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Sarah Pendergrass: Connecting Phenotypes to SNPs

Sarah Pendergrass: Connecting Phenotypes to SNPs
Research fellow, Ritchie lab, Pennsylvania State University
Recommended by Marylyn Ritchie, Pennsylvania State University

Pendergrass.jpgBioinformatics, Sarah Pendergrass said, is a perfect match between her experience in engineering and her desire to be able to solve biological problems.

Pendergrass and her colleagues are working on what's been dubbed phenome-wide association studies, in which they study links between a number of SNPs and a range of phenotypes. She noted that they work with a number of different outcomes, both different types of datasets and different types of diseases or conditions, ranging from autism to lipid levels to cancer and to HIV.

"We're phenotype agnostic," Pendergrass said.

For example, one project she and her colleagues are currently working on is an electronic medical record-based PheWAS. This involves searching through electronic medical records — their source of phenotypes — to find variants linked to immune response. Another one, which she said is more pharmacogenomics-centered, draws on an HIV dataset as part of a clinical trial.

A challenge she faces is setting up collaborations, not just finding times for those involved to meet, but particularly because many of those meetings have to take place before there's any funding in place. "It's like bootstrapping," she added.

But collaborations are increasingly important. "We're at a point where no lab can work alone and everyone needs to work together to utilize the huge amounts of data that we have," she said.

Paper of note

In a phenome-wide association study published last January in PLOS Genetics, Pendergrass and her colleagues reported that such an approach was able to replicate previously uncovered associations as well as uncover new ones.

For the study, they drew on phenotype and SNP data from more than 70,000 people participating in the Population Architecture using Genomics and Epidemiology network. They replicated finding between certain SNPS and lipid traits, type 2 diabetes, and more, as well as found some 33 potentially novel genotype-phenotype associations.

"It was the first where we really able to make a phenome-wide association study possible," she noted.

In a separate study, Pendergrass and her colleagues presented a new method to visualize data. As they reported in BioData Mining in 2010, their Synthesis-View software enabled researchers to integrate different types of data from genetic association studies. "Through visually incorporating results, details of individual SNP-phenotype relationships as well as larger trends in the interplay between information such as SNP location, sample size, data stratification, and allele frequencies can be viewed," she and her colleagues wrote.

Looking ahead

In the coming years, Pendergrass said that collaborations between researchers with different backgrounds and experience will be important. This will not only be important in crafting better tools, but also to integrate all the data that has been collected, she said.

"We can take new data that we're collecting across all the omes — genomes to gene expression data and across to other ways of measuring outcomes — to really put it together to get a bigger picture of this relationship between our genetic architecture and outcome," she added.

And the Nobel goes to…

If she were to win the Nobel Prize, Pendergrass would like it to be for teasing out the etiology of a complex outcome to help human health. "We work with a lot of different types of data, so from autism datasets to cancer datasets, so if in any of those kinds of complex outcomes that affect human health, we found something that really helped elucidate that, that would make it worthwhile," she said.

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