Genetic effects of prolonged UV-B exposure in a Namaqualand daisy - Dimorphotheca sinuata

Bibliography: leaves 122-139.This thesis describes investigations into the genetic effects of long term UV-B exposure in Namaqualand daisies (Dimorphotheea sinuata) grown for several generations under ambient and enhanced UV-B levels. Enhanced UV-B radiation was found to have a major effect on the biochemical composition of the chloroplast accompanied by impairment of photosynthetic function, involving a down-regulation of photosynthetic genes and an up-regulation of flavonoid biosynthesis

Genetic variation and population structure of Botswana populations as identified with AmpFLSTR Identifiler short tandem repeat (STR) loci

Population structure was investigated in 990 Botswana individuals according to ethno-linguistics, Bantu and Khoisan, and geography (the nine administrative districts) using the Identifiler autosomal microsatellite markers. Genetic diversity and forensic parameters were calculated for the overall population, and according to ethno-linguistics and geography. The overall combined power of exclusion (CPE) was 0.9999965412 and the combined match probability 6,28 × 10−19. CPE was highest for the Khoisan Tuu ethnolinguistic group and the Northeast District at 0.9999582029 and 0.9999922652 respectively. CMP ranged from 6.28 × 10−19 (Khoisan Tuu) to 1,02 × 10−18 (Northwest district). Using pairwise genetic distances (FST), analysis of molecular variance (AMOVA), factorial correspondence analysis (FCA), and the unsupervised Bayesian clustering method found in STRUCTURE and TESS, ethno-linguistics were found to have a greater influence on population structure than geography. FCA showed clustering between Bantu and Khoisan, and within the Bantu. This Bantu sub-structuring was not seen with STRUCTURE and TESS, which detected clustering only between Bantu and Khoisan. The patterns of population structure revealed highlight the need for regional reference databases that include ethno-linguistic and geographic location information. These markers have important potential for bio-anthropological studies as well as for forensic applications.Web of Scienc

Genetics and geography of leukocyte telomere length in sub-Saharan Africans

Leukocyte telomere length (LTL) might be causal in cardiovascular disease and major cancers. To elucidate the roles of genetics and geography in LTL variability across humans, we compared LTL measured in 1295 sub-Saharan Africans (SSAs) with 559 African-Americans (AAms) and 2464 European-Americans (EAms). LTL differed significantly across SSAs (P = 0.003), with the San from Botswana (with the oldest genomic ancestry) having the longest LTL and populations from Ethiopia having the shortest LTL. SSAs had significantly longer LTL than AAms [P = 6.5(e-16)] whose LTL was significantly longer than EAms [P = 2.5(e-7)]. Genetic variation in SSAs explained 52% of LTL variance versus 27% in AAms and 34% in EAms. Adjustment for genetic variation removed the LTL differences among SSAs. LTL genetic variation among SSAs, with the longest LTL in the San, supports the hypothesis that longer LTL was ancestral in humans. Identifying factors driving LTL variation in Africa may have important ramifications for LTL-associated diseases

Y-chromosomal variation in Sub-Saharan Africa: insights into the history of Niger-Congo groups

Technological and cultural innovations as well as climate changes are thought to have influenced the diffusion of major language phyla in sub-Saharan Africa. The most widespread and the richest in diversity is the Niger-Congo phylum, thought to have originated in West Africa ∼10,000 years ago (ya). The expansion of Bantu languages (a family within the Niger-Congo phylum) ∼5,000 ya represents a major event in the past demography of the continent. Many previous studies on Y chromosomal variation in Africa associated the Bantu expansion with haplogroup E1b1a (and sometimes its sublineage E1b1a7). However, the distribution of these two lineages extends far beyond the area occupied nowadays by Bantu-speaking people, raising questions on the actual genetic structure behind this expansion. To address these issues, we directly genotyped 31 biallelic markers and 12 microsatellites on the Y chromosome in 1,195 individuals of African ancestry focusing on areas that were previously poorly characterized (Botswana, Burkina Faso, Democratic Republic of Congo, and Zambia). With the inclusion of published data, we analyzed 2,736 individuals from 26 groups representing all linguistic phyla and covering a large portion of sub-Saharan Africa. Within the Niger-Congo phylum, we ascertain for the first time differences in haplogroup composition between Bantu and non-Bantu groups via two markers (U174 and U175) on the background of haplogroup E1b1a (and E1b1a7), which were directly genotyped in our samples and for which genotypes were inferred from published data using linear discriminant analysis on short tandem repeat (STR) haplotypes. No reduction in STR diversity levels was found across the Bantu groups, suggesting the absence of serial founder effects. In addition, the homogeneity of haplogroup composition and pattern of haplotype sharing between Western and Eastern Bantu groups suggests that their expansion throughout sub-Saharan Africa reflects a rapid spread followed by backward and forward migrations. Overall, we found that linguistic affiliations played a notable role in shaping sub-Saharan African Y chromosomal diversity, although the impact of geography is clearly discernible. © The Author 2010. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved.(submitted in July 2010, accepted)SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana

