shown. TABLE S4. Summary statistics per year and for all years pooled. Produced using the basicStats command of the diveRsity package v1.9.90 in R v3.6.2 and RStudio v1.2.5033. Standard deviation was calculated across loci in Microsoft Excel (STDEV.S). TABLE S5. Pairwise FST values between farms with the full dataset (below diagonal) and associated significance at a level of 0.05 (above diagonal), where significant values are indicated by a “+” and non-significant values by a “−”. Calculated in Arlequin 3.5.2.2. TABLE S6. Pairwise FST values between farms with relatives removed (below diagonal) and associated significance at a level of 0.05 (above diagonal), where significant values are indicated by a “+” and non-significant values by a “−”. Calculated in Arlequin 3.5.2.2. TABLE S7. Comparison of mean pairwise relatedness (r) between years and mean individual inbreeding coefficients (F) between years. P-values for the Wilcoxon tests for difference in means are shown on the inside of the table (bordered by grey), with P-values for inbreeding comparisons shown below the diagonal (bottom left) and P-values for relatedness comparisons shown above the diagonal (top right). The mean F for each year is shown in the left-most column “outside” the main table, with the mean r for each year shown in the top row “outside” the main table. The numbers in parentheses after each year are the number of observations/data points for that year (number of samples for F and number of pairwise relatedness comparisons for r).SUPPLEMENTARY FIGURES. FIGURE S1. STRUCTURE HARVESTER results for (a) Delta K values and (b) probability (-LnPr) of K = 1–27 averaged over 20 runs and (c) genetic differentiation between the jackal sample locations (farms) based on STRUCTURE analysis (performed with K = 2–6) of 1 = GV, 2 = BB, 3 = BR, 4 = BD, 5 = DS, 6 = GG, 7 = HK, 8 = KD, 9 = KW, 10 = KK, 11 = KT, 12 = NG, 13 = ND, 14 = OG, 15 = RV, 16 = RE, 17 = RT, 18 = RD, 19 = SG, 20 = SK, 21 = VR, 22 = WK, 23 = CL, 24 = KR, 25 = WB and 26 = TD. FIGURE S2. STRUCTURE HARVESTER results for (a) Delta K values and (b) probability (-LnPr) of K = 1–27 averaged over 20 runs and (c) genetic differentiation between the jackal sample locations (farms) based on STRUCTURE analysis (performed with K = 2–6 and K = 14) of 1 = GV, 2 = BB, 3 = BD, 4 = DS, 5 = GG, 6 = HK, 7 = KW, 8 = KT, 9 = NG, 10 = ND, 11 = OG, 12 = RV, 13 = RE, 14 = RD, 15 = SG, 16 = SK, 17 = VR, 18 = WK and 19 = CL. After removing relatives, some localities had no samples, hence fewer sampling localities as compared to the full dataset. Note: The Evanno method (DeltaK) does not evaluate K = 1. FIGURE S3. Principal component analysis (PCA) of the different jackal sampling locations (farms) with related individuals removed. FIGURE S4. Plot comparing the relatedness estimates using six estimators and simulated individuals of known relatedness. Di, Dyadic likelihood estimator “DyadML”; LL, Lynch-Li estimator; LR, Lynch and Ritland estimator; QG, Queller and Goodnight estimator; Tri, Triadic likelihood estimator “TrioML”; W, Wang estimator. Plot produced with ggplot2 3.3.0 (Wickham, 2016). FIGURE S5. Results of the spatial autocorrelation analysis for A females and B males. The blue line indicates the autocorrelation coefficient of the data, with the 95% confidence interval at each distance class indicated by the black error bars, as determined by 1000 bootstrap resampling replicates. The red dashed lines indicate the 95% confidence interval around the null hypothesis (no spatial structure, i.e. rauto = 0), as determined by permutation (999 steps). Thus, if the error bars around the blue line do not overlap with the red dashed lines in a distance class, then genotypes were more (positive rauto) or less (negative rauto) similar than expected under the null hypothesis in that distance class. Such cases are indicated with an asterisk (*).The National Zoological Gardens, Pretoria and the University of South Africa.https://zslpublications.onlinelibrary.wiley.com/journal/14697998hj2023BiochemistryGeneticsMicrobiology and Plant Patholog

