Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit.

The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified relative to the population mean, inevitably differences in disease or trait prevalence among social strata influence the relationship between PGS and risk. Here we quantify the magnitude of PGS-by-Exposure (PGSxE) interactions for seven human diseases (coronary artery disease, type 2 diabetes, obesity thresholded to body mass index and to waist-to-hip ratio, inflammatory bowel disease, chronic kidney disease, and asthma) and pairs of 75 exposures in the White-British subset of the UK Biobank study (n=408,801). Across 24,198 PGSxE models, 746 (3.1%) were significant by two criteria, at least three-fold more than expected by chance under each criterion. Predictive accuracy is significantly improved in the high-risk exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. The predominant mechanism for PGS×E interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. We introduce the notion of the proportion needed to benefit (PNB) which is the cumulative number needed to treat across the range of the PGS and show that typically this is halved in the 70th to 80th percentile. These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of genome-wide association studies.

Genetically transitional disease: conceptual understanding and applicability to rheumatic disease.

In genomic medicine, the concept of genetically transitional disease (GTD) refers to cases in which gene mutation is necessary but not sufficient to cause disease. In this Perspective, we apply this novel concept to rheumatic diseases, which have been linked to hundreds of genetic variants via association studies. These variants are in the 'grey zone' between monogenic variants with large effect sizes and common susceptibility alleles with small effect sizes. Among genes associated with rare autoinflammatory diseases, many low-frequency and/or low-penetrance variants are known to increase susceptibility to systemic inflammation. In autoimmune diseases, hundreds of HLA and non-HLA genetic variants have been revealed to be modest- to moderate-risk alleles. These diseases can be reclassified as GTDs. The same concept could apply to many other human diseases. GTD could improve the reporting of genetic testing results, diagnostic yields, genetic counselling and selection of therapy, as well as facilitating research using a novel approach to human genetic diseases.

Loss of immune cell identity with age inferred from large atlases of single cell transcriptomes.

By analyzing two large atlases of almost 4 million cells, we show that immune-senescence involves a gradual loss of cellular identity, reflecting increased cellular heterogeneity, for effector, and cytotoxic immune cells. The effects are largely similar in both males and females and were robustly reproduced in two atlases, one assembled from 35 diverse studies including 678 adults, the other the OneK1K study of 982 adults. Since the mean transcriptional differences among cell-types remain constant across age deciles, there is little evidence for the alternative mechanism of convergence of cell-type identity. Key pathways promoting activation and stemness are down-regulated in aged T cells, while CD8 TEM and CD4 CTLs exhibited elevated inflammatory, and cytotoxicity in older individuals. Elevated inflammatory signaling pathways, such as MAPK and TNF-alpha signaling via NF-kB, also occur across all aged immune cells, particularly amongst effector immune cells. This finding of lost transcriptional identity with age carries several implications, spanning from a fundamental biological understanding of aging mechanisms to clinical perspectives on the efficacy of immunomodulation in elderly people.

Concordant B and T Cell Heterogeneity Inferred from the Multiomic Landscape of Peripheral Blood Mononuclear Cells in a Crohn's Disease Cohort.

BACKGROUND AND AIMS: Crohn's disease is characterized by inflammation in the gastrointestinal tract due to a combination of genetic, immune, and environmental factors. Transcriptomic and epigenomic profiling of intestinal tissue of Crohn's disease patients have revealed valuable insights into pathology, however have not been conducted jointly on less invasive peripheral blood mononuclear cells (PBMCs). Furthermore, the heterogeneous responses to treatments among individuals with Crohn's disease imply hidden diversity of pathological mechanisms. METHODS: We employed single nucleus multiomic analysis, integrating both snRNA-seq and snATAC-seq of PBMCs with a variety of open source bioinformatics applications. RESULTS: Our findings reveal a diverse range of transcriptional signatures among individuals, highlighting the heterogeneity in PBMC profiles. Nevertheless, striking concordance between three heterogeneous groups was observed across B cells and T cells. Differential gene regulatory mechanisms partially explain these profiles, notably including a signature involving TGFß signaling in two individuals with Crohn's disease. A mutation mapped to a transcription factor binding site within a differentially accessible peak associated with the expression of this pathway, with implications for a personalized approach to understanding disease pathology. CONCLUSIONS: This study highlights how multiomic analysis can reveal common regulatory mechanisms that underlie heterogeneity of PBMC profiles, one of which may be specific to inflammatory disease.

