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Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction

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Genomic Prediction of Complex Traits

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2467))

Abstract

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium–based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype–phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker–QTL association data into polygenic risk score that captures part of an individual’s susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker–QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

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Abbreviations

AUC:

Area under the receiver-operating characteristic (ROC) curves

BLUE:

Best linear unbiased estimate

BLUP:

Best linear unbiased prediction

BV:

Breeding value

CIM:

Composite interval mapping

EM:

Expectation maximization

G × E:

Genotype by environment

GWAS :

Genome-wide association study

IBD:

Identity by descent

IM:

Interval mapping

Lasso:

Least absolute shrinkage and selection operator

LD:

Linkage disequilibrium

LE:

Linkage equilibrium

LR:

Linear regression

MAF :

Minor allele frequency

MAS :

Marker assisted selection

MIM:

Multiple interval mapping

ML:

Maximum Likelihood

MLM:

Mixed linear model

QTL:

Quantitative trait loci

RR:

Ridge regression

SM:

Single marker

SNP:

Single nucleotide polymorphism

References

  1. Lander ES, Schork NJ (1994) Genetic dissection of complex traits. Science 265:2037–2048

    Article  CAS  PubMed  Google Scholar 

  2. Galton F (1877) Typical laws of heredity. Nature 15:492–495, 512–514, 532–533

    Article  Google Scholar 

  3. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Proc Royal Soc Edinburgh 52:399–433

    Article  Google Scholar 

  4. Henderson CR (1953) Estimation of variance and covariance components. Biometrics 9:226–252

    Article  Google Scholar 

  5. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Cambridge University Press, p 980

    Google Scholar 

  6. Sax K (1923) The association of size differences with seed-coat pattern and pigmentation in Phaseolus vulgaris. Genetics 8:552

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Morgan TH, Sturtevant AH, Muller HJ, Bridges CB (1915) The mechanism of Mendelian heredity. Henry Holt, New York

    Book  Google Scholar 

  8. Morton NE (1955) Sequential tests for the detection of linkage. Am J Hum Genet 20:277–318

    Google Scholar 

  9. Botstein D, Whit RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Rendel J (1961) Relationships between blood groups and the fat percentage of the milk in cattle. Nature 189:408–409

    Article  CAS  PubMed  Google Scholar 

  11. Tanksley SD, Medina-Filho R, Rick DM (1982) Use of naturally-occurring enzyme variation to detect and map genes controlling quantitative traits in an interspecific backcross of tomato. Heredity 49:11

    Article  Google Scholar 

  12. Kahler AL, Wherhahn CF (1986) Associations between quantitative traits and enzyme loci in the F2 population of a maize hybrid. Theor Appl Genet 72:15

    Article  CAS  PubMed  Google Scholar 

  13. Paterson AH, Lander ES, Hewiit JD, Peterson S et al (1988) Resolution of quantitative traits into Mendelian factors by using a complete RFLP linkage map. Nature 335:721–726

    Article  CAS  PubMed  Google Scholar 

  14. Cardon LR, Smith SD, Fulker DW, Kimberling WJ et al (1994) Quantitative trait locus for reading disability on chromosome 6. Science 266(5183):276–279

    Article  CAS  PubMed  Google Scholar 

  15. Peleman JD, van der Voort JR (2003) Breeding by design. Trends Plant Sci 8(7):330–334

    Article  CAS  PubMed  Google Scholar 

  16. van Arendonk JAM, Tied B, Kinghorn BP (1994) Use of multiple genetic markers in prediction of breeding values. Genetics 137:319–329

    Article  PubMed  Google Scholar 

  17. MacArthur J, Bowler E, Cerezo M, Gil L et al (2017) The new NHGRI-EBI catalog of published genome-wide association studies. Nucleic Acids Res 45(1):896–901

    Article  Google Scholar 

  18. De los Campos G, Sorensen D, Gianola D (2015) Genomic heritability: what is it? PLoS Genet 11:e1005048

    Article  PubMed Central  Google Scholar 

  19. Miedaner T, Galiano-Carneiro Boeven AL, Gaikpa DS, Kistner MB, Grote CP (2020) Genomics-assisted breeding for quantitative disease resistances in small-grain cereals and maize. Int J Mol Sci 21:9717

    Article  CAS  PubMed Central  Google Scholar 

  20. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lander ES, Kruglyak L (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 11:241–247

    Article  CAS  PubMed  Google Scholar 

  22. Bohra A, Pandey MK, Jha UC, Singh B et al (2014) · genomics-assisted breeding in four major pulse crops of developing countries: present status and prospects. Theor Appl Genet 127:1263–1291. https://doi.org/10.1007/s00122-014-2301-3