Assessing introgressive hybridization in roan antelope (Hippotragus equinus):Lessons from South Africa

Biological diversity is being lost at unprecedented rates, with genetic admixture and introgression presenting major threats to biodiversity. Our ability to accurately identify introgression is critical to manage species, obtain insights into evolutionary processes, and ultimately contribute to the Aichi Targets developed under the Convention on Biological Diversity. The current study concerns roan antelope, the second largest antelope in Africa. Despite their large size, these antelope are sensitive to habitat disturbance and interspecific competition, leading to the species being listed as Least Concern but with decreasing population trends, and as extinct over parts of its range. Molecular research identified the presence of two evolutionary significant units across their sub-Saharan range, corresponding to a West African lineage and a second larger group which includes animals from East, Central and Southern Africa. Within South Africa, one of the remaining bastions with increasing population sizes, there are a number of West African roan antelope populations on private farms, and concerns are that these animals hybridize with roan that naturally occur in the southern African region. We used a suite of 27 microsatellite markers to conduct admixture analysis. Our results indicate evidence of hybridization, with our developed tests using a simulated dataset being able to accurately identify F1, F2 and non-admixed individuals at threshold values of qi > 0.80 and qi > 0.85. However, further backcrosses were not always detectable with backcrossed-Western roan individuals (46.7-60%), backcrossed-East, Central and Southern African roan individuals (28.3-45%) and double backcrossed (83.3-98.3%) being incorrectly classified as non-admixed. Our study is the first to confirm ongoing hybridization in this within this iconic African antelope, and we provide recommendations for the future conservation and management of this species

