title: Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18.
abstract: Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18, seven research groups applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. Annotation of genetic variants is inconsistent across databases, incomplete, and biased toward known genes. At the analysis stage insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type I error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric. Interpretation of findings is further complicated by evidence in support of interactions between pathways and by the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false-positive findings and to identify specific causal variants within a given pathway. Despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed.
authors.affiliation: Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
authors.forename: Stella
authors.initials: S
authors.lastname: Aslibekyan
authors.forename: Marcio
authors.initials: M
authors.lastname: Almeida
authors.forename: Nathan
authors.initials: N
authors.lastname: Tintle
mesh_terms: D005787:Gene Frequency; D014644:Genetic Variation; D015894:Genome, Human; D005838:Genotype; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D006973:Hypertension / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D017422:Sequence Analysis, DNA / Q000379:methods*
keywords: family studies; hypertension; pathway analysis; whole-genome sequence
references.citation: PLoS One. 2013 May 31;8(5):e62161
references.pmid: 23741293
references.citation: Proc Natl Acad Sci U S A. 2004 Dec 7;101(49):17156-61
references.pmid: 15572454
references.citation: BMC Proc. 2011 Nov 29;5 Suppl 9:S52
references.pmid: 22373052
references.citation: Nucleic Acids Res. 2011 Jan;39(Database issue):D691-7
references.pmid: 21067998
references.citation: Nature. 2012 Sep 6;489(7414):57-74
references.pmid: 22955616
references.citation: BMC Genomics. 2008 Jul 31;9:363
references.pmid: 18671852
references.citation: BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S102
references.pmid: 25519356
references.citation: BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S103
references.pmid: 25519357
references.citation: Trends Genet. 2012 Jul;28(7):323-32
references.pmid: 22480918
references.citation: Stat Med. 2005 Oct 15;24(19):2911-35
references.pmid: 16152135
references.citation: BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S101
references.pmid: 25519355
references.citation: Genet Epidemiol. 2011;35 Suppl 1:S56-60
references.pmid: 22128060
references.citation: BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S104
references.pmid: 25519358
references.citation: Nucleic Acids Res. 2012 Jan;40(Database issue):D109-14
references.pmid: 22080510
references.citation: Genet Epidemiol. 2013 May;37(4):345-57
references.pmid: 23526307
references.citation: BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S100
references.pmid: 25519354
references.citation: Nat Genet. 2000 May;25(1):25-9
references.pmid: 10802651
references.citation: Genet Epidemiol. 2013 May;37(4):366-76
references.pmid: 23529756
references.citation: Genome Res. 2003 Nov;13(11):2498-504
references.pmid: 14597658
references.citation: BMC Proc. 2011 Nov 29;5 Suppl 9:S45
references.pmid: 22373371
references.citation: BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S106
references.pmid: 25519360
references.citation: Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
references.pmid: 16199517
references.citation: BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S105
references.pmid: 25519359
references.citation: PLoS Comput Biol. 2012;8(2):e1002375
references.pmid: 22383865
references.citation: BMC Proc. 2011 Nov 29;5 Suppl 9:S48
references.pmid: 22373429