scholarly journals Molecular Determination of mirRNA-126 rs4636297, Phosphoinositide-3-kinase Regulatory Subunit 1-gene Variability rs7713645, rs706713 (Tyr73Tyr), rs3730089 (Met326Ile) and Their Association with Susceptibility to T2D

2021 ◽  
Vol 11 (9) ◽  
pp. 861
Author(s):  
Rashid Mir ◽  
Imadeldin Elfaki ◽  
Faisel M. Abu Duhier ◽  
Maeidh A. Alotaibi ◽  
Adel Ibrahim AlAlawy ◽  
...  
Keyword(s):  
Association Studies ◽  
Minor Allele ◽  
Regulatory Subunit ◽  
P Value ◽  
Amplification Refractory Mutation System ◽  
Genome Wide Association Studies ◽  
Genotype Distribution ◽  
Rna Molecules ◽  
Phosphoinositide 3 Kinase ◽  
Gene Variability

Type 2 diabetes is a metabolic disease characterized by elevated blood sugar. It has serious complications and socioeconomic impact. The MicroRNAs are short single-stranded and non-coding RNA molecules. They regulate gene expression at the post-transcriptional levels. They are important for many physiological processes including metabolism, growth, and others. The phosphoinositide 3-kinase (PI3K) is important for insulin signaling and glucose uptake. The genome wide association studies have identified the association of certain loci with diseases including T2D. In this study we have examined the association of miR126 rs4636297 and Phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1) gene Variations rs7713645, rs706713 (Tyr73Tyr), and rs3730089 (Met326Ile) with T2D using the amplification refractory mutation system PCR. Results indicated that there was a significant different (p-value < 0.05) in the Mir126 rs4636297 genotypes distribution between cases and controls, and the minor allele of the rs4636297 was also associated with T2D with OR = 0.58, p-value < 0.05. In addition results showed that there were significant differences (p-value < 0.05) of rs4636297 genotype distribution of patients with normal and patient with abnormal lipid profile. Results also showed that the PIK3R1 rs7713645 and rs3730089 genotype distribution was significantly different between cases and controls with a p-values < 0.05. In addition, the minor allele of the rs7713645 and rs3730089 were associated with T2D with OR = 0.58, p-value < 0.05). We conclude that the Mir126 rs4636297 and PIK3R1 SNPs (rs7713645 and rs3730089) were associated with T2D. These results need verification in future studies with larger sample sizes and in different populations. Protein-protein interaction and enzyme assay studies are also required to uncover the effect of the SNPs on the PI3K regulatory subunit (PI3KR1) and PI3K catalytic activity.

Strong Association between Serotonin Transporter 5-HTTVNTR Variant and Psychoactive Substance (Nicotine) Use in the Turkish Cypriot Population

2020 ◽  
Vol 21 (6) ◽  
pp. 466-470
Author(s):  
Emine Kandemis ◽  
Gulten Tuncel ◽  
Ozen Asut ◽  
Sehime G. Temel ◽  
Mahmut C. Ergoren
Keyword(s):  
Serotonin Transporter ◽  
Association Studies ◽  
Strong Association ◽  
Smoking Behavior ◽  
Population Based ◽  
Genome Wide Association Studies ◽  
Genotype Distribution ◽  
Psychological Traits ◽  
Wide Range ◽  
Turkish Cypriot

Background: The use of psychoactive substances is one of the most dangerous social problems worldwide. Nicotine dependence results from the interaction between neurobiological, environmental and genetic factors. Serotonin is a neurotransmitter that has a wide range of central nervous system activities. The serotonin transporter gene has been previously linked to psychological traits. Objective: A variable number of tandem repeats within the serotonin transporter-linked polymorphic gene region are believed to alter the transcriptional efficiency of the 5-HTT gene. Therefore, we aimed to investigate the association between this polymorphic site and smoking behavior in the Turkish Cypriot population. Methods: A total of 259 (100 smokers, 100 non-smokers and 59 ex-smokers) Turkish Cypriots were included in this population-based cross-sectional study. Genomic DNA was extracted from peripheral blood samples and the 5-HTTVNTR2 polymorphisms were determined by the PCR-RFLP. Results: The allelic frequency and genotype distribution results of this study showed a strong association (P<0.0001) between smokers and non-smokers. No statistical significance was found between non-smokers and ex-smokers. Conclusion: This is the first genetic epidemiology study to investigate the allelic frequencies of 5-HTTVNTR2 polymorphisms associated with smoking behavior in the Turkish Cypriot population. Based on the results of this study, genome-wide association studies should be designed for preventive medicine in this population.

