Implicating Sequence Variants in Human Disease
On September 12-13, 2012, the National Human Genome Research Institute (NHGRI), sponsored a workshop — Implicating Sequence Variants in Human Disease — at the Hyatt Regency Bethesda, in Bethesda, Md. Teri Manolio, M.D., Ph.D., NHGRI, and Daniel MacArthur, Ph.D., Massachusetts General Hospital, co-chaired the workshop. The goal of the workshop was to develop guidelines for investigators, reviewers and editors to consider in assessing the evidence implicating sequence variants or genes as causal in a specific disease.
![video](https://webarchive.library.unt.edu/web/20120915124100im_/http://www.genome.gov/images/icons/video_icon.gif)
Time: 8:00 a.m.-5:00 p.m., Sept. 13, 2012
Wednesday, September 12, 2012
Topic | Speaker | |
---|---|---|
1 | Welcome and Introductions | Daniel MacArthur, Ph.D. Massachusetts General Hospital |
2 | The Need for Criteria to Implicate DNA Variants: Real-World Examples | Mark Daly, Ph.D. Massachusetts General Hospital |
3 | Large Sample Sequencing and NHGRI Priorities |
Heidi Rehm, Ph.D., F.A.C.M.G Partners Healthcare |
4 | Laboratory Perspectives on Variant Implication | David Dimmock, M.D. Medical College of Wisconsin |
5 | Discussion | All |
6 | Overview of Second Day Format and Plans | Daniel MacArthur, Ph.D. Massachusetts General Hospital Teri Manolio, M.D., Ph.D. National Human Genome Research Institute |
Thursday, September 13, 2012
Topic | Speaker | |
---|---|---|
7 | Revisions (as needed) to Plans for Day's Discussions | Daniel MacArthur, Ph.D. Massachusetts General Hospital |
START OF WORKGROUP DISCUSSIONS | ||
8 | Study Design: What sample selection and data processing procedures maximize power and minimize false positives in identifying causal variants? | Study Design Working Group |
9 | Statistical Analysis: How can we robustly identify variants underlying disease? | Statistical Analysis Working Group |
10 | Known Variants: Which candidate casual variants/denes in a patient or cohort have been previously implicated in similar phenotypes? | Known Variants Working Group |
11 | Functional Annotation: How can we predict the variants most likely to have an effect on biological function? | Functional Annotation Working Group |
12 | Experimental Data: How can we investigate whether candidate causal variants have a biological effect on disease risk? | Experimental Data Working Group |
13 | Clinical Implications: How could these guidelines be used in context of diagnostic-decision-making? | Clinical Implications Working Group |
14 | Integrated Approach: How can we sum across different classes of evidence to assess overall confidence in variant causality? | Integrated Approach Working Group |
END OF WORKGROUP DISCUSSIONS | ||
15 | Outline of White Paper | Daniel MacArthur, Ph.D. Massachusetts General Hospital |
16 | Summary of Key Messages and Next Steps | Teri Manolio, M.D., Ph.D. National Human Genome Research Institute |
Posted: September 13, 2012
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