Application of machine learning algorithms to predict coronary artery calcification with a sibship‐based design

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Abstract
As part of the Genetic Epidemiology Network of Arteriopathy study, hypertensive non‐Hispanic White sibships were screened using 471 single nucleotide polymorphisms (SNPs) to identify genes influencing coronary artery calcification (CAC) measured by computed tomography. Individuals with detectable CAC and CAC quantity ≥70th age‐ and sex‐specific percentile were classified as having a high CAC burden and compared to individuals with CAC quantity P‐valueGPR35 and NOS3) and 12 risk factors (age, body mass index, sex, serum glucose, high‐density lipoprotein cholesterol, systolic blood pressure, cholesterol, homocysteine, triglycerides, fibrinogen, Lp(a) and low‐density lipoprotein particle size) were identified by both methods. This study illustrates how machine learning methods can be used in sibships to identify important, replicable predictors of subclinical coronary atherosclerosis. Genet. Epidemiol. 2008.