Insights into the genomic histories of diverse human populations using whole-genome sequencing analysis.

Despite the progress in sampling many populations, human genomics research is still not fully reflective of the diversity found globally. Understudied populations limit our knowledge of genetic variation and population history, and their inclusion is needed to ensure they benefit from future developments in genomic medicine. In this thesis, I describe extending our understanding of global genetic diversity and population history by two main projects. The first is focused on structural variation in a diverse set of 54 human populations which are part of the Human Genome Diversity Project (HGDP-CEPH) panel. Using whole-genome sequences previously produced at the Wellcome Sanger Institute, I generated a comprehensive catalogue of structural variation identifying a total of 126,018 variants, of which 78% are novel. Some reach high frequency and are private to continental groups or even individual populations, including regionally-restricted runaway duplications and putatively introgressed variants from archaic hominins. By de novo assembly of 25 genomes using linked-read sequencing, I discovered 1643 breakpoint-resolved unique insertions, in aggregate accounting for 1.9 Mb of sequence absent from the GRCh38 reference genome, highlighting the limitation of a single human reference genome. In the second project I collected and analysed a dataset of 137 high-coverage physically-phased genome sequences from eight Middle Eastern populations using linked-read sequencing. Focusing on the population history using single nucleotide variants, I found no genetic traces of archeologically documented early expansions out-of-Africa in present-day populations in the region. I show that Arabian populations have the lowest Neanderthal ancestry of all non-African populations tested, which is explained by them having elevated Basal Eurasian ancestry. By comparing Levantines and Arabian historical population sizes, I find a divergence that starts before the Neolithic era, when Levantines expanded while Arabians maintained small populations that could have derived ancestry from local epipaleolithic hunter-gatherers. All populations suffered a bottleneck overlapping the archaeologically-documented aridification events, with Arabians decreasing in size with the onset of the desert climate in Arabia ~6 kya while the Levantine bottleneck overlaps the 4.2 kiloyear aridification event. I also identify an ancestry that is associated with the spread of Semitic languages across the region during the Bronze Age. Finally, I identify novel variants that show evidence of selection, including signals of polygenic selection. This thesis fills an important gap in the study of diverse human populations, although further work is needed to sequence and characterize additional genetically underrepresented groups.Government of Dubai - Dubai Police GH

Insights from Population Genomics to Enhance and Sustain Biological Control of Insect Pests

Biological control—the use of organisms (e.g., nematodes, arthropods, bacteria, fungi, viruses) for the suppression of insect pest species—is a well-established, ecologically sound and economically profitable tactic for crop protection. This approach has served as a sustainable solution for many insect pest problems for over a century in North America. However, all pest management tactics have associated risks. Specifically, the ecological non-target effects of biological control have been examined in numerous systems. In contrast, the need to understand the short- and long-term evolutionary consequences of human-mediated manipulation of biological control organisms for importation, augmentation and conservation biological control has only recently been acknowledged. Particularly, population genomics presents exceptional opportunities to study adaptive evolution and invasiveness of pests and biological control organisms. Population genomics also provides insights into (1) long-term biological consequences of releases, (2) the ecological success and sustainability of this pest management tactic and (3) non-target effects on native species, populations and ecosystems. Recent advances in genomic sequencing technology and model-based statistical methods to analyze population-scale genomic data provide a much needed impetus for biological control programs to benefit by incorporating a consideration of evolutionary consequences. Here, we review current technology and methods in population genomics and their applications to biological control and include basic guidelines for biological control researchers for implementing genomic technology and statistical modeling

Complex population structure and haplotype patterns in the Western European honey bee from sequencing a large panel of haploid drones:Sequencing haploid honey bee drones

International audienceHoney bee subspecies originate from specific geographical areas in Africa, Europe and the Middle East, and beekeepers interested in specific phenotypes have imported genetic material to regions outside of the bees' original range for use either in pure lines or controlled crosses. Moreover, imported drones are present in the environment and mate naturally with queens from the local subspecies. The resulting admixture complicates population genetics analyses, and population stratification can be a major problem for association studies. To better understand Western European honey bee populations, we produced a whole genome sequence and single nucleotide polymorphism (SNP) genotype data set from 870 haploid drones and demonstrate its utility for the identification of nine genetic backgrounds and various degrees of admixture in a subset of 629 samples. Five backgrounds identified correspond to subspecies, two to isolated populations on islands and two to managed populations. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long and represents 21% of chromosome 11, with two major haplotypes corresponding to the two dominant genetic backgrounds identified. This large naturally phased data set is available as a single vcf file that can now serve as a reference for subsequent populations genomics studies in the honey bee, such as (i) selecting individuals of verified homogeneous genetic backgrounds as references, (ii) imputing genotypes from a lower-density data set generated by an SNP-chip or by low-pass sequencing, or (iii) selecting SNPs compatible with the requirements of genotyping chips

