Studying the effects of genetic variation on the aminoacyl-tRNA charging pathway using the Diversity Outbred mouse population

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In: Student Reports, Summer 2018, The Jackson Laboratory


Genome wide studies have shown that genetic variation among the population can lead to difference in quantitative traits, such as height and disease risk. A recent study has shown that genetic variants leading to changes in transcript abundance do not always explain the variability in protein abundance. In order to better understand this discrepancy, we investigated how genetic variation plays a role in the arni11oacyl-tRNA charging pathway, an integral step in protein synthesis. We worked with transcript and protein abundance data obtained from the livers of the Diversity Outbred mouse population, fed regular (chow) or high fat diet. First, we compared the peptide sequences of aminoacyl-tRNA synthetases, and confirmed that protein sequences were largely unique to each gene and therefore estimates of protein abundance were likely to be accurate and not compromised by higl1 sequence similarity among family members. Next, we used R/qtl2 to perform quantitative trait loci (QTL) mapping and identified 19 expression QTL ( eQTL) - variants that affect mRNA abundance - and 40 protein QTL (pQTL) - variants that affect protein abundance for aminoacyl-tRNA synthetase genes. Out of the total QTL, -50% are located in close proximity to the gene ( <5Mb, local) ru1d -50% can be classified as distant - not located close to the gene (>5 Mbp), often on another chromosome. Next, we studied the contribution of the eight founder strain allele effects at each QTL to narrow down variants likely to be causal in a QTL region and identify one or more founder &trains with biased expression of tRNA pathway genes. Further, we examined how the inferred effects correlated to the measure expression in the founder strains. Doing so we identified interesting cases such as C57BL/6J which has a negative correlation. We followed this up with inediation analysis to understand-the regulatory mechanisms driving this discrepancy. The full set ofQTL can be followed up through mediation analysis to understand protein-protein interaction in the tRNA charging pathway. These findings will help us better understand quantitative traits like protein abundance and disease risk.

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