Dataset Information

The Impact of Evolutionary Driving Forces on Human Complex Diseases: A Population Genetics Approach.

ABSTRACT: Investigating the molecular evolution of human genome has paved the way to understand genetic adaptation of humans to the environmental changes and corresponding complex diseases. In this review, we discussed the historical origin of genetic diversity among human populations, the evolutionary driving forces that can affect genetic diversity among populations, and the effects of human movement into new environments and gene flow on population genetic diversity. Furthermore, we presented the role of natural selection on genetic diversity and complex diseases. Then we reviewed the disadvantageous consequences of historical selection events in modern time and their relation to the development of complex diseases. In addition, we discussed the effect of consanguinity on the incidence of complex diseases in human populations. Finally, we presented the latest information about the role of ancient genes acquired from interbreeding with ancient hominids in the development of complex diseases.

SUBMITTER: Saeb AT

PROVIDER: S-EPMC4904122 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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The Impact of Evolutionary Driving Forces on Human Complex Diseases: A Population Genetics Approach.

Saeb Amr T M AT Al-Naqeb Dhekra D

Scientifica 20160530

Investigating the molecular evolution of human genome has paved the way to understand genetic adaptation of humans to the environmental changes and corresponding complex diseases. In this review, we discussed the historical origin of genetic diversity among human populations, the evolutionary driving forces that can affect genetic diversity among populations, and the effects of human movement into new environments and gene flow on population genetic diversity. Furthermore, we presented the role ...[more]

PMID: 27313952

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Project description:Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high-throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history.Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with many human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral.Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enable deleterious rare variants to persist at low frequencies just as long as neutral variants, but low-frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function and that potentially contribute to disease susceptibility.The key question for sequencing-based association studies of complex traits is how to distinguish between deleterious and benign genetic variation. We used population genetic simulations to uncover patterns of genetic variation that distinguish these two categories, especially derived allele age, thereby providing inroads into novel methods for characterizing rare genetic variation driving complex diseases.

Project description:To generate a parsimonious gene set for understanding the mechanisms underlying complex diseases, we reasoned it was necessary to combine the curation of public literature, review of experimental databases and interpolation of pathway-associated genes. Using this strategy, we previously built the following two databases for reproductive disorders: The Database for Preterm Birth (dbPTB) and The Database for Preeclampsia (dbPEC). The completeness and accuracy of these databases is essential for supporting our understanding of these complex conditions. Given the exponential increase in biomedical literature, it is becoming increasingly difficult to manually maintain these databases. Using our curated databases as reference data sets, we implemented a machine learning-based approach to optimize article selection for manual curation. We used logistic regression, random forests and neural networks as our machine learning algorithms to classify articles. We examined features derived from abstract text, annotations and metadata that we hypothesized would best classify articles with genetically relevant content associated to the disorder of interest. Combinations of these features were used build the classifiers and the performance of these feature sets were compared to a standard 'Bag-of-Words'. Several combinations of these genetic based feature sets outperformed 'Bag-of-Words' at a threshold such that 95% of the curated gene set obtained from the original manual curation of all articles were extracted from the articles classified by machine learning as 'considered'. The performance was superior in terms of the reduction of required manual curation and two measures of the harmonic mean of precision and recall. The reduction in workload ranged from 0.814 to 0.846 for the dbPTB and 0.301 to 0.371 for the dbPEC. Additionally, a database of metadata and annotations is generated which allows for rapid query of individual features. Our results demonstrate that machine learning algorithms can identify articles with relevant data for databases of genes associated with complex diseases.

