Integration of polygenic and individual SNP effects in genome-wide association analyses

N. V.L. Serão, Jonathan Edward Beever, D. B. Faulkner, Sandra Luisa Rodriguez-Zas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The lack of consideration of polygenic effects in genome-wide association studies (GWAS) may bias the results in complex traits controlled by multiple genes. The goal of this study is to develop a composite-GWAS model that identifies individual SNPs while adjusting for polygenic effects. The complex trait residual feed intake (RFI), an indicator of the feed efficiency based on maintenance and growth, was modeled. RFI and genotypic data (5,910 SNPs from chromosomes 3, 11 and 24) from 1,387 steers from different breeds and receiving different diets were analyzed, with and without the additive polygenic effect. The model included the fixed effects of days of feed, diet, breed and interaction between diet and breed, and the random effects of contemporary group and additive polygenic effect. A total of 69 and 141 SNPs were detected (P-value < 0.01) with the model including and excluding polygenic effects, respectively. The higher number of SNPs identified by the second model confirms that ignoring polygenic effects in GWAS of multi-gene traits can lead to false positives due to linkage disequilibrium. Seven SNPs (P-value < 0.001), four in chromosomes 3, two in chromosome 11 and one in chromosome 24, were detected using the polygenic model. Two SNPs, one from chromosome 3 and one from 11 are located within coding gene regions. Our results demonstrate the need to use composite-GWAS that include polygenic effects in complex multi-gene traits. These results indicated that the genetic improvement of feed efficiency in beef cattle may be accelerated by the incorporation of these markers in genomic selection strategies.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Pages985-987
Number of pages3
DOIs
StatePublished - Dec 1 2011
Event2011 IEEE International Conference onBioinformatics and Biomedicine Workshops, BIBMW 2011 - Atlanta, GA, United States
Duration: Nov 12 2011Nov 15 2011

Publication series

Name2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Other

Other2011 IEEE International Conference onBioinformatics and Biomedicine Workshops, BIBMW 2011
CountryUnited States
CityAtlanta, GA
Period11/12/1111/15/11

Fingerprint

Genome-Wide Association Study
Single Nucleotide Polymorphism
Genes
Chromosomes
Chromosomes, Human, Pair 3
Chromosomes, Human, Pair 11
Nutrition
Diet
Linkage Disequilibrium
Beef
Composite materials
Maintenance
Growth

Keywords

  • FCGR1A
  • GWAS
  • IMMT
  • pedigree information
  • polygenic effect

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Serão, N. V. L., Beever, J. E., Faulkner, D. B., & Rodriguez-Zas, S. L. (2011). Integration of polygenic and individual SNP effects in genome-wide association analyses. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 (pp. 985-987). [6112531] (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011). https://doi.org/10.1109/BIBMW.2011.6112531

Integration of polygenic and individual SNP effects in genome-wide association analyses. / Serão, N. V.L.; Beever, Jonathan Edward; Faulkner, D. B.; Rodriguez-Zas, Sandra Luisa.

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. p. 985-987 6112531 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Serão, NVL, Beever, JE, Faulkner, DB & Rodriguez-Zas, SL 2011, Integration of polygenic and individual SNP effects in genome-wide association analyses. in 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011., 6112531, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, pp. 985-987, 2011 IEEE International Conference onBioinformatics and Biomedicine Workshops, BIBMW 2011, Atlanta, GA, United States, 11/12/11. https://doi.org/10.1109/BIBMW.2011.6112531
Serão NVL, Beever JE, Faulkner DB, Rodriguez-Zas SL. Integration of polygenic and individual SNP effects in genome-wide association analyses. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. p. 985-987. 6112531. (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011). https://doi.org/10.1109/BIBMW.2011.6112531
Serão, N. V.L. ; Beever, Jonathan Edward ; Faulkner, D. B. ; Rodriguez-Zas, Sandra Luisa. / Integration of polygenic and individual SNP effects in genome-wide association analyses. 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. pp. 985-987 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011).
@inproceedings{139aaec63bba470ea5ef999cf5aed4f4,
title = "Integration of polygenic and individual SNP effects in genome-wide association analyses",
abstract = "The lack of consideration of polygenic effects in genome-wide association studies (GWAS) may bias the results in complex traits controlled by multiple genes. The goal of this study is to develop a composite-GWAS model that identifies individual SNPs while adjusting for polygenic effects. The complex trait residual feed intake (RFI), an indicator of the feed efficiency based on maintenance and growth, was modeled. RFI and genotypic data (5,910 SNPs from chromosomes 3, 11 and 24) from 1,387 steers from different breeds and receiving different diets were analyzed, with and without the additive polygenic effect. The model included the fixed effects of days of feed, diet, breed and interaction between diet and breed, and the random effects of contemporary group and additive polygenic effect. A total of 69 and 141 SNPs were detected (P-value < 0.01) with the model including and excluding polygenic effects, respectively. The higher number of SNPs identified by the second model confirms that ignoring polygenic effects in GWAS of multi-gene traits can lead to false positives due to linkage disequilibrium. Seven SNPs (P-value < 0.001), four in chromosomes 3, two in chromosome 11 and one in chromosome 24, were detected using the polygenic model. Two SNPs, one from chromosome 3 and one from 11 are located within coding gene regions. Our results demonstrate the need to use composite-GWAS that include polygenic effects in complex multi-gene traits. These results indicated that the genetic improvement of feed efficiency in beef cattle may be accelerated by the incorporation of these markers in genomic selection strategies.",
keywords = "FCGR1A, GWAS, IMMT, pedigree information, polygenic effect",
author = "Ser{\~a}o, {N. V.L.} and Beever, {Jonathan Edward} and Faulkner, {D. B.} and Rodriguez-Zas, {Sandra Luisa}",
year = "2011",
month = "12",
day = "1",
doi = "10.1109/BIBMW.2011.6112531",
language = "English (US)",
isbn = "9781457716133",
series = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",
pages = "985--987",
booktitle = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

