Little evidence for association between the TGFBR1*6A variant and colorectal cancer: a family-based association study on non-syndromic family members from Australia and Spain.

Background: Genome-wide linkage studies have identified the 9q22 chromosomal region as linked with colorectal cancer (CRC) predisposition. A candidate gene in this region is transforming growth factor β receptor 1 (TGFBR1). Investigation of TGFBR1 has focused on the common genetic variant rs11466445, a short exonic deletion of nine base pairs which results in truncation of a stretch of nine alanine residues to six alanine residues in the gene product.

A simulation study to assess a variable selection method for selecting single nucleotide polymorphisms associated with disease.

In genome-wide association studies, where hundreds of thousands of single nucleotide polymorphisms (SNPs) are genotyped, the potential for false positives is high and methods for selecting models with only a few SNPs are required. Methods for variable selection giving sets of SNPs associated with disease have been developed, but are still less common than evaluation of individual SNPs one at a time. To assess the potential improvement available from multi-SNP approaches, we examined the performance of the software GeneRaVE as a variable selection method when applied to SNP data in case-control studies.

Evidence of linkage to chromosomes 10p15.3-p15.1, 14q24.3-q31.1 and 9q33.3-q34.3 in non-syndromic colorectal cancer families.

Up to 25% of colorectal cancer (CRC) may be caused by inherited genetic variants that have yet to be identified. Previous genome-wide linkage studies (GWLSs) have identified a new loci postulated to contain novel CRC risk genes amongst affected families carrying no identifiable mutations in any of the known susceptibility genes for familial CRC syndromes. To undertake a new GWLS, we recruited members from 54 non-syndromic families from Australia and Spain where at least two first-degree relatives were affected by CRC.

Assessing the potential usefulness of IGF-related peptides and adiponectin for predicting disease risk.

Objective: Insulin-like growth factors (IGF), their binding proteins and adiponectin have been investigated as potential blood-based biomarkers for a variety of diseases. Before these circulating proteins can be considered as biomarkers, their variation within and between individuals and between published studies must be critically assessed. The purpose of this study was to use the D-value to predict the potential usefulness of IGF-related peptides and adiponectin as biomarkers for the diagnosis of colorectal cancer (CRC).

Estimating genotyping error rates from Mendelian errors in SNP array genotypes and their impact on inference.

A simple method of inferring the genotyping error rate of SNP arrays and similar high-throughput genotyping methods from Mendelian errors is described. Application to genotypes from small families using the Affymetrix GeneChip Human Mapping 50 k Array indicates an error rate of about 0.1%, and this rate can be reduced by increasing the quality criterion for calls, though at the cost of a reduced genotype call rate, which limits the benefit available.

A range of simple summary genome-wide statistics for detecting genetic linkage using high density marker data.

A simple approach to design and analysis of genome-wide linkage scans is described, based on an approximation to the joint distribution of likelihood ratio statistics at a large number of single nucleotide polymorphism (SNP) loci. The approximation is readily calculated and makes it feasible to study the test properties of a range of summary statistics for the entire sequence of point-wise test values. Both the null distribution in the absence of genetic effects and the alternative distribution under various models of single or multi-gene inheritance can readily be simulated.

A measure of the performance of biomarkers for disease.

The statistical properties required for effective biomarkers for disease are examined. It is shown that an "effectiveness parameter" D can be calculated that summarises the performance of a given biomarker and can distinguish between effective and ineffective biomarkers. D can be readily calculated from published summaries of biomarker levels and provides a simpler alternative to the commonly used "Area under the Curve" statistic.