Dataset Information

Increasing the yield in targeted next-generation sequencing by implicating CNV analysis, non-coding exons and the overall variant load: the example of retinal dystrophies.

ABSTRACT: Retinitis pigmentosa (RP) and Leber congenital amaurosis (LCA) are major causes of blindness. They result from mutations in many genes which has long hampered comprehensive genetic analysis. Recently, targeted next-generation sequencing (NGS) has proven useful to overcome this limitation. To uncover "hidden mutations" such as copy number variations (CNVs) and mutations in non-coding regions, we extended the use of NGS data by quantitative readout for the exons of 55 RP and LCA genes in 126 patients, and by including non-coding 5' exons. We detected several causative CNVs which were key to the diagnosis in hitherto unsolved constellations, e.g. hemizygous point mutations in consanguineous families, and CNVs complemented apparently monoallelic recessive alleles. Mutations of non-coding exon 1 of EYS revealed its contribution to disease. In view of the high carrier frequency for retinal disease gene mutations in the general population, we considered the overall variant load in each patient to assess if a mutation was causative or reflected accidental carriership in patients with mutations in several genes or with single recessive alleles. For example, truncating mutations in RP1, a gene implicated in both recessive and dominant RP, were causative in biallelic constellations, unrelated to disease when heterozygous on a biallelic mutation background of another gene, or even non-pathogenic if close to the C-terminus. Patients with mutations in several loci were common, but without evidence for di- or oligogenic inheritance. Although the number of targeted genes was low compared to previous studies, the mutation detection rate was highest (70%) which likely results from completeness and depth of coverage, and quantitative data analysis. CNV analysis should routinely be applied in targeted NGS, and mutations in non-coding exons give reason to systematically include 5'-UTRs in disease gene or exome panels. Consideration of all variants is indispensable because even truncating mutations may be misleading.

SUBMITTER: Eisenberger T

PROVIDER: S-EPMC3827063 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Increasing the yield in targeted next-generation sequencing by implicating CNV analysis, non-coding exons and the overall variant load: the example of retinal dystrophies.

Eisenberger Tobias T Neuhaus Christine C Khan Arif O AO Decker Christian C Preising Markus N MN Friedburg Christoph C Bieg Anika A Gliem Martin M Charbel Issa Peter P Holz Frank G FG Baig Shahid M SM Hellenbroich Yorck Y Galvez Alberto A Platzer Konrad K Wollnik Bernd B Laddach Nadja N Ghaffari Saeed Reza SR Rafati Maryam M Botzenhart Elke E Tinschert Sigrid S Börger Doris D Bohring Axel A Schreml Julia J Körtge-Jung Stefani S Schell-Apacik Chayim C Bakur Khadijah K Al-Aama Jumana Y JY Neuhann Teresa T Herkenrath Peter P Nürnberg Gudrun G Nürnberg Peter P Davis John S JS Gal Andreas A Bergmann Carsten C Lorenz Birgit B Bolz Hanno J HJ

PloS one 20131112 11

Retinitis pigmentosa (RP) and Leber congenital amaurosis (LCA) are major causes of blindness. They result from mutations in many genes which has long hampered comprehensive genetic analysis. Recently, targeted next-generation sequencing (NGS) has proven useful to overcome this limitation. To uncover "hidden mutations" such as copy number variations (CNVs) and mutations in non-coding regions, we extended the use of NGS data by quantitative readout for the exons of 55 RP and LCA genes in 126 patie ...[more]

PMID: 24265693

Similar Datasets

Project description:The ability to alter the morphology of cattle towards greater yields of higher value primal cuts has the potential to increase the value of animals at slaughter. Using weight records of 14 primal cuts from 31,827 cattle, the objective of the present study was to quantify the extent of genetic variability in these primal cuts; also of interest was the degree of genetic variability in the primal cuts adjusted to a common carcass weight. Variance components were estimated for each primal cut using animal linear mixed models. The coefficient of genetic variation in the different primal cuts ranged from 0.05 (bavette) to 0.10 (eye of round) with a mean coefficient of genetic variation of 0.07. When phenotypically adjusted to a common carcass weight, the coefficient of genetic variation of the primal cuts was lesser ranging from 0.02 to 0.07 with a mean of 0.04. The heritability of the 14 primal cuts ranged from 0.14 (bavette) to 0.75 (topside) with a mean heritability across all cuts of 0.48; the heritability estimates reduced, and ranged from 0.12 (bavette) to 0.56 (topside), when differences in carcass weight were accounted for in the statistical model. Genetic correlations between each primal cut and carcass weight were all ?0.77; genetic correlations between each primal cut and carcass conformation score were, on average, 0.59 but when adjusted to a common carcass weight, the correlations weakened to, on average, 0.27. The genetic correlations among all 14 primal cut weights was, on average, strong (mean correlation of 0.72 with all correlations being ?0.37); when adjusted to a common carcass weight, the mean of the genetic correlations among all primal cuts was 0.10. The ability of estimated breeding values for a selection of primal cuts to stratify animals phenotypically on the respective cut weight was demonstrated; the weight of the rump, striploin, and fillet of animals estimated to be in the top 25% genetically for the respective cut, were 10 to 24%, 12 to 24%, and 7 to 17% heavier than the weight of cuts from animals predicted to be in the worst 25% genetically for that cut. Significant exploitable genetic variability in primal carcass cuts was clearly evident even when adjusted to a common carcass weight. The high heritability of many of the primal cuts infers that large datasets are not actually required to achieve high accuracy of selection once the structure of the data and the number of progeny per sire is adequate.

Dataset Information

Increasing the yield in targeted next-generation sequencing by implicating CNV analysis, non-coding exons and the overall variant load: the example of retinal dystrophies.

Publications

Increasing the yield in targeted next-generation sequencing by implicating CNV analysis, non-coding exons and the overall variant load: the example of retinal dystrophies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets