Difference between revisions of "Genetics of different body mass measurements"
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When confronting all the genes of all phenotypes, a total of 6097, we wanted to visualize how they were dispersed between all the phenotypes. | When confronting all the genes of all phenotypes, a total of 6097, we wanted to visualize how they were dispersed between all the phenotypes. | ||
| − | [Figure_1.png] | + | [File:Figure_1.png] |
At first sight, we can see that the majority of the genes are shared between the different phenotypes | At first sight, we can see that the majority of the genes are shared between the different phenotypes | ||
Revision as of 15:59, 3 June 2022
File:Heritability of BMI Sofia.pdf
Contents
Introduction
Obesity is a "condition in which excess body fat has accumulated to such an extent that it may have a negative effect on health[1]". It is correlated to a lot of diseases, but particularly with cardiovascular diseases. They include heart attacks, strokes or even heart failures. The World Health Organization (WHO) states that cardiovascular diseases are the first cause of mortality in the world. 31% of deaths are attributable to cardiovascular diseases[2]. The systolic blood pressure is a potential indicator for these diseases. Finding a good definition of obesity seemed important. That is what we tried to do in the first part of the project. The most used definition is the BMI. This index is defined by dividing the weight by the square of the height. The BMI has a lot of limitations, so we tried to find another definition. We tried different combinations of diverse body measurements that potentially correlated to systolic blood pressure. The combinations are called the phenotypes. In the second part of the project, we performed a GWAS. That is an "observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait[3]". The study focused on associations between SNPs and our phenotypes which are potentially correlated to high systolic blood pressure. GWAS have already been performed on height, weight and BMI. The goal of this part was to see if other phenotypes showed better signals than BMI and bring different biology by looking at related genes. Heritability helped us also to determine whether a phenotype is relevant or not.
Methodology
Linear Regression
GWAS
Results
Linear Regression
GWAS
Manhattan plots
Qq plots
Heritability
Venn Diagrams
When confronting all the genes of all phenotypes, a total of 6097, we wanted to visualize how they were dispersed between all the phenotypes.
[File:Figure_1.png]
At first sight, we can see that the majority of the genes are shared between the different phenotypes 686 genes of them are shared between all phenotypes, which represents 11.3%
We also remark than BAI and weight have a lot of genes that are not shared with the other ones.
We can see in a better way the repartition of the genes containing significatn SNPs in Fig.2, Fig.3, Fig.4 and Fig. 5.
