Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering
Autoři:
Simon Rio aff001; Tristan Mary-Huard aff001; Laurence Moreau aff001; Cyril Bauland aff001; Carine Palaffre aff003; Delphine Madur aff001; Valérie Combes aff001; Alain Charcosset aff001
Působiště autorů:
Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
aff001; MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
aff002; UE 0394 SMH, INRAE, 2297 Route de l’INRA, 40390, Saint-Martin-de-Hinx, France
aff003
Vyšlo v časopise:
Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering. PLoS Genet 16(3): e32767. doi:10.1371/journal.pgen.1008241
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008241
Souhrn
When handling a structured population in association mapping, group-specific allele effects may be observed at quantitative trait loci (QTLs) for several reasons: (i) a different linkage disequilibrium (LD) between SNPs and QTLs across groups, (ii) group-specific genetic mutations in QTL regions, and/or (iii) epistatic interactions between QTLs and other loci that have differentiated allele frequencies between groups. We present here a new genome-wide association (GWAS) approach to identify QTLs exhibiting such group-specific allele effects. We developed genetic materials including admixed progeny from different genetic groups with known genome-wide ancestries (local admixture). A dedicated statistical methodology was developed to analyze pure and admixed individuals jointly, allowing one to disentangle the factors causing the heterogeneity of allele effects across groups. This approach was applied to maize by developing an inbred “Flint-Dent” panel including admixed individuals that was evaluated for flowering time. Several associations were detected revealing a wide range of configurations of allele effects, both at known flowering QTLs (Vgt1, Vgt2 and Vgt3) and new loci. We found several QTLs whose effect depended on the group ancestry of alleles while others interacted with the genetic background. Our GWAS approach provides useful information on the stability of QTL effects across genetic groups and can be applied to a wide range of species.
Klíčová slova:
Alleles – Genetic loci – Genome-wide association studies – Maize – Molecular genetics – Plant genomics – Population genetics – Quantitative trait loci
Zdroje
1. Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics. 2006;38:203–208. doi: 10.1038/ng1702 16380716
2. Wright S. Evolution in Mendelian populations. Genetics. 1931;16:97–159. 17246615
3. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics. 2006;38:904–909. doi: 10.1038/ng1847 16862161
4. Rincent R, Moreau L, Monod H, Kuhn E, Melchinger AE, Malvar RA, et al. Recovering Power in Association Mapping Panels with Variable Levels of Linkage Disequilibrium. Genetics. 2014;197(1):375–387. doi: 10.1534/genetics.113.159731 24532779
5. Pritchard JK, Przeworski M. Linkage Disequilibrium in Humans: Models and Data. The American Journal of Human Genetics. 2001;69(1):1–14. doi: 10.1086/321275 11410837
6. Rogers AR. How Population Growth Affects Linkage Disequilibrium. Genetics. 2014;197(4):1329–1341. doi: 10.1534/genetics.114.166454 24907258
7. Sawyer SL, Mukherjee N, Pakstis AJ, Feuk L, Kidd JR, Brookes AJ, et al. Linkage disequilibrium patterns vary substantially among populations. European Journal Of Human Genetics. 2005;13:677–686. doi: 10.1038/sj.ejhg.5201368 15657612
8. Evans DM, Cardon LR. A comparison of linkage disequilibrium patterns and estimated population recombination rates across multiple populations. The American Journal of Human Genetics. 2005;76:681–687. doi: 10.1086/429274 15719321
9. de Roos APWM, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics. 2008;179:1503–1512. doi: 10.1534/genetics.107.084301 18622038
10. Porto-Neto LR, Kijas JW, Reverter A. The extent of linkage disequilibrium in beef cattle breeds using high-density SNP genotypes. Genetics Selection Evolution. 2014;46(1):22. doi: 10.1186/1297-9686-46-22
11. Badke YM, Bates RO, Ernst CW, Schwab C, Steibel JP. Estimation of linkage disequilibrium in four US pig breeds. BMC Genomics. 2012;13(1):24. doi: 10.1186/1471-2164-13-24 22252454
