A type 2 diabetes-associated SNP in KCNQ1 (rs163184) modulates the binding activity of the locus for Sp3 and Lsd1/Kdm1a, potentially affecting CDKN1C expression

Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease

Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures.

The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

Background

The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants.

Methods

To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles.

Results

We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33).

Conclusions

This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01216-w.

Synergistic effect between the KCNQ1 haplotype and alcohol consumption on the development of type 2 diabetes mellitus in Korean cohorts

Potassium voltage-gated channel subfamily Q member 1 (KCNQ1) is one of the strongest susceptibility genes for type 2 diabetes mellitus (T2DM). Association studies between KCNQ1 genetic variants and T2DM have been reported. The multifactorial disease T2DM is caused by interactions between genetic susceptibility and environmental factors. In this study, we examined the associations between the KCNQ1 haplotype, which consists of the major alleles rs3852528, rs11024175, and rs2237892 (ht: ACC), and environmental factors such as alcohol consumption, which are related to the risk of T2DM, in two independent Korean populations. Data from health examination studies, i.e., HEXA (n = 50,357 subjects) and the Ansung–Ansan community-based Korean cohort study (n = 7603), were analyzed. In both cohorts, fasting blood glucose levels were significantly increased in moderate-to-heavy drinkers and carriers of the homozygous ACC haplotype. A significant association between the KCNQ1 haplotype and alcohol consumption in the risk of diabetes was observed in the HEXA (OR 1.587; 95% CI 1.128–2.234) and Ansung–Ansan (OR 2.165; 95% CI 1.175–3.989) cohorts compared with abstainers not carrying the KCNQ1 haplotype. Associations of the KCNQ1 haplotype with alcohol consumption and β-cell function were observed in the Ansung–Ansan cohort. Moderate-to-heavy drinkers with the ACC haplotype had lower fasting insulin levels and mean 60 min insulinogenic index (IGI₆₀) compared with light drinkers and abstainers not carrying the ACC haplotype. These findings indicate that KCNQ1 variants play a synergistic role with alcohol consumption in the development of T2DM and impaired β-cell function.

Genetic Variants in Transcription Factor Binding Sites in Humans: Triggered by Natural Selection and Triggers of Diseases

Variants of transcription factor binding sites (TFBSs) constitute an important part of the human genome. Current evidence demonstrates close links between nucleotides within TFBSs and gene expression. There are multiple pathways through which genomic sequences located in TFBSs regulate gene expression, and recent genome-wide association studies have shown the biological significance of TFBS variation in human phenotypes. However, numerous challenges remain in the study of TFBS polymorphisms. This article aims to cover the current state of understanding as regards the genomic features of TFBSs and TFBS variants; the mechanisms through which TFBS variants regulate gene expression; the approaches to studying the effects of nucleotide changes that create or disrupt TFBSs; the challenges faced in studies of TFBS sequence variations; the effects of natural selection on collections of TFBSs; in addition to the insights gained from the study of TFBS alleles related to gout, its associated comorbidities (increased body mass index, chronic kidney disease, diabetes, dyslipidemia, coronary artery disease, ischemic heart disease, hypertension, hyperuricemia, osteoporosis, and prostate cancer), and the treatment responses of patients.

Polymorphic genetic markers and how they are associated with clinical and metabolic indicators of type 2 diabetes mellitus in the Kazakh population

Background

Type 2 diabetes mellitus is a serious public health problem worldwide. The aim of the study was to analyze the relationship of eight polymorphic gene variants with the development of clinical-metabolic rates of type 2 diabetes mellitus inside Kazakh population.

Materials and methods

139 patients with type 2 diabetes mellitus and 100 patients in the control group were examined. Genotyping of polymorphisms of candidate genes was carried out on a next generation QuantStudio 12 K Flex unit.

Results

Gene TCF7L2 locus rs7901695 and rs7903146, gene KCNQ1 locus rs2237892, rs7756992, and gene CDKAL1 locus rs7754840 demonstrated statistically significant associations with glucose metabolism, lipid profile and body mass index (BMI) in type 2 DM inside the population. Statistically significant difference in anthropometric and biochemical measures of rs17584499, rs4712523 and rs163184 has not been revealed.

Conclusions

Genetic polymorphisms that influence pancreatic gland beta-cells insulin release and secretion associate with metabolic and anthropometric measures definitive for type 2 DM in Kazakh population.