snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions

Kari Nousiainen; Kartiek Kanduri; Isis Ricaño-Ponce; Cisca Wijmenga; Riitta Lahesmaa; Vinod Kumar; Harri Lähdesmäki

doi:10.1093/bioinformatics/bty460

snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions

Kari Nousiainen, Kartiek Kanduri, Isis Ricaño-Ponce, Cisca Wijmenga, Riitta Lahesmaa, Vinod Kumar, Harri Lähdesmäki

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Abstract

Motivation: Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed. Results: We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection. Availability and implementation: The software is available at https://github.com/kartiek/snpEnrichR.

Original language	English
Pages (from-to)	4112-4114
Number of pages	3
Journal	Bioinformatics (Oxford, England)
Volume	34
Issue number	23
DOIs	https://doi.org/10.1093/bioinformatics/bty460
Publication status	Published - 1 Dec 2018
MoE publication type	A1 Journal article-refereed

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title = "snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions",

abstract = "Motivation: Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed. Results: We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection. Availability and implementation: The software is available at https://github.com/kartiek/snpEnrichR.",

author = "Kari Nousiainen and Kartiek Kanduri and Isis Rica{\~n}o-Ponce and Cisca Wijmenga and Riitta Lahesmaa and Vinod Kumar and Harri L{\"a}hdesm{\"a}ki",

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month = dec,

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doi = "10.1093/bioinformatics/bty460",

language = "English",

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T2 - analyzing co-localization of SNPs and their proxies in genomic regions

AU - Nousiainen, Kari

AU - Kanduri, Kartiek

AU - Ricaño-Ponce, Isis

AU - Wijmenga, Cisca

AU - Lahesmaa, Riitta

AU - Kumar, Vinod

AU - Lähdesmäki, Harri

PY - 2018/12/1

Y1 - 2018/12/1

N2 - Motivation: Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed. Results: We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection. Availability and implementation: The software is available at https://github.com/kartiek/snpEnrichR.

AB - Motivation: Co-localization of trait associated SNPs for specific transcription-factor binding sites or regulatory regions in the genome can yield profound insight into underlying causal mechanisms. Analysis is complicated because the truly causal SNPs are generally unknown and can be either SNPs reported in GWAS studies or other proxy SNPs in their linkage disequilibrium. Hence, a comprehensive pipeline for SNP co-localization analysis that utilizes all relevant information about both the genotyped SNPs and their proxies is needed. Results: We developed an R package snpEnrichR for SNP co-localization analysis. The software integrates different tools for random SNP generation and genome co-localization analysis to automatize and help users to create custom SNP co-localization analysis. We show via an example that including proxy SNPs in SNP co-localization analysis enhances the sensitivity of co-localization detection. Availability and implementation: The software is available at https://github.com/kartiek/snpEnrichR.

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snpEnrichR: analyzing co-localization of SNPs and their proxies in genomic regions

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