    Article  PubMed  PubMed Central  Google Scholar 

  23. Van Ooijen J, Jansen J (2013) Genetic mapping in experimental populations. Cambridge University Press, Cambridge, p 155

    Book  Google Scholar 

  24. Soller M (1990) Genetic mapping of the bovine genome using deoxyribonucleic acid-level markers to identify loci affecting quantitative traits of economic importance. Dairy Sci 73:2628–2646

    Article  CAS  Google Scholar 

  25. Fulker DW, Cardon LR (1994) A sib-pair approach to interval mapping of quantitative trait loci. Am J Hum Genet 54:1092–1103

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang Q, Boichard D, Hoeschele I, Ernst C et al (1998) Mapping quantitative trait loci for milk production and health of dairy cattle in a large outbred pedigree. Genetics 149(4):1959–1973

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kissebah AH, Sonnenberg GE, Myklebust J, Goldstein M et al (2000) Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc Natl Acad Sci U S A 97(26):14478–14483

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Knott SA, Elsen JM, Haley CS (1996) Multiple marker mapping of quantitative trait loci in half-sib populations. Theor Appl Genet 93:71–80

    Article  CAS  PubMed  Google Scholar 

  29. Uimari P, Zhan Q, Grignolia FG, Hoeschelaned I, Thaller G (1996) Granddaughter design data using least-squares, residual maximum likelihood and Bayesian methods for QTL analysis. J Agri Genomics 2:1–20

    Google Scholar 

  30. Chuechill G, Doerge R (1994) Empirical threshold values for quantitative trait mapping. Genetics 138(3):963–971

    Article  Google Scholar 

  31. Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm (with discussion). J R Stat Soc 39:1–38

    Google Scholar 

  33. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324

    Article  CAS  PubMed  Google Scholar 

  34. Sen S, Churchill GA (2001) A statistical framework for quantitative trait mapping. Genetics 159:371–387

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Jansen RC (1993) Interval mapping of multiple quantitative trait loci. Genetics 135:205–211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Zeng ZB (1993) Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci U S A 90:10972–10976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152:1203–1216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Carlborg O, Andersson L, Kinghorn B (2000) The use of a genetic algorithm for simultaneous mapping of multiple interacting quantitative trait loci. Genetics 155:2003–2010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Baierl A, Bogdan M, Frommlet F, Futschik A (2006) On locating multiple interacting quantitative trait loci in intercross designs. Genetics 173:1693–1703

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Grignola FE, Zhang Q, Hoeschele I (1997) Mapping linked quantitative trait loci via residual maximum likelihood. Genet Sel Evol 29:529–544

    Article  PubMed Central  Google Scholar 

  42. Thomas C, Cortessis V (1992) A Gibbs sampling approach to linkage analysis. Hum Hered 42:63–76

    Article  CAS  PubMed  Google Scholar 

  43. Hoeschele I, VanRaden P (1993) Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence. Theor Appl Genet 85:946–952

    Article  CAS  PubMed  Google Scholar 

  44. Satagopan JM, Yandell BS, Newton MA, Osborn TC (1996) Markov chain Monte Carlo approach to detect polygene loci for complex traits. Genetics 144:805–816

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Banerjee S, Yandell BS, Yi NJ (2008) Bayesian quantitative trait loci mapping for multiple traits. Genetics 179:2275–2289

    Article  PubMed  PubMed Central  Google Scholar 

  46. Haley CS, Knott SA, Elsen JM (1994) Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics 136:1195–1207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Chen M, Kendziorski C (2007) A statistical framework for expression quantitative trait loci mapping. Genetics 117:761–771

    Article  Google Scholar 

  48. Bedo J, Wenzl P, Kowalczyk A, Kilian A (2008) Precision-mapping and statistical validation of quantitative trait loci by machine learning. BMC Genet 9:35

    Article  PubMed  PubMed Central  Google Scholar 

  49. Guyon I (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1156–1182

    Google Scholar 

  50. Jannink JL, Jansen RC (2001) Mapping epistatic quantitative trait loci with one-dimensional genome searches. Genetics 157:445–454

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Zhang YM, Xu S (2005) A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity 95:96–104

    Article  CAS  PubMed  Google Scholar 

  52. Manichaikul A, Moon JY, Sen S, Yandell BS, Broman KW (2009) A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. Genetics 181:1077–1086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang DL, Zhu J, Li ZKL, Paterson AH (1999) Mapping QTLs with epistatic effects and QTL × environment interactions by mixed linear model approaches. Theor Appl Genet 99:1255–1264

