Genetic disease risks of under-represented founder populations in New York City.

The detection of founder pathogenic variants, those observed in high frequency only in a group of individuals with increased inter-relatedness, can help improve delivery of health care for that community. We identified 16 groups with shared ancestry, based on genomic segments that are shared through identity by descent (IBD), in New York City using the genomic data of 25,366 residents from the All Of Us Research Program and the Mount Sinai Bio biobank. From these groups we defined 8 as founder populations, mostly communities currently under-represented in medical genomics research, such as Puerto Rican, Garifuna and Filipino/Pacific Islanders.

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.

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.

Using epigenomics to understand cellular responses to environmental influences in diseases.

It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures.

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

Unlabelled: 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).

Genome-wide analysis of cold adaptation in indigenous Siberian populations.

Following the dispersal out of Africa, where hominins evolved in warm environments for millions of years, our species has colonised different climate zones of the world, including high latitudes and cold environments. The extent to which human habitation in (sub-)Arctic regions has been enabled by cultural buffering, short-term acclimatization and genetic adaptations is not clearly understood. Present day indigenous populations of Siberia show a number of phenotypic features, such as increased basal metabolic rate, low serum lipid levels and increased blood pressure that have been attributed to adaptation to the extreme cold climate.

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).

Human expression QTLs are enriched in signals of environmental adaptation.

Expression quantitative trait loci (eQTLs) have been found to be enriched in trait-associated single-nucleotide polymorphisms (SNPs). However, whether eQTLs are adaptive to different environmental factors and its relative evolutionary significance compared with nonsynonymous SNPs (NS SNPs) are still elusive. Compiling environmental correlation data from three studies for more than 500,000 SNPs and 42 environmental factors, including climate, subsistence, pathogens, and dietary patterns, we performed a systematic examination of the adaptive patterns of eQTLs to local environment.

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.

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)-1β, 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.

Validating discovered Cis-acting regulatory genetic variants: application of an allele specific expression approach to HapMap populations.

Background: Localising regulatory variants that control gene expression is a challenge for genome research. Several studies have recently identified non-coding polymorphisms associated with inter-individual differences in gene expression. These approaches rely on the identification of signals of association against a background of variation due to other genetic and environmental factors.

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.

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.

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.