Abstract Background Gut microbiota from individuals in rural, non-industrialized societies differ from those in individuals from industrialized societies. Here, we use 16S rRNA sequencing to survey the gut bacteria of seven non-industrialized populations from Tanzania and Botswana. These include populations practicing traditional hunter-gatherer, pastoralist, and agropastoralist subsistence lifestyles and a comparative urban cohort from the greater Philadelphia region. Results We find that bacterial diversity per individual and within-population phylogenetic dissimilarity differs between Botswanan and Tanzanian populations, with Tanzania generally having higher diversity per individual and lower dissimilarity between individuals. Among subsistence groups, the gut bacteria of hunter-gatherers are phylogenetically distinct from both agropastoralists and pastoralists, but that of agropastoralists and pastoralists were not significantly different from each other. Nearly half of the Bantu-speaking agropastoralists from Botswana have gut bacteria that are very similar to the Philadelphian cohort. Based on imputed metagenomic content, US samples have a relative enrichment of genes found in pathways for degradation of several common industrial pollutants. Within two African populations, we find evidence that bacterial composition correlates with the genetic relatedness between individuals. Conclusions Across the cohort, similarity in bacterial presence/absence compositions between people increases with both geographic proximity and genetic relatedness, while abundance weighted bacterial composition varies more significantly with geographic proximity than with genetic relatedness

The genetic prehistory of southern Africa.

Southern and eastern African populations that speak non-Bantu languages with click consonants are known to harbour some of the most ancient genetic lineages in humans, but their relationships are poorly understood. Here, we report data from 23 populations analysed at over half a million single-nucleotide polymorphisms, using a genome-wide array designed for studying human history. The southern African Khoisan fall into two genetic groups, loosely corresponding to the northwestern and southeastern Kalahari, which we show separated within the last 30,000 years. We find that all individuals derive at least a few percent of their genomes from admixture with non-Khoisan populations that began ∼1,200 years ago. In addition, the East African Hadza and Sandawe derive a fraction of their ancestry from admixture with a population related to the Khoisan, supporting the hypothesis of an ancient link between southern and eastern Africa

Unmapped exome reads implicate a role for Anelloviridae in childhood HIV-1 long-term non-progression

Human immunodeficiency virus (HIV) infection remains a significant public health burden globally. The role of viral co-infection in the rate of progression of HIV infection has been suggested but not empirically tested, particularly among children. We extracted and classified 42 viral species from whole-exome sequencing (WES) data of 813 HIV-infected children in Botswana and Uganda categorised as either long-term non-progressors (LTNPs) or rapid progressors (RPs). The Ugandan participants had a higher viral community diversity index compared to Batswana (p = 4.6 × 10−13), and viral sequences were more frequently detected among LTNPs than RPs (24% vs 16%; p = 0.008; OR, 1.9; 95% CI, 1.6–2.3), with Anelloviridae showing strong association with LTNP status (p = 3 × 10−4; q = 0.004, OR, 3.99; 95% CI, 1.74–10.25). This trend was still evident when stratified by country, sex, and sequencing platform, and after a logistic regression analysis adjusting for age, sex, country, and the sequencing platform (p = 0.02; q = 0.03; OR, 7.3; 95% CI, 1.6–40.5). Torque teno virus (TTV), which made up 95% of the Anelloviridae reads, has been associated with reduced immune activation. We identify an association between viral co-infection and prolonged AIDs-free survival status that may have utility as a biomarker of LTNP and could provide mechanistic insights to HIV progression in children, demonstrating the added value of interrogating off-target WES reads in cohort studies

Genetic signatures of gene flow and malaria-driven natural selection in sub-Saharan populations of the "endemic Burkitt Lymphoma belt"

