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Newswise To reduce false positives when identifying genetic variations associated with human disease through genome-wide association studies (GWAS), Dartmouth researchers have identified nine traits that are not dependent on P values to predict single nucleotide polymorphisms (SNP) reproducibility as reported in Human Genetics on October 2, 2014.

Reproducibility rates of SNPs based solely on P values is low. Dartmouth authors analysis of GWAS studies published in Nature Genetics showed a 1-5 percent replication rate.

It is important to improve our ability to select SNPs for validation using a formalized process. In this paper, we propose a combination of traits that improve replication success, said first author Ivan P. Gorlov, PhD, DSC, associate professor of Community and Family Medicine, Geisel School of Medicine at Dartmouth.

The team assigned a value of zero or one to nine different predictors. To compute the Replication Score (RS), one totals the individual scores for all significant predictors. The predictors include Online Mendelian Inheritance in Man (OMIM, a list of genetically caused diseases), receptors, kinases, growth factors, transcription factors, tissue specific, plasma membrane localization, nuclear localization and conversation index. The authors provided detailed information to construct the RS in supplementary material to the paper.

An RS score is not disease specific but shows the potential for impact on human disease. The disease-associated genes have something in common, said Gorlov. And we know what specific characteristics should be present to ensure the SNP is likely to be replicated

Gorlov says the empirical model can be used to select SNPs for validation and prioritization. We believe that RS-based SNP prioritization may provide guidance for more targeted and powered approach to detecting the disease-associated SNPs with small effect size, he concluded.

This work was supported in part by the National Institutes of Health U19 CA148127 Grant and the National Institutes of Health Grants 5 P30 CA016672, LM009012, LM010098 and GM103534. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

About Norris Cotton Cancer Center at Dartmouth-Hitchcock Norris Cotton Cancer Center combines advanced cancer research at Dartmouth and the Geisel School of Medicine with patient-centered cancer care provided at Dartmouth-Hitchcock Medical Center, at Dartmouth-Hitchcock regional locations in Manchester, Nashua, and Keene, NH, and St. Johnsbury, VT, and at 12 partner hospitals throughout New Hampshire and Vermont. It is one of 41 centers nationwide to earn the National Cancer Institutes Comprehensive Cancer Center designation. Learn more about Norris Cotton Cancer Center research, programs, and clinical trials online at cancer.dartmouth.edu.

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Dartmouth Researchers Develop Reproducibility Score for SNPs Associated with Human Disease in GWAS

PUBLIC RELEASE DATE:

8-Oct-2014

Contact: Robin Dutcher robin.Dutcher@hitchcock.org 603-653-9056 The Geisel School of Medicine at Dartmouth

Lebanon, NH, 10/8/14 To reduce false positives when identifying genetic variations associated with human disease through genome-wide association studies (GWAS), Dartmouth researchers have identified nine traits that are not dependent on P values to predict single nucleotide polymorphisms (SNP) reproducibility as reported in Human Genetics on October 2, 2014.

Reproducibility rates of SNPs based solely on P values is low. Dartmouth authors' analysis of GWAS studies published in Nature Genetics showed a 1-5 percent replication rate.

"It is important to improve our ability to select SNPs for validation using a formalized process. In this paper, we propose a combination of traits that improve replication success," said first author Ivan P. Gorlov, PhD, DSC, associate professor of Community and Family Medicine, Geisel School of Medicine at Dartmouth.

The team assigned a value of zero or one to nine different predictors. To compute the Replication Score (RS), one totals the individual scores for all significant predictors. The predictors include "Online Mendelian Inheritance in Man" (OMIM, a list of genetically caused diseases), receptors, kinases, growth factors, transcription factors, tissue specific, plasma membrane localization, nuclear localization and conversation index. The authors provided detailed information to construct the RS in supplementary material to the paper.

An RS score is not disease specific but shows the potential for impact on human disease. "The disease-associated genes have something in common," said Gorlov. "And we know what specific characteristics should be present to ensure the SNP is likely to be replicated"

Gorlov says the empirical model can be used to select SNPs for validation and prioritization. "We believe that RS-based SNP prioritization may provide guidance for more targeted and powered approach to detecting the disease-associated SNPs with small effect size," he concluded.

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Researchers develop reproducibility score for SNPs associated with human disease in GWAS

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Newswise The Gordon and Betty Moore Foundation today announced the University of Chicagos Matthew Stephens as the recipient of a Moore Investigator in Data-Driven Discovery award. Stephens, a professor in statistics and human genetics, is among 14 scientists from academic institutions nationwide who will receive a total of $21 million over five years to catalyze new data-driven scientific discoveries. Stephens grant is for $1.5 million.

These Moore Investigator Awards are part of a $60 million, five-year Data-Driven Discovery Initiative within the Gordon and Betty Moores Science Program. The initiativeone of the largest privately funded data scientist programs of its kindis committed to enabling new types of scientific breakthroughs by supporting interdisciplinary, data-driven researchers.

Science is generating data at unprecedented volume, variety and velocity, but many areas of science dont reward the kind of expertise needed to capitalize on this explosion of information, said Chris Mentzel, program director of the Data-Driven Discovery Initiative. We are proud to recognize these outstanding scientists, and we hope these awards will help cultivate a new type of researcher and accelerate the use of interdisciplinary, data-driven science in academia.

Stephens is a data scientist who develops statistical and computational analysis tools for the large datasets being generated in the biological sciences. Over the last 15 years, Stephens and his collaborators have made seminal contributions to several problems in population genetics, including identifying structure (clusters) in genetic data, and modeling correlations among genetic variants.

The methods for identifying structure, which Stephens developed with his collaborators (Jonathan Pritchard, Peter Donnelly and Daniel Falush), have driven scientific discoveries in hundreds of organisms. Science papers in 2002, 2003, and 2004 used their method to elucidate the genetic structure of human populations, the Heliobacter pylori stomach bacterium, and domestic dog breeds, respectively. The original paper of Stephens and his collaborators has been cited more than 11,000 times. And, in an example of the potential for cross-fertilization of ideas across disciplines, similar methods have also become popular in machine learning to identify structure in large collections of text documents.

Stephenss work modeling correlations among genetic variants began with a paper in 2003, with graduate student Na Li, PhD03. At the time scientists were grappling with a problem: they had an elegant model (based on work by UChicagos Richard Hudson, professor in ecology & evolution) relating these correlations to the underlying recombination process, which mixes a parents genetic material before transmission to an offspring, but these models were computationally intractable for even small datasets.

Li and Stephens solved this problem by simplifying the model enough to make it computationally tractable. This new simplified model has found widespread application in the last 10 years: Stephens, Li and their collaborators used their model to demonstrate that most recombination in human genes occurs in relatively narrow channels (``hotspots) rather than being spread uniformly. And thousands of scientists conducting genomic studies now make regular use of these models to impute missing genotype data to substantially improve the efficacy of their studies.

Stephenss recent focus has been on developing methods for data integration combining information on multiple related processes. An important application of these methods which he has been pursuing with collaborators, including Yoav Gilad, Jonathan Pritchard and Anna DiRienzo - is to combine information measured on cellular processes, such as gene expression, and transcription factor binding, to help understand the mechanisms of genetic regulation within living cells.

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Moore Foundation Selects Matthew Stephens for Data-Driven-Discovery Grant