Chiang.Lab

Charleston Chiang is the principal investigator of the group. He is a tenure-track Assistant Professor in the Department of Population and Public Health Sciences at USC Keck School of Medicine and Department of Quantitative and Computational Biology at USC Dornsife College of Letters, Arts, and Sciences. He is broadly interested in using genetic approaches to understand how natural selection and demographic history shaped the variations in complex traits within and between diverse human populations.

Prior to setting up his lab in 2018, he was an NRSA postdoctoral fellow working with Nelson Freimer and John Novembre at UCLA. Charleston received his Ph.D. in Genetics from Harvard University where he worked with Joel Hirschhorn.

Charleston's CV and Google Scholar profile
ORCID: 0000-0002-0668-7865
Email: charleston [dot] chiang [at] med [dot] usc [dot] edu
Follow him on twitter: @CharlestonCWKC

Tsz Fung Chan is a Ph.D student in the group since 2019. He is pursuing his Ph.D. in Epidemiology in the Department of Preventive Medicine. Co-advised by both Dr. Chiang and Dr. Nicholas Mancuso in the Center for Genetic Epidemiology, he is currently developing methods to estimate heritability of complex traits explained by local ancestry and to evaluate stratification bias in admixture mapping studies. Before joining the group, Tsz Fung studied the genetics of Epstein-Barr virus under the guidance of Alan Chiang and Wanling Yang at the University of Hong Kong. He received his Bachelor of Biomedical Sciences (with a minor in Computer Sciences) and M.Phil in Bioinformatics from University of Hong Kong in 2017 and 2019, respectively.

Recent publications:

1. Chan TF, Rui X, Conti DV, Fornage M, Graff M, Haessler J, Haiman C, Highland HM, Jung SY, Kenny E, Kooperberg C, Le Marchand L, North KE, Tao R, Wojcik G, Gignoux CR, PAGE Consortium, Chiang CWK*, Mancuso N*. “Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics.” bioRxiv 2023. doi: 10.1101/2023.04.10.536252. link
   
   
2. Li S, Chiang CWK, Myint SS, Arroyo K, Chan TF, Morimoto L, Metayer C, de Smith AJ, Walsh KM, Wiemels JL. “Localized variation in ancestral admixture identifies pilocytic astrocytoma risk loci among Latino children.” PLoS Genet. 2022 Sep 7;18(9):e1010388. doi: 10.1371/journal.pgen.1010388.link
   
   
3. Jeon S, de Smith AJ, Li S, Chen M, Chan TF, Muskens IS, Morimoto LM, Dewan AT, Mancuso N, Metayer C, Ma X, Wiemels JL, Chiang CWK. “Genome-wide trans-ethnic meta-analysis identifies novel susceptibility loci for childhood acute lymphoblastic leukemia.” Leukemia. 2022 Mar;36(3):865-868. doi: 10.1038/s41375-021-01465-1. Epub 2021 Nov 8. link
   
   
4. Sun H*, Lin M*, Russell EM, Minster RL, Chan TF, Dinh BL, Naseri T, Reupena MS, Lum-Jones A, OLaGA Study Group, Cheng I, Wilkens LR, Le Marchand L, Haiman CA, Chiang CWK. “The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians.” PLoS Genet. 2021 Feb 11; doi: 10.1371/journal.pgen.1009273.link
   
   

Bryan Dinh is a Ph.D. student in the group since 2020. He is an F31 predoctoral fellow pursuing his Ph.D. in the Computational Biology and Bioinformatics program in the Department of Biological Sciences. Bryan is interested in algorithms and their applications to biology, genetics, and diverse populations. Supported by a F31 predoctoral fellowship from NHGRI, Bryan's dissertation focuses on constructing and evaluating genomic resources for Native Hawaiian community, with the goal to empower cutting edge genomic studies in this understudied population. Before joining the group, Bryan received a Master of Science in Computer Science and a Bachelor of Science in Mathematics and Economics from UCSD.

Recent publications:

1. Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. “Recombination map tailored to Native Hawaiians reduces false positive findings in genomic scans of positive selection.” bioRxiv 2023. doi: 10.1101/2023.07.12.548735v2. link
   
   
2. Link V, Schraiber JG, Fan C, Dinh BL, Mancuso N, Chiang CWK, Edge MD. “Tree-based QTL mapping with expected local genetic relatedness matrices.” bioRxiv 2023. doi: 10.1101/2023.04.07.536093. link
   
   
3. Sun H*, Lin M*, Russell EM, Minster RL, Chan TF, Dinh BL, Naseri T, Reupena MS, Lum-Jones A, OLaGA Study Group, Cheng I, Wilkens LR, Le Marchand L, Haiman CA, Chiang CWK. “The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians.” PLoS Genet. 2021 Feb 11; doi: 10.1371/journal.pgen.1009273.link
   
   

Echo Tang is an undergraduate student in the group since 2021. She is pursuing a B.S. degree in Quantitative Biology from USC Dornsife College of Letters, Arts and Sciences and a minor in Health Policy. Before joining the group, she worked as a research assistant studying the genetics of aging and sex-specific effects of mutations. Currently, she is part of a team of researchers evaluating the current efficacy for imputing populations around the globe.