PSVIII-10 Genome-wide CNV analysis unravels a deletion associated with parasite resistance in Florida native sheep

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 243-244
Author(s):  
Brittany N Diehl ◽  
Andres A Pech-Cervantes ◽  
Thomas H Terrill ◽  
Ibukun M Ogunade ◽  
Owen Rae ◽  
...  
Keyword(s):  
Copy Number ◽  
Association Studies ◽  
Chromosome 21 ◽  
Trend Test ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Parasite Resistance ◽  
Association Testing ◽  
Genome Wide ◽  
Native Sheep

Abstract Florida Native sheep is an indigenous breed from Florida and expresses superior parasite resistance. Previous candidate and genome wide association studies with Florida Native sheep have identified single nucleotide polymorphisms with additive and non-additive effects associated with parasite resistance. However, the role of other potential DNA variants, such as copy number variants (CNVs), controlling this complex trait have not been evaluated. The objective of the present study was to investigate the importance of CNVs on resistance to natural Haemonchus contortus infections in Florida Native sheep. A total of 200 sheep were evaluated in the present study. Phenotypic records included fecal egg count (FEC, eggs/gram), FAMACHA score, and packed cell volume (PCV, %). Sheep were genotyped using the GGP Ovine 50K SNP chip. The copy number analysis was used to identify CNVs using the univariate method. A total of 170 animals with CNVs and phenotypic data were used for the association testing. Association tests were carried out using single linear regression and Principal Component Analysis (PCA) correction to identify CNVs associated with FEC, FAMACHA, and PCV. To confirm our results, a second association testing using the correlation-trend test with PCA correction was performed. Significant CNVs were detected when their adjusted p-value was &lt; 0.05 after FDR correction. A deletion CNV in chromosome 21 was associated with FEC. This DNA variant was located in intron 2 of RAB3IL gene and overlapped a QTL associated with changes in eosinophil number. Our study demonstrated for the first time that CNVs could be potentially involved with parasite resistance in this heritage sheep breed.

scholarly journals Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer

2021 ◽  
Vol 17 (3) ◽  
pp. e1008819
Author(s):  
Héctor Climente-González ◽  
Christine Lonjou ◽  
Fabienne Lesueur ◽  
Dominique Stoppa-Lyonnet ◽  
Nadine Andrieu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Progression ◽  
Biological Networks ◽  
Familial Breast Cancer ◽  
Association Studies ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Functional Relationships ◽  
Network Methods ◽  
The Impact

Genome-wide association studies (GWAS) explore the genetic causes of complex diseases. However, classical approaches ignore the biological context of the genetic variants and genes under study. To address this shortcoming, one can use biological networks, which model functional relationships, to search for functionally related susceptibility loci. Many such network methods exist, each arising from different mathematical frameworks, pre-processing steps, and assumptions about the network properties of the susceptibility mechanism. Unsurprisingly, this results in disparate solutions. To explore how to exploit these heterogeneous approaches, we selected six network methods and applied them to GENESIS, a nationwide French study on familial breast cancer. First, we verified that network methods recovered more interpretable results than a standard GWAS. We addressed the heterogeneity of their solutions by studying their overlap, computing what we called the consensus. The key gene in this consensus solution was COPS5, a gene related to multiple cancer hallmarks. Another issue we observed was that network methods were unstable, selecting very different genes on different subsamples of GENESIS. Therefore, we proposed a stable consensus solution formed by the 68 genes most consistently selected across multiple subsamples. This solution was also enriched in genes known to be associated with breast cancer susceptibility (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10−4). The most connected gene was CUL3, a regulator of several genes linked to cancer progression. Lastly, we evaluated the biases of each method and the impact of their parameters on the outcome. In general, network methods preferred highly connected genes, even after random rewirings that stripped the connections of any biological meaning. In conclusion, we present the advantages of network-guided GWAS, characterize their shortcomings, and provide strategies to address them. To compute the consensus networks, implementations of all six methods are available at https://github.com/hclimente/gwas-tools.

