We now take for granted the idea that a genome wide association study, or GWAS, can help identify genetic variants associated with complex traits, and have identified loci associated with everything from breast cancer to heart disease to diabetes.
However, most GWAS do not reveal the actual genes associated with those traits but variations that fall within areas that control gene expression.
And to truly understand the connection between genetic variants and disease and complex traits requires understanding how gene expression and splicing are controlled.
And to do that, you need a lot of data, more than previous studies have used , finds a recent study published in Science .
Many previous studies used Genotype-Tissue Expression, or GTEx, data to map genetic variants from GWAS to expression quantitative trait loci, or eQTL, and splicing quantitative trait loci, or sQTL—areas of the genome associated with gene expression and splicing, respectively—and quickly reached a limit to how many such loci they could find for each gene.
The new study, led by Peter Orchard, Ph.D., and Steve Parker, Ph.D., of the Gilbert S. Omenn Department of Computational Medicine and Bioinformatics, at the University of Michigan (U-M), Laura Scott, Ph.D. of U-M and Laura Raffield, Ph.D. of UNC Chapel Hill and Francois Aguet, Ph.D. of the Broad Institute, reveal that the limit may be in the size of the data set.
“An individual can have one of three possible genotypes, based on which alleles they get from Mom and Dad,” explained Parker.
Those three genotypes are associated with dosage of gene expression: low, intermediate or high.
Part of what determines our phenotype, our set of observable traits, is how much certain genes are expressed.
This can be mapped by looking at eQTL and sQTL.
“Genetic regulation of the expression levels of one part of a biological unit’s machinery, such as a regulator of a cellular stress response, can affect the expression levels of other parts of the same biological machinery,” said Scott.
Parker added, “One reason this is super important is because genetic evidence of drug targets is a huge predictor of clinical success for therapeutics,” increasing the rate of success more than two-fold. The ultimate goal then is to link genetic variation with target gene expression.
In the new study, the team examined 14,324 RNA sequencing samples, including 6,454 whole blood and 1,291 lung samples from the Trans-Omics for Precision Medicine, also known as TOPMed, program, sponsored by the National Heart, Lung and Blood Institute.
After performing e/sQTL analysis, they were able to map tens of thousands of e/sQTL signals with high resolution.
The TOPMed cohorts are also more genetically diverse than most previous studies, and that turned out to matter.
Roughly one in six of the signals the team found in whole blood came from variants that are rare or absent in people of European ancestry.
Almost none of them, 1.3%, appear in the four large eQTL resources built mostly from European samples.
“The more samples you add, the more e/sQTL signals that you detect,” said Parker.
“I think NHLBI has invested very smartly in TOPMed. Although you saturate discovery of main effects after a few thousand samples… the number of independent e/sQTL signals just keeps on growing and growing, which is really cool to see.”
Then, they fine-mapped GWAS signals from the UK Biobank and saw that the more eQTL signals you get, the more overlap of GWAS you have as well.
The extra signals weren't just more of the same.
Most genes turn out to have several independent control switches, and the second, third, and fourth ones are harder for smaller studies to detect.
Of the 10,611 GWAS signals the team was able to connect to a specific gene, roughly two-thirds involved one of these secondary switches.
“With GWAS, you find regions of the genome that correlate with the probability of having a particular disease, like Type 2 diabetes for instance,” said Peter Orchard, first author on the paper.
“You know there’s a correlation, but you just don't know why. With the eQTL, you're trying to bridge that gap and add more context to the associations between variants and the traits we care about.”
“Another powerful aspect of larger-scale studies is the ability to identify QTL for rarer variants, which tend to have larger effects,” said Aguet.
“As further such studies are conducted across more tissues and cell types, we will likely find a lot more of these effects and increase our understanding of their contribution to complex traits.”
The findings emphasize the importance of collecting tissue samples beyond whole blood for larger sample sizes that can be analyzed to uncover these important loci.
“In TOPMed, we still have many more distinct signals to discover at most genes, even in whole blood, which will almost certainly provide still more links between GWAS loci and their likely mechanisms,” added Raffield.
“Also, many non-European ancestry populations are still underrepresented in our current dataset, despite efforts to be more representative than other projects. This means that important ancestry-differentiated signals may be missed, even in blood, and certainly in other tissues. These issues will continue to be addressed in future work from TOPMed.”
“Science moves forward with bigger and better data, always. It’s the equivalent of getting a fancy new microscope except we're not seeing things in higher resolution in a microscopy image, we’re seeing things in much higher resolution from a genetic association with gene expression perspective,” Parker said.
This study was made possible by support from the Foundation for the National Institutes of Health , National Heart, Lung, and Blood Institute , National Cancer Institute , National Institute of Environmental Health Sciences , COPD Foundation , American Asthma Foundation , and the Evans Medical Foundation
Paper cited: Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed. Science . DOI: 10.1126/science.adx2989
Science