Due to patterns of migration, selection, and population expansion, founder effects are common among humans. In Southern Brazil, a recurrent TP53 mutation, p.R337H, is detected in families with cancer predisposition. We have used whole locus resequencing and high‐density single nucleotide polymorphism (SNP) genotyping to refine TP53 locus haplotype definitions. Haplotyping of 12 unrelated p.R337H carriers using a set of 29 tag SNPs, revealed that all subjects carried the same haplotype, and presence of the mutation on this haplotype was confirmed by allele‐specific PCR. The probability that this haplotype occurs independently in all index cases was of 3.1×10⁻⁹, demonstrating a founder effect. Analysis of the patterns of 103 tumors diagnosed in 12 families showed that the presence of p.R337H is associated with multiple cancers of the Li‐Fraumeni Syndrome (LFS) spectrum, with relatively low penetrance before the age of 30 but a lifetime risk comparable to classical LFS. The p.R337H families are mostly distributed along a road axis historically known as the main route used by merchants of Portuguese origin in the XVIII and XIX century. This historical circumstance and the relatively low penetrance before the age of 30 may have contributed to the maintenance of this pathogenic mutation in a large, open population. Hum Mutat 30:1–8, 2009.

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co‐segregation, family cancer history profile, co‐occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case‐control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene‐specific calibration of evidence types used for variant classification.

Molecular biology is currently a fast-advancing science. Sequencing techniques are getting cheaper, but the interpretation of genetic variants requires expertise and computational power, therefore is still a challenge. Next-generation sequencing releases thousands of variants and to classify them, researchers propose protocols with several parameters. Here we present a review of several in silico pathogenicity prediction tools involved in the variant prioritization/classification process used by some international protocols for variant analysis and studies evaluating their efficiency.