Complex population structure and haplotype patterns in the Western European honey bee from sequencing a large panel of haploid drones

Honey bee subspecies originate from specific geographical areas in Africa, Europe and the Middle East, and beekeepers interested in specific phenotypes have imported genetic material to regions outside of the bees' original range for use either in pure lines or controlled crosses. Moreover, imported drones are present in the environment and mate naturally with queens from the local subspecies. The resulting admixture complicates population genetics analyses, and population stratification can be a major problem for association studies. To better understand Western European honey bee populations, we produced a whole genome sequence and single nucleotide polymorphism (SNP) genotype data set from 870 haploid drones and demonstrate its utility for the identification of nine genetic backgrounds and various degrees of admixture in a subset of 629 samples. Five backgrounds identified correspond to subspecies, two to isolated populations on islands and two to managed populations. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long and represents 21% of chromosome 11, with two major haplotypes corresponding to the two dominant genetic backgrounds identified. This large naturally phased data set is available as a single vcf file that can now serve as a reference for subsequent populations genomics studies in the honey bee, such as (i) selecting individuals of verified homogeneous genetic backgrounds as references, (ii) imputing genotypes from a lower-density data set generated by an SNP-chip or by low-pass sequencing, or (iii) selecting SNPs compatible with the requirements of genotyping chips.This work was performed in collaboration with the GeT platform, Toulouse (France), a partner of the National Infrastructure France Génomique, thanks to support by the Commissariat aux Grands Invetissements (ANR-10-INBS-0009). Bioinformatics analyses were performed on the GenoToul Bioinfo computer cluster. This work was funded by a grant from the INRA Département de Génétique Animale (INRA Animal Genetics division) and by the SeqApiPop programme, funded by the FranceAgriMer grant 14-21-AT. We thank John Kefuss for helpful discussions. We thank Andrew Abrahams for providing honey bee samples from Colonsay (Scotland), the Association Conservatoire de l'Abeille Noire Bretonne (ACANB) for samples from Ouessant (France), CETA de Savoie for sample from Savoie, ADAPI for samples from Porquerolles and all beekeepers and bee breeders who kindly participated in this study by providing samples from their colonies.info:eu-repo/semantics/publishedVersio

Inference of natural selection from ancient DNA.

Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods

Integrating Human Population Genetics And Genomics To Elucidate The Etiology Of Brain Disorders

Brain disorders present a significant burden on affected individuals, their families and society at large. Existing diagnostic tests suffer from a lack of genetic biomarkers, particularly for substance use disorders, such as alcohol dependence (AD). Numerous studies have demonstrated that AD has a genetic heritability of 40-60%. The existing genetics literature of AD has primarily focused on linkage analyses in small family cohorts and more recently on genome-wide association analyses (GWAS) in large case-control cohorts, fueled by rapid advances in next generation sequencing (NGS). Numerous AD-associated genomic variations are present at a common frequency in the general population, making these variants of public health significance. However, known AD-associated variants explain only a fraction of the expected heritability. In this dissertation, we demonstrate that systems biology applications that integrate evolutionary genomics, rare variants and structural variation can dissect the genetic architecture of AD and elucidate its heritability. We identified several complex human diseases, including AD and other brain disorders, as potential targets of natural selection forces in diverse world populations. Further evidence of natural selection forces affecting AD was revealed when we identified an association between eye color, a trait under strong selection, and AD. These findings provide strong support for conducting GWAS on brain disorder phenotypes. However, with the ever-increasing abundance of rare genomic variants and large cohorts of multi-ethnic samples, population stratification becomes a serious confounding factor for GWAS. To address this problem, we designed a novel approach to identify ancestry informative single nucleotide polymorphisms (SNPs) for population stratification adjustment in association analyses. Furthermore, to leverage untyped variants from genotyping arrays – particularly rare variants – for GWAS and meta-analysis through rapid imputation, we designed a tool that converts genotype definitions across various array platforms. To further elucidate the genetic heritability of brain disorders, we designed approaches aimed at identifying Copy Number Variations (CNVs) and viral insertions into the human genome. We conducted the first CNV-based whole genome meta-analysis for AD. We also designed an integrated approach to estimate the sensitivity of NGS-based methods of viral insertion detection. For the first time in the literature, we identified herpesvirus in NGS data from an Alzheimer’s disease brain sample. The work in this dissertation represents a three-faceted advance in our understanding of brain disease etiology: 1) evolutionary genomic insights, 2) novel resources and tools to leverage rare variants, and 3) the discovery of disease-associated structural genomic aberrations. Our findings have broad implications on the genetics of complex human disease and hold promise for delivering clinically useful knowledge and resources

