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 | In a search of a relevant phenotype for normalized weight, and what

===Introduction ===

Most common human traits and diseases have a polygenic pattern of inheritance. The variation of DNA sequence at many genetic loci, influence the phenotype. GWAS have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation.

Background

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.

GWAS on height

First, a study showed that about 180 loci 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.

GWAS on weight

Then, many genes related to the weight were found in a GWAS including covariants such as for example triglyceride level, or BMI. Two main genes have been found: the FTO and the IRS1.

GWAS on BMI

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.

More studies on BMI have shown that other genes such as NEGR1 were also associated with BMI. We know that this gene is highly expressed in the brain and has a role in the body weight and food intake. Identifying the genetic determinants of BMI could lead for example to a better understanding of the biological basis of obesity.

Study on heritability

Estimation of the heritability of BMI differ between experimental designs and also because of the different types of heritability. However, the results of the studies show that the heritability of SNPs for BMI is greater than 0.2, approximately half of the heritability of height which is greater than 0.5.

Aim of the project 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 is also the fact that very tall people have a BMI that is too high in comparison to their body fat. There are therefore limitations to the use of BMI as a measure of body fat.

A question arises here, why not use another formula than the BMI. The BMI is a function of height and weight, but it is associated with different genes. We can therefore assume that other phenotypes based on height and weight could also bring different biological results. With a power of 3, we know that this is the formula for the ponderal index, generally used for babies. Since there is no GWAS on the PI, we can wonder if it has something relevant. Based on the BMI formula, many other phenotypes would be possible.

So, the goal of the project is to see if other phenotypes based on height and weight show better signals than BMI and bring different biology by looking at related genes and pathways. Heritability is also an aspect to determine if a phenotype seems relevant or not.

=== Methodology ===

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

File:Fig pathwayMirrorBarChart tau3 PCA median tortuosity.pdf

Choose phenotypes and covariants

Make a GWAS

Results of the GWAS

SNPs: Manhattan plot, QQplots

Heritability

Genes

Pathways

Conclusion