Quantitative genomics, bioinformatics and computational biology

Quantitative genetics / genomics

Quantitative genetics / genomics is closely related to population genetics, statistical genetics and genetic epidemiology. This area of research is concerned with diseases and traits that have a complex or mixed inheritance background, influenced by external environmental effects and their interactions with genes. In this research, we estimate population genetic parameters such as heritability, genetic correlation and variance components for quantitative traits and diseases in animals and human using population-level genetics/genomics, epidemiological and environmental data. We use various statistical methods including linear mixed models (Best Linear Unbiased Prediction or BLUP methods), restricted maximum likelihood and Bayesian methods.

Using Quantitative / statistical genetics methods, we conduct research in identification of novel genes and novel genetic variants for quantitative traits & diseases using data on phenotypes, treatment records, familial/pedigree information, high-throughput SNPchip genotypic data, exome or whole genome sequence data from a large number of individuals. We work with data from farm animals, animal models for human diseases and on human obesity/diabetes and psychiatric disorders in collaboration with national and international partners. The other aspect of our research in this area involves genomic prediction for disease risks or phenotypes whereby using training and testing populations with genome-wide genetic/genomic and phenotype data, we develop highly accurate statistical “genomic prediction” of disease risks or phenotype manifestation in individuals measured only for genetic/genomic data.

Bioinformatics and Computational Systems Biology

Hugely comprehensive genomic and other omics datasets (Big Data) are now available from individuals or populations registered for diseases and phenotypes / traits. Biological data types come from genome-wide, epigenome-wide, transcriptome-wide, proteome-wide, metabolomics and metagenomics measurements, made using high-throughput biotechnologies including next generation sequencing or NGS methods. Our own experiments or field trials in our funded projects generate these omics datasets and/or our partners supply these datasets where we provide data analysis and interpretation support. We apply advanced big data integration and bio-statistical methods and bio-computing algorithms to analyze these data sets in high performance computing (HPC) environments.

In addition to analyzing individual data types (e.g. genetic data alone), we apply integrative “systems biology” approaches. We integrate various omics measurements made on the same individuals (e.g. from DNA through mRNA to metabolites) and analyze these multi-omic datasets jointly using a combination of mathematical, computational biology and bioinformatics principles and tools. We, in the past decade, have heavily focused on integrating genomics data with transcriptomic datasets (to identify eQTLs) and recently included metabolomics and metagenomics datasets. The “systems” aspects of systems biology is focused on testing and correlating genetic variants for a range of intermediate omics phenotypes and observed traits in related or unrelated individuals as well as characterizing those parts of the molecular networks that drive these complex phenotypes. We apply systems biology approaches for detecting causal genetic factors, predictive biomarkers and constructing causal and regulatory networks underlying important diseases and traits.

This research topic involves also construction of scale-free weighted biological networks that characterizes a disease or healthy state or phenotype classes of groups of individuals. The networks are built using genomic or transcriptomic or metabolomics datasets and using graphical theory/models, computational and visualization algorithms.

Societal impact and Application domains

  • Statistical methods and Software development based on Quantitative Genomics, Bioinformatics and Computational Systems Biology principles for applications in animal, human and life sciences
  • BIG biological data analytics using high performance scientific computing in animal and human sciences
  • Gene, gene variant & biomarker discovery and pathway profiling for animal production, health and welfare
  • Develop highly accurate statistical genomic prediction of diseases and other quantitative traits in animals and humans.
  • Gene, gene variant & biomarker discovery and pathway profiling for disease diagnostics and disease risk prediction with a focus on human diseases such as obesity, diabetes, metabolic disorders, cardio-vascular diseases, psychiatric diseases and maternal-fetal interactions in disease development.
  • Contribute to strengthening data analytics in personalized and precision medicine initiatives and health technologies based on Quantitative Genomics, Bioinformatics and Computational Systems Biology
  • Innovation in animal-biotech and human biomedical industries related to animal production & human health.

Research Consortia and Research Projects:

  • EliteOva: In vitro embryo production and genomic selection harnessing Danish elite cattle breeding
  • FeedOMICS: Systems Genomics, Transcriptomics and Metabolomics approaches for simultaneous improvement of feed efficiency and production in Danish pigs.
  • MiSys: Microbiome Systems Biology - Novel alternatives to antibiotics by mining human gut microbiome data
  • GEM: Genomic, Epigenetic and Metabolomics analyses of production and welfare in Danish cattle and Pigs
  • GIFT Research Consortium: Genomic Improvement of Fertilization Traits (GIFT) in Danish and Brazilian Cattle: www.gift.ku.dk
  • BioChild Research Consortium: Genetics and Systems Biology of Childhood Obesity in India and Denmark: www.biochild.ku.dk
  • DSPEM: Danish-Singaporean Personalized Medicine Initiative
  • GUDP: Biomarkers for Boar Taint and Sensory Meat Quality in Danish Pigs
  • SYSFEED: Genomics, Transcriptomics, Metabolomics and Metagenomics of Feed Efficiency in Nordic Cattle
  • BioSheep: Biomarkers for fetal programming induced by late gestation malnutrition and for susceptibility to an early postnatal obesogenic diet of sheep
  • IntBio: Integrative genetic and regulatory network analysis to detect genes related to muscle iron content in Nelore cattle
  • ArthGene: Association between Pro-inflammatory interleukin genes and Rheumatoid arthritis.
  • GenPred: Genomic Prediction and Systems Genomics of Schizophrenia and Autism

Contact

Haja Kadarmideen
Professor
DTU Compute
+45 45 25 52 23