Investigations on pleiotropy and genome wide association analyses by random effects using QTL-MAS 2010 dataset
Abstract
Recent advances in molecular genetics have provided hundreds of thousands of single nucleotide polymorphisms to detect mutations at the vicinity of genes related with quantitative traits. Breeding values could be used as response variable in mixed model framework to detect possible associations with genomic relationship matrix. It is known that most of quantitative traits are correlated which leads to construct of networks and pathways of genes due to pleiotropy. Hence the main aim of this paper is to a) detecting pleiotropy by principal component analyses methods b) prediction of genomic breeding values by ridge regression c) detecting associations based on predicted genomic breeding values obtained from b) using QTLMAS 2010 simulated dataset. Most of the Quantitative Trait Locus (QTLs) were located at chromosome 1 and 3. Highest correlation between true breeding value and predicted breeding value were obtained by Gaussian Kernel function as 0.557. To detect pleiotropy we used first and second principal components as response variable and success rates found to be 0.2727 and 0.1714 and error rates found to be 0.5952 to 0.6400 for first two principal component loadings respectively. Using genomic breeding values as response variable gave better success rate and lower error rate compared with when using raw phenotypes. We found that using the most heritable and variable component (first component) had higher change to detect pleiotropic genes using QTLMAS-2010 dataset
References
- Aulchenko YS, Ripke S, Isaacs A, van Dujin, CM (2007): GenABEL: An R library for genome-wide association analysis. Bioinformatics, 23, 1294-1296.
- Bensen JT, Lange LA, Langefeld CD, Chang BL, Bleecker ER, Meyers DA, Xu J (2003): Exploring pleiotropy using principal components. BMC Genet, 4,S53.
- Endelman JB (2011): Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Gen, 4,250–255.
- Everitt BS, Landau S, Leese M (2001): Cluster Analysis. National Academy Press, Washington, DC.
- Guo G, Lund MS, Zhang Y, Su G (2010): Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables. J Anim Breed Genet, 127,423–432.
- Hill GW, Zhang SX (2012): On the pleiotropic structure of the genotype-phenotype map and the evolvability of complex organisms. Genetics, 3 Jan 2012(doi: 10.1534/genetics.111.135681). Burak Karacaören
- Karacaören B, Kadarmideen H (2008): Principal component and clustering analyses of functional traits in swiss dairy cattle. Turk. J. Vet. Anim. Sci, 32, 163-167.
- Karacaören B, Silander T, Alvarez-Castro MJ, Haley CS, de Koning DJ (2011): Association analyses of the MAS-QTL dataset using GRAMMAR, principal components and Bayesian network methodologies. BMC Proc, 5 (Suppl 3), S8
- Karacaören B, Janss L LG, Kadarmideen HN (2012): Predicting breeding values in animals by kalman filter: application to body condition scores in dairy cattle. Kafkas Univ Vet Fak Derg, 18, 627-632.
- Karacaören B (2012): Some observations for discordant sib pair design using QTL-MAS 2010 dataset. Kafkas Univ Vet Fak Derg, 18:857-860.
- Mei H, Chen W, Dellinger A, He J, Wang M, Yau C, Srinivasan SR, Berenson GS (2010): Principal- component-based multivariate regression for genetic association studies of metabolic syndrome components. BMC Genetics, 11:100.
- Meuwissen THE, Hayes BJ, Goddard ME (2001): Prediction of total genetic value using genome wide dense marker maps. Genetics,157,1819–1829.
- Mucha S, Pszczola M, Strabel T, Wolc A, Pacynska P, Szydlowski M (2011): Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis. BMC Proc, 5 (Suppl 3), S2.
- Mukhopadhyay I, Saha S, Ghosh S (2011): Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes. BMC Proc, 5,S73.
- Pszczola M, Strabel T, Wolc A, Mucha S, Szydlowski M (2011): Comparison of analyses of the QTLMAS XIV common dataset. I: genomic selection. BMC Proc, 5(Suppl 3),S1.
- Szydlowski M, Paczynska P (2011) QTLMAS 2010: Simulated dataset. BMC Proc, 5 (Suppl 3), S3.
- Wang X, Kammerer CM, Anderson S, Lu J, Feingold E (2009): A comparison of principal component analysis and factor analysis strategies for uncovering pleiotropic factors. Genet Epidemiolgy, 33, 325-331.
