Melanie Swan1. 1. MS Futures Group, Palo Alto, California 94306, USA. m@melanieswan.com
Abstract
PURPOSE: Gene carrier status and pharmacogenomic data may be detectable from single nucleotide polymorphisms (SNPs), but SNP-based research concerning multigenic common disease such as diabetes, cancers, and cardiovascular disease is an emerging field. The many SNPs and loci that may relate to common disease have not yet been comprehensively identified and understood scientifically. In the interim, direct-to-consumer (DTC) genomic companies have forged ahead in developing composite risk interpretations for multigenic conditions. It is useful to understand how variance in risk interpretation may arise. METHODS: A comprehensive study was conducted to analyze the 213 conditions covered by the 5 identifiable genome-wide DTC genomic companies, and the total SNPs (401) and loci (224) assessed in the 20 common disease conditions with the greatest overlapping coverage. RESULTS: Variance in multigenic condition risk interpretation can be explained by differences in the average lifetime risk assigned to similar underlying populations, the loci and SNPs selected for analysis, and the quantitative risk assignment methodologies used by DTC genomic companies. CONCLUSION: At present, multigenic condition analysis is a complicated process. DTC genomic companies have made laudable efforts to deliver risk predictions, but greater consistency is needed for the long-term validity, utility, and credibility of the sector.
PURPOSE: Gene carrier status and pharmacogenomic data may be detectable from single nucleotide polymorphisms (SNPs), but SNP-based research concerning multigenic common disease such as diabetes, cancers, and cardiovascular disease is an emerging field. The many SNPs and loci that may relate to common disease have not yet been comprehensively identified and understood scientifically. In the interim, direct-to-consumer (DTC) genomic companies have forged ahead in developing composite risk interpretations for multigenic conditions. It is useful to understand how variance in risk interpretation may arise. METHODS: A comprehensive study was conducted to analyze the 213 conditions covered by the 5 identifiable genome-wide DTC genomic companies, and the total SNPs (401) and loci (224) assessed in the 20 common disease conditions with the greatest overlapping coverage. RESULTS: Variance in multigenic condition risk interpretation can be explained by differences in the average lifetime risk assigned to similar underlying populations, the loci and SNPs selected for analysis, and the quantitative risk assignment methodologies used by DTC genomic companies. CONCLUSION: At present, multigenic condition analysis is a complicated process. DTC genomic companies have made laudable efforts to deliver risk predictions, but greater consistency is needed for the long-term validity, utility, and credibility of the sector.
Authors: Karen P Powell; Whitney A Cogswell; Carol A Christianson; Gaurav Dave; Amit Verma; Sonja Eubanks; Vincent C Henrich Journal: J Genet Couns Date: 2011-07-16 Impact factor: 2.537
Authors: Rachel Nusbaum; Kara-Grace Leventhal; Gillian W Hooker; Beth N Peshkin; Morgan Butrick; Yasmin Salehizadeh; William Tuong; Susan Eggly; Jeena Mathew; David Goerlitz; Peter G Shields; Marc D Schwartz; Kristi D Graves Journal: Transl Behav Med Date: 2012-06-10 Impact factor: 3.046
Authors: Rachel R J Kalf; Raluca Mihaescu; Suman Kundu; Peter de Knijff; Robert C Green; A Cecile J W Janssens Journal: Genet Med Date: 2013-06-27 Impact factor: 8.822