Dataset Information

Prediction of individual genetic risk to prostate cancer using a polygenic score.

ABSTRACT:

Background

Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction.

Methods

We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls.

Results

The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P?=?0.0012) and the net reclassification index with 0.21 (P?=?8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk.

Conclusions

Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.

SUBMITTER: Szulkin R

PROVIDER: S-EPMC7745998 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Publications

Prediction of individual genetic risk to prostate cancer using a polygenic score.

Szulkin Robert R Whitington Thomas T Eklund Martin M Aly Markus M Eeles Rosalind A RA Easton Douglas D Kote-Jarai Z Sofia ZS Amin Al Olama Ali A Benlloch Sara S Muir Kenneth K Giles Graham G GG Southey Melissa C MC Fitzgerald Liesel M LM Henderson Brian E BE Schumacher Fredrick F Haiman Christopher A CA Schleutker Johanna J Wahlfors Tiina T Tammela Teuvo L J TL Nordestgaard Børge G BG Key Tim J TJ Travis Ruth C RC Neal David E DE Donovan Jenny L JL Hamdy Freddie C FC Pharoah Paul P Pashayan Nora N Khaw Kay-Tee KT Stanford Janet L JL Thibodeau Stephen N SN McDonnell Shannon K SK Schaid Daniel J DJ Maier Christiane C Vogel Walther W Luedeke Manuel M Herkommer Kathleen K Kibel Adam S AS Cybulski Cezary C Lubiński Jan J Kluźniak Wojciech W Cannon-Albright Lisa L Brenner Hermann H Butterbach Katja K Stegmaier Christa C Park Jong Y JY Sellers Thomas T Lin Hui-Yi HY Slavov Chavdar C Kaneva Radka R Mitev Vanio V Batra Jyotsna J Clements Judith A JA Spurdle Amanda A Teixeira Manuel R MR Paulo Paula P Maia Sofia S Pandha Hardev H Michael Agnieszka A Michael Agnieszka A Kierzek Andrzej A Gronberg Henrik H Wiklund Fredrik F

The Prostate 20150714 13

<h4>Background</h4>Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction.<h4>Methods</h4>We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 ...[more]

PMID: 26177737

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Project description:BackgroundPolygenic risk score (PRS) analysis is used to predict disease risk. Although PRS has been shown to have great potential in improving clinical care, PRS accuracy assessment has been mainly focused on European ancestry. This study aimed to develop an accurate genetic risk score for knee osteoarthritis (OA) using a multi-population PRS and leveraging a multi-trait PRS in the Japanese population.MethodsWe calculated PRS using PRS-CS-auto, derived from genome-wide association study (GWAS) summary statistics for knee OA in the Japanese population (same ancestry) and multi-population. We further identified risk factor traits for which PRS could predict knee OA and subsequently developed an integrated PRS based on multi-trait analysis of GWAS (MTAG), including genetically correlated risk traits. PRS performance was evaluated in participants of the Nagahama cohort study who underwent radiographic evaluation of the knees (n = 3,279). PRSs were incorporated into knee OA integrated risk models along with clinical risk factors.ResultsA total of 2,852 genotyped individuals were included in the PRS analysis. The PRS based on Japanese knee OA GWAS was not associated with knee OA (p = 0.228). In contrast, PRS based on multi-population knee OA GWAS showed a significant association with knee OA (p = 6.7 × 10-5, odds ratio (OR) per standard deviation = 1.19), whereas PRS based on MTAG of multi-population knee OA, along with risk factor traits such as body mass index GWAS, displayed an even stronger association with knee OA (p = 5.4 × 10-7, OR = 1.24). Incorporating this PRS into traditional risk factors improved the predictive ability of knee OA (area under the curve, 74.4% to 74.7%; p = 0.029).ConclusionsThis study showed that multi-trait PRS based on MTAG, combined with traditional risk factors, and using large sample size multi-population GWAS, significantly improved predictive accuracy for knee OA in the Japanese population, even when the sample size of GWAS of the same ancestry was small. To the best of our knowledge, this is the first study to show a statistically significant association between the PRS and knee OA in a non-European population.Trial registrationNo. C278.

Dataset Information

Prediction of individual genetic risk to prostate cancer using a polygenic score.

Background

Methods

Results

Conclusions

Publications

Prediction of individual genetic risk to prostate cancer using a polygenic score.

Similar Datasets

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