Recent publications:

1. Dinh BL, Tang E, Taparra K, Nakatsuka N, Chen F, Chiang CWK. “Recombination map tailored to Native Hawaiians reduces false positive findings in genomic scans of positive selection.” bioRxiv 2023. doi: 10.1101/2023.07.12.548735v2. link
   
   
2. Cahoon JL, Rui X*, Tang E*, Simons C, Langie J, Chen M, Lo YC, Chiang CWK. “Imputation Efficacy Across Global Human Populations.” bioRxiv 2023. doi: 10.1101/2023.05.22.541241. link
   
   

Jordan Cahoon is an undergraduate student in the group since 2021. She is pursuing a B.S. degree in Computer Science at the USC Viterbi School of Engineering with a minor in Computational Biology and Bioinformatics. She is one of only ~400 undergraduates nationwide to be awarded the Barry Goldwater Scholarship. Jordan is interested in analyzing and visualizing genetic disease factors in diverse populations. Prior to joining the lab, she worked as an undergraduate research assistant in single-cell transcriptomics research. Supported by the Viterbi Merit Scholarship, she is leading a team of undergraduate researchers to evaluate the current efficacy for imputing populations around the globe, supporting algorithm development in the lab, and exploring the application of machine learning methods for population genetic inference using the ancestral recombination graph.

Recent publications:

1. Cahoon JL, Rui X*, Tang E*, Simons C, Langie J, Chen M, Lo YC, Chiang CWK. “Imputation Efficacy Across Global Human Populations.” bioRxiv 2023. doi: 10.1101/2023.05.22.541241. link
   
   
2. Sheng X, Xia L, Cahoon JL, Conti DV, Haiman CA, Kachuri L, Chiang CWK. “Inverted genomic regions between reference genome builds in humans impact imputation accuracy and decrease the power of association testing.” HGG Adv. 2022 Nov 11;4(1):100159. doi: 10.1016/j.xhgg.2022.100159. eCollection 2023 Jan 12. link
   
   

Jalen Langie is a Ph.D. student in the group since 2021. She is a F31 predoctoral fellow pursuing a Ph.D. in Epidemiology (Genetic Track) in the Department of Population and Public Health Sciences. Overall, she aims to investigate genetic contributions to disease through the lens of evolutionary history. Her interests include admixture mapping, fine mapping, and SNP-to-gene annotations in multiethnic populations. Prior to joining the lab, she received her Bachelor of Science in Microbiology from California State University Los Angeles in 2019. During undergraduate, she worked as a research assistant for three years studying fungal pathogenesis and regulatory pathways and completed an internship studying viral kinetics and chemokine expression at Johns Hopkins University.

Recent publications:

1. Cahoon JL, Rui X*, Tang E*, Simons C, Langie J, Chen M, Lo YC, Chiang CWK. “Imputation Efficacy Across Global Human Populations.” bioRxiv 2023. doi: 10.1101/2023.05.22.541241. link
   
   

Dorcus Kholofelo Malomane is a postdoctoral research scholar in the group since 2022. She is interested in exploring patterns of genetic variation in diverse human populations and their relation to complex traits. Dorcus has a background in population and evolutionary genomics. She obtained her PhD in 2019 from the University of Goettingen, Germany. In her PhD work she studied how different geographic distributions, management and breeding practices have shaped genetic diversity in a wide variety of globally collected chicken populations. Furthermore, she investigated the genetic patterns of different genomic regions, pathways and genes in the chicken to explore their subjection to different evolutionary dynamics. Prior to joining the group, she was shortly a postdoctoral research associate at the University of Goettingen and her work was aimed at developing genetic markers for ash dieback disease. Her current project utilizes the genome-wide array data, whole exome and genome sequencing data to understand the impact of population structure and history on patterns of variation in a Saudi population.

