18 research outputs found
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 Previous version was withdrawn to be updated by the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 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
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