Presenter/Creator Information

James A. Diao, Yale UniversityFollow

Website

https://github.com/jamesdiao/2016-paper-ACMG-penetrance

Description

The dropping costs and rising popularity of next-generation sequencing has introduced the possibility of personalizing medical treatments and screening for genetic diseases. Still, the clinical community’s understanding remains incomplete, with limited consensus on the proper interpretation for many genetic variants. Thus, the standard procedure when returning sequencing results has been to report findings only in genes related to the diagnostic indication, and not incidental findings in other genes. To balance the threat of false positives with the medical benefits of true findings, the American College on Medical Genetics and Genomics (ACMG) recommends an exception: that clinical sequencing laboratories seek and report incidental findings in a specific set of 56 genes (ACMG-56), in which variants are considered to have a greater likelihood of causing disease.

The clinical value of these recommendations are evaluated using the metric of penetrance, defined as the probability that a patient who tests positive will later develop the disease. We queried the public 1000 Genomes Project to obtain whole genome data for 2,504 healthy individuals from diverse ethnic populations. Pathogenic variants were identified using the central repository ClinVar, and found to be distributed unevenly across ancestral groups in this cohort, and incidental findings were found to be inflated relative to empirical disease prevalence. Quantitative risk estimates were derived by modeling penetrance as a function of disease prevalence, allele frequency, and allelic heterogeneity. Plausible ranges for these parameters were estimated from the 1000 Genomes Project cohort and the medical literature. Under the most generous assumptions, the maximum overall penetrance estimates for the majority of diseases fall under 50%, with many under 5%. Penetrance estimates were also shown to vary significantly between ancestral groups, stemming from allele frequency differences between these groups.

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Penetrance estimates for incidental genomic findings in ACMG-59

The dropping costs and rising popularity of next-generation sequencing has introduced the possibility of personalizing medical treatments and screening for genetic diseases. Still, the clinical community’s understanding remains incomplete, with limited consensus on the proper interpretation for many genetic variants. Thus, the standard procedure when returning sequencing results has been to report findings only in genes related to the diagnostic indication, and not incidental findings in other genes. To balance the threat of false positives with the medical benefits of true findings, the American College on Medical Genetics and Genomics (ACMG) recommends an exception: that clinical sequencing laboratories seek and report incidental findings in a specific set of 56 genes (ACMG-56), in which variants are considered to have a greater likelihood of causing disease.

The clinical value of these recommendations are evaluated using the metric of penetrance, defined as the probability that a patient who tests positive will later develop the disease. We queried the public 1000 Genomes Project to obtain whole genome data for 2,504 healthy individuals from diverse ethnic populations. Pathogenic variants were identified using the central repository ClinVar, and found to be distributed unevenly across ancestral groups in this cohort, and incidental findings were found to be inflated relative to empirical disease prevalence. Quantitative risk estimates were derived by modeling penetrance as a function of disease prevalence, allele frequency, and allelic heterogeneity. Plausible ranges for these parameters were estimated from the 1000 Genomes Project cohort and the medical literature. Under the most generous assumptions, the maximum overall penetrance estimates for the majority of diseases fall under 50%, with many under 5%. Penetrance estimates were also shown to vary significantly between ancestral groups, stemming from allele frequency differences between these groups.

http://elischolar.library.yale.edu/dayofdata/2016/posters/10