Genomic characterization of large rearrangements of the LDLR gene in Czech patients with familial hypercholesterolemia

<p>Abstract</p> <p>Background</p> <p>Mutations in the <it>LDLR </it>gene are the most frequent cause of Familial hypercholesterolemia, an autosomal dominant disease characterised by elevated concentrations of LDL in blood plasma. In many populations, large genomic rearrangements account for approximately 10% of mutations in the <it>LDLR </it>gene.</p> <p>Methods</p> <p>DNA diagnostics of large genomic rearrangements was based on Multiple Ligation dependent Probe Amplification (MLPA). Subsequent analyses of deletion and duplication breakpoints were performed using long-range PCR, PCR, and DNA sequencing.</p> <p>Results</p> <p>In set of 1441 unrelated FH patients, large genomic rearrangements were found in 37 probands. Eight different types of rearrangements were detected, from them 6 types were novel, not described so far. In all rearrangements, we characterized their exact extent and breakpoint sequences.</p> <p>Conclusions</p> <p>Sequence analysis of deletion and duplication breakpoints indicates that intrachromatid non-allelic homologous recombination (NAHR) between <it>Alu </it>elements is involved in 6 events, while a non-homologous end joining (NHEJ) is implicated in 2 rearrangements. Our study thus describes for the first time NHEJ as a mechanism involved in genomic rearrangements in the <it>LDLR </it>gene.</p

Biophysical ambiguities prevent accurate genetic prediction

A goal of biology is to predict how mutations combine to alter phenotypes, fitness and disease. It is often assumed that mutations combine additively or with interactions that can be predicted. Here, we show using simulations that, even for the simple example of the lambda phage transcription factor CI repressing a gene, this assumption is incorrect and that perfect measurements of the effects of mutations on a trait and mechanistic understanding can be insufficient to predict what happens when two mutations are combined. This apparent paradox arises because mutations can have different biophysical effects to cause the same change in a phenotype and the outcome in a double mutant depends upon what these hidden biophysical changes actually are. Pleiotropy and non-monotonic functions further confound prediction of how mutations interact. Accurate prediction of phenotypes and disease will sometimes not be possible unless these biophysical ambiguities can be resolved using additional measurements.This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2017-89488-P and SEV-2012-0208), the Bettencourt Schueller Foundation, Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR, 2017 SGR 1322), and the CERCA Program/Generalitat de Catalunya. We also acknowledge the support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership and the Centro de Excelencia Severo Ochoa