Submitted by Nuzia Santos ([email protected]) on 2020-02-04T14:35:16Z No. of bitstreams: 1 Genetic signatures of gene flow and malaria-driven.pdf: 11801795 bytes, checksum: fd87b07ab4fac498d62a72df070e920d (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2020-02-04T16:03:51Z (GMT) No. of bitstreams: 1 Genetic signatures of gene flow and malaria-driven.pdf: 11801795 bytes, checksum: fd87b07ab4fac498d62a72df070e920d (MD5)Made available in DSpace on 2020-02-04T16:03:51Z (GMT). No. of bitstreams: 1 Genetic signatures of gene flow and malaria-driven.pdf: 11801795 bytes, checksum: fd87b07ab4fac498d62a72df070e920d (MD5) Previous issue date: 2019Fundação Oswaldo Cruz. Instituto René Rachou. Belo Horizonte, MG, Brasil / Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia. Belo Horizonte, MG, Brasil /Center for Research on Genomics & Global Health, National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia. Belo Horizonte, MG, Brasil.Universidade de São Paulo. Instituto de Biociências. Departamento de Genética e Biologia Evolutiva. São Paulo, SP, Brasil.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia. Belo Horizonte, MG, Brasil / Universidade Federal de Minas Gerais. Departamento de Estatística. Belo Horizonte, MG, Brasil.EMBLEM Study. African Field Epidemiology Network. Kampala, Uganda.University of Ghana Medical School, Accra, Ghana.University of Ghana Medical School, Accra, Ghana.EMBLEM Study. African Field Epidemiology Network. Kampala, Uganda.EMBLEM Study. African Field Epidemiology Network. Kampala, Uganda.EMBLEM Study. African Field Epidemiology Network. Kampala, Uganda.EMBLEM Study. African Field Epidemiology Network. Kampala, Uganda.Department of Biological Sciences. University of Botswana. Gaborone, Botswana.Department of Biomedical Sciences. University of Botswana School of Medicine. Gaborone, Botswana.EMBLEM Study. African Field Epidemiology Network. Kampala, Uganda.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Division of Intramural Research. National Institute of Allergy and Infectious Diseases. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.University of Ghana Medical School. Accra, Ghana.University of Ghana Medical School. Accra, Ghana.University of Ghana Medical School. Accra, Ghana.University of Ghana Medical School. Accra, Ghana.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Laboratory of Translational Genomics. Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. USDepartment of Health and Human Services. Bethesda, Maryland, USA.Laboratory of Translational Genomics. Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Cancer Genomics Research Laboratory. Leidos Biomedical Research. Frederick National Laboratory for Cancer Research. US Department of Health and Human Services. Frederick, Maryland, USA.Fundação Oswaldo Cruz. Instituto René Rachou. Belo Horizonte, MG, Brasil.Stanford Cancer Institute. Stanford University. Stanford, California, USA.Department of Genetics and Biology, University of Pennsylvania, Philadelphia, USA.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Biologia Geral. Belo Horizonte, MG, Brasil.Division of Cancer Epidemiology and Genetics. National Cancer Institute. National Institutes of Health. US Department of Health and Human Services. Bethesda, Maryland, USA.Populations in sub-Saharan Africa have historically been exposed to intense selection from chronic infection with falciparum malaria. Interestingly, populations with the highest malaria intensity can be identified by the increased occurrence of endemic Burkitt Lymphoma (eBL), a pediatric cancer that affects populations with intense malaria exposure, in the so called “eBL belt” in sub-Saharan Africa. However, the effects of intense malaria exposure and sub-Saharan populations’ genetic histories remain poorly explored. To determine if historical migrations and intense malaria exposure have shaped the genetic composition of the eBL belt populations, we genotyped ~4.3 million SNPs in 1,708 individuals from Ghana and Northern Uganda, located on opposite sides of eBL belt and with ≥ 7 months/year of intense malaria exposure and published evidence of high incidence of BL. Among 35 Ghanaian tribes, we showed a predominantly West-Central African ancestry and genomic footprints of gene flow from Gambian and East African populations. In Uganda, the North West population showed a predominantly Nilotic ancestry, and the North Central population was a mixture of Nilotic and Southern Bantu ancestry, while the Southwest Ugandan population showed a predominant Southern Bantu ancestry. Our results support the hypothesis of diverse ancestral origins of the Ugandan, Kenyan and Tanzanian Great Lakes African populations, reflecting a confluence of Nilotic, Cushitic and Bantu migrations in the last 3000 years. Natural selection analyses suggest, for the first time, a strong positive selection signal in the ATP2B4 gene (rs10900588) in Northern Ugandan populations. These findings provide important baseline genomic data to facilitate disease association studies, including of eBL, in eBL belt populations