    Article  Google Scholar 

  54. Knott SA, Haley CS (2000) Multitrait least squares for quantitative trait loci detection. Genetics 156:899–911

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Jiang C, Zeng ZB (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140:1111–1127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Lange C, Whittaker JC (2001) Mapping quantitative trait loci using generalized estimating equations. Genetics 159:1325–1337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Weller JI, Wiggans GR, Vanraden PM, Ron M (1996) Application of a canonical transformation to detection of quantitative trait loci with the aid of genetic markers in a multitrait experiment. Theor Appl Genet 92:998–1002

    Article  CAS  PubMed  Google Scholar 

  58. Liu J, Liu Y, Liu X, Deng HW (2007) Bayesian mapping of quantitative trait loci for multiple complex traits with the use of variance components. Am J Hum Genet 81:304–320

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Goffinet B, Gerber S (2000) Quantitative trait loci: a meta-analysis. Genetics 155:463–473

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Veyrieras JB, Goffinet B, Charcosset A (2007) MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinformatics 8(49):1–16

    Google Scholar 

  61. Rebai A, Goffinet B (2000) More about quantitative trait locus mapping with diallel designs. Genet Res 75:243–247

    Article  CAS  PubMed  Google Scholar 

  62. Demarest K, Koyner J, McCaughran J, Cipp L, Hitzzeman R (2001) Further characterisation and high-resolution mapping of quantitative trait loci for ethanol induced locomotor activity. Behave Genet 31:79–91

    Article  CAS  Google Scholar 

  63. Cavanagh C, Morell M, Mackay I, Powel W (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol 1:215–221

    Article  Google Scholar 

  64. Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551

    Article  PubMed  PubMed Central  Google Scholar 

  65. Beavis WD (1998) QTL analyses: power, precision, and accuracy. In: Paterson AH (ed) Molecular dissection of complex traits. CRC Press, Boca Raton, FL, pp 145–162

    Google Scholar 

  66. Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–131

    Article  CAS  PubMed  Google Scholar 

  67. Balding D (2006) A tutorial on statistical methods for population association studies. Nat Rev Genet 7:781–791

    Article  CAS  PubMed  Google Scholar 

  68. Visscher PM, Wray NR, Zhang Q, Sklar P et al (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101:5–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Hayes B (2014) Overview of Statistical methods for genome-wide association studies (GWAS). In: Gondro C, van der Werf J, Hayes B (eds) Genome-wide association Studies and genomic prediction. Springer, Berlin/Heidelberg, pp 149–169

    Google Scholar 

  70. Spielman RS, McGinnis RE, Ewens WJ (1993) Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 52:506–516

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Nejati-Javaremi A, Smith C, Gibson JP (1997) Effect of total allelic relationship on accuracy of evaluation and response to selection. J Anim Sci 75:1738–1745

    Article  CAS  PubMed  Google Scholar 

  72. Devlin B, Bacanu SA, Roeder K (2004) Genomic control in the extreme. Nat Genet 36:1129–1130

    Article  CAS  PubMed  Google Scholar 

  73. Pritchard JK et al (2000) Association mapping in structured populations. Am J Hum Genet 67:170–181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Yu J, Pressoir G, Briggs WH, Bi IV et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    Article  CAS  PubMed  Google Scholar 

  75. Kang HM, Zaitlen NA, Wade CM, Kirby A et al (2008) Efficient control of population structure in model organism association mapping. Genetics 178:1709–1723

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Aulchenko YS, Koning DJ, Haley C (2007) Genome wide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics 177:577–585

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Wen YJ, Zhang H, Ni YL, Huang B et al (2018) Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief Bioinform 19(4):700–712

    Article  PubMed  Google Scholar 

  80. Fernando RA, Toosi A, Wolc D, Garrick N, Dekkers J (2017) Application of whole-genome prediction methods for genome-wide association studies: a Bayesian approach. J Agric Biol Environ Stat 22:172–193

    Article  Google Scholar 

  81. Pan Q, Hu T, Moore JH (2014) Epistasis, complexity, and multifactor dimensionality reduction. In: Gondro C et al (eds) Genome-wide association studies and genomic prediction. Springer, Berlin/Heidelberg, pp 465–478

    Google Scholar 

  82. Korte A, Vilhjálmsson BJ, Segura V, Platt P et al (2012) A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat Genet 44:1066–1071

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Zhu X, Stephens M (2017) Bayesian large-scale multiple regression with summary statistics from genome-wide association studies. Ann Appl Stat 11(3):1561–1592

