Collaboration

We can help analyze your biobank data

Precision health aims to predict disease risk or response to medical treatment based on an individual’s DNA profile. It holds the potential to improve decision-making which could improve patient health and lower health care costs. Predictions of disease risk and treatment-response are based on statistical models that are developed using large-scale genotype and phenotype data from biobank projects such as UK Biobank.

We have expertise in developing statistical and genetic models for understanding and predicting complex traits and diseases.

We have expertise in analysing large-scale genotype and phenotype data using R, a widely used tool for statistical computing and graphics.

We offer analysis of biobank data or development of workflows in R that allow efficient analysis of large-scale genotype and phenotype data.

Software

Software for analyzing biobank data

We have developed an R package, qgg, that is suitable for large-scale quantitative genetic analyses of complex traits. It is publicly available on CRAN and github with online tutorials and a scientific publication in bioinformatics.

qgg provides an infrastructure for efficient processing of large-scale genotype and phenotype data, including core functions for:

  • fitting linear mixed models
  • estimating genetic parameters (heritability and correlation)
  • genomic prediction using Bayesian linear regression methods
  • single marker association analysis
  • gene set enrichment analysis

qgg handles large-scale data using efficient algorithms and by taking advantage of:

  • multi-core processing using openMP
  • multithreaded matrix operations implemented in BLAS libraries (e.g. OpenBLAS, ATLAS or MKL)
  • fast and memory-efficient batch processing of genotype data stored in binary files (e.g. PLINK bedfiles)

Contact

Get in touch by filling out the form below and briefly mention your analytical needs.

Publications

  1. Cuyabano BCD, Sørensen AC, Sørensen P. 2018. Understanding the potential bias of variance components estimators when using genomic models. Genet Sel Evol 50: 41. doi: 10.1186/s12711-018-0411-0.
  2. Edwards SM, Thomsen B, Madsen P, Sørensen P. 2015. Partitioning of genomic variance reveals biological pathways associated with udder health and milk production traits in dairy cattle. Genet Sel Evol 47:60. doi: 10.1186/s12711-015-0132-6.
  3. Edwards SM, Sørensen IF, Sarup P, Mackay TFC, Sørensen P. 2016. Genomic prediction for quantitative traits is improved by mapping variants to gene ontology categories in Drosophila melanogaster. Genetics 203:1871–1883. doi: 10.1534/genetics.116.187161.
  4. Ehsani A, Janss L, Pomp D, Sørensen P. 2015. Decomposing genomic variance using information from GWA, GWE and eQTL analysis. Anim Genet 47:165–173. doi:10.1111/age.12396.
  5. Fang L, Sahana G, Ma P, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. 2017. Exploring the genetic architecture and improving genomic prediction accuracy for mastitis and milk production traits in dairy cattle by mapping variants to hepatic transcriptomic regions responsive to intra-mammary infection. Genet Sel Evol 49:1–18. doi: 10.1186/s12711-017-0319-0.
  6. Fang L, Sahana G, Su G, Yu Y, Zhang S, Lund MS, Sørensen P. 2017. Integrating sequence-based GWAS and RNA-seq provides novel insights into the genetic basis of mastitis and milk production in dairy cattle. Sci Rep 7:45560. doi:10.1038/srep45560.
  7. Fang L, Sørensen P, Sahana G, Panitz F, Su G, Zhang S, Yu Y, Li B, Ma L, Liu G, Lund MS, Thomsen B. 2018. MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle. Sci Rep 8:1–14. doi: 10.1038/s41598-018-27729-y.
  8. Rohde PD, Krag K, Loeschcke V, Overgaard J, Sørensen P, Kristensen TN. 2016. A quantitative genomic approach for analysis of fitness and stress related traits in a Drosophila melanogaster model population. Int J Genomics:1–11. doi: 10.1155/2016/2157494.
  9. Rohde PD, Demontis D, Cuyabano BCD, The GEMS Group, Børglum AD, Sørensen P. 2016. Covariance Association Test (CVAT) identify genetic markers associated with schizophrenia in functionally associated biological processes. Genetics 203:1901–1913. doi: 10.1534/genetics.116.189498.
  10. Rohde PD, Gaertner B, Ward K, Sørensen P, Mackay TFC. 2017. Genomic analysis of genotype-by-social environment interaction for Drosophila melanogaster. Genetics 206:1969–1984. doi: 10.1534/genetics.117.200642/-/DC1.1.
  11. Rohde PD, Østergaard S, Kristensen TN, Sørensen P, Loeschcke V, Mackay TFC, Sarup P. 2018. Functional validation of candidate genes detected by genomic feature models. G3 Genes, Genomes, Genet 8:1659–1668. doi: 10.1534/g3.118.200082.
  12. Rohde PD, Jensen IR, Sarup PM, Ørsted M, Demontis D, Sørensen P, & Kristensen TN. 2019. Genetic signatures of drug response variability in Drosophila melanogaster. Genetics, 213(2), 633–650. doi: 10.1534/genetics.119.302381.
  13. Rohde PD, Sørensen IF, Sørensen P. 2020. qgg: an R package for large-scale quantitative genetic analyses. Bioinformatics, 36(8), 2614–2615. doi: 10.1093/bioinformatics/btz955.
  14. Rohde PD, Kristensen TN, Sarup P, Muñoz J, Malmendal, A. 2021. Prediction of complex phenotypes using the Drosophila metabolome. Heredity, 126(5), 717–732. doi: 10.1038/s41437-021-00404-1.
  15. Rohde PD, Nyegaard M, Kjolby M, Sørensen P. 2021. Multi-trait genomic risk stratification for type 2 diabetes. Frontiers in Medicine, 8, 711208. doi: 10.3389/fmed.2021.711208.
  16. Sarup P, Jensen J, Ostersen T, Henryon M, Sørensen P. 2016. Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs. BMC Genet 17:11. doi: 10.1186/s12863-015-0322-9.
  17. Skarman A, Shariati M, Janns L, Jiang L, Sørensen P.  2012. A bayesian variable selection procedure for ranking overlapping gene sets. BMC Bioinformatics. 13:73. doi: 10.1186/1471-2105-13-73.
  18. Sørensen P, de los Campos G, Morgante F, Mackay TFC, Sorensen D. 2015. Genetic control of environmental variation of two quantitative traits of Drosophila melanogaster revealed by whole-genome sequencing. Genetics 201:487–497. doi: 10.1534/genetics.115.180273.
  19. Sørensen IF, Edwards SM, Rohde PD, Sørensen P. 2017. Multiple trait covariance association test identifies gene ontology categories associated with chill coma recovery time in Drosophila melanogaster. Sci Rep 7:2413. doi:10.1038/s41598-017-02281-3.
  20. Ørsted M, Rohde PD, Hoffmann AA, Sørensen P, Kristensen TN. 2017. Environmental variation partitioned into separate heritable components. Evolution 72:136–152. doi: 10.1111/evo.13391.
  21. Ørsted M, Hoffmann AA, Rohde PD, Sørensen P, Kristensen TN. 2018. Strong impact of thermal environment on the quantitative genetic basis of a key stress tolerance trait. Heredity . doi: 10.1038/s41437-018-0117-7.