Presymptomatic risk assessment for chronic non-communicable diseases

The prevalence of common chronic non-communicable diseases (CNCDs) far overshadows the prevalence of both monogenic and infectious diseases combined. All CNCDs, also called complex genetic diseases, have a heritable genetic component that can be used for pre-symptomatic risk assessment. Common single nucleotide polymorphisms (SNPs) that tag risk haplotypes across the genome currently account for a non-trivial portion of the germ-line genetic risk and we will likely continue to identify the remaining missing heritability in the form of rare variants, copy number variants and epigenetic modifications. Here, we describe a novel measure for calculating the lifetime risk of a disease, called the genetic composite index (GCI), and demonstrate its predictive value as a clinical classifier. The GCI only considers summary statistics of the effects of genetic variation and hence does not require the results of large-scale studies simultaneously assessing multiple risk factors. Combining GCI scores with environmental risk information provides an additional tool for clinical decision-making. The GCI can be populated with heritable risk information of any type, and thus represents a framework for CNCD pre-symptomatic risk assessment that can be populated as additional risk information is identified through next-generation technologies.Comment: Plos ONE paper. Previous version was withdrawn to be updated by the journal's pdf versio

Allele frequencies and the relative risks of Type 2 Diabetes, Crohn's Disease and Rheumatoid Arthritis SNPs.

<p>1. The relative risks provided here were calculated using the GCI methodology, as explained in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014338#s4" target="_blank">Methods</a> section. RR means risk-risk genotype and RN means risk-nonrisk genotype.</p><p>2. The allele frequencies are taken from the HapMap project's CEU population.</p

ROC curves for the GENEVA dataset.

<p>Effect of genetic (15 SNPs given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014338#pone-0014338-t006" target="_blank">Table 6</a>) and environmental factors (BMI, Smoking) versus genetic factors alone for predicting Type 2 Diabetes in 2600 cases and 3000 controls in the GENEVA data. The AUCs of the two curves are 0.727 and 0.565 respectively. The relative risks for BMI and Smoking are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014338#pone-0014338-t005" target="_blank">Table 5</a>.</p

ROC curves for the WTCCC dataset.

<p><b>A</b>. Crohn's Disease. <b>B</b>. Type 2 Diabetes. <b>C</b>. Rheumatoid Arthritis. In each plot, the black line corresponds to random expectation, the blue lines correspond to theoretical expectations (under the two disease models described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014338#s4" target="_blank">Methods</a>) when the genetic variable is known, the red line corresponds to GCI, and the green line corresponds to logistic regression.</p

The area under the ROC curve for the three diseases under three different scenarios.

<p>1. The ideal score when the complete genetic information is known.</p

The area under the curve (AUC) for the different interaction scenarios.

<p>1. The two columns correspond to the case where there is a SNP-SNP interaction in which the effect of a certain combination of genotypes has two times the product of the marginal effects.</p><p>2. The two columns correspond to the case where there is a SNP-SNP interaction in which the effect of a certain combination of genotypes is 10 times the product of the marginal effects.</p