    Article  PubMed  PubMed Central  Google Scholar 

  84. Wang T, Zhou B, Guo T, Bidlingmaier M et al (2014) A robust method for genome-wide association meta-analysis with the application to circulating insulin-like growth factor I concentrations. Genet Epidemiol 38(2):162–171

    Article  PubMed  Google Scholar 

  85. Turley P, Walters RK, Maghzian O, Okbay A et al (2018) Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 50(2):229–237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Cordell HJ (2009) Detecting gene–gene interactions that underlie human diseases. Nature Rev. Genet 10:393–404

    Article  Google Scholar 

  87. Wu M, Ma S (2019) Robust genetic interaction analysis. Brief Bioinform 20(2):624–637

    Article  PubMed  Google Scholar 

  88. Cantor RM, Lange K, Sinsheimer JS (2010) Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 86:6–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Qin X, Ma S, Wu M (2020) Gene-gene interaction analysis incorporating network information via a structured Bayesian approach. arXiv:2010.10960

    Google Scholar 

  90. Gayán J, González-Pérez A, Bermudo F, Sáez ME et al (2008) A method for detecting epistasis in genome-wide studies using case-control multi-locus association analysis. BMC Genomics 9:360

    Article  PubMed  PubMed Central  Google Scholar 

  91. Han SS, Chatterjee N (2018) Review of statistical methods for gene-environment interaction analysis. Curr Epidemiol Rep 5:39–45

    Article  Google Scholar 

  92. Ott J, Kamatani Y, Lathrop M (2011) Family-based designs for genome-wide association studies. Nat Rev Genet 12:465–474

    Article  CAS  PubMed  Google Scholar 

  93. Meuwissen THE, Goddard ME (2000) Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci. Genetics 155:421–430

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Long AD, Mullaney SL, Reid LA, Fry JD et al (1995) High resolution mapping of genetic factors affecting abdominal bristle number in Drosophila melanogaster. Genetics 139:1273–1291

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Brut DW (2002) A comprehensive review on the analysis of QTL in animals. Trends Genet 18(9):488

    Article  Google Scholar 

  96. Kearsey MJ, Farquhar AGL (1998) QTL analysis in plants; where are we now? Heredity 80:137–142

    Article  PubMed  Google Scholar 

  97. Hayes B, Goddard ME (2001) The distribution of the effects of genes affecting quantitative traits in livestock. Genet Sel Evol 33:209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Barton NH, Keightley PD (2002) Understanding quantitative genetic variation. Nat Rev Genet 3:11–21

    Article  CAS  PubMed  Google Scholar 

  99. Hyne V, Kearsey MJ (1995) QTL analysis further uses of marker regression. Theor Appl Genet 91:471–476

    Article  CAS  PubMed  Google Scholar 

  100. Robertson A (1967) The nature of quantitative genetic variation. In: Brink RA, Styles ED (eds) Heritage from Mendel. University of Wisconsin, Madison, WI, pp 265–280

    Google Scholar 

  101. Flint J, Mackay TFC (2009) Genetic architecture of quantitative traits in mice, flies, and humans. Genome Res 19:723–733

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Goddard ME, Kemper KE, MacLeod IM, Chamberlain AJ, Hayes BJ (2016) Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture. Proc R Soc B 283:20160569

    Article  PubMed  PubMed Central  Google Scholar 

  103. Yang J, Benyamin B, McEvoy BP, Gordon S et al (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Yang J, Bakshi A, Zhu Z, Hemani G et al (2015) Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet 47:1114–1120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Park JH, Wacholder S, Gail MH, Peters U et al (2010) Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet 42:570–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Simons YB, Turchin MC, Pritchard JK, Sella G (2014) The deleterious mutation load is insensitive to recent population history. Nat Genet 46:220–224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Makowsky R, Pajewski NM, Klimentidis YC, Vazquez AI et al (2011) Beyond missing heritability: prediction of complex traits. PLoS Genet 7(4):e1002051

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Phillips PC (1998) The language of gene interaction. Genetics 149:1167–1171

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Cheverud JM, Routman EJ (1995) Epistasis and its contribution to genetic variance components. Genetics 139:1455–1461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Mackay TFC (2001) The genetic architecture of quantitative traits. Ann Rev Genet 35:303–339

    Article  CAS  PubMed  Google Scholar 

  111. Nagel RL (2005) Epistasis and the genetics of human diseases. C R Biol 328:606–615

    Article  CAS  PubMed  Google Scholar 

  112. Jannink JL, Moreau L, Charmet G, Charcosset A (2008) Overview of QTL detection in plants and tests for synergistic epistatic interactions. Genitica 136(2):225–236

