Interrogating gene-by-environment
interactions using genetic risk scores
Nicholas A. Furlotte, Vladimir Vacic, Geoffrey Benton, Adam Auton and David Hinds
23andMe, Inc., Mountain View, CA, USA
The effect of genetic variation is dependent on the environment in which
it is expressed. This interaction between genetic and environmental
variation is of fundamental importance to our understanding of basic
biology as well as the etiology and progression of disease. As such, the
ability to quantify gene-by-environment (GxE) interactions will play a key
role in the application of personalized medicine. By understanding the
effect of genetic risk factors in differing environmental contexts,
clinicians will be better able to tailor therapeutic recommendations to
specific patients. For example, the effectiveness of certain dietary and
exercise recommendations may depend on underlying genetic
propensity for obesity.
Genetic risk score for BMI
(A)
Feature Selection
Training Data
s=
top 5% of GRS
29.5
bottom 5% of GRS
(B)
X
i snpi
i
GWAS
25.5
Mean BMI
Unfortunately, GxE interactions remain notoriously difficult to identify,
due in part to the low statistical power for discovery resulting from small
effect sizes. However, recent work has demonstrated that it is possible
to detect interactions by aggregating genetic effects, even when the
power to detect individual GxE interactions is low. In this approach,
GxE interactions are identified by constructing a genetic risk score
(GRS) and testing for interaction between the score and an
environmental condition. Here, we investigate interactions between a GRS computed using over
450 BMI-associated SNPs and different environmental conditions. Using
a cohort of over 400,000 consented research participants from the
23andMe customer base, we show that there is a statistically significant
interaction between the GRS for BMI (grsBMI) and exercise frequency (p
= 1.32e-17) as well as fast food intake (p = 6.34e-18). To quantify the
effect of the GRS on true BMI, we estimate the amount of increase in
true BMI expected from a one unit increase in grsBMI and then compare
estimates under different environmental conditions. The effect size
among those who exercise is 0.97, increasing to 1.7 for those who do
not exercise. Similarly, we find that the effect among those who
frequently eat fast food is 1.87, while only 1.21 in those who consume
infrequently. This discrepancy becomes more extreme depending on
how you define “frequently.” Additionally, we explore how environment
affects heritability estimates. Taken together, our observations imply that
the expected effects of diet and exercise are dependent on genetic
predisposition. Genetic Risk Score
GRS
Distribution
(C)
Observed BMI
Abstract
GRS Bin
Expected BMI
Figure 1: Building a genetic risk score (GRS) for BMI.
(A) We built a genetic risk score (GRS) for BMI by first conducting a GWAS in a training set comprised of over 400,000 23andMe research participants.
Next, we selected SNPs to represent associated loci (450 SNPs) and then we fit a joint linear model to the training data. The genetic risk score (GRS) is
calculated by taking a weighted sum of the SNPs included in the model weighted by their regression coefficients. (B) We show that the GRS can be used
to stratify individuals by average BMI. By splitting the risk distribution into bins containing 5% of the total training data. (C) We determine expected BMI
in a validation set using the bins defined in the training set. Then we evaluate “calibration" by comparing the expected BMI to the average observed BMI
in each GRS risk bin.
Changes in BMI due to GxE interactions
Searching for GRS Interactions
Age, Sex, PCs
GRS/Phenotype
Phenotype
Interaction
GRS
log(BM I) = X + s
S
+p
p
+s·p
int
+✏
We evaluated the potential for GRS-by-Phenotype
interactions by conducting a scan across about 1000
phenotypes from the 23andMe database.
For each phenotype, we regress log(BMI) against age,
sex, top 5 PCs, GRS, the phenotype and then we test the
hypothesis that the interaction between the GRS and the
phenotype has a zero coefficient.
Diet Interactions
-18
-12
-6
0
log10 Interaction P-value
Percent Change in BMI
Figure 2: Effect sizes and p-values for phenotypes with a GRS interaction
We interrogated a set of over 1000 phenotypes when looking for interaction with GRS. We identified slightly over 300 with significant interactions at a Bonferroni threshold. The
above represents a curated list of these phenotypes. The figure on the left shows the log10 p-value for the interaction term. The figure on the right shows the effect sizes of the
main and interaction effects for each phenotype. The effect is in terms of percent change in BMI per one standard deviation change in phenotype value. Most of these
phenotypes are discrete with a different numbers of levels. The percent change per standard deviation representation is a simple way to standardize the effect sizes.
Lifestyle Interactions
23andMe Data
All participants were drawn from the customer base of
23andMe, Inc., a consumer personal genetics company.
Customers were genotyped on custom HumanHap550 or
Illumina HumanOmniExpress+ genotyping chips.
Phenotyping was accomplished through online surveys.
Acknowledgments
Figure 3: Regression plots for diet and lifestyle interactions
We thank 23andMe customers who consented to
participate in research for enabling this study. We also
thank employees of 23andMe who contributed to the
development of the infrastructure that made this research
possible.
Here we selected diet and lifestyle traits and show the relationship between
GRS and BMI for each phenotypic level. The general theme is that the effect
of GRS on BMI is reduced with “healthy” eating habits and lifestyle choices.
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