Publications by authors named "Britt Kilian"

5 Publications

  • Page 1 of 1

External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank.

Cancer Prev Res (Phila) 2020 06 18;13(6):509-520. Epub 2020 Feb 18.

The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.

The aim of this study was to compare and externally validate risk scores developed to predict incident colorectal cancer that include common genetic variants (SNPs), with or without established lifestyle/environmental (questionnaire-based/classical/phenotypic) risk factors. We externally validated 23 risk models from a previous systematic review in 443,888 participants ages 37 to 73 from the UK Biobank cohort who had 6-year prospective follow-up, no prior history of colorectal cancer, and data for incidence of colorectal cancer through linkage to national cancer registries. There were 2,679 (0.6%) cases of incident colorectal cancer. We assessed model discrimination using the area under the operating characteristic curve (AUC) and relative risk calibration. The AUC of models including only SNPs increased with the number of included SNPs and was similar in men and women: the model by Huyghe with 120 SNPs had the highest AUC of 0.62 [95% confidence interval (CI), 0.59-0.64] in women and 0.64 (95% CI, 0.61-0.66) in men. Adding phenotypic risk factors without age improved discrimination in men but not in women. Adding phenotypic risk factors and age increased discrimination in all cases ( < 0.05), with the best performing models including SNPs, phenotypic risk factors, and age having AUCs between 0.64 and 0.67 in women and 0.67 and 0.71 in men. Relative risk calibration varied substantially across the models. Among middle-aged people in the UK, existing polygenic risk scores discriminate moderately well between those who do and do not develop colorectal cancer over 6 years. Consideration should be given to exploring the feasibility of incorporating genetic and lifestyle/environmental information in any future stratified colorectal cancer screening program.
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http://dx.doi.org/10.1158/1940-6207.CAPR-19-0521DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610623PMC
June 2020

Very low-depth whole-genome sequencing in complex trait association studies.

Bioinformatics 2019 08;35(15):2555-2561

Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.

Motivation: Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking.

Results: We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design.

Availability And Implementation: The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter, the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/bty1032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662288PMC
August 2019

Author Correction: Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits.

Nat Commun 2018 12 19;9(1):5460. Epub 2018 Dec 19.

Department of Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, United Kingdom.

The original version of this Article contained an error in Fig. 2. In panel a, the two legend items "rare" and "common" were inadvertently swapped. This has been corrected in both the PDF and HTML versions of the Article.
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http://dx.doi.org/10.1038/s41467-018-07730-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300593PMC
December 2018

Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits.

Nat Commun 2018 11 7;9(1):4674. Epub 2018 Nov 7.

Department of Human Genetics, Wellcome Sanger Institute, Hinxton, CB10 1SA, United Kingdom.

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens that are independent of established common variant signals (ADIPOQ and adiponectin, P = 4.2 × 10; APOC3 and triglyceride levels, P = 1.5 × 10), and identify replicating evidence for a burden associated with triglyceride levels in FAM189B (P = 2.2 × 10), indicating a role for this gene in lipid metabolism.
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http://dx.doi.org/10.1038/s41467-018-07070-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220258PMC
November 2018

The zebrafish reference genome sequence and its relationship to the human genome.

Nature 2013 Apr 17;496(7446):498-503. Epub 2013 Apr 17.

Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK.

Zebrafish have become a popular organism for the study of vertebrate gene function. The virtually transparent embryos of this species, and the ability to accelerate genetic studies by gene knockdown or overexpression, have led to the widespread use of zebrafish in the detailed investigation of vertebrate gene function and increasingly, the study of human genetic disease. However, for effective modelling of human genetic disease it is important to understand the extent to which zebrafish genes and gene structures are related to orthologous human genes. To examine this, we generated a high-quality sequence assembly of the zebrafish genome, made up of an overlapping set of completely sequenced large-insert clones that were ordered and oriented using a high-resolution high-density meiotic map. Detailed automatic and manual annotation provides evidence of more than 26,000 protein-coding genes, the largest gene set of any vertebrate so far sequenced. Comparison to the human reference genome shows that approximately 70% of human genes have at least one obvious zebrafish orthologue. In addition, the high quality of this genome assembly provides a clearer understanding of key genomic features such as a unique repeat content, a scarcity of pseudogenes, an enrichment of zebrafish-specific genes on chromosome 4 and chromosomal regions that influence sex determination.
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http://dx.doi.org/10.1038/nature12111DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703927PMC
April 2013
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