Landscape of pharmacogenetic variants associated with non-insulin antidiabetic drugs in the Indian population.

INTRODUCTION: Genetic variants contribute to differential responses to non-insulin antidiabetic drugs (NIADs), and consequently to variable plasma glucose control. Optimal control of plasma glucose is paramount to minimizing type 2 diabetes-related long-term complications. India's distinct genetic architecture and its exploding burden of type 2 diabetes warrants a population-specific survey of NIAD-associated pharmacogenetic (PGx) variants. The recent availability of large-scale whole genomes from the Indian population provides a unique opportunity to generate a population-specific map of NIAD-associated PGx variants. RESEARCH DESIGN AND METHODS: We mined 1029 Indian whole genomes for PGx variants, drug-drug interaction (DDI) and drug-drug-gene interactions (DDGI) associated with 44 NIADs. Population-wise allele frequencies were estimated and compared using Fisher's exact test. RESULTS: Overall, we found 76 known and 52 predicted deleterious common PGx variants associated with response to type 2 diabetes therapy among Indians. We report remarkable interethnic differences in the relative cumulative counts of decreased and increased response-associated alleles across NIAD classes. Indians and South Asians showed a significant excess of decreased metformin response-associated alleles compared with other global populations. Network analysis of shared PGx genes predicts high DDI risk during coadministration of NIADs with other metabolic disease drugs. We also predict an increased CYP2C19-mediated DDGI risk for CYP3A4/3A5-metabolized NIADs, saxagliptin, linagliptin and glyburide when coadministered with proton-pump inhibitors (PPIs). CONCLUSIONS: Indians and South Asians have a distinct PGx profile for antidiabetes drugs, marked by an excess of poor treatment response-associated alleles for various NIAD classes. This suggests the possibility of a population-specific reduced drug response in atleast some NIADs. In addition, our findings provide an actionable resource for accelerating future diabetes PGx studies in Indians and South Asians and reconsidering NIAD dosing guidelines to ensure maximum efficacy and safety in the population.

Integrative polygenic analysis of the protective effects of fatty acid metabolism on disease as modified by obesity.

Dysregulation of fatty acid metabolites can play a crucial role in the progression of complex diseases, such as cardiovascular disease, digestive diseases, and metabolic diseases. Metabolites can have either protective or risk effects on a disease; however, the details of such associations remain contentious. In this study, we demonstrate an integrative PheWAS approach to establish high confidence, causally suggestive of metabolite-disease associations for three fatty acid metabolites, namely, omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, for 1,254 disease endpoints. Metabolite-disease associations were established if there was a concordant direction of effect and significance for metabolite level and genetic risk score for the metabolite. There was enrichment for metabolite associations with diseases of the respiratory system for omega-3 fatty acids, diseases of the circulatory system and endocrine system for omega-6 fatty acids, and diseases of the digestive system for docosahexaenoic acid. Upon performing Mendelian randomization on a subset of the outcomes, we identified 3, 6, and 15 significant diseases associated with omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, respectively. We then demonstrate a class of prevalence-risk relationships indicative of (de)canalization of disease under high and low fatty acid metabolite levels. Finally, we show that the interaction between the metabolites and obesity demonstrates that the degree of protection afforded by fatty acid metabolites is strongly modulated by underlying metabolic health. This study evaluated the disease architectures of three polyunsaturated fatty acids (PUFAs), which were validated by several PheWAS modes of support. Our results not only highlight specific diseases associated with each metabolite but also disease group enrichments. In addition, we demonstrate an integrative PheWAS methodology that can be applied to other components of the human metabolome or other traits of interest. The results of this study can be used as an atlas to cross-compare genetic with non-genetic disease associations for the three PUFAs investigated. The findings can be explored through our R shiny app at https://pufa.biosci.gatech.edu.