Variabilité Génétique des Populations Ouest-Africaines

Notre patrimoine génétique dévoile, de plus en plus, les passerelles démogénétiques d’une susceptibilité plus accrue de certains individus à des maladies infectieuses complexes. En vue d’une caractérisation de la variabilité génétique des populations ouest-africaines, nous avons analysé 659 chromosomes X au locus dys44 qui comprend, 35 SNPs et un microsatellite distribués sur 2853 pb en amont et 5034 pb en aval de l’exon 44 du gène de la dystrophine en Xp21.3. Les génotypes obtenus, par ASO dynamique et électrophorèse sur gel d’acrylamide, ont servi à la détermination des haplotypes. Des paramètres comme la diversité haplotypique (G) et l'indice de fixation (Fst) ont été calculés. Des analyses en composantes principales ainsi que multidimensionnelles ont été réalisées. Sur 68 haplotypes détectés, 26 sont nouveaux, et cette région, avec une diversité haplotypique moyenne (Gmoy) de 0,91 ± 0,03, se révèle beaucoup plus hétérogène que le reste du continent (Gmoy = 0,85 ± 0,04). Toutefois, malgré l’existence de disparités sous régionales dans la distribution des variants du marqueur dys44, l’AMOVA montre d’une manière générale, une faible érosion de l’éloignement génétique entre les populations subsahariennes (Fst = 1,5% ; p<10-5). Certains variants tel que l’haplotype eurasien B006 paraissent indiquer des flux transsahariens de gènes entre les populations nord-africaines et celles subsahariennes, comme l’exemplifie le pool génétique de l’une des populations ubiquitaires de la famille linguistique Nigéro-congolaise : Les Fulani. Nos résultats vont aussi dans le sens d’un héritage phylétique commun entre les Biaka, les Afro-américains et les populations de la sous-famille de langues Volta-Congo.The unravelling of our genetic heritage has revealed a demogenetic segueway leading to an increased susceptibility of certain individuals to complex infectious diseases. In order to characterize genetic variability among the West African populations, we analyzed 659 X chromosomes at the dys44 locus which comprises 35 SNPs and a microsatellite spanning a region 2853 bp upstream and 5034 bp downstream of exon 44 of the dystrophine gene in Xp21.3. The resulting genotypes, obtained by dynamic allele specific oligonucleotide hybridization and acrylamide gel electrophoresis, were used for haplotype construction. Gene diversity parameters such as the haplotypic diversity (G) and fixation indexes (Fst) were estimated. Multidimensional analysis of the data, including principal component analysis was also performed. Of the 68 distinct haplotypes detected in our data set, 26 were novel. The mean haplotypic diversity (Gmoy) was 0.91 ± 0.03 for this West African region which was shown to be more heterogeneous than the rest of the continent (Gmoy = 0.85 ± 0.04). However, despite certain sub-regional differences in the distribution of dys44 variants, the analysis of molecular variance showed an overall decline in the genetic distance between Sub-Saharan populations (Fst = 1.5% ; p<10-5). Certain variants, such as the Eurasian-specific haplotype B006, appear to suggest a Trans-Saharan gene flux between North African and Sub-Saharan populations as exemplified by the observed genetic pool of one of the ubiquitous populations of the Nigerian-Congolese linguistic family: The Fulani. Our results are also in agreement with a phyletic heritage between the Biaka, the Afro-Americans and the populations of the Volta-Congo language subfamilies

High-depth African genomes inform human migration and health

The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals—comprising 50 ethnolinguistic groups, including previously unsampled populations—to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that Zambia was a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon—but in other genes, variants denoted as ‘likely pathogenic’ in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health

High-depth African genomes inform human migration and health

The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals—comprising 50 ethnolinguistic groups, including previously unsampled populations—to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that Zambia was a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon—but in other genes, variants denoted as ‘likely pathogenic’ in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health

A haplotype-based normalization technique for the analysis and detection of allele specific expression

BACKGROUND: Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects. RESULTS: Here, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking. CONCLUSIONS: The correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1238-8) contains supplementary material, which is available to authorized users

A haplotype-based normalization technique for the analysis and detection of allele specific expression

Abstract Background Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects. Results Here, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking. Conclusions The correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes

Additional file 3: of A haplotype-based normalization technique for the analysis and detection of allele specific expression

Table S2 detailing the numbers of covered sites and ASE events within RNA sequencing data from 93 individuals. (XLSX 45 kb

Additional file 2: of A haplotype-based normalization technique for the analysis and detection of allele specific expression

Supplementary Figures and Tables. (DOCX 978 kb

Gene-by-environment interactions in urban populations modulate risk phenotypes

Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec

High-Resolution Genomic Analysis of Human Mitochondrial RNA Sequence Variation

RNA Heteroplasmy Like nuclear DNA, the mitochondrial genome has to be posttranscriptionally modified to function properly; however, among individuals, mitochondrial RNA (mtRNA) transcripts vary in ways that are poorly understood. Hodgkinson et al. (p. 413 ) looked at mtRNA editing events and posttranscriptional methylation in more than 700 individuals. Interestingly, variation at the ninth position within transfer RNAs showed a high frequency of variation that, in some cases, is genetically attributable. </jats:p

Genomic architecture of sickle cell disease in West African children

Sickle cell disease (SCD) is a congenital blood disease, affecting predominantly children from sub-Saharan Africa, but also populations world-wide. Although the causal mutation of SCD is known, the sources of clinical variability of SCD remain poorly understood, with only a few highly heritable traits associated with SCD having been identified. Phenotypic heterogeneity in the clinical expression of SCD is problematic for follow-up (FU), management, and treatment of patients. Here we used the joint analysis of gene expression and whole genome genotyping data to identify the genetic regulatory effects contributing to gene expression variation among groups of patients exhibiting clinical variability, as well as unaffected siblings, in Benin, West Africa. We characterized and replicated patterns of whole blood gene expression variation within and between SCD patients at entry to clinic, as well as in follow-up programs. We present a global map of genes involved in the disease through analysis of whole blood sampled from the cohort. Genome-wide association mapping of gene expression revealed 390 peak genome-wide significant expression SNPs (eSNPs) and 6 significant eSNP-by-clinical status interaction effects. The strong modulation of the transcriptome implicates pathways affecting core circulating cell functions and shows how genotypic regulatory variation likely contributes to the clinical variation observed in SCD