Welcome to our Research Group's webpage!
We are a group of human geneticists and computational biologists. We utilize cutting-edge analytic tools to address questions at the intersection of human medical and population genetics. These insights will be critical for future medical genetics studies and in practicing personalized medicine.
11.2023: New members joining the lab before we wrap up the Fall semester! Indu Sharma is a postdoc who previously working on characterizing isolated populations in India/Himalayas, Indu is interested in examining the signature of selection and its modern day consequences on complex traits and diseases. Eunice Lee joined as a senior research associate. Trained as an environmental epidemiologist, she will incorporate her background in working with environmental variables with genetics data! Welcome Indu and Eunice!
11.2023: Jordan Cahoon's project for evaluating imputation accuracy for populations across the globe received the best poster award at International Genetic Epidemiology Society annual meeting in Nashville, TN! (though unfortunately she could not make it).
11.2023: Charleston Chiang was awarded the early career investigator award by HGG Advances this year, though the credit should really go to Grace Sheng who relentlessly tracked down a QC anomaly impacting only 0.2% of the data, but in the end taught all of us something new in genomic analysis!
11.2023: Our lab is well-represented at the 2023 American Society of Human Genetics meeting, including three posters and a talk from Jalen Langie on her work in admixture mapping for childhood leukemia! Check out the photos and links to posters and presentation here!
10.2023: Caoqi Fan's second chapter of his dissertation is now on bioRxiv! In a method we called gLike, we can obtain maximum likelihood estimates of demographic history parameters based on the genealogical trees from the genetic data. We showed that this method is much more accurate over previous methods based on summary statistics of the genetic data. Please let us know if you have any questions or comments! A tweetorial is here.
10.2023: A pair of preprints from members in the group are officially published! Tsz Fung Chan's method HAMSTA that estimates genome-wide heritability and evaluates biases in test statistics of admixture mapping studies was published in American Journal of Human Genetics, and Soyoung Jeon's paper in constructing and evaluating genome-wide PRS for childhood leukemia was published in HGG Advances! Congrats to Tsz Fung and Soyoung!
9.2023: Our lab has been awarded a R01 grant from NIH/NHGRI (R01HG012605), where we will develop methods leveraging genome-wide genealogies inferred from the genetic data (aka ancestral recombination graph) to gain knowledge about population history and to improve trait mapping. This is the third major grant obtained by the lab over the last three years, thanks so much for everyone involved every step along the way!
9.2023: New members joining the lab! While dear old friends left the lab over the summer, we also have multiple new members joining the lab. These include Xinran Wang, who is taking a year focusing on research before applying for Ph.D. this fall, joins us as a Program Analyst; He Tian, a second year Epidemiology PhD student joining the lab after completing her screening exam; and Ji Tang, a new postdoc interested in developing machine learning methods to address population genetic questions. Welcome Xinran, He, and Ji!
8.2023: Charleston Chiang gave a talk on the "Imputation around the World" project at the International Society of Evolution, Medicine, and Public Health (ISEMPH) annual meeting in Irvine, CA.
7.2023: Bryan Dinh's first author paper is released on bioRxiv! In this study we investigated the recombination landscapes of Native Hawaiians and Polynesian-ancestry individuals, and evaluated the impact of a population-specific recombination map for downstream genetic analyses. The maps are released on github and a tweetorial can be found here! Congrats Bryan!
7.6.2023: Caoqi Fan successfully defended his PhD dissertation titled "Beyond Genotypes: Genealogy-Based Inference of Population Structure and Demographic History." Congrats Dr. Fan!
Read our older news here.
The overarching theme of our research group is to use genetic approaches to understand how evolutionary forces shaped the genetic architecture of complex traits within and between populations. To this end, we have been involved in a number of past and ongoing medical genetics studies in mapping genetic loci underlying human complex traits. We are also continually interested in investigating the evolutionary forces, namely demography and selection, that shaped the pattern of genetic variability and phenotypic distribution. We are particularly interested in diverse, global human populations and our successes result from collaborating with innovative colleagues and thriving in resourceful consortiums. Read a more detailed description of our work here.
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.
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.
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.
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.
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.
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.
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.
Indu Sharma is a postdoctoral research fellow since 2023. She is a highly accomplished researcher with a Ph.D. in Biotechnology from India, having previously worked as a Population Geneticist at New York University Abu Dhabi campus. Indu's research has predominantly centered on underrepresented and tribal populations, particularly those from Northwestern India and the Northwestern Himalayas. Her expertise in population genomics has allowed her to delve into the evolutionary impacts and gene-based associations of various complex disorders prevalent in South Asian endogamous groups. Her current research aims at unraveling the selection signatures within the Native Hawaiian population. Her work combines the precision of population genomics with a keen understanding of complex genetic traits.
Eunice Lee is a senior research associate who joined the lab in 2023. She was born in Seoul, South Korea, and moved to and grew up in Southern California when she was young. She was trained as an environmental and genetic epidemiologist focusing on asthma in minority children and cardiovascular diseases in the adult U.S. population. Her main research interests are population genetics, computational toxicology, and gene and environment interactions using various statistical methods and machine learning algorithms. Outside of work, Eunice started rock climbing, taking pottery lessons, and enjoys outdoor activities such as hiking and camping.
We don't just work hard, we play hard too! Click here for some pictures of the lab events and activities over the years!
Image credit: modified from "Group" by Nawicon / CC
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
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
We are part of the Center for Genetic Epidemiology, in the Department of Population and Public Health Sciences at USC Keck School of Medicine, and jointly affiliated with the Department of Quantitative and Computational Biology at USC Dornsife College of Letters, Arts, and Sciences.
Charleston can be reached via email at:
charleston [dot] chiang [at] med [dot] usc [dot] edu
We are located in the Harlyne J. Norris Research Tower (NRT) on the Health Science Campus at USC
1450 Biggy Street
Los Angeles, CA 90033
Postdoctoral Fellows: We are always looking for talented and motivated individuals to join our group! Interested individuals are encouraged to contact Charleston Chiang for inquiries. There are multiple ways a postdoctoral fellow can be funded, through NIH grants in the lab, collaborative funds, or unrestricted funds. This means you have the stability as well as the flexibility to explore topics broadly of interest to the lab. Positions are generally for at least 2 years, with salary commensurate with experience and adjusted for cost of living in LA (definitely higher than NIH scale!). Applicants ideally should have extensive exposure and experience in human medical and/or population genetics research and have analyzed large genetic datasets. Programming skills (in at least one of, 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.
Inquiries or applications (cover letter, CV, and contact information to at least two references) should be emailed to Charleston Chiang.
Graduate Students:We are open to accepting doctoral and master students. Generally, doctoral students are enrolled through Program in Biomedical and Biological 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.
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