Research fine tunes tools used to search for genetic causes of asthma

Genome wide association studies (GWAS) have identified hundreds of genome regions containing thousands of genetic variants associated with asthma, but it’s still not clear which variants have an actual causal link to the disease. This “variant-to-function” gap is one of the biggest challenges to the usefulness of these genomic studies and has motivated researchers to develop new tools to make sense of GWAS results.

A new study by researchers from the University of Chicago combines genetic data and improved computational tools to look more closely at GWAS results for both adult-onset and childhood-onset asthma. The research identified many genetic variants with a high likelihood of having a causal effect on both types of asthma, paving the way for further studies to target the genes connected to these variants as potential treatments.

The study, published in Genome Medicine, also found significant differences in the sets of genes that could be linked to adult-onset and childhood-onset asthma, with relatively little overlap between the two.

“The real uniqueness of our study is that the differences between childhood- and adult-onset asthma were evident at every level that we looked at,” said Carole Ober, PhD, the Blum-Riese Distinguished Service Professor and Chair of Human Genetics at UChicago, and co-senior author of the paper. “You find out it's actually different variants that are contributing to asthma. Even when the GWAS locus looks the same, the genes functionally linked to these variants are also different. So, they're really quite different diseases.”

Fine-mapping causal variants

Researchers use GWAS to compare genome sequences from a large group of people with a disease to another set of sequences from healthy individuals. The differences identified in the disease group could point to genetic variants that increase risk for that disease and warrant further study. Most human diseases—including asthma—are not caused by a single genetic variant, however. Instead, they are the result of complex interactions among multiple genes, environmental factors, and host of other variables. As a result, GWAS often identifies too many variants across the genome to be of use without further refinement.

GWAS also identifies association only, not causality. In a typical genomic region, many variants are highly correlated with each other, due to a phenomenon called linkage disequilibrium. This is because DNA is passed from one generation to the next in entire blocks, not as individual variants. Therefore, variants nearby each other tend to be correlated. To make the problem more difficult, most of the genetic variants associated with diseases are located in non-coding regions of the genome, making their effects difficult to interpret.

In the new study, Ethan Zhong, a graduate student working with Ober and Xin He, PhD, Associate Professor of Human Genetics and another co-senior author of the paper, wanted to bridge the variant-to-function gap and find more concrete biological insights from different sets of asthma GWAS data. He worked with data from the UK Biobank, a large-scale biomedical database and research resource containing de-identified genetic data from nearly 500,000 people in the United Kingdom. Using a statistical method called “fine-mapping,” he was able to estimate the probability that a given genetic variant has a causal relationship to asthma.

The new estimates incorporated data on the accessibility of chromatin, the bundle of DNA and proteins that make up chromosomes. When a region is involved in regulating gene expression, the chromatin “opens” to become more accessible. The amount of open chromatin can be measured and used as an indicator of regulatory activity; when combined with statistical evidence, it builds an even stronger case that the variant is causally linked to asthma.

“The GWAS associations provide sets of variants associated with the disease,” Zhong said. “So, when those variants overlap with open chromatin regions in cell types that are relevant to asthma pathogenesis like lung epithelial cells, we think that they are more likely to be causal to these asthma phenotypes.”

Zhong also included data on expression quantitative trait loci (eQTLs), genetic variants associated with differences in gene expression, and chromatin interactions from blood and lung cell types, to link fine-mapped variants to their target genes. Using this information, he built a list of likely causal genes supported by genetic evidence.

Closing the gap

The fine-mapping analysis uncovered 21 independent sets of variants (called credible sets) for adult-onset asthma and 67 for childhood-onset, with only 16% shared between the two. Zhong also looked for cis-regulatory elements (CREs), short DNA sequences that control expression of nearby genes, that were linked to asthma and found 62 and 169 candidate genes for adult-onset and childhood-onset, respectively. More than 60% of these had open chromatin in different cell types, including many genes involved in immune and inflammatory responses.

The team selected six of the candidate CREs and tested them in bronchial epithelial cells to see if the variants had a regulatory effect; four of the six did, meaning their efforts are getting closer to the mark in the right kind of cells involved in asthma. The variant-to-function gap closes ever so slightly, opening the door to further studies of these candidate genes as potential targets for treatment.

The study was supported in part by a National Institutes of Health grant to discover genes in asthma and allergy, in collaboration with Marcelo Nobrega, MD, PhD, A.N. Pritzker Professor of Human Genetics at UChicago, Nathan Schoettler, MD, PhD, Assistant Professor of Medicine, and Anne Sperling, PhD, formerly of UChicago and now Professor of Medicine at the University of Virginia.

Additional authors include Robert Mitchell, Christine Billstrand, Emma Thompson, Noboru J. Sakabe, Ivy Aneas, Isabella M. Salamone, and Jing Gu.