The statistical genetics faculty at the University of Washington propose renewal of the predoctoral training program in biostatistics that emphasizes applications to genetics. The faculty members belong to the very strong departments of Biostatistics, Genome Sciences or Statistics at the University of Washington. The trainees will pursue Ph.D. degrees in one of the three departments. The training program will include the current Ph.D. tracks in statistical genetics offered by the Departments of Biostatistics and Statistics but it will have the additional feature of rotations in experimental laboratories and opportunities for internships in local companies. In addition to formal courses taught by international experts in statistical genetics, the training program features journal club, seminars and retreats. Trainees will also be able to attend modules in the annual Summer Institute in Statistical Genetics held at the University of Washington.

Public Health Relevance

Biology in general and genetics in particular, is becoming increasingly quantitative. Many major advances in medical science are being made possible by the massive amounts of data from the Human Genome Project, the HapMap project, the 1000 Genomes project and related activities. Managing and interpreting these data requires the development and application of new statistical methodologies. This training grant will support highly talented students in the Departments of Biostatistics, Genome Sciences or Statistics and ensure that they are trained to lead such development and application.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Institutional National Research Service Award (T32)
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National Institute of General Medical Sciences Initial Review Group (BRT)
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Flicker, Paula F
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University of Washington
Biostatistics & Other Math Sci
Schools of Public Health
United States
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Grinde, Kelsey E; Arbet, Jaron; Green, Alden et al. (2017) Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association. Front Genet 8:117
Hodonsky, Chani J; Jain, Deepti; Schick, Ursula M et al. (2017) Genome-wide association study of red blood cell traits in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos. PLoS Genet 13:e1006760
Jain, Deepti; Hodonsky, Chani J; Schick, Ursula M et al. (2017) Genome-wide association of white blood cell counts in Hispanic/Latino Americans: the Hispanic Community Health Study/Study of Latinos. Hum Mol Genet 26:1193-1204
Baraff, Aaron J; McCormick, Tyler H; Raftery, Adrian E (2016) Estimating uncertainty in respondent-driven sampling using a tree bootstrap method. Proc Natl Acad Sci U S A 113:14668-14673
Browning, Sharon R; Grinde, Kelsey; Plantinga, Anna et al. (2016) Local Ancestry Inference in a Large US-Based Hispanic/Latino Study: Hispanic Community Health Study/Study of Latinos (HCHS/SOL). G3 (Bethesda) 6:1525-34
Greco, Brian; Hainline, Allison; Arbet, Jaron et al. (2016) A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures. Eur J Hum Genet 24:767-73
Blue, Elizabeth M; Brown, Lisa A; Conomos, Matthew P et al. (2016) Estimating relationships between phenotypes and subjects drawn from admixed families. BMC Proc 10:357-362
Saad, Mohamad; Nato Jr, Alejandro Q; Grimson, Fiona L et al. (2016) Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data. BMC Proc 10:295-301
Conomos, Matthew P; Laurie, Cecelia A; Stilp, Adrienne M et al. (2016) Genetic Diversity and Association Studies in US Hispanic/Latino Populations: Applications in the Hispanic Community Health Study/Study of Latinos. Am J Hum Genet 98:165-84
Valcarcel, Alessandra; Grinde, Kelsey; Cook, Kaitlyn et al. (2016) A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests. BMC Proc 10:349-355

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