Prioritization of family member sequencing for the detection of rare variants

Rachel Sippy; Jill M Kolesar; Burcu F Darst; Corinne D Engelman

doi:10.1186/s12919-016-0035-8

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Prioritization of family member sequencing for the detection of rare variants

Journal article

Open access

Peer reviewed

Prioritization of family member sequencing for the detection of rare variants

Rachel Sippy, Jill M Kolesar, Burcu F Darst and Corinne D Engelman

BMC proceedings, Vol.10(Suppl 7), pp.227-231

2016

DOI: 10.1186/s12919-016-0035-8

PMCID: PMC5133500

PMID: 27980641

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https://doi.org/10.1186/s12919-016-0035-8View

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Abstract

Background The advent of affordable sequencing has enabled researchers to discover many variants contributing to disease, including rare variants. There are methods for determining the most informative individuals for sequencing, but the application of these methods is more complex when working with families. Sets of large families can be beneficial in finding rare variants, but it may be unfeasible to sequence all members of these family sets. Methods Using simulated data from the Genetic Analysis Workshop 19, we apply multiple regression to identify cases and controls. To find the best controls for each case, we used kinship coefficients to match within families. Selected cases and controls were analyzed for rare variants, collapsed by gene, associated with hypertension using the family-based rare variant association test (FARVAT). Results The gene with the strongest simulated effect, MAP4, did not meet the Bonferroni corrected significance threshold. However, analysis of cases and controls using our selection method substantially improved the significance of MAP4, despite the reduction in sample size. Conclusions Taking the additional steps to select the optimal cases and controls from large family data sets can help ensure that only informative individuals are included in analysis and may improve the ability to detect rare variants.

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Title: Subtitle: Prioritization of family member sequencing for the detection of rare variants
Creators: Rachel Sippy - University of Wisconsin–Madison
Jill M Kolesar - University of Wisconsin–Madison
Burcu F Darst - University of Wisconsin–Madison
Corinne D Engelman - University of Wisconsin–Madison
Resource Type: Journal article
Publication Details: BMC proceedings, Vol.10(Suppl 7), pp.227-231
Publisher: BioMed Central
DOI: 10.1186/s12919-016-0035-8
PMID: 27980641
PMCID: PMC5133500
ISSN: 1753-6561
eISSN: 1753-6561
Language: English
Date published: 2016
Academic Unit: Pharmacy; Pharmaceutical Sciences and Experimental Therapeutics
Record Identifier: 9984695791502771

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