Identification of copy number variants from exome sequence data.

BACKGROUND: With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1-4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1-4 exons), combining computational prediction algorithms and a high-resolution custom CGH array. RESULTS: We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate. CONCLUSIONS: In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1-4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

Application of molecular genetics for diagnosing familial hypercholesterolemia in Norway: results from a family-based screening program.

A total of 119 different mutations in the low-density lipoprotein-receptor gene have so far been found to cause familial hypercholesterolemia (FH) among Norwegian patients. As of April 2003, 2390 patients from 959 unrelated families were provided with a molecular genetic diagnosis. Of these, 25.3% had xanthomas and 8.4% had xanthelasma. During the last 2-3 years, a systematic family-based program to identify close relatives of FH patients has been established. A total of 851 relatives of 188 index patients have undergone genetic testing. Four hundred seven people (47.9%) were affected, and 444 (52.1%) were unaffected. Only 41.5% of those affected were on lipid-lowering drugs, and only 6.1% had a value for total serum cholesterol below 193 mg/dL (5.0 mmol/L). A follow-up study was conducted in 146 of 407 affected relatives 6 months after genetic testing was performed. Of those already under treatment at the time of genetic testing, nonsignificant reductions of total serum cholesterol and low-density lipoprotein cholesterol of 3.2% and 7.1% were observed. Of those not under treatment who were aged 18 years and older, the corresponding reductions were 21.2% (p <.0001) and 30.0% (p <.0001), respectively. We conclude that molecular genetic methods represent a feasible and highly efficient tool in a family-based strategy to diagnose FH.