A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants:Application to BRCA1 and BRCA2

A large number of variants identified through clinical genetic testing in disease susceptibility genes are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion) can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analysis of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC) and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared with classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and preformatted Excel calculators for implementation of the method for rare variants in BRCA1, BRCA2, and other high-risk genes with known penetrance.</p

A Likelihood Ratio Approach for Utilizing Case-Control Data in the Clinical Classification of Rare Sequence Variants: Application to BRCA1 and BRCA2

A large number of variants identified through clinical genetic testing in disease susceptibility genes are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion) can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analysis of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC) and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity—findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared with classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and preformatted Excel calculators for implementation of the method for rare variants in BRCA1, BRCA2, and other high-risk genes with known penetrance.</jats:p

The <i>HMMR</i> locus and breast cancer risk in <i>BRCA1</i> mutation carriers.

<p>(<b>A</b>) Forest plots showing rs299290 HRs and 95% CIs (retrospective likelihood trend estimation) for participating countries (relatively small sample sets are not shown) ordered by sample size. Left and right panels show results for <i>BRCA1</i> and <i>BRCA2</i> mutation carriers, respectively. The sizes of the rectangles are proportional to the corresponding country/study precision. (<b>B</b>) The rs299290-containing region, including the genes, variation and regulatory evidence mentioned in HMECs. Exons are marked by black-filled rectangles and the direction of transcription is marked by arrows in the genomic structure. The chromosome 5 positions (base pairs (bp)) and linkage disequilibrium structure from Caucasian HapMap individuals are also shown.</p

Potential GxG associated with breast cancer risk in <i>BRCA1/2</i> mutation carriers.

<p>*Each estimate is derived from the interaction term of a Cox regression model.</p><p>Potential GxG associated with breast cancer risk in <i>BRCA1/2</i> mutation carriers.</p

Gene expression interactions in breast cancer survival.

<p>(<b>A</b>) Kaplan–Meier survival curves based on categorization of <i>HMMR</i> (probe NM_012484) and <i>AURKA</i> (NM_003600) expression in tertiles (low, medium or high expression). For simplicity, only the tertiles for “high” <i>AURKA</i> are shown. The tumours with high expression levels for both genes were not those with the poorest prognosis. (<b>B</b>) Kaplan–Meier survival curves based on categorization of <i>HMMR</i> (NM_012484) and <i>TUBG1</i> (NM_016437) expression in tertiles (low, medium or high expression). For simplicity, only the tertiles for “high” <i>HMMR</i> are shown. The cases with high expression levels for both genes were those with the poorest prognosis.</p