Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization.

BACKGROUND: As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. RESULTS: In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation . CONCLUSIONS: GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.

Genome integrity as a potential index of longevity in Ashkenazi Centenarian's families.

The aging process, or senescence, is characterized by age-specific decline in physical and physiological function, and increased frailty and genomic changes, including mutation accumulation. However, the mechanisms through which changes in genomic architecture influence human longevity have remained obscure. Copy number variants (CNVs), an abundant class of genomic variants, offer unique opportunities for understanding age-related genomic changes. Here we report the spectrum of CNVs in a cohort of 670 Ashkenazi Jewish centenarians, their progeny, and unrelated controls. The average ages of these groups were 97.4 ± 2.8, 69.2 ± 9.2, and 66.5 ± 7.0 respectively. For the first time, we compared different size classes of CNVs, from 1 kB to 100 MB in size. Using a high-resolution custom Affymetrix array, targeting 44,639 genomic regions, we identified a total of 12,166, 22,188, and 10,285 CNVs in centenarians, their progeny, and control groups, respectively. Interestingly, the offspring group showed the highest number of unique CNVs, followed by control and centenarians. While both gains and losses were found in all three groups, centenarians showed a significantly higher average number of both total gains and losses relative to their controls (p < 0.0327, 0.0182, respectively). Moreover, centenarians showed a lower total length of genomic material lost, suggesting that they may maintain superior genomic integrity over time. We also observe a significance fold increase of CNVs among the offspring, implying greater genomic integrity and a putative mechanism for longevity preservation. Genomic regions that experienced loss or gains appear to be distributed across many sites in the genome and contain genes involved in DNA transcription, cellular transport, developmental pathways, and metabolic functions. Our findings suggest that the exceptional longevity observed in centenarians may be attributed to the prolonged maintenance of functionally important genes. These genes are intrinsic to specific genomic regions as well as to the overall integrity of the genomic architecture. Additionally, a strong association between longer CNVs and differential gene expression observed in this study supports the notion that genomic integrity could positively influence longevity.

Regulatory Landscape Enrichment Analysis (RLEA) using gaiaAssociation.

Motivation: To understand whether sets of genomic loci are enriched at the regulatory loci of one or more cell types, we developed the gaiaAssociation package to perform Regulatory Landscape Enrichment Analysis (RLEA). RLEA is a novel analytical process that tests for enrichment of sets of loci in cell type-specific open chromatin regions (OCRs) in the genome. Results: We demonstrate that the application of RLEA to genome-wide association study (GWAS) data reveals cell types likely to be mediating the phenotype studied, and clusters OCRs based on their shared regulatory profiles. GaiaAssociation is Python code that is freely available for use in functional genomics studies. Availability and Implementation: Gaia Association is available on PyPi (https://pypi.org/project/gaiaAssociation/0.6.0/#description) for pip download and use on the command line or as an inline Python package. Gaia Association can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation.

The Genomics of Colorectal Cancer in Populations with African and European Ancestry.

Black people have a higher incidence of colorectal cancer and worse survival rates when compared with white people. Comprehensive genomic profiling was performed in 46,140 colorectal adenocarcinoma cases. Ancestry-informative markers identified 5,301 patients of African descent (AFR) and 33,770 patients of European descent (EUR). AFR were younger, had fewer microsatellite instability-high (MSI-H) tumors, and had significantly more frequent alterations in KRAS, APC, and PIK3CA. AFR had increased frequency of KRAS mutations, specifically KRASG12D and KRASG13. There were no differences in rates of actionable kinase driver alterations (HER2, MET, NTRK, ALK, ROS1, and RET). In patients with young-onset colorectal cancer (<50 years), AFR and EUR had a similar frequency of MSI-H and tumor mutational burden-high (TMB-H) tumors, and strikingly different trends in APC mutations by age, as well as differences in MAPK pathway alterations. These findings inform treatment decisions, impact prognosis, and underscore the need for model systems representative of the diverse U.S. population. SIGNIFICANCE: KRAS (particularly KRASG12D/G13), APC, and PIK3CA were more frequently altered in AFR who had a lower frequency of MSI-H tumors. There were no differences in actionable kinase driver alterations. In young-onset colorectal cancer, both ancestries had a similar frequency of MSI-H/TMB-H tumors, but strikingly different trends in APC. See related commentary by Eng and Holowatyj, p. 1187. This article is highlighted in the In This Issue feature, p. 1171.

A general linear model-based approach for inferring selection to climate.

BACKGROUND: Many efforts have been made to detect signatures of positive selection in the human genome, especially those associated with expansion from Africa and subsequent colonization of all other continents. However, most approaches have not directly probed the relationship between the environment and patterns of variation among humans. We have designed a method to identify regions of the genome under selection based on Mantel tests conducted within a general linear model framework, which we call MAntel-GLM to Infer Clinal Selection (MAGICS). MAGICS explicitly incorporates population-specific and genome-wide patterns of background variation as well as information from environmental values to provide an improved picture of selection and its underlying causes in human populations. RESULTS: Our results significantly overlap with those obtained by other published methodologies, but MAGICS has several advantages. These include improvements that: limit false positives by reducing the number of independent tests conducted and by correcting for geographic distance, which we found to be a major contributor to selection signals; yield absolute rather than relative estimates of significance; identify specific geographic regions linked most strongly to particular signals of selection; and detect recent balancing as well as directional selection. CONCLUSIONS: We find evidence of selection associated with climate (P < 10-5) in 354 genes, and among these observe a highly significant enrichment for directional positive selection. Two of our strongest 'hits', however, ADRA2A and ADRA2C, implicated in vasoconstriction in response to cold and pain stimuli, show evidence of balancing selection. Our results clearly demonstrate evidence of climate-related signals of directional and balancing selection.