Project description:BACKGROUND: Nuclear genome size varies 300 000-fold, whereas transcriptome size varies merely 17-fold. In the largest genomes nearly all DNA is non-genic secondary DNA, mostly intergenic but also within introns. There is now compelling evidence that secondary DNA is functional, i.e. positively selected by organismal selection, not the purely neutral or 'selfish' outcome of mutation pressure. The skeletal DNA theory argued that nuclear volumes are genetically determined primarily by nuclear DNA amounts, modulated somewhat by genes affecting the degree of DNA packing or unfolding; the huge spread of nuclear genome sizes is the necessary consequence of the origin of the nuclear envelope and the nucleation of its assembly by DNA, plus the adaptively significant 300 000-fold range of cell volumes and selection for balanced growth by optimizing karyoplasmic volume ratios (essentially invariant with cell volume in growing/multiplying cells). This simple explanation of the C-value paradox is refined here in the light of new insights into the nature of heterochromatin and the nuclear lamina, the genetic control of cell volume, and large-scale eukaryote phylogeny, placing special emphasis on protist test cases of the basic principles of nuclear genome size evolution. GENOME MINIATURIZATION: and Expansion Intracellular parasites (e.g. Plasmodium, microsporidia) dwarfed their genomes by gene loss and eliminating virtually all secondary DNA. The primary driving forces for genome reduction are metabolic and spatial economy and cell multiplication speed. Most extreme nuclear shrinkage yielded genomes as tiny as 0.38 Mb (making the nuclear genome size range effectively 1.8 million-fold!) in some minute enslaved nuclei (nucleomorphs) of cryptomonads and chlorarachneans, chimaeric cells that also retain a separate normal large nucleus. The latter shows typical correlation between genome size and cell volume, but nucleomorphs do not despite co-existing in the same cell for >500 My. Thus mutation pressure does not inexorably increase genome size; selection can eliminate essentially all non-coding DNA if need be. Nucleomorphs and microsporidia even reduced gene size. Expansion of secondary DNA in the main nucleus, and in large-celled eukaryotes generally, must be positively selected for function. Ciliate nuclear dimorphism provides a key test that refutes the selfish DNA and strongly supports the skeletal DNA/karyoplasmic ratio interpretation of genome size evolution. GENETIC CONTROL OF CELL VOLUME IS MULTIGENIC: The quantitatively proportional correlation between genome size and cell size cannot be explained by purely mutational theories, as eukaryote cell volumes are causally determined by cell cycle control genes, not by DNA amounts.

Project description:BackgroundIn the last four decades, the Asian tiger mosquito, Aedes albopictus, vector of several human arboviruses, has spread from its native range in South-East Asia to all over the world, largely through the transportation of its eggs via the international trade in used tires. Albania was the first country invaded in Europe in 1979, followed by Italy in 1990 and other Mediterranean countries after 2000.Methods/principal findingsWe here inferred the invasion history and migration patterns of Ae. albopictus in Italy (today the most heavily-infested country in Europe), Greece and Albania, by analyzing a panel of >100,000 single nucleotide polymorphisms (SNPs) obtained by sequencing of double-digest Restriction site-Associated DNA (ddRADseq). The obtained dataset was combined with samples previously analyzed from both the native and invasive range worldwide to interpret the results using a broader spatial and historical context. The emerging evolutionary scenario complements the results of other studies in showing that the extraordinary worldwide expansion of Ae. albopictus has occurred thanks to multiple independent invasions by large numbers of colonists from multiple geographic locations in both native and previously invaded areas, consistently with the role of used tires shipments to move large numbers of eggs worldwide. By analyzing mosquitoes from nine sites across ~1,000-km transect in Italy, we were able to detect a complex interplay of drift, isolation by distance mediated divergence, and gene flow in shaping the species very recent invasion and range expansion, suggesting overall high connectivity, likely due to passive transportation of adults via ground transportation, as well as specific adaptations to local conditions.Conclusions/significanceResults contribute to characterize one of the most successful histories of animal invasion, and could be used as a baseline for future studies to track epidemiologically relevant characters (e.g. insecticide resistance).

Dataset Information

The Impact of Evolutionary Driving Forces on Human Complex Diseases: A Population Genetics Approach.

Publications

The Impact of Evolutionary Driving Forces on Human Complex Diseases: A Population Genetics Approach.

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