Systematic detection of positive selection in the human-pathogen interactome and lasting effects on infectious disease susceptibility.

Infectious disease has shaped the natural genetic diversity of humans throughout the world. A new approach to capture positive selection driven by pathogens would provide information regarding pathogen exposure in distinct human populations and the constantly evolving arms race between host and disease-causing agents. We created a human pathogen interaction database and used the integrated haplotype score (iHS) to detect recent positive selection in genes that interact with proteins from 26 different pathogens. We used the Human Genome Diversity Panel to identify specific populations harboring pathogen-interacting genes that have undergone positive selection. We found that human genes that interact with 9 pathogen species show evidence of recent positive selection. These pathogens are Yersenia pestis, human immunodeficiency virus (HIV) 1, Zaire ebolavirus, Francisella tularensis, dengue virus, human respiratory syncytial virus, measles virus, Rubella virus, and Bacillus anthracis. For HIV-1, GWAS demonstrate that some naturally selected variants in the host-pathogen protein interaction networks continue to have functional consequences for susceptibility to these pathogens. We show that selected human genes were enriched for HIV susceptibility variants (identified through GWAS), providing further support for the hypothesis that ancient humans were exposed to lentivirus pandemics. Human genes in the Italian, Miao, and Biaka Pygmy populations that interact with Y. pestis show significant signs of selection. These results reveal some of the genetic footprints created by pathogens in the human genome that may have left lasting marks on susceptibility to infectious disease.

Systematic correlation of environmental exposure and physiological and self-reported behaviour factors with leukocyte telomere length.

Background: It is hypothesized that environmental exposures and behaviour influence telomere length, an indicator of cellular ageing. We systematically associated 461 indicators of environmental exposures, physiology and self-reported behaviour with telomere length in data from the US National Health and Nutrition Examination Survey (NHANES) in 1999-2002. Further, we tested whether factors identified in the NHANES participants are also correlated with gene expression of telomere length modifying genes. Methods: We correlated 461 environmental exposures, behaviours and clinical variables with telomere length, using survey-weighted linear regression, adjusting for sex, age, age squared, race/ethnicity, poverty level, education and born outside the USA, and estimated the false discovery rate to adjust for multiple hypotheses. We conducted a secondary analysis to investigate the correlation between identified environmental variables and gene expression levels of telomere-associated genes in publicly available gene expression samples. Results: After correlating 461 variables with telomere length, we found 22 variables significantly associated with telomere length after adjustment for multiple hypotheses. Of these varaibales, 14 were associated with longer telomeres, including biomarkers of polychlorinated biphenyls([PCBs; 0.1 to 0.2 standard deviation (SD) increase for 1 SD increase in PCB level, P < 0.002] and a form of vitamin A, retinyl stearate. Eight variables associated with shorter telomeres, including biomarkers of cadmium, C-reactive protein and lack of physical activity. We could not conclude that PCBs are correlated with gene expression of telomere-associated genes. Conclusions: Both environmental exposures and chronic disease-related risk factors may play a role in telomere length. Our secondary analysis found no evidence of association between PCBs/smoking and gene expression of telomere-associated genes. All correlations between exposures, behaviours and clinical factors and changes in telomere length will require further investigation regarding biological influence of exposure.

Systematic identification of DNA variants associated with ultraviolet radiation using a novel Geographic-Wide Association Study (GeoWAS).

BACKGROUND: Long-term environmental variables are widely understood to play important roles in DNA variation. Previously, clinical studies examining the impacts of these variables on the human genome were localized to a single country, and used preselected DNA variants. Furthermore, clinical studies or surveys are either not available or difficult to carry out for developing countries. A systematic approach utilizing bioinformatics to identify associations among environmental variables, genetic variation, and diseases across various geographical locations is needed but has been lacking. METHODS: Using a novel Geographic-Wide Association Study (GeoWAS) methodology, we identified Single Nucleotide Polymorphisms (SNPs) in the Human Genome Diversity Project (HGDP) with population allele frequencies associated geographical ultraviolet radiation exposure, and then assessed the diseases known to be assigned with these SNPs. RESULTS: 2,857 radiation SNPs were identified from over 650,000 SNPs in 52 indigenous populations across the world. Using a quantitative disease-SNP database curated from 5,065 human genetic papers, we identified disease associations with those radiation SNPs. The correlation of the rs16891982 SNP in the SLC45A2 gene with melanoma was used as a case study for analysis of disease risk, and the results were consistent with the incidence and mortality rates of melanoma in published scientific literature. Finally, by analyzing the ontology of genes in which the radiation SNPs were significantly enriched, potential associations between SNPs and neurological disorders such as Alzheimer's disease were hypothesized. CONCLUSION: A systematic approach using GeoWAS has enabled us to identify DNA variation associated with ultraviolet radiation and their connections to diseases such as skin cancers. Our analyses have led to a better understating at the genetic level of why certain diseases are more predominant in specific geographical locations, due to the interactions between environmental variables such as ultraviolet radiation and the population types in those regions. The hypotheses proposed in GeoWAS can lead to future testing and interdisciplinary research.