    Article  Google Scholar 

  113. Albar L, Lorieux M, Ahmadi N, Rimbault I et al (1998) Genetic basis and mapping of the resistance to rice yellow mottle virus. I. QTLs identification and relationship between resistance and plant morphology. Theor Appl Genet 97:1145–1154

    Article  CAS  Google Scholar 

  114. Pressoir G, Albar L, Ahmadi N, Rimbault I et al (1998) Genetic basis and mapping of the resistance to the rice yellow mottle virus. II. Evidence of a complementary epistasis between two QTLs. Theor Appl Genet 97:1155–1161

    Article  CAS  Google Scholar 

  115. Ahmadi N, Albar L, Pressoir G, Pinel A et al (2001) Genetic basis and mapping of the resistance to Rice yellow mottle virus. III. Analysis of QTLs efficiency in introgressed progenies confirmed the hypothesis of complementary epistasis between two resistance QTLs. Theor Appl Genet 103:1084–1092

    Article  CAS  Google Scholar 

  116. Yi N, Zinniel DK, Kim K, Eisen EJ et al (2006) Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice. Genet Res 87:45–60

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Mackay TFC, Roshina NV, Leips JW, Pasyukova EG (2006) Complex genetic architecture of drosophila longevity. In: Masaro EJ, Austad SN (eds) Handbook of the biology of aging. Elsevier, Academic Press, San Diego, CA, pp 181–216

    Google Scholar 

  118. Carlborg O, Hocking PM, Burt DW, Haley CS (2004) Simultaneous mapping of epistatic QTL in chickens reveals clusters of QTL pairs with similar genetic effects on growth. Genet Res 83:197–209

    Article  CAS  PubMed  Google Scholar 

  119. Grobe-Brinkhaus C, Jonas E, Buschbell H, Phatsara C et al (2010) Epistatic QTL pairs associated with meat quality and carcass composition traits in a porcine Duroc × Pietrain population. Genet Sel Evol 42:39

    Article  Google Scholar 

  120. Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–U614

    Article  CAS  PubMed  Google Scholar 

  121. Barendse W, Harrison BE, Hawken RJ, Ferguson DM (2007) Epistasis between Calpain-1 and its inhibitor Calpastatin within breeds of cattle. Genetics 176(4):2601–2610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Zhang J, Wei Z, Cardinale CJ, Gusareva LS et al (2019) Multiple epistasis interactions within MHC are associated with ulcerative colitis. Front Genet 10:257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Strange T, Ask B, Nielsen B (2013) Genetic parameters of the piglet mortality traits stillborn, weak at birth, starvation, crushing, and miscellaneous in crossbred pigs. J Anim Sci 91:1562–1569

    Article  CAS  PubMed  Google Scholar 

  124. Huang A, Xu S, Cai X (2014) Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of Rice. PLoS One 9:e87330

    Article  PubMed  PubMed Central  Google Scholar 

  125. Mackay TFC (2014) Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat Rev Genet 15(1):22–33

    Article  CAS  PubMed  Google Scholar 

  126. de los Campos G, Sorensen DA, Toro MA (2019) Imperfect linkage disequilibrium generates phantom epistasis (& Perils of Big Data). G3 (Bethesda) 9:1429–1436

    Article  Google Scholar 

  127. Burch CL, Chao L (2004) Epistasis and its relationship to canalization in the RNA virus 6. Genetics 167:559–567

    Article  PubMed  PubMed Central  Google Scholar 

  128. Eitan Y, Soller M (2004) Selection induced genetic variation. In: Wasser SP (ed) Evolutionary theory and processes: modern horizons. Springer, Berlin/Heidelberg, pp 153–176

    Chapter  Google Scholar 

  129. Sonawane AR, Weiss ST, Glass K, Sharma A (2019) Network medicine in the age of biomedical big data. Front Genet 10:294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Amaral AJ, Bressan MC, Almeida J, Bettencourt C et al (2019) Combining genome-wide association analyses and gene interaction networks to reveal new genes associated with carcass traits, meat quality and fatty acid profiles in pigs. Livest Sci 220:180–189

    Article  Google Scholar 

  131. Ko DK, Brandizzi F (2020) Network-based approaches for understanding gene regulation and function in plants. Plant J 104:302–317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Ebbert MTW, Ridge PG, Kauwe JSK (2015) Bridging the gap between statistical and biological epistasis in Alzheimer’s disease. Biomed Res Int 2015:870123. https://doi.org/10.1155/2015/870123

