Genetics of different body mass measurements

File:Heritability of BMI Sofia.pdf

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

Phenotypic part

Linear Regression

Genotypic part

Manhattan plots

A Manhattan plot displays the significant SNPs. SNPs are shown along the x-axis, sorted by chromosomes The relative negative logarithm of the association p-value for each SNP is displayed along the y-axis. Each SNP is thus represented with a dot. The strongest associations have the smallest p-value and thus their negative logarithm will be the greatest and high on the graph. All the SNP that are above the red line are significant. All the phenotypes show a lot of significant SNPs. All the phenotypes have similar results. We also see that there are more genes on the first chromosomes.

Qq plots

It is a different representation of the same results. A QQ plot plots the quantile distribution of observed p-values (on the y-axis) versus the quantile distribution of expected p-values. The SNPs here are all mixed, not sorted by chromosomes. All the SNP that are not on the red line are significant. The red line represents the probability of it occurring by chance. The plots are similar between the different phenotypes even if the upper right differs a little bit. Again, we see that a lot of SNPs are relevant with our phenotypes.

Heritability

From the GWAS we performed we also calculated the heritability of each phenotype. The heritability can be defined such as the « amount of phenotypic (observable) variation in a population that is attributable to individual genetic differences ». If the heritability is very high, it means that the phenotype is a lot attributable to individual genetic differences and thus that the environment has a low impact. For us, as we want to have influence on the phenotype, the phenotypes with a low heritability are more interesting. The BMI 2.5 and the BMI 3 have the lowest 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.