Genome-wide association (GWA) studies aim to dissect the relationship between genotype and phenotype. So far, the focus has been on nuclear genetic determinants as variation in the mitochondrial genome is often low in the study cohorts. Despite this observation, mitochondrial genomic variants are increasingly linked to metabolic and neurological disorders. Thus, there is much to gain from studying how mitochondrial variation interacts with nuclear variation and/or environment, and how this subsequently affects phenotypes.
In my thesis I address these topics using Drosophila melanogaster as a model system, and specifically the Drosophila Genetic Reference Panel (DGRP). The DGRP consists of 200 sequenced and genetically diverse Drosophila populations and has been extensively used to study a wide-range of quantitative traits. However, in all of these studies, researchers have focussed on the influence of nuclear genetic variants in the absence of a high-resolution map of mitochondrial variants. To address this problem, we have developed a high-throughput method to specifically sequence mitochondrial DNA that enabled us to characterize mitochondrial genomic variation to an unprecedented depth. On average, each DGRP line had 25 mitochondrial variants (1 in 600 bp) and that most of the variants detected were unique for each line. However, we also found that some variants are more prevalent and show they can be used to construct mitochondrial haplotypes. We then used these haplotypes to test whether mitochondrial variation affected previously published phenotypes. We detected significant associations with 12 out of 259 publicly accessible phenotypes and found that these were mostly related to metabolism and responses to environmental cues. We selected one of the significant phenotypes, food intake in males, to verify our findings. We demonstrated the effect of mitochondrial haplotypes on the phenotype by swapping mitochondria from ¿high food intake¿ lines with mitochondria from ¿low food intake¿ lines and vice versa. We observed that, largely independent of the nuclear background, DGRP lines with a ¿high food intake mitochondria¿ consumed more food and concomitantly, DGRP lines with the ¿low food intake mitochondria¿ consumed less food.
Furthermore, we investigated the influence of natural genetic variation was on the response to a high-fat diet. We designed a control and a high-fat diet and fed this to >100 DGRP lines and measured the lifespan on each diet. On average, lifespan was decreased by 48% on a high-fat diet, however this ranged from an increase of 2% to a decrease of 60% accentuating the complexity of the underlying genetic architecture. Our dataset was relatively small and limited our genome-wide and mitochondrial haplotype association analyses. Nonetheless, our GWA analysis detected variants related to genes involved in lipid metabolism and mitochondria but we did not detect significant associations with mitochondrial haplotypes. This suggests that while mitochondrial function may be important for the response to a high-fat diet, it is not dependent on the mitochondrial haplotype.
Taken together, the work described in my thesis demonstrates the significance of accounting for mitochondrial haplotypes in phenotype association analyses. By doing so, it can open up new insights into genotype-to-phenotype relationships and gene regulation.
EPFL2018