scholarly journals Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

2020 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P. A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Uk ◽  
Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

scholarly journals Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

2019 ◽  
Author(s):  
Zijie Zhao ◽  
Yanyao Yi ◽  
Yuchang Wu ◽  
Xiaoyuan Zhong ◽  
Yupei Lin ◽  
...  
Keyword(s):  
Association Studies ◽  
Fine Tuning ◽  
Risk Scores ◽  
Training Dataset ◽  
Validation Dataset ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Polygenic Risk ◽  
Model Tuning

AbstractPolygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require individual-level genetic data as the training dataset or as a validation dataset independent from both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS (Parameter-tuning Using Marginal Association Statistics), a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. On average, PUMAS improves the predictive R2 by 205.6% and 62.5% compared to PRSs with arbitrary p-value cutoffs of 0.01 and 1, respectively. Applied to 211 neuroimaging traits and Alzheimer’s disease, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.

scholarly journals An approach to gene-based testing accounting for dependence of tests among nearby genes

2021 ◽  
Author(s):  
Ronald J Yurko ◽  
Kathryn Roeder ◽  
Bernie Devlin ◽  
Max G'Sell
Keyword(s):  
Multiple Testing ◽  
Association Studies ◽  
Autism Spectrum ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Strongly Correlated ◽  
Test Statistics ◽  
Test Statistic ◽  
Genome Wide ◽  
Insight Into

In genome-wide association studies (GWAS), it has become commonplace to test millions of SNPs for phenotypic association. Gene-based testing can improve power to detect weak signal by reducing multiple testing and pooling signal strength. While such tests account for linkage disequilibrium (LD) structure of SNP alleles within each gene, current approaches do not capture LD of SNPs falling in different nearby genes, which can induce correlation of gene-based test statistics. We introduce an algorithm to account for this correlation. When a gene's test statistic is independent of others, it is assessed separately; when test statistics for nearby genes are strongly correlated, their SNPs are agglomerated and tested as a locus. To provide insight into SNPs and genes driving association within loci, we develop an interactive visualization tool to explore localized signal. We demonstrate our approach in the context of weakly powered GWAS for autism spectrum disorder, which is contrasted to more highly powered GWAS for schizophrenia and educational attainment. To increase power for these analyses, especially those for autism, we use adaptive p-value thresholding (AdaPT), guided by high-dimensional metadata modeled with gradient boosted trees, highlighting when and how it can be most useful. Notably our workflow is based on summary statistics.

scholarly journals 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Mathias Gorski ◽  
Peter J. van der Most ◽  
Alexander Teumer ◽  
Audrey Y. Chu ◽  
Man Li ◽  
...  
Keyword(s):  
Kidney Function ◽  
Kidney Development ◽  
Association Studies ◽  
Meta Analysis ◽  
European Ancestry ◽  
P Value ◽  
Genome Wide Association Studies ◽  
1000 Genomes ◽  
Gene Sets ◽  
Project Promise

Abstract HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10−8 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples.

scholarly journals A health disparities study of MicroRNA-146a expression in prostate cancer samples derived from African American and European American patients

2020 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Monet Stevenson ◽  
Narendra Narendra Banerjee ◽  
Narendra Banerjee ◽  
Kuldeep Rawat ◽  
Lin Chen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
African American ◽  
Association Studies ◽  
Single Gene ◽  
Micro Rna ◽  
Genome Wide Association Studies ◽  
Racial Groups ◽  
Protein Coding ◽  
Rna Molecules ◽  
Prostate Carcinogenesis

Considering the prevalence of prostate cancer all over the world, it is desired to have tools, technologies, and biomarkers which help in early detection of the disease and discriminate different races and ethnic groups. Genetic information from the single gene analysis and genome-wide association studies have identified few biomarkers, however, the drivers of prostate cancer remain unknown in the majority of prostate cancer patients. In those cases where genetic association has been identified, the genes confer only a modest risk of this cancer, hence, making them less relevant for risk counseling and disease management. There is a need for additional biomarkers for diagnosis and prognosis of prostate cancer. MicroRNAs are a class of non-protein coding RNA molecules that are frequently dysregulated in different cancers including prostate cancer and show promise as diagnostic biomarkers and targets for therapy. Here we describe the role of micro RNA 146a (miR-146a) which may serve as a diagnostic and prognostic marker for prostate cancer, as indicated from the data presented in this report. Also, a pilot study indicated differential expression of miR-146a in prostate cancer cell lines and tissues from different racial groups. Reduced expression of miR-146a was observed in African American tumor tissues compared to those from European Whites This report provides a novel insight into understanding the prostate carcinogenesis.

