A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis
JSON
Rentoft M, Svensson D, Sjödin A, Olason PI, Sjöström O, Nylander C, Osterman P, Sjögren R, Netotea S, Wibom C, Cederquist K, Chabes A, Trygg J, Melin BS, Johansson E
PLoS ONE 14 (3) e0213350 [2019-03-27; online 2019-03-27]
Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e.g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide.
Bioinformatics Long-term Support WABI [Collaborative]
Bioinformatics Support for Computational Resources [Service]
Bioinformatics Support, Infrastructure and Training [Collaborative]
NGI Stockholm (Genomics Applications) [Service]
NGI Stockholm (Genomics Production) [Service]
NGI Uppsala (SNP&SEQ Technology Platform) [Service]
National Genomics Infrastructure [Service]
Systems Biology [Collaborative]
PubMed 30917156
DOI 10.1371/journal.pone.0213350
Crossref 10.1371/journal.pone.0213350