Tracking medical genetic literature through machine learning.

There has been remarkable progress in identifying the causes of genetic conditions as well as understanding how changes in specific genes cause disease. Though difficult (and often superficial) to parse, an interesting tension involves emphasis on basic research aimed to dissect normal and abnormal biology versus more clearly clinical and therapeutic investigations. To examine one facet of this question and to better understand progress in Mendelian-related research, we developed an algorithm that classifies medical literature into three categories (Basic, Clinical, and Management) and conducted a retrospective analysis. We built a supervised machine learning classification model using the Azure Machine Learning (ML) Platform and analyzed the literature (1970-2014) from NCBI's Entrez Gene2Pubmed Database (http://www.ncbi.nlm.nih.gov/gene) using genes from the NHGRI's Clinical Genomics Database (http://research.nhgri.nih.gov/CGD/). We applied our model to 376,738 articles: 288,639 (76.6%) were classified as Basic, 54,178 (14.4%) as Clinical, and 24,569 (6.5%) as Management. The average classification accuracy was 92.2%. The rate of Clinical publication was significantly higher than Basic or Management. The rate of publication of article types differed significantly when divided into key eras: Human Genome Project (HGP) planning phase (1984-1990); HGP launch (1990) to publication (2001); following HGP completion to the "Next Generation" advent (2009); the era following 2009. In conclusion, in addition to the findings regarding the pace and focus of genetic progress, our algorithm produced a database that can be used in a variety of contexts including automating the identification of management-related literature.

Multiplex ligation-dependent probe amplification copy number variant analysis in patients with acquired long QT syndrome.

AIMS: Thirteen genetic loci map to families with congenital long QT syndrome (cLQT) and multiple single nucleotide mutations have been functionally implicated in cLQT. Studies have investigated copy number variations (CNVs) in the cLQT genes to ascertain their involvement in cLQT. In these studies 3-12% of cLQT patients who were mutation negative by all other methods carried CNVs in cLQT genes. Prolongation of the QT interval can also be acquired after exposure to certain drugs [acquired LQT (aLQT)]. Single nucleotide mutations in cLQT genes have also been associated with and functionally implicated in aLQT, but to date no studies have explored CNVs as an additional susceptibility factor in aLQT. The aim of this study was to explore the contribution of CNVs in determining susceptibility to aLQT. METHODS AND RESULTS: In this study we screened the commonest cLQT genes (KCNQ1; KCNH2; SCN5A; KCNE1, and KCNE2) in a general population of healthy volunteers and in a cohort of subjects presenting with aLQT for CNVs using the multiplex ligation-dependent probe amplification method. Copy number variants were detected and confirmed in 1 of 197 of the healthy volunteers and in 1 of 90 subjects with aLQT. The CNV in the aLQT subject was functionally characterized and demonstrated impaired channel function. CONCLUSION: Copy number variation is a possible additional risk factor for aLQT and should be considered for incorporation into pharmacogenetic screening of LQTS genes in addition to mutation detection to improve the safety of medication administration.