Heritability of BMI

• Project name: Heritability of BMI - in a search of a relevant phenotype for normalized weight, and what

heritability says about it

• Tutor: Sofia Ortin Vela (sofia.ortinvela_AT_unil.ch)

• Slides: File:BMI heritability.pdf

Heritability of BMI

Aim of the project

The aim of this project is to do a genome-wide association study on a large population using phenotypes based on height and weight. We will get different genes and pathways associated with the chosen phenotypes, and then we will also see the heritability of certain traits.

Previous research

A GWAS is an analysis of many genetic variations in many individuals to study their correlations with phenotypic traits. GWAS have already been done on height, weight and BMI.The development of genome-wide association studies has been made possible by advances in genotyping technology, and has greatly accelerated gene discovery. GWAS studies have identified many genes with strong associations for phenotypic traits such as diseases. GWAS generally focus on associations between SNPs and phenotypes. GWAS have already been carried out on height, weight and BMI.

First, a study showed that about 180 loci (SNPs associated with height) influence adult height. The most strongly connected genes include the Hedgehog and TGF-b signaling pathways. These signaling pathways are involved in chondrocyte proliferation and differentiation, growth plate signaling and bone formation. Other genes such as ECM2 involved in the formation of the extracellular matrix influence size. We also know that mutations in the STAT2 and FGFR3 genes cause growth failure and skeletal dysplasia as well as dwarfism.

Next, a bigger cluster of weight-related SNPs are located in intron 1 of the fat mass and obesity associated (FTO) gene. The FTO gene encodes an RNA demethylase, and is the most associated SNP with obesity throughout life and across generations. SNPs at this locus have also been associated with other specificities such as type 2 diabetes, osteoarthritis or cardiometabolic characteristics. This is implied by the effect of the FTO gene on BMI. The study concludes that FTO increases the risk of obesity through changes in food consumption and preference. 23 other SNPs were studied such as BDNF, FAIM2, TFAP2B, FTO.

Then, we know that variations in BMI are explained by genetics at about 50-75%. For BMI, the genes discovered so far are mainly located in the brain. For example, neuronal growth regulator 1 (NEGR1) was one of the genes identified in the first GWAS of BMI. This gene is highly expressed in the brain, particularly in the hippocampus. In recent years, by performing GWAS for BMI, the number of genes linked to obesity has risen to over 200.

But it is also known that studies report that BMI as a measure of body fat is inaccurate and can lead to bias in measuring the health effects of obesity. The problems arise because BMI does not take into account the difference between fat and non-fat mass, such as bone and muscle, and also does not include changes in body composition that occur with age. For example, very muscular people may have very low body fat, but their BMI puts them in the obese category. There are therefore limitations to the use of BMI as a measure of body fat.

The question arises as to why the square should be used in the BMI formula and not another index for our study. With a power of 3, we know that this is the formula for the ponderal index (PI = weight/height3). And we know that according to some studies, the PI is used to check whether newborns are malnourished, normal weight or overweight. According to some studies, it is also one of the methods used to diagnose fetal growth retardation.

And what if we used other index to obtain different phenotypes?

The aim of our project is to do a GWAS on different phenotypes. So before doing GWAS, we first had to analyse our data and then choose different phenotypes with the formula weight divided by height power gamma. We must therefore choose a gamma that gives a relevant phenotype and represents the whole population.

Data Exploration

Size of our sample

As there was a lot of incomplete data at the genotype level, the dataset was cleaned to remove all individuals without genotype information.

Our sample size is different from the number of data in the UKB database. This is due to the fact that there is not necessarily the genotype collected for each individual. In the weight dataset in the UK biobank, we have 499,806 individuals. While in our sample we have only 45829 individuals. lees data we just have the data who came for the 3rd time. BMI data height data from the third time. It limits our number.

For height and weight independently

Central tendency

Mean

Median

Mode

Min

Max

Mesure of dispersion

Range

Quantile

IQR

Variance

Standard deviation

Skewness

Kurtosis

Visualisation

Histograms

Boxplot

For height and weight together

Covariance

Correlation

Scatterplot

Choose phenotypes

Prepare phenotype

Make a GWAS

Results of the GWAS

Manhattan plot

QQplots

Heritability

Genes

Pathways

Conclusion