A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis
Artikel i vetenskaplig tidskrift, 2019

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.


Matilda Rentoft

Umeå universitet

Daniel Svensson

Umeå universitet

Andreas Sjodin

Totalförsvarets forskningsinstitut (FOI)

Umeå universitet

Pall I. Olason

Uppsala universitet

Olle Sjostrom

Unit Res Educ & Dev

Umeå universitet

Carin Nylander

Umeå universitet

Pia Osterman

Umeå universitet

Rickard Sjogren

Umeå universitet

Sergiu Netotea

Chalmers, Biologi och bioteknik

Umeå universitet

Carl Wibom

Umeå universitet

Kristina Cederquist

Umeå universitet

Andrei Chabes

Umeå universitet

Johan Trygg

Umeå universitet

Beatrice S. Melin

Umeå universitet

Erik Johansson

Umeå universitet


1932-6203 (ISSN)

Vol. 14 3 e0213350



Medicinsk genetik






Mer information

Senast uppdaterat