#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

The Complex Genetic Architecture of the Metabolome


Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable.


Vyšlo v časopise: The Complex Genetic Architecture of the Metabolome. PLoS Genet 6(11): e32767. doi:10.1371/journal.pgen.1001198
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1001198

Souhrn

Discovering links between the genotype of an organism and its metabolite levels can increase our understanding of metabolism, its controls, and the indirect effects of metabolism on other quantitative traits. Recent technological advances in both DNA sequencing and metabolite profiling allow the use of broad-spectrum, untargeted metabolite profiling to generate phenotypic data for genome-wide association studies that investigate quantitative genetic control of metabolism within species. We conducted a genome-wide association study of natural variation in plant metabolism using the results of untargeted metabolite analyses performed on a collection of wild Arabidopsis thaliana accessions. Testing 327 metabolites against >200,000 single nucleotide polymorphisms identified numerous genotype–metabolite associations distributed non-randomly within the genome. These clusters of genotype–metabolite associations (hotspots) included regions of the A. thaliana genome previously identified as subject to recent strong positive selection (selective sweeps) and regions showing trans-linkage to these putative sweeps, suggesting that these selective forces have impacted genome-wide control of A. thaliana metabolism. Comparing the metabolic variation detected within this collection of wild accessions to a laboratory-derived population of recombinant inbred lines (derived from two of the accessions used in this study) showed that the higher level of genetic variation present within the wild accessions did not correspond to higher variance in metabolic phenotypes, suggesting that evolutionary constraints limit metabolic variation. While a major goal of genome-wide association studies is to develop catalogues of intraspecific variation, the results of multiple independent experiments performed for this study showed that the genotype–metabolite associations identified are sensitive to environmental fluctuations. Thus, studies of intraspecific variation conducted via genome-wide association will require analyses of genotype by environment interaction. Interestingly, the network structure of metabolite linkages was also sensitive to environmental differences, suggesting that key aspects of network architecture are malleable.


Zdroje

1. FiehnO

2002 Metabolomics - the link between genotypes and phenotypes. Plant Molecular Biology 48 155 171

2. MeyerRC

SteinfathM

LisecJ

BecherM

Witucka-WallH

2007 The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America 104 4759 4764

3. LisecJ

MeyerRC

SteinfathM

RedestigH

BecherM

2008 Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations. Plant Journal 53 960 972

4. WentzellAM

RoweHC

HansenBG

TicconiC

HalkierBA

2007 Linking metabolic QTL with network and cis-eQTL controlling biosynthetic pathways. PLoS Genet 3 e162 doi:10.1371/journal.pgen.0030162

5. YagilC

BarkalifaR

SapojnikovM

WechslerA

Ben-DorD

2007 Metabolic and genomic dissection of diabetes in the Cohen rat. Physiol Genomics

6. StittM

FernieAR

2003 From measurements of metabolites to metabolomics: an ‘on the fly’ perspective illustrated by recent studies of carbon-nitrogen interactions. Curr Opin Biotechnol 14 136 144

7. SchauerN

SemelY

RoessnerU

GurA

BalboI

2006 Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotechnology 24 447 454

8. SchauerN

SemelY

BalboI

SteinfathM

RepsilberD

2008 Mode of inheritance of primary metabolic traits in tomato. Plant Cell 20 509 523

9. RoweHC

KliebensteinDJ

2008 Complex Genetics Control Natural Variation in Arabidopsis thaliana Resistance to Botrytis cinerea. Genetics 180 2237 2250

10. RoweHC

HansenBG

HalkierBA

KliebensteinDJ

2008 Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell 20 1199 1216

11. BeloA

ZhengPZ

LuckS

ShenB

MeyerDJ

2008 Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Molecular Genetics and Genomics 279 1 10

12. HarjesCE

RochefordTR

BaiL

BrutnellTP

KandianisCB

2008 Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319 330 333

13. HarriganGG

StorkLG

RiordanSG

ReynoldsTL

RidleyWP

2007 Impact of genetics and environment on nutritional and metabolite components of maize grain. J Agric Food Chem 55 6177 6185

14. SkogersonK

HarriganGG

ReynoldsTL

HallsSC

RuebeltM

Impact of Genetics and Environment on the Metabolite Composition of Maize Grain. J Agric Food Chem

15. FridmanE

CarrariF

LiuYS

FernieAR

ZamirD

2004 Zooming in on a quantitative trait for tomato yield using interspecific introgressions. Science 305 1786 1789

16. HirschhornJN

DalyMJ

2005 Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics 6 95 108

17. ChanEKF

RoweHC

KliebensteinDJ

2010 Understanding the evolution of defense metabolites in Arabidopsis thaliana using genome-wide association mapping. Genetics 185 991 1007

