Subject(s)
Algorithms , Prenatal Diagnosis , Trisomy , Whole Genome Sequencing , Female , Humans , PregnancyABSTRACT
Optimal molecular diagnosis of primary dyslipidemia is challenging to confirm the diagnosis, test and identify at risk relatives. The aim of this study was to test the application of a single targeted next-generation sequencing (NGS) panel for hypercholesterolemia, hypocholesterolemia, and hypertriglyceridemia molecular diagnosis. NGS workflow based on a custom AmpliSeq panel was designed for sequencing the most prevalent dyslipidemia-causing genes (ANGPTL3, APOA5, APOC2, APOB, GPIHBP1, LDLR, LMF1, LPL, PCSK9) on the Ion PGM Sequencer. One hundred and forty patients without molecular diagnosis were studied. In silico analyses were performed using the NextGENe software and homemade tools for detection of copy number variations (CNV). All mutations were confirmed using appropriate tools. Eighty seven variations and 4 CNV were identified, allowing a molecular diagnosis for 40/116 hypercholesterolemic patients, 5/13 hypocholesterolemic patients, and 2/11, hypertriglyceridemic patients respectively. This workflow allowed the detection of CNV contrary to our previous strategy. Some variations were found in previously unexplored regions providing an added value for genotype-phenotype correlation and familial screening. In conclusion, this new NGS process is an effective mutation detection method and allows better understanding of phenotype. Consequently this assay meets the medical need for individualized diagnosis of dyslipidemia.
Subject(s)
DNA Copy Number Variations , Dyslipidemias/diagnosis , Dyslipidemias/genetics , INDEL Mutation , Adolescent , Adult , Aged , Aged, 80 and over , Biomarkers , Child , Child, Preschool , Comorbidity , Diagnosis, Differential , Genetic Association Studies , Genetic Predisposition to Disease , Genetic Testing , High-Throughput Nucleotide Sequencing , Humans , Middle Aged , Workflow , Young AdultABSTRACT
BACKGROUND: Today, sequencing is frequently carried out by Massive Parallel Sequencing (MPS) that cuts drastically sequencing time and expenses. Nevertheless, Sanger sequencing remains the main validation method to confirm the presence of variants. The analysis of MPS data involves the development of several bioinformatic tools, academic or commercial. We present here a statistical method to compare MPS pipelines and test it in a comparison between an academic (BWA-GATK) and a commercial pipeline (TMAP-NextGENe®), with and without reference to a gold standard (here, Sanger sequencing), on a panel of 41 genes in 43 epileptic patients. This method used the number of variants to fit log-linear models for pairwise agreements between pipelines. To assess the heterogeneity of the margins and the odds ratios of agreement, four log-linear models were used: a full model, a homogeneous-margin model, a model with single odds ratio for all patients, and a model with single intercept. Then a log-linear mixed model was fitted considering the biological variability as a random effect. RESULTS: Among the 390,339 base-pairs sequenced, TMAP-NextGENe® and BWA-GATK found, on average, 2253.49 and 1857.14 variants (single nucleotide variants and indels), respectively. Against the gold standard, the pipelines had similar sensitivities (63.47% vs. 63.42%) and close but significantly different specificities (99.57% vs. 99.65%; p < 0.001). Same-trend results were obtained when only single nucleotide variants were considered (99.98% specificity and 76.81% sensitivity for both pipelines). CONCLUSIONS: The method allows thus pipeline comparison and selection. It is generalizable to all types of MPS data and all pipelines.
Subject(s)
Computational Biology/methods , Models, Statistical , Epilepsy/genetics , Epilepsy/pathology , High-Throughput Nucleotide Sequencing , Humans , INDEL Mutation , Polymorphism, Single Nucleotide , Sequence Analysis, DNAABSTRACT
Genetic association studies have the potential to identify causative genetic variants with small effects in complex diseases, but it is not at all clear which study designs best balance power with sample size, especially when taking into account the difficulty of obtaining a sample of the necessary structure. The 14 contributions from the Genetic Analysis Workshop 15 group 3 used data sets with rheumatoid arthritis as primary phenotype from problem 2 (real data) and Problem 3 (simulated data) to investigate design and analysis problems that arise in candidate-gene, candidate-region, and genome-wide association studies. We identified three major themes that were addressed by multiple groups: (1) comparing family-based and case-control study designs with each other and with hybrid designs incorporating both related and unrelated individuals; (2) exploring and comparing techniques of combining information from multiple, correlated single-nucleotide polymorphisms; and (3) comparing analyses that select the model(s) of best fit with the ultimate aim of detecting the joint effects of several unlinked single-nucleotide polymorphisms. These contributions achieved some success in improving upon existing methods. For example, tests using related cases and unrelated controls can achieve higher power than the tests using unrelated cases and unrelated controls. Aside from these successes, the group 3 contributions highlight some interesting areas for future research.