Xinran Wang has been working as a Program Analyst since 2023. She is passionate about leveraging genomic data to gain insights into disease-trait associations, the evolution and architecture of complex traits, and the history of diverse human populations. At present, Xinran's work primarily focuses on evaluating imputation reference panel for Japanese and Native Hawaiian populations, as well as conducting GWAS and meta-analyses on hyperemesis gravidarum. Before joining the lab, she earned her Master of Science in Applied Biostatistics and Epidemiology from the University of Southern California and her Bachelor of Arts in Public Health from UC Irvine. During her master's program, Xinran gained valuable experience in GWAS, meta-analysis, and TWAS while working under the supervision of Dr. Nicholas Mancuso at USC and Dr. Marlena Fejzo at USC and UCLA.

He Tian is a Ph.D. student in the group since 2023. She is pursuing her Ph.D. in Epidemiology in the Department of Population and Public Health Sciences. She is interested in Polygenic Risk Scores and cancers among ethnic minority populations. Before joining the lab, she received her Bachelor of Medicine in Preventive Medicine from Shandong University (Jinan, China) in 2020. During graduate school, she focused on studying the racial disparity in ovarian cancer and received her MPH in 2022.

Ji Tang obtained a Bioinformatics Ph.D. in July 2023 from the Southern Medical University of China. He was awarded as the Outstanding Graduate in his Ph.D., where he developed a deep learning method for identifying the genomic mutations favored by recent natural selection and investigated the association between adaptive evolution and disease susceptibility in human populations. In general, Ji is interested in investigating the influence of population history on the genetic architecture of traits/diseases in diverse human populations by developing computational methods (especially machine learning methods) and performing statistical analyses. Currently, he is focusing on developing a method to infer more accurate ARG (Ancestral Recombination Graph) when applied to empirical sequencing data with errors, and on demonstrating the enrichment of functionally important or consequential alleles in Polynesian-ancestry individuals, whose ancestors went through several bottlenecks and persisted in small population sizes for a long time.

Lab Activities

Covid certainly made it hard for us to do a lot of fun things together, but click here for some pictures of the lab over the years!

Image credit: modified from "Group" by Nawicon / CC

Lab Alumni

All of the wonderful trainees, visitors, and colleagues that have contributed to our work!

Hanxiao Sun, M.S. Biostatistics 2018-2019. Last known location: Ph.D. student at UT Health School of Public Health
Meng Lin, postdoc 2018-2020. Last known location: postdoc at University of Colorado
Samuel Sommerer, B.S. Quantitative Biology 2019-2020. Last known location: B.S. student in Computer Science at USC
Sydney Rashid, B.S. Quantitative Biology 2019-2020. Last known location: B.S. student in Quantitative Biology at USC
Minhui Chen, postdoc 2018-2021. Last known location: postdoc at University of Chicago
Camellia Xinyue Rui, M.S. Biostatistics 2020-2022. Last known location: Ph.D. student at USC
Ying Chu Lo, postdoc 2021-2023.
Christopher Simons, B.S./M.S. Quantitative Biology 2021-2023. Last known location: Bioinformatician at Design Therapeutics
Soyoung Jeon, Ph.D. Cancer Biology and Genomics 2018-2023. Last known location: Bioinformatic scientist at Active Motif
Caoqi Fan, Ph.D. Computational Biology and Bioinformatics 2019-2023. Last known location: Bioinformatic scientist at Vibrant Therapeutics

Image credit: modified from "Graduation" by Wilson Joseph / CC

Join Our Group

We are always interested in talented and highly motivated individuals to join our team! We currently have space and funding for one or more postdoctoral fellows in genetic epidemiology and population genetics to join our group. Please check out this page for expectations and resources for lab members. See below for how to apply:

Postdoctoral Fellows: Highly motivated individuals are encouraged to contact Charleston Chiang for inquiries. In particular, there are openings for postdocs either interested in population genetics or in genetic epidemiology. Funding could be through a R35 grant, an anticipated R01, unrestricted funds, or through the USC-UH Multiethnic Cohort T32 Postdoctoral Training Program. Applicants ideally should have extensive exposure and experience in human medical and/or population genetics research and have analyzed large genetic datasets. Programming skills (e.g. python, perl, C, R, etc.) and proficiency in Unix-based computing environments are very desirable. Successful candidates may also develop projects broadly within the scope of the group's research interests.

Informal inquiries or applications (cover letter, CV, and contact information to at least two references) should be emailed to Charleston Chiang.

Graduate Students: The research group is recruiting prospective PhD students. Generally, doctoral students are enrolled through Program in Biological and Biomedical Sciences (PIBBS), Computational Biology and Bioinformatics (CBB) or one of the Public Health Sciences programs (such as Biostatistics or Epidemiology) at USC. Email Charleston Chiang for inquiries.

Image credit: "Unknown" by Bybzee / CC BY