A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact

Genome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population‐based sam...

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Bibliographic Details
Published in:Genetic epidemiology 2011-09, Vol.35 (6), p.549-556
Main Authors: MacInnis, Robert J., Antoniou, Antonis C., Eeles, Rosalind A., Severi, Gianluca, Al Olama, Ali Amin, McGuffog, Lesley, Kote-Jarai, Zsofia, Guy, Michelle, O'Brien, Lynne T., Hall, Amanda L., Wilkinson, Rosemary A., Sawyer, Emma, Ardern-Jones, Audrey T., Dearnaley, David P., Horwich, Alan, Khoo, Vincent S., Parker, Christopher C., Huddart, Robert A., Van As, Nicholas, McCredie, Margaret R., English, Dallas R., Giles, Graham G., Hopper, John L., Easton, Douglas F.
Format: Article
Language:English
Subjects:
Adult
Aged
Algorithms
Australia
common variant
Data processing
Genetic Variation
Genome-Wide Association Study
Genomes
GWAS
Heredity
Humans
Male
Middle Aged
Models, Genetic
Models, Statistical
Molecular Epidemiology - methods
Polygenic inheritance
Polymorphism, Single Nucleotide
Probability
Prostate cancer
Prostatic Neoplasms - genetics
Risk
risk model
Single-nucleotide polymorphism
United Kingdom
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Summary:Genome wide association studies have identified several single nucleotide polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population‐based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where . Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, , and the residual polygenic component due to the postulated but as yet unknown genetic variants, . The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population‐based family data and established risk variants exist. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. 35: 549‐556, 2011
ISSN:0741-0395
1098-2272
1098-2272
DOI:10.1002/gepi.20605