A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer’s Disease or Mild Cognitive Impairment

Objectives. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain genes have been identified as important clinical risk factors for AD, and technological advances in genomic research, such as genome-wide association studies (GWAS), allow for analysis of polymorphisms and have been widely applied to studies of AD. However, shortcomings of GWAS include sensitivity to sample size and hereditary deletions, which result in low classification and predictive accuracy. Therefore, this paper proposes a novel deep-learning genomics approach and applies it to multitasking classification of AD progression, with the goal of identifying novel genetic biomarkers overlooked by traditional GWAS analysis. Methods. In this study, we selected genotype data from 1461 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative, including 622 AD, 473 mild cognitive impairment (MCI), and 366 healthy control (HC) subjects. The proposed deep-learning genomics (DLG) approach consists of three steps: quality control, coding of single-nucleotide polymorphisms, and classification. The ResNet framework was used for the DLG model, and the results were compared with classifications by simple convolutional neural network structure. All data were randomly assigned to one training/validation group and one test group at a ratio of 9 : 1. And fivefold cross-validation was used. Results. We compared classification results from the DLG model to those from traditional GWAS analysis among the three groups. For the AD and HC groups, the accuracy, sensitivity, and specificity of classification were, respectively, 98.78 ± 1.50 % , 98.39 % ± 2.50 % , and 99.44 % ± 1.11 % using the DLG model, while 71.38 % ± 0.63 % , 63.13 % ± 2.87 % , and 85.59 % ± 6.66 % using traditional GWAS. Similar results were obtained from the other two intergroup classifications. Conclusion. The DLG model can achieve higher accuracy and sensitivity when applied to progression of AD. More importantly, we discovered several novel genetic biomarkers of AD progression, including rs6311 and rs6313 in HTR2A, rs1354269 in NAV2, and rs690705 in RFC3. The roles of these novel loci in AD should be explored in future research.

Genome-wide association analysis reveals quantitative trait loci and candidate genes involved in yield components under multiple field environments in cotton (Gossypium hirsutum)

Background Numerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis. However, most of the phenotypic data were from a single or few environments, and the stable loci remained to be validated under multiple field environments. Results Here, 242 upland cotton accessions collected from different origins were continuously investigated for phenotypic data of four main yield components, including boll weight (BW) and lint percentage (LP) under 13 field environments, and boll number per plant (BN) and seed index (SI) under 11 environments. Correlation analysis revealed a positive correlation between BN and LP, BW and SI, while SI had a negative correlation with LP and BN. Genetic analysis indicated that LP had the highest heritability estimates of 94.97%, followed by 92.08% for SI, 86.09% for BW, and 72.92% for BN, indicating LP and SI were more suitable traits for genetic improvement. Based on 56,010 high-quality single nucleotide polymorphisms (SNPs) and GWAS analysis, a total of 95 non-redundant QTLs were identified, including 12 of BN, 23 of BW, 45 of LP, and 33 of SI, respectively. Of them, 10 pairs of homologous QTLs were detected between A and D sub-genomes. We also found that 15 co-located QTLs with more than two traits and 12 high-confidence QTLs were detected under more than six environments, respectively. Further, two NET genes (GH_A08G0716 and GH_A08G0783), located in a novel QTL hotspot (qtl24, qtl25 and qlt26) were predominately expressed in early fiber development stages, exhibited significant correlation with LP and SI. The GH_A07G1389 in the stable qtl19 region encoded a tetratricopeptide repeat (TPR)-like superfamily protein and was a homologous gene involved in short fiber mutant ligon lintless-y (Liy), implying important roles in cotton yield. Conclusions The present study provides a foundation for understanding the regulatory mechanisms of yield components and may enhance yield improvement through molecular breeding in cotton.

Evolutionary Genomic and Bacterial Genome-Wide Association Study of Mycobacterium avium subsp. paratuberculosis and Dairy Cattle Johne's Disease Phenotypes

ABSTRACT Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease in ruminants, which has important health consequences for dairy cattle. The Regional Dairy Quality Management Alliance (RDQMA) project is a multistate research program involving MAP isolates taken from three intensively studied commercial dairy farms in the northeastern United States, which emphasized longitudinal data collection of both MAP isolates and animal health in three regional dairy herds for a period of about 7 years. This paper reports the results of a pan-GWAS analysis involving 318 MAP isolates and dairy cow Johne’s disease phenotypes, taken from these three farms. Based on our highly curated accessory gene count, the pan-GWAS analysis identified several MAP genes associated with bovine Johne’s disease phenotypes scored from these three farms, with some of the genes having functions suggestive of possible cause/effect relationships with these phenotypes. This paper reports a pangenomic comparative analysis between MAP and Mycobacterium tuberculosis, assessing functional Gene Ontology category enrichments between these taxa. Finally, we also provide a population genomic perspective on the effectiveness of herd isolation, involving closed dairy farms, in preventing MAP interfarm cross infection on a microgeographic scale. IMPORTANCE Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease in ruminants, which has important health consequences for dairy cattle and enormous economic consequences for the dairy industry. Understanding which genes in this bacterium are correlated with key disease phenotypes can lead to functional experiments targeting these genes and ultimately lead to improved control strategies. This study represents a rare example of a prolonged longitudinal study of dairy cattle where the disease was measured and the bacteria were isolated from the same cows. The genome sequences of over 300 MAP isolates were analyzed for genes that were correlated with a wide range of Johne’s disease phenotypes. A number of genes were identified that were significantly associated with several aspects of the disease and suggestive of further experimental follow-up.

Un cadeau empoisonné

GWAS analysis of severe Covid patients implicates a major locus on chromosome 3. The corresponding 50 kb segment appears to originate from Neanderthal/Sapiens crossings, raising interesting evolutionary questions.