Statistical Genomics

Our research aims at discovering genes and biological pathways involved in the etiology of complex human traits and multifactorial diseases such as obesity or type 2 diabetes.

Our research aims at discovering genes and biological pathways involved in the etiology of complex human traits and multifactorial diseases such as obesity or type 2 diabetes.

Our group develops new analysis tools that can maximise the utility of genetics studies across human populations of all ancestries.

A better understanding of genes underlying inter-individual variation in disease susceptibility has the potential to illuminate new and personalised therapies.

Group leader

Dr Loic Yengo

Dr Loic Yengo

Group Leader, Statistical Genomics

ARC DECRA Research Fellow

  +61 7 334 62095
  l.yengo@imb.uq.edu.au
  UQ Researcher Profile

Our group has three main research themes:

  1. Theory for Statistical Genetics Inference. Projects under this theme focus on studying the statistical properties and validity of approximate inferential techniques used in the field of genome-wide association studies (GWAS) from SNP chip or whole genome sequence data.
     
  2. Trans-Ancestry Genomics. Projects under this theme focus on developing new methodologies and software for GWAS analyses including under-represented ancestries.
     
  3. Novel Inference from Biobank Data. Projects under this theme focus on developing methods to diagnose and correct ascertainment biases in biobank data collection. Other projects aim at developing models to detect genetic and phenotypic structures in the population that can inform human behaviours influencing health outcomes (e.g. migration, assortative mating, inbreeding).
  • Statistical Properties of the LD score regression-based methodologies for analysing summary statistics from genome-wide association studies
  • Large scale computation using approximate algorithms
  • Quantifying variation in mate choice and its impact on disease risk

Our Approach

Our research questions can be approached through a series of estimation, extensive computer simulations, and statistical testing and prediction problems.

Each of these problems require developing new theoretical models that can best represent the data and also implementing new software tools for scalable analysis of millions of observations simultaneously.

Research Areas

Ageing

    • Deleterious mutations and lifetime effects of inbreeding
    • Metabolic and Cardiovascular disease
    • Novel Inference from Biobank Data

    Common diseases

    • Obesity
    • Type 2 Diabetes
    • Hypertension
    • Trans-Ancestry Genomics

    Into the future

    • Evolution of mating system in humans
    • Theory for Statistical Genetics Inference
    • Trans-Ancestry Genomics
    • Efficient computation in large scale biobank (hybrid computing)

     

          Our Team

          Group leader

          Researchers

          Alumni

          • Ms Ying Wang

            Higher degree by research (PhD) student
            Institute for Molecular Bioscience