scholarly journals The harmonic mean p-value for combining dependent tests

2019 ◽  
Vol 116 (4) ◽  
pp. 1195-1200 ◽  
Author(s):  
Daniel J. Wilson
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Scientific Discovery ◽  
Association Studies ◽  
Harmonic Mean ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Familywise Error Rate ◽  
Significance Threshold ◽  
Genome Wide

Analysis of “big data” frequently involves statistical comparison of millions of competing hypotheses to discover hidden processes underlying observed patterns of data, for example, in the search for genetic determinants of disease in genome-wide association studies (GWAS). Controlling the familywise error rate (FWER) is considered the strongest protection against false positives but makes it difficult to reach the multiple testing-corrected significance threshold. Here, I introduce the harmonic mean p-value (HMP), which controls the FWER while greatly improving statistical power by combining dependent tests using generalized central limit theorem. I show that the HMP effortlessly combines information to detect statistically significant signals among groups of individually nonsignificant hypotheses in examples of a human GWAS for neuroticism and a joint human–pathogen GWAS for hepatitis C viral load. The HMP simultaneously tests all ways to group hypotheses, allowing the smallest groups of hypotheses that retain significance to be sought. The power of the HMP to detect significant hypothesis groups is greater than the power of the Benjamini–Hochberg procedure to detect significant hypotheses, although the latter only controls the weaker false discovery rate (FDR). The HMP has broad implications for the analysis of large datasets, because it enhances the potential for scientific discovery.

Potential Impact of MicroRNA-423 Gene Variability in Coronary Artery Disease

2019 ◽  
Vol 19 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Chandan K. Jha ◽  
Rashid Mir ◽  
Imadeldin Elfaki ◽  
Naina Khullar ◽  
Suriya Rehman ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Recessive Model ◽  
Amplification Refractory Mutation System ◽  
Genotype Distribution ◽  
Healthy Controls ◽  
Increased Risk ◽  
Significant Difference ◽  
Artery Disease ◽  
Gene Variability

Aim: Studies have evaluated the association of miRNA-423 C>A genotyping with the susceptibility to various diseases such cancers, atherosclerosis and inflammatory bowel disease but the results were contradictory. However, no studies have reported the association between miRNA-423 rs6505162 C>A polymorphism and susceptibility of coronary artery disease. MicroRNAs regulate expression of multiple genes involved in atherogenesis. Therefore, we investigated the association of microRNA-423C>T gene variations with susceptibility to coronary artery disease. Methodology: This study was conducted on 100 coronary artery disease patients and 117 matched healthy controls. The genotyping of the microRNA-423 rs6505162C>A was performed by using Amplification refractory mutation system PCR method (ARMS-PCR). Results: A significant difference was observed in the genotype distribution among the coronary artery disease cases and sex-matched healthy controls (P=0.048). The frequencies of all three genotypes CC, CA, AA reported in the patient’s samples were 55%, 41% and 4% and in the healthy controls samples were 55%, 41% and 4% respectively. Our findings showed that the microRNA-423 C>A variant was associated with an increased risk of coronary artery disease in codominant model (OR = 1.96, 95 % CI, 1.12-3.42; RR 1.35(1.05-1.75, p=0.017) of microRNA-423CA genotype and significant association in dominant model (OR 1.97, 95% CI (1.14-3.39), (CA+AA vs CC) and non-significant association for recessive model (OR=1.42, 95%CI=0.42-4.83, P=0.56, AA vs CC+CA).While, the A allele significantly increased the risk of coronary artery disease (OR =1.56, 95 % CI, 1.03-2.37; p=0.035) compared to C allele. Therefore, it was observed that more than 1.96, 1.97 and 1.56 fold increased risk of developing coronary artery disease. Conclusion: Our findings indicated that microRNA-423 CA genotype and A allele are associated with an increased susceptibility to Coronary artery disease.

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