12. Hao C, Wang L, Ge H, Dong Y, Zhang X. Genetic Diversity and Linkage Disequilibrium in Chinese Bread Wheat (Triticum aestivum L.) Revealed by SSR Markers. PLOS ONE. 2011;6(2):1–13. doi: 10.1371/journal.pone.0017279
13. Van Inghelandt D, Reif JC, Dhillon BS, Flament P, Melchinger AE. Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm. Theoretical and Applied Genetics. 2011;123(1):11–20. doi: 10.1007/s00122-011-1562-3 21404061
14. Technow F, Riedelsheimer C, Schrag TA, Melchinger AE. Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects. Theoretical and Applied Genetics. 2012;125(6):1181–1194. doi: 10.1007/s00122-012-1905-8 22733443
15. Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, et al. Adaptation of Maize to Temperate Climates: Mid-Density Genome-Wide Association Genetics and Diversity Patterns Reveal Key Genomic Regions, with a Major Contribution of the Vgt2 (ZCN8) Locus. PLOS ONE. 2013;8(8):1–17. doi: 10.1371/journal.pone.0071377
16. Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D, Revilla P, et al. Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. Theoretical and Applied Genetics. 2014;127(11):2313–2331. doi: 10.1007/s00122-014-2379-7 25301321
17. Stryjecki C, Alyass A, Meyre D. Ethnic and population differences in the genetic predisposition to human obesity. Obesity Reviews. 2018;19(1):62–80. doi: 10.1111/obr.12604 29024387
18. Tang H. Confronting ethnicity-specific disease risk. Nature Genetics. 2006;38(1):12–15. doi: 10.1038/ng0106-13
19. Helgadottir A, Manolescu A, Helgason A, Thorleifsson G, Thorsteinsdottir U, Gudbjartsson DF, et al. A variant of the gene encoding leukotriene A4 hydrolase confers ethnicity-specific risk of myocardial infarction. Nature Genetics. 2006;38(1):68–74. doi: 10.1038/ng1692 16282974
20. Barroso I, Luan J, Wheeler E, Whittaker P, Wasson J, Zeggini E, et al. Population-Specific Risk of Type 2 Diabetes Conferred by HNF4A P2 Promoter Variants. Diabetes. 2008;57(11):3161–3165. doi: 10.2337/db08-0719 18728231
21. Neuman RJ, Wasson J, Atzmon G, Wainstein J, Yerushalmi Y, Cohen J, et al. Gene-Gene Interactions Lead to Higher Risk for Development of Type 2 Diabetes in an Ashkenazi Jewish Population. PLOS ONE. 2010;5(3):1–6. doi: 10.1371/journal.pone.0009903
22. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, et al. The Genetic Architecture of Maize Flowering Time. Science. 2009;325(5941):714–718. doi: 10.1126/science.1174276 19661422
23. Durand E, Bouchet S, Bertin P, Ressayre A, Jamin P, Charcosset A, et al. Flowering Time in Maize: Linkage and Epistasis at a Major Effect Locus. Genetics. 2012;190(4):1547–1562. doi: 10.1534/genetics.111.136903 22298708
24. Evangelou E, Ioannidis JPA. Meta-analysis methods for genome-wide association studies and beyond. Nature Genetics. 2013;14:379–389. doi: 10.1038/nrg3472
25. Li YR, Keating BJ. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Medicine. 2014;6(10):91. doi: 10.1186/s13073-014-0091-5 25473427
26. Ioannidis JPA, Ntzani EE, Trikalinos TA. ‘Racial’ differences in genetic effects for complex diseases. Nature Genetics. 2004;36(12):1312–1318. doi: 10.1038/ng1474 15543147
27. Marigorta UM, Navarro A. High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants. PLOS Genetics. 2013;9(6):1–13. doi: 10.1371/journal.pgen.1003566
28. Ntzani EE, Liberopoulos G, Manolio TA, Ioannidis JPA. Consistency of genome-wide associations across major ancestral groups. Human Genetics. 2012;131(7):1057–1071. doi: 10.1007/s00439-011-1124-4 22183176
29. Cole JB, VanRaden PM, O’Connell JR, Van Tassell CP, Sonstegard TS, Schnabel RD, et al. Distribution and location of genetic effects for dairy traits. Journal of Dairy Science. 2009;92(6):2931–2946. doi: 10.3168/jds.2008-1762 19448026
30. Hayes BJ, Pryce J, Chamberlain AJ, Bowman PJ, Goddard ME. Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits. PLOS Genetics. 2010;6(9):1–11. doi: 10.1371/journal.pgen.1001139
31. Cole JB, Wiggans GR, Ma L, Sonstegard TS, Lawlor TJ, Crooker BA, et al. Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. BMC Genomics. 2011;12(1):408. doi: 10.1186/1471-2164-12-408 21831322