Variation at diabetes- and obesity-associated Loci may mirror neutral patterns of human population diversity and diabetes prevalence in India.

South Asian populations harbor a high degree of genetic diversity, due in part to demographic history. Two studies on genome-wide variation in Indian populations have shown that most Indian populations show varying degrees of admixture between ancestral north Indian and ancestral south Indian components. As a result of this structure, genetic variation in India appears to follow a geographic cline. Similarly, Indian populations seem to show detectable differences in diabetes and obesity prevalence between different geographic regions of the country. We tested the hypothesis that genetic variation at diabetes- and obesity-associated loci may be potentially related to different genetic ancestries. We genotyped 2977 individuals from 61 populations across India for 18 SNPs in genes implicated in T2D and obesity. We examined patterns of variation in allele frequency across different geographical gradients and considered state of origin and language affiliation. Our results show that most of the 18 SNPs show no significant correlation with latitude, the geographic cline reported in previous studies, or by language family. Exceptions include KCNQ1 with latitude and THADA and JAK1 with language, which suggests that genetic variation at previously ascertained diabetes-associated loci may only partly mirror geographic patterns of genome-wide diversity in Indian populations.

Genetic association analyses of atopic illness and proinflammatory cytokine genes with type 1 diabetes.

BACKGROUND: The genetic basis of the autoimmune disease type 1 diabetes (T1D) has now been largely determined, so now we can compare these findings with emerging genetic knowledge of disorders and phenotypes that have been negatively or positively associated with T1D historically. Here, we assessed the role in T1D of variants previously reported to be associated with atopic diseases and epithelial barrier function, profilaggrin (FLG), and those that affect the expression levels of the proinflammatory cytokines tumour necrosis factor (TNF)-α, interleukin (IL)-1ß, interferon (IFN)γ and IL-18. METHODS: We genotyped single nucleotide polymorphisms (SNPs): -105/rs28665122 in SELS or SEPS1 (selenoprotein), three single nucleotide polymorphisms in IL18 (-105/rs360717, +183/rs5744292 and +1467/rs574456) and R501X/rs61816761 in FLG, the major locus associated with atopic dermatitis and predisposing to asthma, in a minimum of 6743 T1D cases and 7864 controls. RESULTS: No evidence of T1D association was found for any of the SNPs we genotyped at FLG, SELS or IL18 (p≥0.03), nor with haplotypes of IL18 (p=0.82). Review of previous T1D genome-wide association results revealed that four (human leucocyte antigen (HLA), gasdermin B/ORM1 (Saccharomyces cerevisiae)-like/gasdermin B/, GSDMB/ORMDL3/GSDMA and IL2RB) of ten loci recently reported to be associated with asthma were associated with T1D (p≤0.005). CONCLUSIONS: These results show that there are shared genetic associations for atopy-related traits and T1D, and this might help in the future to understand the mechanisms, pathways and environmental factors that underpin the rapid rise in incidence of both disorders in children.

Genetic variation and population structure of interleukin genes among seven ethnic populations from Karnataka, India.

The extent of genetic variation and the degree of genetic differentiation among seven ethnic populations from Karnataka, India (Bunt, Havyak, Iyengar, Lingayath, Smartha, Vaishya, Vokkaliga), was investigated using four single nucleotide polymorphisms (SNPs: IL-1A 4845, IL-1B 3954, IL-1B 511 and IL-1RA 2018) of the interleukin gene cluster. Allele frequencies varied by threefold among these populations, which also differed for gene diversity and heterozygosity levels. The average degree of population subdivision among these castes was low (F(ST) = 0.02). However, pair-wise interpopulation differentiation ranged from 0-7%, indicating no detectable differentiation to moderate differentiation between specific populations. The results of phylogenetic analysis based on genetic distances between populations agreed with known social and cultural data on these ethnic groups. Variation in the allele frequencies, as well as differentiation, may be attributed to differential selection and demographic factors including consanguinity among the ethnic groups. Information on the distribution of functionally relevant polymorphisms among ethnic populations may be important towards developing community medicine and public health policies.

Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators.

Although androgen receptor (AR)-mediated signaling is central to prostate cancer, the ability to modulate AR signaling states is limited. Here we establish a chemical genomic approach for discovery and target prediction of modulators of cancer phenotypes, as exemplified by AR signaling. We first identify AR activation inhibitors, including a group of structurally related compounds comprising celastrol, gedunin, and derivatives. To develop an in silico approach for target pathway identification, we apply a gene expression-based analysis that classifies HSP90 inhibitors as having similar activity to celastrol and gedunin. Validating this prediction, we demonstrate that celastrol and gedunin inhibit HSP90 activity and HSP90 clients, including AR. Broadly, this work identifies new modes of HSP90 modulation through a gene expression-based strategy.

Linkage disequilibria and haplotype structure of four SNPs of the interleukin 1 gene cluster in seven Asian Indian populations.

Variation at four single nucleotide polymorphism (SNP) sites of the interleukin 1 (IL1) gene cluster was investigated among 280 unrelated individuals, representing 7 caste groups from the state of Karnataka, India, and one European American community of Boston, Massachusetts. Allele and haplotype frequencies, strength of linkage disequilibrium, and signatures of recombination varied considerably among populations. Variable community sizes and traditions of consanguinity may account for the observed variation.