    Article  PubMed  PubMed Central  Google Scholar 

  133. Boyle EA, Li YI, Pritchard JK (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell 169:1177–1186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Field Y, Boyle EA, Telis N, Gao Z et al (2016) Detection of human adaptation during the past 2000 years. Science 354:760–764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Via S, Lande R (1987) Evolution of genetic variability in a spatially heterogeneous environment: effects of genotype-environment interaction. Genet Res 49:147–156

    Article  CAS  PubMed  Google Scholar 

  136. Bradshaw AD (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13:115–155

    Article  Google Scholar 

  137. de Leon N, Jannink JL, Edwards JW, Kaeppler SM (2016) Introduction to a special issue on genotype by environment interaction. Crop Sci 56:2081–2089

    Article  Google Scholar 

  138. Gauch HG, Zobel RW (1996) AMMI analysis of yield trials. In: Kang MS, Gauch HG (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, FL, pp 85–122

    Chapter  Google Scholar 

  139. Eberhart SA, Russell WA (1996) Stability parameters for comparing varieties. Crop Sci 6:36–40

    Article  Google Scholar 

  140. Stinchcombe JR, Function-valued Traits Work. Group, Kirkpatrick M (2012) Genetics and evolution of function-valued traits: understanding environmentally responsive phenotypes. Trends Ecol Evol 27:637–647

    Article  PubMed  Google Scholar 

  141. Robinson MR, Beckerman AP (2013) Quantifying multivariate plasticity: genetic variation in resource acquisition drives plasticity in resource allocation to components of life history. Ecol Lett 16:281–290

    Article  PubMed  Google Scholar 

  142. Jansen RC, Van Ooijen JM, Stam P, Lister C, Dean C (1995) Genotype-by-environment interaction in genetic mapping of multiple quantitative trait loci. Theor Appl Genet 91:33–37

    Article  CAS  PubMed  Google Scholar 

  143. Korol AB, Ronin YI, Ne E (1998) Approximate analysis of QTL-environment interaction with no limits on the number of environments. Genetics 148:2015–2028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Hayes BJ, Daetwyler HD, Goddard ME (2016) Models for genome x environment interaction: examples in livestock. Crop Sci 56(5):2251–2259

    Article  Google Scholar 

  145. Valdar W, Solberg LC, Gauguier D, Cookson WO et al (2006b) Genetic and environmental effects on complex traits in mice. Genetics 174:959–984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Vieira C, Pasyukova EG, Zeng ZB, Hackett JB et al (2000) Genotype-environment interaction for quantitative trait loci affecting life span in Drosophila melanogaster. Genetics 154:213–227

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. El-Soda M, Malosetti M, Zwaan BJ, Koornneef M, Aarts MGM (2014) G × E interaction QTL mapping in plants: lessons from Arabidopsis. Trends Plant Sci 19(6):390–398

    Article  CAS  PubMed  Google Scholar 

  148. El-Soda M, Kruijer W, Malosetti M, Koornneef M, Aarts MGM (2015) Quantitative trait loci and candidate genes underlying genotype by environment interaction in the response of Arabidopsis thaliana to drought. Plant Cell Environ 38:585–599

    Article  CAS  PubMed  Google Scholar 

  149. MacMahon B (1968) Gene-environment interaction in human disease. J Psychiatr Res 6:393–402

    Article  Google Scholar 

  150. Hunter DJ (2005) Gene-environment interaction in Humain diseases. Nat Rev Genet 6:287–298

    Article  CAS  PubMed  Google Scholar 

  151. Lillehammer M, Goddard ME, Nilsen H, Sehested E et al (2008) Quantitative trait locus-by-environment interaction for Milk yield traits on Bos taurus autosome 6. Genetics 179:1539–1546

    Article  PubMed  PubMed Central  Google Scholar 

  152. Des Marais DL, Hernandez KM, Juenger TE (2013) Genotype-by-environment interaction and plasticity: exploring genomic responses of plants to the abiotic environment. Annu Rev Ecol Evol Syst 44:5–29

    Article  Google Scholar 

  153. Via S, Gomulkiewicz R, De Jong G, Scheiner SM et al (1995) Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol Evol 10:212–217

    Article  CAS  PubMed  Google Scholar 

  154. Lacaze X, Hayes MP, Koro A (2009) Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare. Heredity 102:163–173

    Article  CAS  PubMed  Google Scholar 

  155. Gutteling EW, Riksen JAG, Bakker J, Kammenga JE (2007) Mapping phenotypic plasticity and genotype-environment interactions affecting life-history traits in Caenorhabditis elegans. Heredity 98(1):28–37