Population genetics of a lethally managed medium-sized predator

Globally, levels of human–wildlife conflict are increasing as a direct consequence of the expansion of people into natural areas resulting in competition with wildlife for food and other resources. By being forced into increasingly smaller pockets of suitable habitat, many animal species are at risk of becoming susceptible to loss of genetic diversity, inbreeding depression and the associated inability to adapt to environmental changes. Predators are often lethally controlled due to their threat to livestock. Predators such as jackals (black backed, golden and side striped; Canis mesomelas, C. aureus and C. adustus, respectively), red foxes (Vulpes vulpes) and coyotes (C. latrans) are highly adaptable and may respond to ongoing persecution through compensatory reproduction such as reproducing at a younger age, producing larger litters and/or compensatory immigration including dispersal into vacant territories. Despite decades of lethal management, jackals are problematic predators of livestock in South Africa and, although considered a temporary measure, culling of jackals is still common. Culling may affect social groups, kinship structure, reproductive strategies and sex-biased dispersal in this species. Here, we investigated genetic structure, variation and relatedness of 178 culled jackals on private small-livestock farms in the central Karoo of South Africa using 13 microsatellites. Genetic variation was moderate to high and was similar per year and per farm. An absence of genetic differentiation was observed based on STRUCTURE, principal component analysis and AMOVA. Relatedness was significantly higher within farms (r = 0.189) than between farms (r = 0.077), a result corroborated by spatial autocorrelation analysis. We documented 18 occurrences of dispersal events where full siblings were detected on different farms (range: 0.78–42.93 km). Distance between identified parent–offspring varied from 0 to 36.49 km. No evidence for sex-biased dispersal was found. Our results suggest that in response to ongoing lethal management, this population is most likely able to maintain genetic diversity through physiological and behavioural compensation mechanisms.APPENDIX S1. Supplementary methods.SUPPLEMENTARY TABLES. TABLE S1. Primer details for microsatellite loci used to genotype black-backed jackals (Canis Mesomelas). TABLE S2. Per-locus summary statistics as calculated in Cervus v3.0.7. The non-exclusion probabilities and combined non-exclusion probabilities (final row, italics) are relevant indicators of the power of the loci for parentage and sibship analyses. TABLE S3. Summary statistics for 20 sampling localities (farms) with >1 sample and for all farms pooled. Produced using the basicStats command of the diveRsity package v1.9.90 in R v3.6.2 and RStudio v1.2.5033. Standard deviation was calculated across loci in Microsoft Excel (stdev.s). Sampling localities with only one sample are not shown. TABLE S4. Summary statistics per year and for all years pooled. Produced using the basicStats command of the diveRsity package v1.9.90 in R v3.6.2 and RStudio v1.2.5033. Standard deviation was calculated across loci in Microsoft Excel (STDEV.S). TABLE S5. Pairwise FST values between farms with the full dataset (below diagonal) and associated significance at a level of 0.05 (above diagonal), where significant values are indicated by a “+” and non-significant values by a “−”. Calculated in Arlequin 3.5.2.2. TABLE S6. Pairwise FST values between farms with relatives removed (below diagonal) and associated significance at a level of 0.05 (above diagonal), where significant values are indicated by a “+” and non-significant values by a “−”. Calculated in Arlequin 3.5.2.2. TABLE S7. Comparison of mean pairwise relatedness (r) between years and mean individual inbreeding coefficients (F) between years. P-values for the Wilcoxon tests for difference in means are shown on the inside of the table (bordered by grey), with P-values for inbreeding comparisons shown below the diagonal (bottom left) and P-values for relatedness comparisons shown above the diagonal (top right). The mean F for each year is shown in the left-most column “outside” the main table, with the mean r for each year shown in the top row “outside” the main table. The numbers in parentheses after each year are the number of observations/data points for that year (number of samples for F and number of pairwise relatedness comparisons for r).SUPPLEMENTARY FIGURES. FIGURE S1. STRUCTURE HARVESTER results for (a) Delta K values and (b) probability (-LnPr) of K = 1–27 averaged over 20 runs and (c) genetic differentiation between the jackal sample locations (farms) based on STRUCTURE analysis (performed with K = 2–6) of 1 = GV, 2 = BB, 3 = BR, 4 = BD, 5 = DS, 6 = GG, 7 = HK, 8 = KD, 9 = KW, 10 = KK, 11 = KT, 12 = NG, 13 = ND, 14 = OG, 15 = RV, 16 = RE, 17 = RT, 18 = RD, 19 = SG, 20 = SK, 21 = VR, 22 = WK, 23 = CL, 24 = KR, 25 = WB and 26 = TD. FIGURE S2. STRUCTURE HARVESTER results for (a) Delta K values and (b) probability (-LnPr) of K = 1–27 averaged over 20 runs and (c) genetic differentiation between the jackal sample locations (farms) based on STRUCTURE analysis (performed with K = 2–6 and K = 14) of 1 = GV, 2 = BB, 3 = BD, 4 = DS, 5 = GG, 6 = HK, 7 = KW, 8 = KT, 9 = NG, 10 = ND, 11 = OG, 12 = RV, 13 = RE, 14 = RD, 15 = SG, 16 = SK, 17 = VR, 18 = WK and 19 = CL. After removing relatives, some localities had no samples, hence fewer sampling localities as compared to the full dataset. Note: The Evanno method (DeltaK) does not evaluate K = 1. FIGURE S3. Principal component analysis (PCA) of the different jackal sampling locations (farms) with related individuals removed. FIGURE S4. Plot comparing the relatedness estimates using six estimators and simulated individuals of known relatedness. Di, Dyadic likelihood estimator “DyadML”; LL, Lynch-Li estimator; LR, Lynch and Ritland estimator; QG, Queller and Goodnight estimator; Tri, Triadic likelihood estimator “TrioML”; W, Wang estimator. Plot produced with ggplot2 3.3.0 (Wickham, 2016). FIGURE S5. Results of the spatial autocorrelation analysis for A females and B males. The blue line indicates the autocorrelation coefficient of the data, with the 95% confidence interval at each distance class indicated by the black error bars, as determined by 1000 bootstrap resampling replicates. The red dashed lines indicate the 95% confidence interval around the null hypothesis (no spatial structure, i.e. rauto = 0), as determined by permutation (999 steps). Thus, if the error bars around the blue line do not overlap with the red dashed lines in a distance class, then genotypes were more (positive rauto) or less (negative rauto) similar than expected under the null hypothesis in that distance class. Such cases are indicated with an asterisk (*).The National Zoological Gardens, Pretoria and the University of South Africa.https://zslpublications.onlinelibrary.wiley.com/journal/14697998hj2023BiochemistryGeneticsMicrobiology and Plant Patholog