32. Raven LA, Cocks BG, Hayes BJ. Multibreed genome wide association can improve precision of mapping causative variants underlying milk production in dairy cattle. BMC Genomics. 2014;15(1):62. doi: 10.1186/1471-2164-15-62 24456127
33. van den Berg I, Boichard D, Lund MS. Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds. Journal of Dairy Science. 2016;99(11):8932–8945. doi: 10.3168/jds.2016-11073 27568046
34. Sanchez MP, Govignon-Gion A, Croiseau P, Fritz S, Hozé C, Miranda G, et al. Within-breed and multi-breed GWAS on imputed whole-genome sequence variants reveal candidate mutations affecting milk protein composition in dairy cattle. Genetics Selection Evolution. 2017;49(1):68. doi: 10.1186/s12711-017-0344-z
35. Flint-Garcia SA, Thuillet AC, Yu J, Pressoir G, Romero SM, Mitchell SE, et al. Maize association population: a high-resolution platform for quantitative trait locus dissection. The Plant Journal. 2005;44(6):1054–1064. doi: 10.1111/j.1365-313X.2005.02591.x 16359397
36. Camus-Kulandaivelu L, Veyrieras JB, Madur D, Combes V, Fourmann M, Barraud S, et al. Maize Adaptation to Temperate Climate: Relationship Between Population Structure and Polymorphism in the Dwarf8 Gene. Genetics. 2006;172(4):2449–2463. doi: 10.1534/genetics.105.048603 16415370
37. Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, et al. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biology. 2013;14(6):R55. doi: 10.1186/gb-2013-14-6-r55 23759205
38. Rius M, Darling JA. How important is intraspecific genetic admixture to the success of colonising populations? Trends in Ecology & Evolution. 2014;29(4):233–242. https://doi.org/10.1016/j.tree.2014.02.003.
39. Brandenburg JT, Mary-Huard T, Rigaill G, Hearne SJ, Corti H, Joets J, et al. Independent introductions and admixtures have contributed to adaptation of European maize and its American counterparts. PLOS Genetics. 2017;13(3):1–30. doi: 10.1371/journal.pgen.1006666
40. McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, et al. Genetic Properties of the Maize Nested Association Mapping Population. Science. 2009;325(5941):737–740. doi: 10.1126/science.1174320 19661427
41. Cavanagh C, Morell M, Mackay I, Powell W. From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Current Opinion in Plant Biology. 2008;11(2):215–221. https://doi.org/10.1016/j.pbi.2008.01.002. 18295532
42. Rio S, Mary-Huard T, Moreau L, Charcosset A. Genomic selection efficiency and a priori estimation of accuracy in a structured dent maize panel. Theoretical and Applied Genetics. 2019;132(1):81–96. doi: 10.1007/s00122-018-3196-1 30288553
43. Bordes J, Dumas de Vaulx R, Lapierre A, Pollacsek M. Haplodiploidization of maize (Zea mays L.) through induced gynogenesis assisted by glossy markers and its use in breeding. Agronomie. 1997;17:291–297. doi: 10.1051/agro:19970504
44. Unterseer S, Bauer E, Haberer G, Seidel M, Knaak C, Ouzunova M, et al. A powerful tool for genome analysis in maize: development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics. 2014;15(1):823. doi: 10.1186/1471-2164-15-823 25266061
45. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. The American Journal of Human Genetics. 2009;84(2):210–23. doi: 10.1016/j.ajhg.2009.01.005 19200528
46. Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, et al. A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome. PLOS ONE. 2011;6(12):1–15. doi: 10.1371/journal.pone.0028334
47. Salvi S, Sponza G, Morgante M, Tomes D, Niu X, Fengler KA, et al. Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(27):11376–11381. doi: 10.1073/pnas.0704145104 17595297
48. Ducrocq S, Madur D, Veyrieras JB, Camus-Kulandaivelu L, Kloiber-Maitz M, Presterl T, et al. Key Impact of Vgt1 on Flowering Time Adaptation in Maize: Evidence From Association Mapping and Ecogeographical Information. Genetics. 2008;178(4):2433–2437. doi: 10.1534/genetics.107.084830 18430961
49. Butler DG, Cullis BR, Gilmour AR, Gogel BJ, Thompson R. ASReml-R Reference Manual Version 4; 2009. VSN International Ltd, Hemel Hempstead, HP1 1ES, UK.