}

TY - GEN

T1 - Integration of polygenic and individual SNP effects in genome-wide association analyses

AU - Serão, N. V.L.

AU - Beever, Jonathan Edward

AU - Faulkner, D. B.

AU - Rodriguez-Zas, Sandra Luisa

PY - 2011/12/1

Y1 - 2011/12/1

N2 - The lack of consideration of polygenic effects in genome-wide association studies (GWAS) may bias the results in complex traits controlled by multiple genes. The goal of this study is to develop a composite-GWAS model that identifies individual SNPs while adjusting for polygenic effects. The complex trait residual feed intake (RFI), an indicator of the feed efficiency based on maintenance and growth, was modeled. RFI and genotypic data (5,910 SNPs from chromosomes 3, 11 and 24) from 1,387 steers from different breeds and receiving different diets were analyzed, with and without the additive polygenic effect. The model included the fixed effects of days of feed, diet, breed and interaction between diet and breed, and the random effects of contemporary group and additive polygenic effect. A total of 69 and 141 SNPs were detected (P-value < 0.01) with the model including and excluding polygenic effects, respectively. The higher number of SNPs identified by the second model confirms that ignoring polygenic effects in GWAS of multi-gene traits can lead to false positives due to linkage disequilibrium. Seven SNPs (P-value < 0.001), four in chromosomes 3, two in chromosome 11 and one in chromosome 24, were detected using the polygenic model. Two SNPs, one from chromosome 3 and one from 11 are located within coding gene regions. Our results demonstrate the need to use composite-GWAS that include polygenic effects in complex multi-gene traits. These results indicated that the genetic improvement of feed efficiency in beef cattle may be accelerated by the incorporation of these markers in genomic selection strategies.

AB - The lack of consideration of polygenic effects in genome-wide association studies (GWAS) may bias the results in complex traits controlled by multiple genes. The goal of this study is to develop a composite-GWAS model that identifies individual SNPs while adjusting for polygenic effects. The complex trait residual feed intake (RFI), an indicator of the feed efficiency based on maintenance and growth, was modeled. RFI and genotypic data (5,910 SNPs from chromosomes 3, 11 and 24) from 1,387 steers from different breeds and receiving different diets were analyzed, with and without the additive polygenic effect. The model included the fixed effects of days of feed, diet, breed and interaction between diet and breed, and the random effects of contemporary group and additive polygenic effect. A total of 69 and 141 SNPs were detected (P-value < 0.01) with the model including and excluding polygenic effects, respectively. The higher number of SNPs identified by the second model confirms that ignoring polygenic effects in GWAS of multi-gene traits can lead to false positives due to linkage disequilibrium. Seven SNPs (P-value < 0.001), four in chromosomes 3, two in chromosome 11 and one in chromosome 24, were detected using the polygenic model. Two SNPs, one from chromosome 3 and one from 11 are located within coding gene regions. Our results demonstrate the need to use composite-GWAS that include polygenic effects in complex multi-gene traits. These results indicated that the genetic improvement of feed efficiency in beef cattle may be accelerated by the incorporation of these markers in genomic selection strategies.

KW - FCGR1A

KW - GWAS

KW - IMMT

KW - pedigree information

KW - polygenic effect

UR - http://www.scopus.com/inward/record.url?scp=84856004723&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856004723&partnerID=8YFLogxK

U2 - 10.1109/BIBMW.2011.6112531

DO - 10.1109/BIBMW.2011.6112531

M3 - Conference contribution

AN - SCOPUS:84856004723

SN - 9781457716133

T3 - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

SP - 985

EP - 987

BT - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

ER -