Bortezomib plus melphalan and prednisone compared with melphalan and prednisone in previously untreated multiple myeloma: updated follow-up and impact of subsequent therapy in the phase III VISTA trial

[EN]The purpose of this study was to confirm overall survival (OS) and other clinical benefits with bortezomib, melphalan, and prednisone (VMP) versus melphalan and prednisone (MP) in the phase III VISTA (Velcade as Initial Standard Therapy in Multiple Myeloma) trial after prolonged follow-up, and evaluate the impact of subsequent therapies. Previously untreated symptomatic patients with myeloma ineligible for high-dose therapy received up to nine 6-week cycles of VMP (n = 344) or MP (n = 338). With a median follow-up of 36.7 months, there was a 35% reduced risk of death with VMP versus MP (hazard ratio, 0.653; P < .001); median OS was not reached with VMP versus 43 months with MP; 3-year OS rates were 68.5% versus 54.0%. Response rates to subsequent thalidomide- (41% v 53%) and lenalidomide-based therapies (59% v 52%) appeared similar after VMP or MP; response rates to subsequent bortezomib-based therapy were 47% versus 59%. Among patients treated with VMP (n = 178) and MP (n = 233), median survival from start of subsequent therapy was 30.2 and 21.9 months, respectively, and there was no difference in survival from salvage among patients who received subsequent bortezomib, thalidomide, or lenalidomide. Rates of adverse events were higher with VMP versus MP during cycles 1 to 4, but similar during cycles 5 to 9. With VMP, 79% of peripheral neuropathy events improved within a median of 1.9 months; 60% completely resolved within a median of 5.7 months. VMP significantly prolongs OS versus MP after lengthy follow-up and extensive subsequent antimyeloma therapy. First-line bortezomib use does not induce more resistant relapse. VMP used upfront appears more beneficial than first treating with conventional agents and saving bortezomib- and other novel agent-based treatment until relapse

Small mammals of a West African hotspot, the Ziama-Wonegizi-Wologizi transfrontier forest landscape

The Upper Guinea rainforest zone in West Africa is considered a biodiversity hotspot and contains important habitats for threatened and endemic mammals, yet this region remains poorly known particularly for small mammals. The aim of this study was to survey small mammals in a Liberian and Guinean cross-border conservation area, the Ziama-Wonegizi-Wologizi landscape. We recorded a total of 52 small mammal species, including 26 bats, 15 rodents, 10 shrews, one otter-shrew, of which one rodent species was new to science (Colomys sp. nov.). We also documented the first country records of the bats Chaerephon aloysiisabaudiae, Pseudoromicia brunnea and Pipistrellus inexspectatus from Guinea, and the shrews Crocidura douceti and Crocidura grandiceps from Liberia. Furthermore, we recorded the recently described bat Nycticeinops happoldorum from Wologizi and Ziama, and we documented the presence of Micropotamogale lamottei at Wologizi, which represents the fourth known locality for this globally threatened species. Finally, the forests of Wologizi and Ziama support numerous threatened species. The results of our survey demonstrate the importance of this region for small mammals and support the creation of a transboundary protected area that will encompass the entire forest landscape.The USAID West Africa Biodiversity and Climate Change Program (USAID WA BiCC).http://www.degruyter.com/view/j/mamm2021-12-08am2021Mammal Research InstituteZoology and Entomolog