Patterns of genetic variation in a prairie wildflower, Silphium integrifolium, suggest a non-prairie origin and locally adaptive variation

PREMISE: Understanding the relationship between genetic structure and geography provides information about a species’ history and can be used for breeding and conservation goals. The North American prairie is interesting because of its recent origin and subsequent fragmentation. Silphium integrifolium, an iconic perennial American prairie wildflower, is targeted for domestication, having undergone a few generations of improvement. We present the first application of population genetic data in this species to address the following goals: (1) improve breeding by characterizing genetic structure and (2) identify the species geographic origin and potential targets and drivers of selection during range expansion. METHODS: We developed a reference transcriptome as a genotyping reference for samples from throughout the species range. Population genetic analyses were used to describe patterns of genetic variation, and demographic modeling was used to characterize potential processes that shaped variation. Outlier scans for selection and associations with environmental variables were used to identify loci linked to putative targets and drivers of selection. RESULTS: Genetic variation partitioned samples into three geographic clusters. Patterns of variation and demographic modeling suggest that the species origin is in the American Southeast. Breeding program accessions are from the region with lowest observed genetic variation. CONCLUSIONS: This prairie species did not originate within the prairie. Breeding may be improved by including accessions from outside of the germplasm founding region. The geographic structuring and the identified targets and drivers of adaptation can guide collecting efforts toward populations with beneficial agronomic traits

The Genomics of Human Local Adaptation

Modern humans inhabit a variety of environments and are exposed to a plethora of selective pressures, leading to multiple genetic adaptations to local environmental conditions. These include adaptations to climate, UV exposure, disease, diet, altitude, or cultural practice and have generated important genetic and phenotypic differences amongst populations. In recent years, new methods to identify the genomic signatures of natural selection underlying these adaptations, combined with novel types of genetic data (e.g., ancient DNA), have provided unprecedented insights into the origin of adaptive alleles and the modes of adaptation. As a result, numerous instances of local adaptation have been identified in humans. Here, we review the most exciting recent developments and discuss, in our view, the future of this field

Candidate Sequence Variants for Polyautoimmunity and Multiple Autoimmune Syndrome from a Colombian Genetic Isolate: Implications for Population Genetics

Autoimmunity is an immunological disorder whereby patients have lost immunological tolerance to self-antigen. It has extreme financial and socioeconomic burden with costs of over 100 billion dollars in the USA alone, and an estimated prevalence of 9.4%, and evidence indicates that this estimate has increased at a rate of 5% per year for the past 3 years. These phenotypes can be manifested in more severe forms through polyautoimmunity, whereby patients are carrying 2 or more autoimmune conditions. In addition to that, there is also the most extreme phenotype of autoimmunity known as the Multiple Autoimmune Syndrome (MAS), consisting of cases where patients have 3 or more autoimmune diseases. These extreme phenotypes are extremely important for genetic research as will be elaborated upon in this thesis. For more than 20 years, pedigrees from the world’s largest known genetic isolate, from the Paisa region of Colombia have been ascertained and thoroughly followed by Dr. Juan-Manuel Anaya and Dr. Mauricio Arcos-Burgos. This population has maintained its status as a genetic isolate since the 16th century, during the early colonization by the Spanish Conquistadors. In this thesis, our attempts in identifying potential candidate variants potentially underpinning the genetic etiology of autoimmune conditions in this population is facilitated by the fact that families are derived from individuals carrying extreme phenotypes, from familial cohorts where genetic homogeneity is maximized. Candidates are identified in both sporadic as well as familial cases. This is primarily achieved through combination of linkage analysis and association tests for both rare and common variants, derived from variant-calling pipelines and that had undergone quality control, filtering and functional annotation, via bioinformatic anlayses. Genes harbouring variants with significant evidence of linkage and association were primarily involved in negative regulation of apoptosis, phagocytosis, regulation of endopeptidase activity, response to lipopolysaccharides and plasminogen urokinase receptor activity. These findings, that were obtained by utilizing the combinations of statistical as well as network-based analyses have relevant potential implications in autoimmunity, and can be further supported with additional studies

Population genomics of speciation and admixture

The application of population genomics to the understanding of speciation has led to the emerging field of speciation genomics. This has brought new insight into how divergence builds up within the genome during speciation and is also revealing the extent to which species can continue to exchange genetic material despite reproductive barriers. It is also providing powerful new approaches for linking genotype to phenotype in admixed populations. In this chapter, we give an overview of some of the methods that have been used and some of the novel insights gained. We also outline some of the pitfalls of the most commonly used methods and possible problems with interpretation of the results