5,10-Methylenetetrahydrofolate reductase single nucleotide polymorphisms and gene-environment interaction analysis in non-syndromic cleft lip/palate

2014 ◽  
Vol 122 (2) ◽  
pp. 109-113 ◽  
Author(s):  
Bernardette Estandia-Ortega ◽  
José A. Velázquez-Aragón ◽  
Miguel A. Alcántara-Ortigoza ◽  
Miriam E. Reyna-Fabian ◽  
Sandra Villagómez-Martínez ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Cleft Lip ◽  
Methylenetetrahydrofolate Reductase ◽  
Interaction Analysis ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Cleft Lip Palate

scholarly journals Association of Single Nucleotide Polymorphisms on Locus 18q21.1 in the Etiology of Nonsyndromic Cleft Lip Palate (NSCLP) in Indian Multiplex Families

2021 ◽  
Vol 08 (01) ◽  
pp. 024-031
Author(s):  
Praveen Kumar Neela ◽  
Gosla Srinivas Reddy ◽  
Akhter Husain ◽  
Vasavi Mohan ◽  
Sravya Thumoju ◽  
...  
Keyword(s):  
High Risk ◽  
Single Nucleotide Polymorphisms ◽  
Cleft Lip ◽  
Genetic Research ◽  
P Value ◽  
Allelic Association ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Cleft Lip Palate ◽  
Multiplex Families

Abstract Background Cleft lip palate (CLP) is a common congenital anomaly with multifactorial etiology. Many polymorphisms at different loci on multiple chromosomes were reported to be involved in its etiology. Genetic research on a single multigenerational American family reported 18q21.1 locus as a high-risk locus for nonsyndromic CLP (NSCLP). However, its association in multiple multiplex families and Indian population is not analyzed for its association in NSCLP. Aim This study was aimed to evaluate whether high-risk single nucleotide polymorphisms (SNPs) on chromosome 18q21.1 are involved in the etiology of NSCLP in multiplex Indian families. Materials and Methods Twenty multigenerational families affected by NSCLP were selected for the study after following inclusion and exclusion criteria. Genomic DNA was isolated from the affected and unaffected members of these 20 multiplex families and sent for genetic analysis. High-risk polymorphisms, such as rs6507872 and rs8091995 of CTIF, rs17715416, rs17713847 and rs183559995 of MYO5B, rs78950893 of SMAD7, rs1450425 of LOXHD1, and rs6507992 of SKA1 candidate genes on the 18q21.1 locus, were analyzed. SNP genotyping was done using the MassARRAY method. Statistical analysis of the genomic data was done by PLINK. Results Polymorphisms followed the Hardy–Weinberg equilibrium. In the allelic association, all the polymorphisms had a p-value more than 0.05. The odds ratio was not more than 1.6 for all the SNPs. Conclusion High-risk polymorphisms, such as rs6507872 and rs8091995 of CTIF, rs17715416, rs17713847 and rs183559995 of MYO5B, rs78950893 of SMAD7, rs1450425 of LOXHD1, and rs6507992 of SKA1 in the locus 18q21.1, are not associated with NSCLP in Indian multiplex families.

scholarly journals Gene–gene and gene-environment interactions on cord blood total IgE in Chinese Han children

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Li Hua ◽  
Quanhua Liu ◽  
Jing Li ◽  
Xianbo Zuo ◽  
Qian Chen ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Cord Blood ◽  
Synergistic Effects ◽  
Gene Interaction ◽  
Independent Effect ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Chinese Han ◽  
Gene Environment Interaction ◽  
Gene Environment

Abstract Background IL13, IL4, IL4RA, FCER1B and ADRB2 are susceptible genes of asthma and atopy. Our previous study has found gene–gene interactions on asthma between these genes in Chinese Han children. Whether the interactions begin in fetal stage, and whether these genes interact with prenatal environment to enhance cord blood IgE (CBIgE) levels and then cause subsequent allergic diseases have yet to be determined. This study aimed to determine whether there are gene–gene and gene-environment interactions on CBIgE elevation among the aforementioned five genes and prenatal environmental factors in Chinese Han population. Methods 989 cord blood samples from a Chinese birth cohort were genotyped for nine single-nucleotide polymorphisms (SNPs) in the five genes, and measured for CBIgE levels. Prenatal environmental factors were collected using a questionnaire. Gene–gene and gene-environment interactions were analyzed with generalized multifactor dimensionality methods. Results A four-way gene–gene interaction model (IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713) was regarded as the optimal one for CBIgE elevation (testing balanced accuracy = 0.5805, P = 9.03 × 10–4). Among the four SNPs, only IL13 rs20541 was identified to have an independent effect on elevated CBIgE (odds ratio (OR) = 1.36, P = 3.57 × 10–3), while the other three had small but synergistic effects. Carriers of IL13 rs20541 TT, IL13 rs1800925 CT/TT, IL4 rs2243250 TT and ADRB2 rs1042713 AA were estimated to be at more than fourfold higher risk for CBIgE elevation (OR = 4.14, P = 2.69 × 10–2). Gene-environment interaction on elevated CBIgE was found between IL4 rs2243250 and maternal atopy (OR = 1.41, P = 2.65 × 10–2). Conclusions Gene–gene interaction between IL13 rs20541, IL13 rs1800925, IL4 rs2243250 and ADRB2 rs1042713, and gene-environment interaction between IL4 rs2243250 and maternal atopy begin in prenatal stage to augment IgE production in Chinese Han children.

