IRP Core: Human Statistical Genetics Unit
Jun Ding, PhD, Chief
The Human Statistical Genetics Unit (HSGU) is an NIA IRP core facility supervised by Jun Ding. It focuses on developing statistical and computational tools for genetic studies and applying them to the analyses of aging-related traits and conditions. Aging-related traits are complex and the risk of a disease is dictated by many genes and environmental factors. The core combines high-throughput technology with powerful computational tools for correspondingly complex genetic analyses in whole populations or case-control studies. The goal of the core is to add additional power with new algorithms to the analyses of variants involved in aging by allying genome-wide association studies (GWAS) with genome sequencing. Concerning analysis tools, the core has been developing new programs to analyze mitochondrial (mt)DNA variation and copy number in next-generation sequencing studies and applying the programs to large population cohorts to assess mitochondrial variation associated with complex diseases and traits. Collaborating with other investigators at the institute, the core has also been developing algorithms for measuring individual rates of aging using multiple quantitative traits. For experimental tests of the algorithms, we have applied our programs to NIA-sponsored large-scale cohort studies: BLSA, InCHIANTI, and SardiNIA. Currently, our applications include analyses of >100,000 samples sequenced by the NIH-sponsored TOPMed Project and analyses of 125,000 UK Biobank samples with a newly approved Alzheimer’s Disease (AD)-related grant.
Curriculum Vitae and Bibliography
- Studying mitochondrial variation and copy number from sequencing data
- Assessing mitochondrial variation associated with diseases and traits
- Measuring the rate and heritability of aging using multiple quantitative traits
Findings and Publications
Sun ED, Qian Y, Oppong R, Butler TJ, Zhao J, Chen BH, Tanaka T, Kang J, Sidore C, Cucca F, Bandinelli S, Abecasis GR, Gorospe M, Ferrucci L#, Schlessinger D#, Goldberg I#, Ding J#. Predicting physiological aging rates from a range of quantitative traits using machine learning. Aging 13: 23471, 2021. #corresponding authors
Butler TJ, Estep KN, Sommers JA, Maul RW, Moore AZ, Bandinelli S, Cucca F, Tuke MA, Wood AR, Bharti SK, Bogenhagen DF, Yakubovskaya E, Garcia-Diaz M, Guilliam TA, Byrd AK, Raney KD, Doherty AJ, Ferrucci L, Schlessinger D, Ding J#, Brosh RM#. Mitochondrial genetic variation is enriched in G-quadruplex regions that stall DNA synthesis in vitro. Human Molecular Genetics 29: 1292, 2020. #corresponding authors
Qian Y, Butler TJ, Opsahl-Ong K, Giroux N, Sidore C, Nagaraja R, Cucca F, Abecasis GR, Schlessinger D, Ding J. fastMitoCalc: an ultra-fast program to estimate mitochondrial DNA copy number from whole-genome sequences. Bioinformatics 33: 1399, 2017.
Ding J, Sidore C, Butler TJ, Wing MK, Qian Y, Meirelles O, Busonero F, Tsoi LC, Maschio A, Angius A, Kang HM, Nagaraja R, Cucca F, Abecasis GR, Schlessinger D. Assessing mitochondrial DNA variation and copy number in lymphocytes of ~2,000 Sardinians using tailored sequencing analysis tools. PLoS Genetics 11(7): e1005306, 2015.
Ding J, Gudjonsson JE, Liang L, Stuart PE, Li Y, Chen W, Weichenthal M, Ellinghaus E, Franke A, Cookson W, Nair RP, Elder JT and Abecasis GR. 2010. Gene expression in skin and lymphoblastoid cells: refined statistical method reveals extensive overlap in cis-eQTL signals. American Journal of Human Genetics 87: 779-789, 2010.
Nair RP*, Duffin KC*, Helms C*, Ding J*, Stuart PE, Goldgar D, Gudjonsson JE, Li Y, Tejasvi T, Feng B, Ruether A, Schreiber S, Weichenthal M, Gladman D, Rahman P, Schrodi SJ, Prahalad S, Guthery SL, Fischer J, Liao W, Kwok P, Menter A, Lathrop GM, Wise C, Begovich AB, Voorhees JJ, Elder JT, Krueger GG, Bowcock AM, Abecasis GR for the Collaborative Association Study of Psoriasis (*joint first author)Genome-wide scan reveals association of psoriasis with IL-23 and NF-κB pathways. Nature Genetics 41:199-204, 2009.