Statistical Genetics and Population Genetics

Singapore Skyline (picture from wikipedia under the GNU Free Documentation License)


We are a small but energetic research group working on statistical and population genetics in Singapore, a metropolitan city close to the Equator of the Earth (see the picture above). As a computational group, we collaborate closely with biologists and clinicians to study population genetics and various human genetic diseases. We develop and distribute novel statistical and computational methods to address new challenges arise from large-scale human genetics and genomics data when there is no off-the-shelf tool available.

Positions available: We are looking for motivated researchers to join the lab at research scientist, postdoctoral fellow, research assistant, and graduate student levels. Click here for more information.

Recent News

  • 30-Sep-2017: New paper in PLOS Genetics! In this paper, we proposed a new method, SEEKIN, to estimate kinship using noisy genotypes obtained from extremely shallow sequencing data, such as 0.1X off-target data from targeted sequencing experiments. Our method is applicable to both homogeneous samples and heterogeneous samples with population structure and admixture. SEEKIN, together with our previous work on acesntry estimation (LASER), enables control of population stratification and cryptic relatedness in targeted sequencing studies by utilizing off-target data. Postdocs in the lab Jinzhuang Dou and Baoluo Sun are joint first authors of the paper. Congratulations!

  • 30-Jun-2017: There are several news since our last website update:
    1. New member Degang Wu joins our lab as a Postdoctoral Fellow. Welcome!
    2. New member Xiaoran Chai joins our lab as a Research Officer. Welcome!
    3. A new paper appears in Bioinformatics! In this work, Daniel Taliun et al. develop a LASER server for tracing individual ancestry using either sequence reads or genotypes. The server includes built-in ancestry reference panels and very nice interactive visualization tools. This paper is based on our previous methodology work on the LASER algorithm (see Wang et al. 2014 and 2015).
    4. Our new software program SEEKIN for inferring genetic relatedness is now available on GitHub.

  • 25-Jul-2016: We welcome our new group member, Baoluo Sun! Baoluo recently finished his Ph.D. in Biostatistics from Harvard T.H. Chan School of Public Health. He join us as a Postdoctoral Fellow, under the support of an A*STAR National Science Scholarship.

  • 15-Apr-2016: Chaolong Wang is appointed as an Adjunct Assistant Professor at the Centre for Computational Biology, Duke-NUS Medical School.

  • 6-Apr-2016: New paper in AJHG! In this work, Han Chen and Chaolong Wang et al. demonstrate that using linear mixed models to correct for population stratification and family relatedness in case-control studies can lead to misleading results. We propose a computationally efficient algorithm to enable the application of generalized linear mixed models in large-scale genome-wide association studies. Our method is publicly available in an R package called GMMAT.