50. Wientjes YCJ, Bijma P, Vandenplas J, Calus MPL. Multi-population Genomic Relationships for Estimating Current Genetic Variances Within and Genetic Correlations Between Populations. Genetics. 2017;207(2):503–515. doi: 10.1534/genetics.117.300152 28821589
51. VanRaden PM. Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science. 2008;91(11):4414–4423. doi: 10.3168/jds.2007-0980 18946147
52. Laporte F, Charcosset A, Mary-Huard T. Efficient ReML inference in Variance Component Mixed Models using Min-Max algorithms. 2019. Forthcoming.
53. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. 1995;57(1):289–300.
54. Salvi S, Corneti S, Bellotti M, Carraro N, Sanguineti MC, Castelletti S, et al. Genetic dissection of maize phenology using an intraspecific introgression library. BMC plant biology. 2011;11:4. doi: 10.1186/1471-2229-11-4 21211047
55. Salvi S, Emanuelli F, Soriano JM, Zamariola L, Giuliani S, Bovina R, et al. Cloning of Vgt3, a major QTL for flowering time in maize. In: 59th Annual Maize Genetics Conference; 2017.
56. Revilla P, Rodríguez VM, Ordás A, Rincent R, Charcosset A, Giauffret C, et al. Association mapping for cold tolerance in two large maize inbred panels. BMC Plant Biology. 2016;16(1):127. doi: 10.1186/s12870-016-0816-2 27267760
57. Buitenhuis B, Janss LL, Poulsen NA, Larsen LB, Larsen MK, Sørensen P. Genome-wide association and biological pathway analysis for milk-fat composition in Danish Holstein and Danish Jersey cattle. BMC Genomics. 2014;15(1):1112. doi: 10.1186/1471-2164-15-1112 25511820
58. Buitenhuis B, Poulsen NA, Larsen LB, Sehested J. Estimation of genetic parameters and detection of quantitative trait loci for minerals in Danish Holstein and Danish Jersey milk. BMC Genetics. 2015;16(1):52. doi: 10.1186/s12863-015-0209-9 25989905
59. Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, et al. Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics. 2004;168(4):2169–2185. doi: 10.1534/genetics.104.032375 15611184
60. Li Yx, Li C, Bradbury PJ, Liu X, Lu F, Romay CM, et al. Identification of genetic variants associated with maize flowering time using an extremely large multi-genetic background population. The Plant Journal. 2016;86(5):391–402. doi: 10.1111/tpj.13174 27012534
61. Giraud H, Lehermeier C, Bauer E, Falque M, Segura V, Baulaud C, et al. Linkage Disequilibrium with Linkage Analysis of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid Performance in the Flint and Dent Heterotic Groups of Maize. Genetics. 2014;198(4):1717–1734. doi: 10.1534/genetics.114.169367 25271305
62. Meng X, Muszynski MG, Danilevskaya ON. The FT-Like ZCN8 Gene Functions as a Floral Activator and Is Involved in Photoperiod Sensitivity in Maize. The Plant Cell. 2011;23(3):942–960. doi: 10.1105/tpc.110.081406 21441432
63. Lazakis CM, Coneva V, Colasanti J. ZCN8 encodes a potential orthologue of Arabidopsis FT florigen that integrates both endogenous and photoperiod flowering signals in maize Journal of Experimental Botany. 2011;62(14):4833–4842. doi: 10.1093/jxb/err129 21730358
64. Guo L, Wang X, Zhao M, Huang C, Li C, Li D, et al. Stepwise cis-Regulatory Changes in ZCN8 Contribute to Maize Flowering-Time Adaptation Current Biology. 2018;28(18):3005–3015. doi: 10.1016/j.cub.2018.07.029 30220503
