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Welcome to the website of the Quantitative and Systems Genomics (QSG) Group. The QSG group, previously known as the Animal Breeding, Quantitative Genetics and Systems Biology (AQS) group  at the Faculty of Health and Medical Sciences, University of Copenhagen has moved to the Institute of Bio and Health Informatics at the Technical University of Denmark (DTU) since 1 March 2017. The QSG group’s main focus is on quantitative genetics and systems genomics of animal and human health (OneHealth) and other complex (quantitative) traits. The group is also actively involved in multi-omic data integration and modeling methods for disease diagnostics and prediction and in development computational-bioinformatic tools in this area of research. The group works, via collaborations, with animal models for unraveling genetics and systems biology of human diseases such as obesity, diabetes, metabolic disorders, cardio-vascular diseases and maternal-fetal interactions in disease development. The QSG group leader is Professor Haja Kadarmideen who is also the Director of Institute of Bio and Health Informatics (DTU Bioinformatics). Besides, he functions the scientific director of two international R&D consortia:  the Danish-Indian BioChild Consortium (BioChild: Genetics and Systems Biology of Childhood Obesity in India and Denmark) and the Danish-Brazilian GIFT Consortium (GIFT: Genomic Improvement of Fertilization traits in Danish and Brazilian Cattle).

In the quantitative genetics and genomics group conducts research and development in whole genomic prediction of disease risks and performance using mixed model methods (baed on Regression and Bayesian approaches) and using clinical phenotypes, treatment records, familial/pedigree information and high-throughput SNPchip genotypic data from a large number of individuals. These whole genomic prediction (WGP) approaches are now extended to include the use of whole genome DNA sequences. The group is involved in WGP work in animal models, human obesity/diabetes and psychiatric disorders in collaboration with national and international partners. It also has a strong interest and activity in developing countries with respect to above research and development.

Hugely comprehensive multiple omic datasets (BIG Data) are now available at individual- and population-level where they are characterized or obsserved for diseases and other complex quantitative traits. Data types come from genome-wide, epigenome-wide, transcriptome-wide, proteome-wide, metabolomic and metagenomic measurements. Systems genomics collectively models and analyzes these multi-omic datasets using a combination of mathematical, computational biology and bioinformatics principles and tools. We aim to study genetic variations of these intermediate molecular phenotypes that eventuate as an observed trait or clinical diseases at the animal or human level. The “systems” aspects of systems genetics is therefore focused on testing and correlating genetic variants for a range of intermediate phenotypes and observed phenotypes in related or unrelated individuals as well as characterizing those parts of the molecular networks that drive these complex phenotypes. We apply systems genetics approaches for detecting causal (genetic) factors, predictive biomarkers and constructing causal and regulatory networks underlying important diseases and traits. We are collaborating partners in human systems medicine, molecular biology, embryology, stem cell, reproductive biology, biotechnology and bioinformatics projects.

With quantitative-systems genomics research, we aim to develop highly accurate genomic prediction of diseases and health, develop predictive genetic- and biomarkers and provide comprehensive system-level understanding of complex traits and diseases in animals and humans. We hope to make an impact in the agri-biotech and biomedical industries via these innovative approaches in animals and humans.