Rassal etkiler kullanılarak yapılan genom tabanlı ilişki ve pleiotropi analizi için QTL-MAS 2010 veriseti üzerine incelemeler
Abstract
Moleküler genetikteki son gelişmeler fenotipler ile ilişkili olabilen başkalaşımların: yüz binlerce tekil nükleotit polimorfizmi ile saptanmasına olanak tanımıştır. Damızlık değerlerin karışık modellerde cevap değişkeni olarak kullanılması ile genom tabanlı ilişkiler tespit edilebilir. Pleiotropi nedeni ile farklı fenotipler birbirleri ile bağıntılı olabilmekte ve böylece gen ağları oluşturulabilmektedir. Dolayısı ile bu çalışmanın amaçları a) pleiotropinin temel bileşenler analizi ile tespiti b) Ridge regresyonu kullanarak genomik damızlık değerlerin tahmini ve c) b)’den elde edilen damızlık değerler ile ilişki analizini benzeşim yolu ile elde edilmiş QTLMAS 2010 veri seti ile yapmaktır. Verimden sorumlu bölge (QTL)’lerin büyük çoğunluğu 1 ve 3. kromozomlarda bulundu. Gerçek ve tahmin edilen damızlık değerler arasındaki en yüksek korelasyon Gausçu çekirdek ile bulundu (0.557). Birinci ve ikinci temel bileşenler ile pleiotropi tespitinde başarı oranları 0.2717 ve 0.1714; hata oranları ise 0.5952 ve 0.6400 olarak bulundu. Genomik damızlık değerlerinin cevap değişkeni olarak kullanılması fenotiplerin kullanımına oranla daha yüksek başarı oranı ve daha düşük hata oranları verdi. Pleiotropik genlerin tespitinde kalıtım derecesi ve çeşitliliği en yüksek olan ilk temel bileşenin kullanılması QTLMAS 2010 veri seti için daha iyi sonuç vermiştir
References
- Aulchenko YS, Ripke S, Isaacs A, van Dujin, CM (2007): GenABEL: An R library for genome-wide association analysis. Bioinformatics, 23, 1294-1296.
- Bensen JT, Lange LA, Langefeld CD, Chang BL, Bleecker ER, Meyers DA, Xu J (2003): Exploring pleiotropy using principal components. BMC Genet, 4,S53.
- Endelman JB (2011): Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Gen, 4,250–255.
- Everitt BS, Landau S, Leese M (2001): Cluster Analysis. National Academy Press, Washington, DC.
- Guo G, Lund MS, Zhang Y, Su G (2010): Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables. J Anim Breed Genet, 127,423–432.
- Hill GW, Zhang SX (2012): On the pleiotropic structure of the genotype-phenotype map and the evolvability of complex organisms. Genetics, 3 Jan 2012(doi: 10.1534/genetics.111.135681). Burak Karacaören
- Karacaören B, Kadarmideen H (2008): Principal component and clustering analyses of functional traits in swiss dairy cattle. Turk. J. Vet. Anim. Sci, 32, 163-167.
- Karacaören B, Silander T, Alvarez-Castro MJ, Haley CS, de Koning DJ (2011): Association analyses of the MAS-QTL dataset using GRAMMAR, principal components and Bayesian network methodologies. BMC Proc, 5 (Suppl 3), S8
- Karacaören B, Janss L LG, Kadarmideen HN (2012): Predicting breeding values in animals by kalman filter: application to body condition scores in dairy cattle. Kafkas Univ Vet Fak Derg, 18, 627-632.
- Karacaören B (2012): Some observations for discordant sib pair design using QTL-MAS 2010 dataset. Kafkas Univ Vet Fak Derg, 18:857-860.
- Mei H, Chen W, Dellinger A, He J, Wang M, Yau C, Srinivasan SR, Berenson GS (2010): Principal- component-based multivariate regression for genetic association studies of metabolic syndrome components. BMC Genetics, 11:100.
- Meuwissen THE, Hayes BJ, Goddard ME (2001): Prediction of total genetic value using genome wide dense marker maps. Genetics,157,1819–1829.
- Mucha S, Pszczola M, Strabel T, Wolc A, Pacynska P, Szydlowski M (2011): Comparison of analyses of the QTLMAS XIV common dataset. II: QTL analysis. BMC Proc, 5 (Suppl 3), S2.
- Mukhopadhyay I, Saha S, Ghosh S (2011): Integrating binary traits with quantitative phenotypes for association mapping of multivariate phenotypes. BMC Proc, 5,S73.
- Pszczola M, Strabel T, Wolc A, Mucha S, Szydlowski M (2011): Comparison of analyses of the QTLMAS XIV common dataset. I: genomic selection. BMC Proc, 5(Suppl 3),S1.
- Szydlowski M, Paczynska P (2011) QTLMAS 2010: Simulated dataset. BMC Proc, 5 (Suppl 3), S3.
- Wang X, Kammerer CM, Anderson S, Lu J, Feingold E (2009): A comparison of principal component analysis and factor analysis strategies for uncovering pleiotropic factors. Genet Epidemiolgy, 33, 325-331.
Details
| Primary Language | English |
|---|---|
| Subjects | Veterinary Surgery |
| Other ID | JA93RC33FH |
| Journal Section | Research Article |
| Authors | |
| Publication Date | September 1, 2013 |
| Published in Issue | Year 2013Volume: 60 Issue: 3 |