Genome-wide admixture and association analysis identifies African ancestry-specific risk loci of eosinophilic esophagitis in African Americans

BACKGROUND

Eosinophilic esophagitis (EoE), a chronic allergic inflammatory disease, is linked to multiple genetic risk factors, but studies have focused on populations of European ancestry. Few studies have assessed Black or African American (AA) populations for loci involved in EoE susceptibility.

OBJECTIVE

We performed admixture mapping (AM) and genome-wide association study (GWAS) of EoE using participants from AA populations.

METHODS

We conducted AM and GWAS of EoE using 137 EoE cases and 1465 healthy controls from the AA population. Samples were genotyped using molecular evolutionary genetics analysis (MEGA). Genotype imputation was carried out with the Consortium on Asthma Among African-Ancestry Populations in the Americas (CAAPA) reference panel using the Michigan Imputation Server. Global and local ancestry inference was carried out, followed by fine mapping and RNA sequencing. After quality control filtering, over 6,000,000 variants were tested by logistic regression adjusted for sex, age, and global ancestry.

RESULTS

The global African ancestry proportion was found to be significantly lower among cases than controls (0.751 vs 0.786, P = .012). Case-only AM identified 3 significant loci (9p13.3, 12q24.22-23, and 15q11.2) associated with EoE, of which 12q24.22-23 and 9p13.3 were further replicated in the case-control analysis, with associations observed with African ancestry. Fine mapping and multiomic functional annotations prioritized the variants rs11068264 (FBXW8) and rs7307331 (VSIG10) at 12q24.23 and rs2297879 (ARHGEF39) at 9p13.3. GWAS identified 1 genome-wide significant locus at chromosome 1p22.3 (rs17131726, DDAH1) and 10 other suggestive loci. Most GWAS variants were low-frequency African ancestry-specific variants. RNA sequencing revealed that esophageal DDAH1 and VSIG10 were downregulated and ARHGEF39 upregulated among EoE cases.

CONCLUSIONS

GWAS and AM for EoE in AA revealed that African ancestry-specific genetic susceptibility loci exist at 1p22.3, 9p13.3, and 12q24.23, providing evidence of ancestry-specific inheritance of EoE. More independent genetic studies of different ancestries for EoE are needed.

Genetic risk score is associated with T2DM and diabetes complications risks

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a prototypical complex disease with polygenic architecture playing an important role in determining susceptibility to develop the disease (and its complications) in subjects exposed to modifiable lifestyle factors. A current challenge is to quantify the degree of the individual's genetic risk using genetic risk scores (GRS) capturing the results of genome-wide association studies while incorporating possible ethnicity- or population-specific differences.

METHODS

This study included three groups of T2DM (T2DM-I, N=1,032; T2DM-II, N=353; and T2DM-III, N=399) patients and 2,481 diabetes-free subjects. The status of the microvascular and macrovascular diabetes complications were known for the T2DM-I patients. Overall, 21 single nucleotide polymorphisms (SNPs) were analyzed, and selected subsets were used to determine the GRS (both weighted - wGRS and unweighted - uGRS) for T2DM risk predictions (6 SNPs) and for predicting the risks of complications (7 SNPs).

RESULTS

The strongest T2DM markers (P<0.0001) were within the genes for TCF7L2 (transcription factor 7-like 2), FTO (fat mass and obesity associated protein) and ARAP1 (ankyrin repeat and PH domain 1). The T2DM-I subjects with uGRS values greater (Odds Ratio, 95% Confidence Interval) than six had at least twice (2.00, 1.72-2.32) the risk of T2DM development (P<0.0001), and these results were confirmed in the independent groups (T2DM-II 1.82, 1.45-2.27; T2DM-III 2.63, 2.11-3.27). The wGRS (>0.6) further improved (P<0.000001) the risk estimations for all three T2DM groups. The uGRS was also a significant predictor of neuropathy (P<0.0001), nephropathy (P<0.005) and leg ischemia (P<0.0005).

CONCLUSIONS

If carefully selected and specified, GRS, both weighted and unweighted, could be significant predictors of T2DM development, as well as the diabetes complications development.

Clinical decision support system for early detection of Alzheimer's disease using an enhanced gradient boosted decision tree classifier

Alzheimer's disease (AD) is one of the most common forms of dementia contributing to more than 70% of the cases. The factors accounting for the cause and progression of neurodegenerative diseases like AD are primarily genetic, in addition to life style and environmental factors. Early and accurate diagnoses of AD empower practitioners to take timely clinical decisions and preventive actions. This being the motivation, the work proposes a novel pattern matching and scoring method on genetic material towards devising an effective classifier. We propose a distinctive disease causing gene sequence pattern identification using suffix trees as a base detection model with an accuracy of 91.5% in linear time complexity. A scoring mechanism is implemented to assign scores to genes based on the severity of the disease causing and disease resistant Single Nucleotide Polymorphisms associated with the genes. These scores are then used as a remarkable feature in the gradient boosted decision tree classifier to enhance the classification of AD versus healthy control. The efficiency of the proposed gene powered EGBDT classifier is evaluated on ADNI benchmark data set with the prediction accuracy of 94.16% and is found to be efficient compared to the recent works in the literature.

Identification of cancer-related module in protein-protein interaction network based on gene prioritization

With the rapid development of deep sequencing technologies, a large amount of high-throughput data has been available for studying the carcinogenic mechanism at the molecular level. It has been widely accepted that the development and progression of cancer are regulated by modules/pathways rather than individual genes. The investigation of identifying cancer-related active modules has received an extensive attention. In this paper, we put forward an identification method ModFinder by integrating both biological networks and gene expression profiles. More concretely, a gene scoring function is devised by using the regression model with [Formula: see text]-step random walk kernel, and the genes are ranked according to both of their active scores and degrees in the PPI network. Then a greedy algorithm NSEA is introduced to find an active module with high score and strong connectivity. Experiments were performed on both simulated data and real biological one, i.e. breast cancer and cervical cancer. Compared with the previous methods SigMod, LEAN and RegMod, ModFinder shows competitive performance. It can successfully identify a well-connected module that contains a large proportion of cancer-related genes, including some well-known oncogenes or tumor suppressors enriched in cancer-related pathways.