    Article  CAS  PubMed  Google Scholar 

  156. Kikuchi S, Bheemanahalli R, Jagadish KSV, Kumagai E et al (2017) Genome-wide association mapping for phenotypic plasticity in rice. Plant Cell Environ 40(8):1565–1575

    Article  CAS  PubMed  Google Scholar 

  157. Lukens LE, Doebley J (1999) Epistatic and environmental interactions for quantitative trait loci involved in maize evolution. Genet Res 74:291–302

    Article  CAS  Google Scholar 

  158. Liu N, Du Y, Warburton ML, Xiao Y, Yan J (2020) Phenotypic plasticity contributes to maize adaptation and Heterosis. Mol Biol Evol 38(4):1262–1275. https://doi.org/10.1093/molbev/msaa283

    Article  CAS  PubMed Central  Google Scholar 

  159. Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. Ecol Lett 7:1225–1241

    Article  Google Scholar 

  160. Lowry DB, Lovell JT, Zhang L, Bonnette J et al (2019) QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient. Proc Natl Acad Sci U S A 116(26):12933–12941

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Wickland DP, Hanzawa Y (2015) The flowering locus t/terminal flower 1 gene family: functional evolution and molecular mechanisms. Mol Plant 8(7):983–997

    Article  CAS  PubMed  Google Scholar 

  162. Dash S, Van Hemert J, Hong L, Wise RP, Dickerson JA (2012) PLEXdb: gene expression resources for plants and plant pathogens. Nucleic Acids Res 40:D1194–D1201

    Article  CAS  PubMed  Google Scholar 

  163. Coughlin SS, Trock B, Criqui MH, Pickle LW et al (1992) The logistic modeling of sensitivity, specificity, and predictive value of a diagnostic test. J Clin Epidemiol 45(1):l–7

    Article  Google Scholar 

  164. Brand A, Brand H, in den Bäumen TS (2008) The impact of genetics and genomics on public health. Eur J Hum Genet 16:5–13

    Article  PubMed  Google Scholar 

  165. Yang Q, Khoury MJ, Botto L, Friedman JM, Flanders WD (2003) Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes. Am J Hum Genet 72:636–649

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genet 9(3):e1003348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Mason SJ, Graham NE (2002) Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: statistical significance and interpretation. Q J R Meteorol Soc 128(584):2145–2166

    Article  Google Scholar 

  168. Janssens ACJW, van Duijn CM (2009) Genome-based prediction of common diseases: Ethodological considerations for future research. Genome Med 1(2):20

    Article  PubMed  PubMed Central  Google Scholar 

  169. Choi SW, Mak TS-H, O’Reilly PF (2020) Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc 15:2759–2772

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. Dreyfuss JM, Levner D, Galagan GE, Church GM, Ramoni MF (2012) How accurate can genetic predictions be? BMC Genomics 13:340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Khera AV, Chaffin M, Aragam KG, Haas ME et al (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 50(9):1219–1224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Vilhjálmsson BJ, Yang J, Finucane HK (2015) Modelling linkage disequilibrium increases accuracy of polygenic risk scores. Am J Hum Genet 97:576–592

    Article  PubMed  PubMed Central  Google Scholar 

  173. Hu Y, Lu Q, Powles R, Yao X et al (2017) Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Comput Biol 13:e1005589

    Article  PubMed  PubMed Central  Google Scholar 

  174. Hu Y, Lu Q, Liu W, Zhang Y et al (2017) Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genet 13:e1006836

    Article  PubMed  PubMed Central  Google Scholar 

  175. Hazel LN (1943) The genetic basis for constructing selection indices. Genetics 38:476–490

    Article  Google Scholar 

  176. Henderson CR (1949) Estimation of changes in herd environment. J Dairy Sci 32:709

    Google Scholar 

  177. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447

    Article  CAS  PubMed  Google Scholar 

  178. Henderson CR (1976) A simple method for computing the inverse of a numerator relationship matrix used in predicting of breeding values. Biometrics 32:69–83

    Article  Google Scholar 

  179. Mrode RA (2005) Linear models for the prediction of animal breeding values. CAB Int:344

    Google Scholar 

  180. Piepho HP, Möhring J, Melchinger AE, Büchse A (2007) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209–228

    Article  Google Scholar 

  181. Bernardo R (1996) Best linear unbiased prediction of maize single-cross performance. Crop Sci 36(1):50–56

    Article  Google Scholar 

  182. Fernando RL, Grossman M (1989) Marker-assisted selection using best linear unbiased prediction. Genet Sel Evol 21:467–477

