DNA sequence variation within human leukocyte antigen (HLA) genes mediate susceptibility to a wide range of human diseases. The complex genetic structure of the major histocompatibility complex (MHC) ...makes it difficult, however, to collect genotyping data in large cohorts. Long-range linkage disequilibrium between HLA loci and SNP markers across the major histocompatibility complex (MHC) region offers an alternative approach through imputation to interrogate HLA variation in existing GWAS data sets. Here we describe a computational strategy, SNP2HLA, to impute classical alleles and amino acid polymorphisms at class I (HLA-A, -B, -C) and class II (-DPA1, -DPB1, -DQA1, -DQB1, and -DRB1) loci. To characterize performance of SNP2HLA, we constructed two European ancestry reference panels, one based on data collected in HapMap-CEPH pedigrees (90 individuals) and another based on data collected by the Type 1 Diabetes Genetics Consortium (T1DGC, 5,225 individuals). We imputed HLA alleles in an independent data set from the British 1958 Birth Cohort (N = 918) with gold standard four-digit HLA types and SNPs genotyped using the Affymetrix GeneChip 500 K and Illumina Immunochip microarrays. We demonstrate that the sample size of the reference panel, rather than SNP density of the genotyping platform, is critical to achieve high imputation accuracy. Using the larger T1DGC reference panel, the average accuracy at four-digit resolution is 94.7% using the low-density Affymetrix GeneChip 500 K, and 96.7% using the high-density Illumina Immunochip. For amino acid polymorphisms within HLA genes, we achieve 98.6% and 99.3% accuracy using the Affymetrix GeneChip 500 K and Illumina Immunochip, respectively. Finally, we demonstrate how imputation and association testing at amino acid resolution can facilitate fine-mapping of primary MHC association signals, giving a specific example from type 1 diabetes.
Motivation: Genome-wide association studies (GWASs) have been widely used to map loci contributing to variation in complex traits and risk of diseases in humans. Accurate specification of familial ...relationships is crucial for family-based GWAS, as well as in population-based GWAS with unknown (or unrecognized) family structure. The family structure in a GWAS should be routinely investigated using the SNP data prior to the analysis of population structure or phenotype. Existing algorithms for relationship inference have a major weakness of estimating allele frequencies at each SNP from the entire sample, under a strong assumption of homogeneous population structure. This assumption is often untenable. Results: Here, we present a rapid algorithm for relationship inference using high-throughput genotype data typical of GWAS that allows the presence of unknown population substructure. The relationship of any pair of individuals can be precisely inferred by robust estimation of their kinship coefficient, independent of sample composition or population structure (sample invariance). We present simulation experiments to demonstrate that the algorithm has sufficient power to provide reliable inference on millions of unrelated pairs and thousands of relative pairs (up to 3rd-degree relationships). Application of our robust algorithm to HapMap and GWAS datasets demonstrates that it performs properly even under extreme population stratification, while algorithms assuming a homogeneous population give systematically biased results. Our extremely efficient implementation performs relationship inference on millions of pairs of individuals in a matter of minutes, dozens of times faster than the most efficient existing algorithm known to us. Availability: Our robust relationship inference algorithm is implemented in a freely available software package, KING, available for download at http://people.virginia.edu/∼wc9c/KING. Contact: wmchen@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Genome-wide strategies have driven the discovery of more than 300 susceptibility loci for autoimmune diseases. However, for almost all loci, understanding of the mechanisms leading to autoimmunity ...remains limited, and most variants that are likely to be causal are in non-coding regions of the genome. A critical next step will be to identify the in vivo and ex vivo immunophenotypes that are affected by risk variants. To do this, key cell types and cell states that are implicated in autoimmune diseases will need to be defined. Functional genomic annotations from these cell types and states can then be used to resolve candidate genes and causal variants. Together with longitudinal studies, this approach may yield pivotal insights into how autoimmunity is triggered.
The convergence of advances in medical science, human biology, data science, and technology has enabled the generation of new insights into the phenotype known as "diabetes." Increased knowledge of ...this condition has emerged from populations around the world, illuminating the differences in how diabetes presents, its variable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and opportunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be done to realize its potential. This, combined with a subsequent, detailed evidence-based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.
Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely ...incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.
In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.
The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve AUC 0.92;
< 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.
An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.
Kwashiorkor, an enigmatic form of severe acute malnutrition, is the consequence of inadequate nutrient intake plus additional environmental insults. To investigate the role of the gut microbiome, we ...studied 317 Malawian twin pairs during the first 3 years of life. During this time, half of the twin pairs remained well nourished, whereas 43% became discordant, and 7% manifested concordance for acute malnutrition. Both children in twin pairs discordant for kwashiorkor were treated with a peanut-based, ready-to-use therapeutic food (RUTF). Time-series metagenomic studies revealed that RUTF produced a transient maturation of metabolic functions in kwashiorkor gut microbiomes that regressed when administration of RUTF was stopped. Previously frozen fecal communities from several discordant pairs were each transplanted into gnotobiotic mice. The combination of Malawian diet and kwashiorkor microbiome produced marked weight loss in recipient mice, accompanied by perturbations in amino acid, carbohydrate, and intermediary metabolism that were only transiently ameliorated with RUTF. These findings implicate the gut microbiome as a causal factor in kwashiorkor.
Genetics of type 1 diabetes .Robertson, Catherine C; Rich, Stephen S
Current opinion in genetics & development,
June 2018, 2018-06-00, 20180601, Letnik:
50
Journal Article
Recenzirano
Type 1 diabetes is the most common autoimmune disorder in childhood, characterized by the development of autoimmunity through unknown environmental insults in genetically susceptible individuals. ...There are now over 50 regions of the genome that harbor type 1 diabetes susceptibility genes, with much of the genetic risk now well-defined in youth of Northern European ancestry. The impact of these genetic variants on initiation and progression of the autoimmune process (islet autoimmunity) is now being understood; however, studies are only now being conducted to understand the function of the variants associated with type 1 diabetes risk. The characteristics of genetic risk of type 1 diabetes across the age spectrum suggests extensive complexity in biological and environmental mechanisms. We propose that both genetic and environmental factors attributed to type 1 and type 2 diabetes may contribute to beta-cell death through multiple mechanisms, leading to the clinical outcome of type 1 diabetes.
Genetics of type 1A diabetes Concannon, Patrick; Rich, Stephen S; Nepom, Gerald T
The New England journal of medicine,
04/2009, Letnik:
360, Številka:
16
Journal Article
Depressive disorders often run in families, which, in addition to the genetic component, may point to the microbiome as a causative agent. Here, we employed a combination of behavioral, molecular and ...computational techniques to test the role of the microbiota in mediating despair behavior. In chronically stressed mice displaying despair behavior, we found that the microbiota composition and the metabolic signature dramatically change. Specifically, we observed reduced Lactobacillus and increased circulating kynurenine levels as the most prominent changes in stressed mice. Restoring intestinal Lactobacillus levels was sufficient to improve the metabolic alterations and behavioral abnormalities. Mechanistically, we identified that Lactobacillus-derived reactive oxygen species may suppress host kynurenine metabolism, by inhibiting the expression of the metabolizing enzyme, IDO1, in the intestine. Moreover, maintaining elevated kynurenine levels during Lactobacillus supplementation diminished the treatment benefits. Collectively, our data provide a mechanistic scenario for how a microbiota player (Lactobacillus) may contribute to regulating metabolism and resilience during stress.
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