Automated Author ProfileByrnes, Jake K.
Stanford University School of Medicine
Byrnes, Jake K.
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.2 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Whole-genome sequencing harbors unprecedented potential for characterization of individual and family genetic variation. Here, we develop a novel synthetic human reference sequence that is ethnically concordant and use it for the analysis of genomes from a nuclear family with history of familial thrombophilia. We demonstrate that the use of the major allele reference sequence results in improved genotype accuracy for disease-associated variant loci. We infer recombination sites to the lowest median resolution demonstrated to date (<1,000 base pairs). We use family inheritance state analysis to control sequencing error and inform family-wide haplotype phasing, allowing quantification of genome-wide compound heterozygosity. We develop a sequence-based methodology for Human Leukocyte Antigen typing that contributes to disease risk prediction. Finally, we advance methods for analysis of disease and pharmacogenomic risk across the coding and non-coding genome that incorporate phased variant data. We show these methods are capable of identifying multigenic risk for inherited thrombophilia and informing the appropriate pharmacological therapy. These ethnicity-specific, family-based approaches to interpretation of genetic variation are emblematic of the next generation of genetic risk assessment using whole-genome sequencing.
Authors
- Dewey, Frederick E. ;
- Chen, Rong ;
- Cordero, Sergio P. ;
- Ormond, Kelly E. ;
- Caleshu, Colleen ;
- Karczewski, Konrad J. ;
- Whirl-Carrillo, Michelle ;
- Wheeler, Matthew T. ;
- Dudley, Joel T. ;
- Byrnes, Jake T. ;
- Cornejo, Omar E. ;
- Knowles, Joshua W. ;
- Woon, Mark ;
- Sangkuhl, Katrin ;
- Gong, Li ;
- Thorn, Caroline F. ;
- Hebert, Joan M. ;
- Capriotti, Emidio ;
- David, Sean P. ;
- Pavlovic, Aleksandra ;
- West, Anne ;
- Thakuria, Joseph V. ;
- Ball, Madeline P. ;
- Zaranek, Alexander W. ;
- Rehm, Heidi L. ;
- Church, George M. ;
- West, John S. ;
- Bustamante, Carlos D. ;
- Snyder, Michael ;
- Altman, Russ B. ;
- Klein, Teri E. ;
- Butte, Atul J. ;
- Ashley, Euan A. ;
- Byrnes, Jake K.