Gene flow from North Africa contributes to differential human genetic diversity in southern Europe.

Human genetic diversity in southern Europe is higher than in other regions of the continent. This difference has been attributed to postglacial expansions, the demic diffusion of agriculture from the Near East, and gene flow from Africa. Using SNP data from 2,099 individuals in 43 populations, we show that estimates of recent shared ancestry between Europe and Africa are substantially increased when gene flow from North Africans, rather than Sub-Saharan Africans, is considered. The gradient of North African ancestry accounts for previous observations of low levels of sharing with Sub-Saharan Africa and is independent of recent gene flow from the Near East. The source of genetic diversity in southern Europe has important biomedical implications; we find that most disease risk alleles from genome-wide association studies follow expected patterns of divergence between Europe and North Africa, with the principal exception of multiple sclerosis.

Analysis of the genetic basis of disease in the context of worldwide human relationships and migration.

Genetic diversity across different human populations can enhance understanding of the genetic basis of disease. We calculated the genetic risk of 102 diseases in 1,043 unrelated individuals across 51 populations of the Human Genome Diversity Panel. We found that genetic risk for type 2 diabetes and pancreatic cancer decreased as humans migrated toward East Asia. In addition, biliary liver cirrhosis, alopecia areata, bladder cancer, inflammatory bowel disease, membranous nephropathy, systemic lupus erythematosus, systemic sclerosis, ulcerative colitis, and vitiligo have undergone genetic risk differentiation. This analysis represents a large-scale attempt to characterize genetic risk differentiation in the context of migration. We anticipate that our findings will enable detailed analysis pertaining to the driving forces behind genetic risk differentiation.

Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases.

Many disease-susceptible SNPs exhibit significant disparity in ancestral and derived allele frequencies across worldwide populations. While previous studies have examined population differentiation of alleles at specific SNPs, global ethnic patterns of ensembles of disease risk alleles across human diseases are unexamined. To examine these patterns, we manually curated ethnic disease association data from 5,065 papers on human genetic studies representing 1,495 diseases, recording the precise risk alleles and their measured population frequencies and estimated effect sizes. We systematically compared the population frequencies of cross-ethnic risk alleles for each disease across 1,397 individuals from 11 HapMap populations, 1,064 individuals from 53 HGDP populations, and 49 individuals with whole-genome sequences from 10 populations. Type 2 diabetes (T2D) demonstrated extreme directional differentiation of risk allele frequencies across human populations, compared with null distributions of European-frequency matched control genomic alleles and risk alleles for other diseases. Most T2D risk alleles share a consistent pattern of decreasing frequencies along human migration into East Asia. Furthermore, we show that these patterns contribute to disparities in predicted genetic risk across 1,397 HapMap individuals, T2D genetic risk being consistently higher for individuals in the African populations and lower in the Asian populations, irrespective of the ethnicity considered in the initial discovery of risk alleles. We observed a similar pattern in the distribution of T2D Genetic Risk Scores, which are associated with an increased risk of developing diabetes in the Diabetes Prevention Program cohort, for the same individuals. This disparity may be attributable to the promotion of energy storage and usage appropriate to environments and inconsistent energy intake. Our results indicate that the differential frequencies of T2D risk alleles may contribute to the observed disparity in T2D incidence rates across ethnic populations.

Extreme evolutionary disparities seen in positive selection across seven complex diseases.

Positive selection is known to occur when the environment that an organism inhabits is suddenly altered, as is the case across recent human history. Genome-wide association studies (GWASs) have successfully illuminated disease-associated variation. However, whether human evolution is heading towards or away from disease susceptibility in general remains an open question. The genetic-basis of common complex disease may partially be caused by positive selection events, which simultaneously increased fitness and susceptibility to disease. We analyze seven diseases studied by the Wellcome Trust Case Control Consortium to compare evidence for selection at every locus associated with disease. We take a large set of the most strongly associated SNPs in each GWA study in order to capture more hidden associations at the cost of introducing false positives into our analysis. We then search for signs of positive selection in this inclusive set of SNPs. There are striking differences between the seven studied diseases. We find alleles increasing susceptibility to Type 1 Diabetes (T1D), Rheumatoid Arthritis (RA), and Crohn's Disease (CD) underwent recent positive selection. There is more selection in alleles increasing, rather than decreasing, susceptibility to T1D. In the 80 SNPs most associated with T1D (p-value <7.01 x 10(-5)) showing strong signs of positive selection, 58 alleles associated with disease susceptibility show signs of positive selection, while only 22 associated with disease protection show signs of positive selection. Alleles increasing susceptibility to RA are under selection as well. In contrast, selection in SNPs associated with CD favors protective alleles. These results inform the current understanding of disease etiology, shed light on potential benefits associated with the genetic-basis of disease, and aid in the efforts to identify causal genetic factors underlying complex disease.