Comparing Effects of FOXO3A and Residing in Urban Areas on Longevity: A Gene-Environment Interaction Study

2021 ◽  
Author(s):  
John S Ji ◽  
Linxin Liu ◽  
Lijing Yan ◽  
Yi Zeng
Keyword(s):  
Protective Effect ◽  
Effect Size ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fine Particulate Matter ◽  
Environment Interaction ◽  
Nucleotide Polymorphisms ◽  
Gene Environment Interaction ◽  
Gene Environment

Abstract Forkhead box O3 (FOXO3A) is a candidate longevity gene. Urban residents are also positively associated with longer life expectancy. We conducted a gene-environment interaction to assess the synergistic effect of FOXO3A and urban/rural environments on mortality. We included 3085 older adults from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used single nucleotide polymorphisms (SNPs) rs2253310, rs2802292, and rs4946936 to identify the FOXO3A gene and classified residential locations as "urban" and "rural." Given the open cohort design, we used the Cox-proportional hazard regression models to assess the mortality risk. We found the minor allele homozygotes of FOXO3A to have a protective effect on mortality [HR (95% CI) for rs4946936 TT vs. CC: 0.807 (0.653, 0.996); rs2802292 GG vs TT: 0.812 (0.67, 0.985); rs2253310 CC vs. GG: 0.808 (0.667, 0.978)]. Participants living in urban areas had a lower risk of mortality [HR of the urban vs. the rural: 0.854 (0.759, 0.962)]. The interaction between FOXO3A and urban and rural regions was statistically significant (pinteraction<0.01). Higher air pollution (fine particulate matter: PM2.5) and lower residential greenness (Normalized Difference Vegetation Index: NDVI) both contributed to higher mortality. After adjusting for NDVI and PM2.5, the protective effect size of FOXO3A SNPs was slightly attenuated while the protective effect size of living in an urban environment increased. The effect size of the beneficial effect of FOXO3 on mortality is roughly equivalent to that of living in urban areas. Our research findings indicate the effect of places of residence and genetic predisposition of longevity are intertwined.

scholarly journals Effect of Occupational Exposures on Lung Cancer Susceptibility: A Study of Gene–Environment Interaction Analysis

2015 ◽  
Vol 24 (3) ◽  
pp. 570-579 ◽  
Author(s):  
Jyoti Malhotra ◽  
Samantha Sartori ◽  
Paul Brennan ◽  
David Zaridze ◽  
Neonila Szeszenia-Dabrowska ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Susceptibility ◽  
Interaction Analysis ◽  
Occupational Exposures ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Lung Cancer Susceptibility

Abstract MP11: Systematic Discovery Of Thousands Of Gene-Environment Interactions For Cardiometabolic Serum Biomarkers

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Kenneth E Westerman
Keyword(s):  
Genetic Variants ◽  
Cardiometabolic Risk ◽  
Interaction Analysis ◽  
Environmental Exposures ◽  
Cardiometabolic Health ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
A Genome ◽  
The Mean

Background: Gene-environment interaction (GEI) analysis enables us to understand how genetic variants modify the effects of environmental exposures on cardiometabolic risk factors, providing a foundation for genome-based precision medicine. Ideally, these interactions could be mapped comprehensively across all measured genetic variants, exposures, and outcomes, but this approach is computationally intensive and statistically underpowered. Recent studies have shown that variance-quantitative trait loci (vQTLs), or genetic variants that associate with differential variance of an outcome, are substantially enriched for underlying GEIs. Here, we sought to first identify vQTLs for cardiometabolic traits, then use this smaller genetic search space to uncover novel gene-environment interactions across thousands of environmental exposures. Methods: A two-stage, multi-ancestry analysis was conducted in 355,790 unrelated participants from the UK Biobank. First, we performed a genome-wide vQTL scan for each of 20 serum metabolic biomarkers, including but not limited to lipids, lipoproteins, and glycemic measures. This scan used Levene’s test to identify genetic markers whose genotypes are associated with the variance, rather than the mean, of the biomarker. Next, we collected over 2000 variables corresponding to socioeconomic, dietary, lifestyle, and clinical exposures, and conducted an interaction analysis for each combination of exposure and vQTL-biomarker pair. For each stage, the analysis was initially stratified by ancestry then meta-analyzed to generate the primary set of results. Results: vQTLs were identified at 514 independent regions in the genome, with most of these genetic variants already known to affect the mean biomarker level. In the subsequent gene-environment interaction analysis, we found 2,162 significant interactions passing a stringent significance threshold adjusted for multiple testing ( p < 0.05 / 578 vQTL-biomarker pairs / 2140 exposures = 4х10 -8 ). Some of these expanded on existing findings; for example, genetic marker rs2393775 in the HNF1A gene interacted with education level (as a proxy for socioeconomic status) to influence hsCRP ( p = 1.3х10 -10 ), building on a previous finding that HNF1A variants modify the effect of perceived stress on cardiovascular outcomes. Others highlighted novel biology, such as an interaction between variants near the fatty liver-associated gene TM6SF2 and oily fish intake on total and LDL-cholesterol levels ( p = 6.6х10 -9 ). Conclusions: Our systematic GEI discovery effort identified thousands of interactions that may impact cardiometabolic risk, both expanding on previous research and identifying novel biological mechanisms. This catalog of vQTLs and interactions can inform future mechanistic studies and provides a knowledge base for genome-centered precision approaches to cardiometabolic health.

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