    Article  PubMed Central  Google Scholar 

  183. Guimarães EP, Ruane J, Scherf BD, Sonnino A, Dargie JD (2007) Marker-assisted selection. Current status and future perspectives in crops, livestock, forestry and fish, Rome, ISBN 978-92-5-105717-9, p 471

    Google Scholar 

  184. Goddard ME (1992) A mixed model for the analyses of data on multiple genetic markers. Theor Appl Genet 83:878–886

    Article  CAS  PubMed  Google Scholar 

  185. Goddard ME, Hayes BJ (2002) Optimisation of response using molecular data. 7th world congress on genetics applied to livestock production, Montpellier, France, Communication no. 22–01

    Google Scholar 

  186. Hayes BJ, Goddard ME (2003) Evaluation of marker assisted selection in pig enterprises. Livest Prod Sci 81(2–3):197–121

    Article  Google Scholar 

  187. Boichard D, Fritz S, Rossignol MN, Guillaume F, et al. (2006) Implementation of marker-assisted selection: practical lessons from dairy cattle. 8th world congress on genetics applied to livestock production, Belo Horizonte, MG, Brasil

    Google Scholar 

  188. Hayes BJ, Chamberlain J, Mcpartlan H, Macleod I et al (2007) Accuracy of marker-assisted selection with single markers and marker haplotypes in cattle. Genet Res 89:215–220

    Article  CAS  PubMed  Google Scholar 

  189. Dekkers JCM, van Arendonk JAM (1998) Optimizing selection for quantitative traits with information on an identified locus in outbred populations. Genet Res 71:257–275

    Article  Google Scholar 

  190. Hospital F, Charcosset A (1997) Marker-assisted introgression of quantitative trait loci. Genetics 147:1469–1485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. Bouchez A, Hospital F, Causse M, Gallais A, Charcosset A (2002) Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite lines. Genetics 162:1945–1959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Peleman JD, van der Voort JR (2003) Breeding by design. Trends Plant Sci 7:330–334

    Article  Google Scholar 

  193. van Berloo R, Stam P (1999) Comparison between marker-assisted selection and phenotypical selection in a set of Arabidopsis thaliana recombinant inbred lines. Theor Appl Genet 98:113–118

    Article  Google Scholar 

  194. Charmet G, Robert N, Perretant MR, Gay G et al (1999) Marker-assisted recurrent selection for cumulating additive and interactive QTL’s in recombinant inbred lines. Theor Appl Genet 99:1143–1148

    Article  Google Scholar 

  195. Ragot M, Lee M (2007) Marker-assisted selection in maize: current status, potential, limitations and perspectives from the private and public sectors. In: Guiamaraes et al (eds) Marker assisted selection. FAO, Rome, pp 117–150

    Google Scholar 

  196. Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet 3:22–32

    Article  CAS  PubMed  Google Scholar 

  197. Moreau L, Charcosset A, Hospital F, Gallais A (1998) Marker-assisted selection efficiency in populations of finite size. Genetics 148:1353–1365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  198. Moreau L, Charcosset A, Gallais A (2004) Experimental evaluation of several cycles of marker-assisted selection in maize. Euphytica 137:111–118

    Article  CAS  Google Scholar 

  199. Melchinger AE (1999) Genetic Diversity and Heterosis. In: Coors JG, Pandey S (eds) Genetics and exploitation of Heterosis in crops. ASA, CSSA, and SSSA Books. Wiley, Hoboken, New Jersey, pp 99–118

    Google Scholar 

  200. Bernardo R (1999) Marker-assisted best linear unbiased prediction of single cross performance. Crop Sci 39:1277–1282

    Article  Google Scholar 

  201. Spelman RJ, Garrick DJ (1998) Genetic and economic responses for within-family marker-assisted selection in dairy cattle breeding schemes. J Dairy Sci 81:2942–2950

    Article  CAS  PubMed  Google Scholar 

  202. Ribaut JM, Hoisington D (1998) Marker-assisted selection: new tools and strategies. Trends Plant Sci 3:236–239

    Article  Google Scholar 

  203. Jansen RC, Jannink J-L, Beavis WD (2003) Mapping quantitative trait loci in plant breeding populations: use of parental haplotype sharing. Crop Sci 43:829–834

    CAS  Google Scholar 

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Ahmadi, N. (2022). Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction. In: Ahmadi, N., Bartholomé, J. (eds) Genomic Prediction of Complex Traits. Methods in Molecular Biology, vol 2467. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2205-6_1

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