Biology subjects

Nordestgaard, B.

Publications and source records attributed to Nordestgaard, B..

3 recordsLinked to original sources

bioRxiv2019

The effect of sample size on polygenic hazard models for prostate cancer

We aimed to determine the effect of sample size on performance of polygenic hazard score (PHS) models in predicting the age at onset of prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training (34,444 samples) and testing (6,417 samples) sets. Two PHS model-building strategies were investigated. Established-SNP model considered 65 SNPs that had been associated with prostate cancer in the literature. A stepwise SNP selection was used to develop Discovery-SNP models. The performance of each PHS model was calculated for random sizes of the training set (1 to 30 thousand). The performance of a representative Established-SNP model was estimated for random sizes of the testing set (0.5 to 6 thousand). Mean HR98/50 (hazard ratio of top 2% to the average in the test set) of the Established-SNP model increased from 1.73[95%CI: 1.69-1.77] to 2.41[2.40-2.43] when the number of training samples was increased from 1 to 30 thousand. The corresponding HR98/50 of the Discovery-SNP model increased from 1.05[0.93-1.18] to 2.19[2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 and 6 thousand observations were 1.78[1.70-1.85] and 1.73[1.71-1.76], respectively. We estimate that a study population of 20 to 30 thousand men is required to develop Discovery-SNP PHS models for prostate cancer. The required sample size could be reduced to 10 thousand samples, if a set of SNPs associated with the disease has already been established.\n\nAuthor summaryPolygenic hazard scores represent a recent advancement in polygenic prediction to model the age of onset of various diseases, such as Alzheimers disease or prostate cancer. These scores accumulate small effect sizes from several tens of genetic variants and can be used to establish an individuals risk of experiencing an event relative to a control population across time. The largest barrier to the development of polygenic hazard scores is the large number of study subjects needed to develop the underlying models. We sought to understand the effect of varying the total number of samples on the performance of a polygenic hazard score in the context of prostate cancer. We found that the performance of the score did not appreciably change beyond 20 to 30 thousand observations when developing the model from scratch. However, when the discovery of the genetic variants can be borrowed from those already identified in the literature to be associated with the disease, the required number of samples is reduced to 10 thousand with no appreciable detriment in performance. We hope that these results can guide the design of future studies of polygenic scores in other diseases and demonstrate the importance of genome-wide association studies.

genetics↗

bioRxiv2018

Appraising the causal relevance of DNA methylation for risk of lung cancer

DNA methylation changes in peripheral blood have been identified in relation to lung cancer risk. However, the causal nature of these associations remains to be fully elucidated. Meta-analysis of four epigenome-wide association studies (918 cases, 918 controls) revealed differential methylation at 16 CpG sites (FDR < 0.05) in relation to lung cancer risk. A two-sample Mendelian randomization analysis, using genetic instruments for methylation at 14 of the 16 CpG sites, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, was performed to appraise the causal role of methylation at these sites on lung cancer. This approach provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development, including for cg05575921 AHRR, where methylation is strongly associated with lung cancer risk. Further studies are needed to investigate the causal role played by DNA methylation in lung tissue.

epidemiology↗

bioRxiv2017

Cigarette smoking increases coffee consumption: findings from a Mendelian randomisation analysis

BackgroundSmokers tend to consume more coffee than non-smokers and there is evidence for a positive relationship between cigarette and coffee consumption in smokers. Cigarette smoke increases the metabolism of caffeine, so this association may represent a causal effect of smoking on caffeine intake.\n\nMethodsWe performed a Mendelian randomisation analysis in 114,029 individuals from the UK Biobank, 56,664 from the Norwegian HUNT study and 78,650 from the Copenhagen General Population Study. We used a genetic variant in the CHRNA5 nicotinic receptor (rs16969968) as a proxy for smoking heaviness. Coffee and tea consumption were self-reported. Analyses were conducted using linear regression and meta-analysed across studies.\n\nResultsEach additional cigarette per day consumed by current smokers was associated with higher coffee consumption (0.10 cups per day, 95% CI:0.03,0.17). There was weak evidence for an increase in tea consumption per additional cigarette smoked per day (0.04 cups per day, 95% CI:-0.002,0.07). There was strong evidence that each additional copy of the minor allele of rs16969968 (which increases daily cigarette consumption) in current smokers was associated with higher coffee consumption (0.15 cups per day, 95% CI:0.11,0.20), but only weak evidence for an association with tea consumption (0.04 cups per day, 95% CI:- 0.01,0.09). There was no clear evidence that rs16969968 was associated with coffee or tea consumption in never or former smokers.\n\nConclusionThese findings suggest that higher cigarette consumption causally increases coffee intake. This is consistent with faster metabolism of caffeine by smokers, but may also reflect behavioural links between smoking and coffee.

genetics↗