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Rare variants as low-penetrance risk alleles for colorectal cancer

Rare variants as low-penetrance alleles


Rare variants will not be detectable by population association studies based on the use of linked polymorphic markers, even with very large case/control cohort studies.  This is because of low allelic frequency and individually small contributions to the overall inherited susceptibility of a disease.  These variants are less common than those studied in association studies (i.e. minor allele frequency (MAF) <0.05) but not as rare as obvious mutations (MAF >0.01), although such mutations may also be identified.  Finding rare variants requires nomination of candidate genes likely to have a role in disease aetiology, which are then directly screened for sequence variants which may affect protein function.  This is known as the ‘common-disease/rare-variant’ hypothesis (Pritchard 2001).

Allele frequency and effect sizes for genetic variants associated with colorectal cancer. Hindorff L A et al. Carcinogenesis 2011;32:945-954

So far there have been few rare variants identified in colorectal cancer, partially because candidate genes are not easily identified, and because there have only been a few studies performed.   In one such study variants in APC I1307K and E1317Q, in AXIN1, CTNNB1, and the mismatch repair genes hMLH1 and hMSH2 were more common in 124 multiple adenoma cases than in controls (Fearnhead et al. 2004).   Studies of other candidate genes have produced results of low or no significance however (Dallosso et al. 2008; Zogopoulos et al. 2008).

Labelling APC I1307K a rare variant may not be accurate, as the frequency of the polymorphism in the Ashkenazi population where it is present is 6%, thus potentially suitable for large association studies.  This distinction underlines the arbitrary nature of how such polymorphisms are labelled as rare or common variants.

Although the population attributable risk (PAR) of rare variants may be relatively high, the relative influence of these common variants is low, with reported odds ratios below 2 and peaking at approximately 1.2 (Easton and Eeles 2008).  Most rare variants have odds ratios a little higher than 2 but not above 5, with a mean of 3.7 in observations thus far (Bodmer and Bonilla 2008).  Their individual contributions are small, and they do not give rise to familial concentrations of cases.  As techniques improve to interrogate genetic sequence in an inexpensive, high-throughput and efficient manner this method of identifying variants is likely to generate a higher yield of significant results in the near future.

A candidate gene approach demonstrated rare novel low penetrance breast cancer predisposition loci in three genes, PALB2, BRIP1, and RAD51C.  (Seal et al 2006; Rahman et al 2007; Meindl et al 2010).   This discovery was assisted by the identification of breast cancer cases in Fanconi Anaemia pedigrees.  In general however, it is not a simple task to prioritize candidates for rare variant studies.  In the short term, it is likely that discovery efforts will be focused largely on sequencing candidate genes. Nevertheless, it is becoming feasible to sequence entire genomes to discover variants, due to decreased costs and increased efficiency of such methods.  In a proof of principle study, complete exomic sequencing of a patient with familial pancreatic cancer identified a germline truncating mutation in PALB2 which appeared responsible for this individual’s predisposition to the disease (Jones et al 2009), although mutations in this gene are thought to be rare events in familial pancreatic cancer (Tischkowitz et al 2010).

The above mentioned rare variant loci for breast cancer in PALB2, BRIP1, and RAD51C were present in 10, 8 and 2 cases and 0, 1 and 0 controls respectively.  Due to lack of power rare variants are difficult to validate by frequency alone in an association-type study. If we assume that a single variant or a set of related variants (for example, in the same gene) occurs at a general population frequency of 0.01–0.001, as many as 1000 unselected cases or controls will be required to detect with probability of about 0.7 more than one variant in a discovery screen (Bodmer & Tomlinson 2010).

Nevertheless, in principle the more common a variant is in the population the less its biological impact, thus allowing it to be passed on through generations without affecting reproductive ability.  Rare variants are likely to reveal more about the pathophysiology of the disease process than common variants, as they are likely to have functional significance, as opposed to common variants which are probably in linkage disequilibrium with the causative mutations.

However it is more problematic to design useful studies of rare variants, as random variation identified cannot be readily assumed to be of functional significance, for example over 1500 variants of uncertain significance (VUSs) have been identified in BRCA1 using a sequencing based approach in breast cancer cases.  The difficulty with rare variant discovery, particularly with whole exomic sequence analysis, will be to sort out the candidate functional variation from an almost overwhelming background of functionally irrelevant variation.  The choice of targets will, in general, require some a priori assessment of functional effects.  In silico biometric approaches have been developed with increasing predictive ability, although in vitro demonstration of effects are generally preferable in order to determine functional effects, for example simple effects on expression or protein truncation.

Studying a cohort of affected cases and subsequently examining a control set for variants identified can cause ascertainment bias.  Thus it would be preferable to search for them in affected individuals and controls with equal rigour, and to use a statistical framework to determine whether variants are truly more common in the affected.  These studies are likely to require extremely large and/or enriched data sets in order to identify and verify significant rare variants.  Nevertheless it is becoming increasingly cost and time effective to perform even whole genome sequencing to determine genetic predisposition to both common and rare disease.


Copyright, Dr Kevin Monahan


About kjmonahan

Service lead for Family History of Bowel Cancer Clinic

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