65. Liang Y, Liu Q, Wang X, Huang C, Xu G, Hey S, et al. ZmMADS69 functions as a flowering activator through the ZmRap2.7-ZCN8 regulatory module and contributes to maize flowering time adaptation. New Phytologist. 2019;221(4):2335–2347. doi: 10.1111/nph.15512 30288760
66. Chardon F, Hourcade D, Combes V, Charcosset A. Mapping of a spontaneous mutation for early flowering time in maize highlights contrasting allelic series at two-linked QTL on chromosome 8. Theoretical and Applied Genetics. 2005;112(1):1–11. doi: 10.1007/s00122-005-0050-z 16244856
67. Bouché F, Lobet G, Tocquin P, Périlleux C. FLOR-ID: an interactive database of flowering-time gene networks in Arabidopsis thaliana. Nucleic Acids Research. 2015;44(D1):D1167–D1171. doi: 10.1093/nar/gkv1054 26476447
68. Vitezica ZG, Legarra A, Toro MA, Varona L. Orthogonal Estimates of Variances for Additive, Dominance, and Epistatic Effects in Populations. Genetics. 2017;206(3):1297–1307. doi: 10.1534/genetics.116.199406 28522540
69. Jannink JL. Identifying Quantitative Trait Locus by Genetic Background Interactions in Association Studies. Genetics. 2007;176(1):553–561. doi: 10.1534/genetics.106.062992 17179077
70. Crawford L, Zeng P, Mukherjee S, Zhou X. Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLOS Genetics. 2017;13(7):1–37. doi: 10.1371/journal.pgen.1006869
71. Legarra A, Vitezica ZG, Naval-Sánchez M, Henshall J, Raidan F, Li Y, et al. Association analysis of loci implied in “buffering” epistasis. bioRxiv. 2019;637579.
72. de Roos APW, Hayes BJ, Goddard ME. Reliability of Genomic Predictions Across Multiple Populations. Genetics. 2009;183(4):1545–1553. doi: 10.1534/genetics.109.104935 19822733
73. Chen L, Schenkel F, Vinsky M, Crews DH, Li C. Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle. Journal of Animal Science. 2013;91(10):4669–4678. doi: 10.2527/jas.2013-5715 24078618
74. Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E, Guo B, et al. The impact of population structure on genomic prediction in stratified populations. Theoretical and Applied Genetics. 2014;127(3):749–762. doi: 10.1007/s00122-013-2255-x 24452438
75. Duhnen A, Gras A, Teyssèdre S, Romestant M, Claustres B, Dayde J, et al. Genomic Selection for Yield and Seed Protein Content in Soybean: A Study of Breeding Program Data and Assessment of Prediction Accuracy. Crop Science. 2017;57(3):1325–1337. doi: 10.2135/cropsci2016.06.0496
Článek vyšel v časopise
PLOS Genetics
2020 Číslo 3
- Může hubnutí souviset s vyšším rizikem nádorových onemocnění?
- Raději si zajděte na oční! Jak souvisí citlivost zraku s rozvojem demence?
- Co způsobuje pooperační infekce? Na vině může být i naše vlastní mikrobiota
- Čeká nás průlom v diagnostice karcinomu pankreatu?
- Polibek, který mi „vzal nohy“ aneb vzácný výskyt EBV u 70leté ženy – kazuistika
Nejčtenější v tomto čísle
- Evidence of defined temporal expression patterns that lead a gram-negative cell out of dormancy
- The Lid/KDM5 histone demethylase complex activates a critical effector of the oocyte-to-zygote transition
- The alarmones (p)ppGpp are part of the heat shock response of Bacillus subtilis
- Modeling cancer genomic data in yeast reveals selection against ATM function during tumorigenesis