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Genome-Analysis of weight gain under psychotropic drugs

Denomination Master's degree in Biology orientation in Bioinformatics and data analysis in biology
Defense Maîtrise : Univ. Genève, 2015
Abstract It is well-known that most of the common diseases are caused by genetic variations (mutations/polymorphisms) under the influence of environment. Researcher’s interest in understanding these variations, together with the development of genotyping such as “micro arrays” and “high-through put sequencing” paved a path to evolution of a field known as Genome Wide Association Study (GWAS). GWAS includes finding an association of genetic variations (SNPs) at genome level to a specific phenotype or trait. In this study, we investigated the association of genetic variants (SNPs) and weight gain in a Swiss cohort receiving anti-psychotic drug treatment at Lausanne University Hospital. Weight gain with anti-psychotic drug treatment is commonly seen and other factors such as change in life style and diet also contribute to weight gain in these patients. Various factors such as the type of drug, the dosage of drug, the duration of treatment, age, gender, and the ethnicity determine the weight gain or changes in body mass index (BMI) due to antipsychotic drug treatment. Merit is unknown, but a genetic component is suspected. The Cardio-MetaboChip (CMC) is genotyping array accommodating 196,725 SNPs of interest for metabolic and cardiovascular diseases. Our CMC contained additionally custom content. In total we genotyped 203,796 SNPs in 1,161 individuals. Quality assurance and control (QA&QC) steps were carried out both on genotype and phenotype data before proceeding with statistical analysis. The steps included verifying the completeness of the genotype at each SNP and in each individual, population stratification, and sex check. QA&QC procedures were performed using PLINK and EIGENSTRAT software packages, custom written JAVA, and R scripts respectively. After performing QA&QC, a multiple linear regression model was built which showed that BMI change is significantly associated with the age and the duration of treatment. The residuals of the model were used in the association analysis. After quality control on phenotype data and genotype, a total of 464 individuals with 166,779 SNPs were left for the association analysis. Quantitative Trait Locus Analysis (QTA) is a statistical analysis performed by using genotype and phenotype information to study the variation at genetic level in complex traits. To explore the data further, data from the GIANT (The Genetic Investigation of ANthropometric Traits) consortium was used for the SNPs which have shown evidence for association with anthropometric traits previously. When considering SNPs with a p-value of < 5x10-8 from the GIANT, 654 SNPs overlapped with our cleaned dataset. After linkage disequilibrium (LD) pruning (<0.7) 637 SNPs remained. No statistical significance was observed in a quantitative trait analysis (QTA) using only these SNPs. QTA was performed on the complete cleaned genotype dataset and we found SNPs that were associated with the phenotype after correcting for multiple testing (p< 5*10-8). Four SNPs on chromosome 10, one SNP each on chromosome 1, 2, 4 and 21 were significant. All significant SNPs were rare. The SNP on chromosome 1 is located in the intronic region of FPGT-TNNI3K, the SNP on chromosome 2 is located in NBEAL1. One SNP on chromosome 4 and two SNPs on chromosome 10 are intergenic. Two SNPs on chromosome 10 are located in CXCL12. The SNP on chromosome 21 is located within the TIAM1 (T-lymphoma invasion and metastasis-inducing protein 1) gene. None of the genes in the vicinity of the SNPs ±1 Mb identified have been described to be associated with weight gain previously. Conclusion – In this study, we identified five SNPs associated with weight gain in patients receiving anti-psychotic medication. None of the SNPs is near genes that have previously been described to be associated with weight gain. A shortcoming of our study is that we could not perform replication analyses due to lack of another cohort at the current time. These analyses are planned in the future to confirm the associations. Functional studies with gene manipulations in-vivo in mice might confirm the role of these SNPs on weight gain associated with anti-psychotic treatment. Analyses of eQTLs, by verifying the expression levels of SNPs in adipose tissue are planned, by using the eQTLs in data of the GTEx consortium. In summary, the current study identifies new potential avenues to explore the mechanisms of weight gain with psychotropic drugs that are a valuable resource for further projects.
Keywords Anti-psychoticsCardio-MetaboChipComplex traitsEIGENSTRATGIANT consortiumGWASPharmacogenomicsPLINKWeight gainQTA
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JAYARAM, Somasekhar. Genome-Analysis of weight gain under psychotropic drugs. Université de Genève. Maîtrise, 2015. https://archive-ouverte.unige.ch/unige:46845

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Deposited on : 2015-02-18

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