Design considerations in a sib-pair study of linkage for susceptibility loci in cancer

Investigation of discordant sibling pairs from hereditary breast cancer families and analysis of a rare PMS1 variant

BACKGROUND

It is likely that additional genes for hereditary breast cancer can be identified using a discordant sib pair design. Using this design we identified individuals harboring a rare PMS1 c.605G>A variant previously predicted to result in loss of function.

OBJECTIVES

A family-based design and predictive algorithms were used to prioritize candidate variants possibly associated with an increased risk of hereditary breast cancer. Functional analyses were performed for one of the candidate variants, PMS1 c.605G>A.

METHODS

1) 14 discordant sister-pairs from hereditary breast cancer families were identified. 2) Whole exome sequencing was performed and candidate risk variants identified. 3) A rare PMS variant was identified in 2 unrelated affected sisters but no unaffected siblings. 4) Functional analysis of this variant was carried out using targeted mRNA sequencing.

RESULTS

Genotype-phenotype correlation did not demonstrate tracking of the variant with cancer in the family. Functional analysis revealed no difference in exon 6 incorporation, which was validated by analyzing PMS1 allele specific expression.

CONCLUSIONS

The PMS1 c.605G>A variant did not segregate with disease, and there was no variant-dependent impact on PMS1 exon 6 splicing, supporting this variant is likely benign. Functional analyses are imperative to understanding the clinical significance of predictive algorithms.

Parametric model-based statistics for possible genotyping errors and sample stratification in sibling-pair SNP data

The detection of genotyping errors, based on apparent Mendelian incompatibilities in a sample of sib-pairs, is a complicated problem. In the case of a single marker and unknown parental genotypes, all combinations of sib-pair genotypes are self-consistent. Moreover, the observed deviation from equilibrium genotype frequencies may result from genotyping errors as well as from the sample's stratification. This in turn, may profoundly affect the results of association and linkage analyses, and therefore an estimation of these factors should be done beforehand. Here we present several parametric models, and using likelihood ratio statistics, we suggest a method of combined analysis of genotyping errors and a sample stratification for randomly ascertained sib-pair single nucleotide polymorphism (SNP) data. Specifically, we implemented two models of genotyping errors in either heterozygotes or homozygotes, and two models of sample stratification resulting from either the presence of families of different ethnic origin (e.g., a population admixture) or from a different ethnic origin of the parents in the family (e.g., intermarriage). The power of this method was established by Monte Carlo data simulation. The results clearly suggest that the proposed method is most efficient for detecting genotyping errors in heterozygotes, a common error caused by incorrect SNP data interpretation. We also provide an example of its application to real data.

The identification of colon cancer susceptibility genes by using genome-wide scans

Recent studies have indicated that in approximately 35% of all colorectal cancer (CRC) cases, the CRC was inherited. Although a number of high-risk familial variants have been identified, these mutations explain <6% of CRC cases; therefore, further genome-wide scans will need to be conducted in the future. There are two popular approaches to genome-wide scans, namely linkage and association. The linkage approach utilizes several hundred markers (typically between 300 and 500 markers) throughout the genome and identifies candidate regions shared among affected family members. Candidate regions are then scrutinized for the presence of susceptibility loci. Linkage studies require no prior information and can provide new avenues for future research, but the regions identified are often large and include many candidate genes. The second and more recent approach is the genome-wide association study (GWAS) in which hundreds of thousands of markers called single nucleotide polymorphisms (SNPs) are used to identify the SNPs associated with traits of interest by employing family-based or case-control association methods. GWAS studies require no prior information and, because they use hundreds of thousands of SNPs, they can target specific candidate genes and/or narrow regions for investigation. Study design considerations, methodology, and the execution of linkage and genome-wide association studies that use both family and case-control designs are covered in this chapter.

Common familial colorectal cancer linked to chromosome 7q31: a genome-wide analysis

Present investigations suggest that approximately 30% of colorectal cancer cases arise on the basis of inherited factors. We hypothesize that the majority of inherited factors are moderately penetrant genes, common in the population. We use an affected sibling pair approach to identify genetic regions that are coinherited by siblings with colorectal cancer. Individuals from families with at least two siblings diagnosed with colorectal adenocarcinoma or high-grade dysplasia were enrolled. Known familial colorectal cancer syndromes were excluded. A genome-wide scan on 151 DNA samples from 70 kindreds was completed using deCODE 1100 short tandem repeat marker set at an average 4-cM density. Fine mapping on a total of 184 DNAs from 83 kindreds was done in regions suggesting linkage. Linkage analysis was accomplished with Merlin analysis package. Nonparametric linkage analysis revealed three genetic regions with logarithm of the odds (LOD) scores >or=2.0: Ch. 3q29, LOD 2.61 (P = 0.0003); Ch. 4q31.3, LOD 2.13 (P = 0.0009); and Ch. 7q31.31, LOD 3.08 (P = 0.00008). Affected siblings with increased sharing at the 7q31 locus have a 3.8-year (+/- 3.5) earlier age of colorectal cancer onset although this is not statistically significant (P = 0.11). No significant linkage was found near genes causing known syndromes or regions previously reported (8q24, 9q22, and 11q23). The chromosome 3q21-q24 region reported to be linked in colorectal cancer relative pairs is supported by our study, albeit a minor peak (LOD 0.9; P = 0.02). No known familial cancer genes reside in the 7q31 locus, and thus the identified region may contain a novel susceptibility gene responsible for common familial colorectal cancer.