Gene-based partial least-squares approaches for detecting rare variant associations with complex traits

Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for st...
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Autor*in:	Turkmen, Asuman S [verfasserIn] Lin, Shili

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Rare Variant Gene Score Rare Variant Association Outer Weight Latent Variable Path

Anmerkung:	© Turkmen and Lin; licensee BioMed Central Ltd. 2011

Übergeordnetes Werk:	Enthalten in: BMC proceedings - London : BioMed Central, 2007, 5(2011), Suppl 9 vom: 29. Nov.
Übergeordnetes Werk:	volume:5 ; year:2011 ; number:Suppl 9 ; day:29 ; month:11

Links:	Volltext

DOI / URN:	10.1186/1753-6561-5-S9-S19

Katalog-ID:	SPR028444981

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spelling	10.1186/1753-6561-5-S9-S19 doi (DE-627)SPR028444981 (SPR)1753-6561-5-S9-S19-e DE-627 ger DE-627 rakwb eng Turkmen, Asuman S verfasserin aut Gene-based partial least-squares approaches for detecting rare variant associations with complex traits 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Turkmen and Lin; licensee BioMed Central Ltd. 2011 Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis. Rare Variant (dpeaa)DE-He213 Gene Score (dpeaa)DE-He213 Rare Variant Association (dpeaa)DE-He213 Outer Weight (dpeaa)DE-He213 Latent Variable Path (dpeaa)DE-He213 Lin, Shili aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11
allfields_unstemmed	10.1186/1753-6561-5-S9-S19 doi (DE-627)SPR028444981 (SPR)1753-6561-5-S9-S19-e DE-627 ger DE-627 rakwb eng Turkmen, Asuman S verfasserin aut Gene-based partial least-squares approaches for detecting rare variant associations with complex traits 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Turkmen and Lin; licensee BioMed Central Ltd. 2011 Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis. Rare Variant (dpeaa)DE-He213 Gene Score (dpeaa)DE-He213 Rare Variant Association (dpeaa)DE-He213 Outer Weight (dpeaa)DE-He213 Latent Variable Path (dpeaa)DE-He213 Lin, Shili aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11
allfieldsGer	10.1186/1753-6561-5-S9-S19 doi (DE-627)SPR028444981 (SPR)1753-6561-5-S9-S19-e DE-627 ger DE-627 rakwb eng Turkmen, Asuman S verfasserin aut Gene-based partial least-squares approaches for detecting rare variant associations with complex traits 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Turkmen and Lin; licensee BioMed Central Ltd. 2011 Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis. Rare Variant (dpeaa)DE-He213 Gene Score (dpeaa)DE-He213 Rare Variant Association (dpeaa)DE-He213 Outer Weight (dpeaa)DE-He213 Latent Variable Path (dpeaa)DE-He213 Lin, Shili aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11
allfieldsSound	10.1186/1753-6561-5-S9-S19 doi (DE-627)SPR028444981 (SPR)1753-6561-5-S9-S19-e DE-627 ger DE-627 rakwb eng Turkmen, Asuman S verfasserin aut Gene-based partial least-squares approaches for detecting rare variant associations with complex traits 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Turkmen and Lin; licensee BioMed Central Ltd. 2011 Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis. Rare Variant (dpeaa)DE-He213 Gene Score (dpeaa)DE-He213 Rare Variant Association (dpeaa)DE-He213 Outer Weight (dpeaa)DE-He213 Latent Variable Path (dpeaa)DE-He213 Lin, Shili aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11
language	English
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author	Turkmen, Asuman S
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topic_title	Gene-based partial least-squares approaches for detecting rare variant associations with complex traits Rare Variant (dpeaa)DE-He213 Gene Score (dpeaa)DE-He213 Rare Variant Association (dpeaa)DE-He213 Outer Weight (dpeaa)DE-He213 Latent Variable Path (dpeaa)DE-He213
topic	misc Rare Variant misc Gene Score misc Rare Variant Association misc Outer Weight misc Latent Variable Path
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title	Gene-based partial least-squares approaches for detecting rare variant associations with complex traits
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title_sort	gene-based partial least-squares approaches for detecting rare variant associations with complex traits
title_auth	Gene-based partial least-squares approaches for detecting rare variant associations with complex traits
abstract	Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis. © Turkmen and Lin; licensee BioMed Central Ltd. 2011
abstractGer	Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis. © Turkmen and Lin; licensee BioMed Central Ltd. 2011
abstract_unstemmed	Abstract Genome-wide association studies are largely based on single-nucleotide polymorphisms and rest on the common disease/common variants (single-nucleotide polymorphisms) hypothesis. However, it has been argued in the last few years and is well accepted now that rare variants are valuable for studying common diseases. Although current genome-wide association studies have successfully discovered many genetic variants that are associated with common diseases, detecting associated rare variants remains a great challenge. Here, we propose two partial least-squares approaches to aggregate the signals of many single-nucleotide polymorphisms (SNPs) within a gene to reveal possible genetic effects related to rare variants. The availability of the 1000 Genomes Project offers us the opportunity to evaluate the effectiveness of these two gene-based approaches. Compared to results from a SNP-based analysis, the proposed methods were able to identify some (rare) SNPs that were missed by the SNP-based analysis. © Turkmen and Lin; licensee BioMed Central Ltd. 2011
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title_short	Gene-based partial least-squares approaches for detecting rare variant associations with complex traits
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Gene-based partial least-squares approaches for detecting rare variant associations with complex traits

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