Turning the data collected by the recent quantum leap in DNA sequencing technology into useful biomedical knowledge is one of the greatest scientific challenges of the 21st Century. The enormous volume of DNA data now collected cannot be accessed, manipulated, or analyzed without computers. However, the greatest bottlenecks involve software rather than hardware. In spite of current levels of sophistication, we still lack adequate theory and algorithms to perform many fundamental tasks in molecular genetics and genetic epidemiology with speed and precision. It will take a new generation of scientists trained in both the biological and mathematical sciences to push forward our nation's genomic agenda. Few universities have the infrastructure and human resources to mount a genomic analysis training program of the scope possible at UCLA. Our training program's core curriculum includes work in molecular biology, human genetics, probability and statistics, bioinformatics, and biomedical ethics. Students must also prepare for and attend pertinent weekly scientific seminars. The trainees collectively meet on a monthly basis with the program faculty and directors to learn about career matters not covered in regular coursework. Each year we will train 12 predoctoral students drawn preferentially from students in the first half of their doctoral programs. Each trainee will be funded for two years contingent on satisfactory progress. In exceptional cases a third year of funding will be considered. This training grant has brought together faculty from four schools and 14 PhD-granting departments within the university to address the current gap in scientific training. During the years 2002 to 2010, this training grant has successfully completed training of 22 predoctoral students in the art of genomic analysis and interpretation. These doctorates have gone on to productive careers in genomic sciences in industry, government, and academia.

Public Health Relevance

Genetics is now a major driving force for modern biomedical research. As genetic experiments become more automated and miniaturized, data analysis and interpretation become the biggest bottlenecks to discovery. This program seeks to train a new generation of computational geneticists able to turn this flood of data into useful biomedical knowledge.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Ethical, Legal, Social Implications Review Committee (GNOM)
Program Officer
Junkins, Heather
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California Los Angeles
Schools of Medicine
Los Angeles
United States
Zip Code
Duong, Dat; Zou, Jennifer; Hormozdiari, Farhad et al. (2016) Using genomic annotations increases statistical power to detect eGenes. Bioinformatics 32:i156-i163
Crawford, Forrest W; Stutz, Timothy C; Lange, Kenneth (2016) Coupling bounds for approximating birth-death processes by truncation. Stat Probab Lett 109:30-38
Brown, Robert; Lee, Hane; Eskin, Ascia et al. (2016) Leveraging ancestry to improve causal variant identification in exome sequencing for monogenic disorders. Eur J Hum Genet 24:113-9
Clark, Michelle M; Blangero, John; Dyer, Thomas D et al. (2016) The Quantitative-MFG Test: A Linear Mixed Effect Model to Detect Maternal-Offspring Gene Interactions. Ann Hum Genet 80:63-80
Han, Buhm; Duong, Dat; Sul, Jae Hoon et al. (2016) A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping. Hum Mol Genet 25:1857-66
Gusev, Alexander; Ko, Arthur; Shi, Huwenbo et al. (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245-52
Traller, Jesse C; Cokus, Shawn J; Lopez, David A et al. (2016) Genome and methylome of the oleaginous diatom Cyclotella cryptica reveal genetic flexibility toward a high lipid phenotype. Biotechnol Biofuels 9:258
Hamaji, Takashi; Lopez, David; Pellegrini, Matteo et al. (2016) Identification and Characterization of a cis-Regulatory Element for Zygotic Gene Expression in Chlamydomonas reinhardtii. G3 (Bethesda) 6:1541-8
Janowitz Koch, Ilana; Clark, Michelle M; Thompson, Michael J et al. (2016) The concerted impact of domestication and transposon insertions on methylation patterns between dogs and grey wolves. Mol Ecol 25:1838-55
Wu, Tong Tong; Lange, Kenneth (2015) Matrix Completion Discriminant Analysis. Comput Stat Data Anal 92:115-125

Showing the most recent 10 out of 112 publications