Genetics of Cardiovascular Disease: How Far Are We from Personalized CVD Risk Prediction and Management?

Despite the rapid progress in diagnosis and treatment of cardiovascular disease (CVD), this disease remains a major cause of mortality and morbidity. Recent progress over the last two decades in the field of molecular genetics, especially with new tools such as genome-wide association studies, has helped to identify new genes and their variants, which can be used for calculations of risk, prediction of treatment efficacy, or detection of subjects prone to drug side effects. Although the use of genetic risk scores further improves CVD prediction, the significance is not unambiguous, and some subjects at risk remain undetected. Further research directions should focus on the “second level” of genetic information, namely, regulatory molecules (miRNAs) and epigenetic changes, predominantly DNA methylation and gene-environment interactions.

Genetics of Familial Hypercholesterolemia: New Insights

Familial hypercholesterolemia (FH) is one of the most common monogenic diseases, leading to an increased risk of premature atherosclerosis and its cardiovascular complications due to its effect on plasma cholesterol levels. Variants of three genes (LDL-R, APOB and PCSK9) are the major causes of FH, but in some probands, the FH phenotype is associated with variants of other genes. Alternatively, the typical clinical picture of FH can result from the accumulation of common cholesterol-increasing alleles (polygenic FH). Although the Czech Republic is one of the most successful countries with respect to FH detection, approximately 80% of FH patients remain undiagnosed. The opportunities for international collaboration and experience sharing within international programs (e.g., EAS FHSC, ScreenPro FH, etc.) will improve the detection of FH patients in the future and enable even more accessible and accurate genetic diagnostics.

Different prevalence of T2DM risk alleles in Roma population in comparison with the majority Czech population

Background

The Czech governmental study suggests up to a 25% higher prevalence of type 2 diabetes mellitus (T2DM) in the Roma population than within the majority population. It is not known whether and to what extent these differences have a genetic background.

Methods

To analyze whether the frequencies of the alleles/genotypes of the FTO, TCF7L2, CDKN2A/2B, MAEA, TLE4, IGF2BP2, ARAP1, and KCNJ11 genes differ between the two major ethnic groups in the Czech Republic, we examined them in DNA samples from 302 Roma individuals and 298 Czech individuals.

Results

Compared to the majority population, Roma are more likely to carry risk alleles in the FTO (26% vs. 16% GG homozygotes, p < .01), IGF2BP2 (22% vs. 10% TT homozygotes, p < .0001), ARAP1 (98% vs. 95% of A allele carriers, p < .005), and CDKN2A/2B (81% vs. 66% of TT homozygotes, p < .001) genes; however, less frequently they are carriers of the TCF7L2 risk allele (34% vs. 48% of the T allele p < .0005). Finally, we found significant accumulation of T2DM‐associated alleles between the Roma population in comparison with the majority population (25.4% vs. 15.2% of the carriers of at least 12 risk alleles; p < .0001).

Conclusion

The increased prevalence of T2DM in the Roma population may have a background in different frequencies of the risk alleles of genes associated with T2DM development.

Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution

A growing number of single-nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effects of these obesity-susceptibility loci are uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of nine SNPs (24%)-rs1421085 (FTO; p = 8.69 × 10-15), rs6235 (PCSK1; p = 7.11 × 10-6), rs7903146 (TCF7L2; p = 9.60 × 10-6), rs11873305 (MC4R; p = 5.08 × 10-5), rs12617233 (FANCL; p = 5.30 × 10-5), rs11672660 (GIPR; p = 1.64 × 10-4), rs997295 (MAP2K5; p = 3.25 × 10-4), rs6499653 (FTO; p = 6.23 × 10-4), and rs3824755 (NT5C2; p = 7.90 × 10-4)-increased significantly across the sample BMI distribution. We showed that such increases stemmed from unadjusted gene interactions that enhanced the effects of SNPs in persons with a high BMI. When 125 height-associated SNPs were analyzed for comparison, only one (<1%), rs6219 (IGF1, p = 1.80 × 10-4), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-height) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p = 7.03 × 10-37; height: p = 0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method for detecting such interactions by using only the sample outcome distribution.

The association of environmental, individual factors, and dopamine pathway gene variation with smoking cessation

This study aimed to examine whether dopamine (DA) pathway gene variation were associated with smoking cessation, and compare the relative importance of infulence factors on smoking cessation. Participants were recruited from 17 villages of Shandong Province, China. Twenty-five single nucleotide polymorphisms in 8 DA pathway genes were genotyped. Weighted gene score of each gene was used to analyze the whole gene effect. Logistic regression was used to calculate odds ratios (OR) of the total gene score for smoking cessation. Dominance analysis was employed to compare the relative importance of individual, heaviness of smoking, psychological and genetic factors on smoking cessation. 415 successful spontaneous smoking quitters served as the cases, and 404 unsuccessful quitters served as the controls. A significant negative association of total DA pathway gene score and smoking cessation was observed (p < 0.001, OR: 0.25, 95% CI 0.16-0.38). Dominance analysis showed that the most important predictor for smoking cessation was heaviness of smoking score (42%), following by individual (40%), genetic (10%) and psychological score (8%). In conclusion, although the DA pathway gene variation was significantly associated with successful smoking cessation, heaviness of smoking and individual factors had bigger effect than genetic factors on smoking cessation.