18. AtwellS

HuangY

VilhjalmssonBJ

WillemsG

HortonM

2010 Genome-wide association study of 107 phenotypes in a common set of Arabidopsis thaliana in-bred lines. Nature In press

19. WangWYS

BarrattBJ

ClaytonDG

ToddJA

2005 Genome-wide association studies: Theoretical and practical concerns. Nature Reviews Genetics 6 109 118

20. JansenRC

1994 Controlling the Type-I and Type-II errors in mapping quantitative trait loci. Genetics 138 871 881

21. FiehnO

2001 Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comparative And Functional Genomics 2 155 168

22. RoessnerU

LuedemannA

BrustD

FiehnO

LinkeT

2001 Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13 11 29

23. SteuerR

KurthsJ

FiehnO

WeckwerthW

2003 Interpreting correlations in metabolomic networks. Biochemical Society Transactions 31 1476 1478

24. WeckwerthW

LoureiroME

WenzelK

FiehnO

2004 Differential metabolic networks unravel the effects of silent plant phenotypes. Proceedings Of The National Academy Of Sciences Of The United States Of America 101 7809 7814

25. FukushimaA

KusanoM

NakamichiN

KobayashiM

HayashiN

2009 Impact of clock-associated Arabidopsis pseudo-response regulators in metabolic coordination. Proceedings of the National Academy of Sciences of the United States of America 106 7251 7256

26. VerdonkJC

de VosCHR

VerhoevenHA

HaringMA

van TunenAJ

2003 Regulation of floral scent production in petunia revealed by targeted metabolomics. Phytochemistry 62 997 1008

27. RoessnerU

LuedemannA

BrustD

FiehnO

LinkeT

2001 Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13 11 29

28. WentzellAM

KliebensteinDJ

2008 Genotype, age, tissue, and environment regulate the structural outcome of glucosinolate activation. Plant Physiology 147 415 428

29. KliebensteinDJ

FiguthA

Mitchell-OldsT

2002 Genetic architecture of plastic methyl jasmonate responses in Arabidopsis thaliana. Genetics 161 1685 1696

30. WentzellAM

BoeyeI

ZhangZY

KliebensteinDJ

2008 Genetic Networks Controlling Structural Outcome of Glucosinolate Activation across Development. PLoS Genet 4 e1000234 doi:10.1371/journal.pgen.1000234

31. KorvesTM

SchmidKJ

CaicedoAL

MaysC

StinchcombeJR

2007 Fitness Effects Associated with the Major Flowering Time Gene FRIGIDA in Arabidopsis thaliana in the Field American Naturalist 169 E141 E157

32. WilczekAM

RoeJL

KnappMC

CooperMD

Lopez-GallegoC

2009 Effects of Genetic Perturbation on Seasonal Life History Plasticity. Science 323 930 934

33. SteffensD

BlosI

SchochS

RudigerW

1976 Light dependence of phytol accumulation - Contribution to question of chlorophyll biosynthesis. Planta 130 151 158

34. BartoliCG

YuJP

GomezF

FernandezL

McIntoshL

2006 Inter-relationships between light and respiration in the control of ascorbic acid synthesis and accumulation in Arabidopsis thaliana leaves. Journal of Experimental Botany 57 1621 1631

35. RudellDR

MattheisJP

CurryEA

2008 Prestorage ultraviolet-white light irradiation alters apple peel metabolome. J Agric Food Chem 56 1138 1147

36. Opgen-RheinR

StrimmerK

2007 From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol 1 37

37. SchaferJ

StrimmerK

2005 An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21 754 764

38. de la FuenteA

BingN

HoescheleI

MendesP

2004 Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20 3565 3574

39. SulpiceR

PylET

IshiharaH

TrenkampS

SteinfathM

2009 Starch as a major integrator in the regulation of plant growth. Proceedings of the National Academy of Sciences of the United States of America 106 10348 10353

40. KeurentjesJJB

SulpiceR

GibonY

SteinhauserM-C

FuJ

2008 Integrative analysis of genetic variation in enzyme activities of primary carbohydrate metabolism reveal distinct modes of regulation in Arabidopsis thaliana. Genome Biology In Press

41. FujitaniY

NakajimaN

IshiharaK

OikawaT

ItoK

2006 Molecular and biochemical characterization of a serine racemase from Arabidopsis thaliana. Phytochemistry 67 668 674

42. FiehnO

WohlgemuthG

ScholzM

2005 Setup and annotation of metabolomic experiments by integrating biological and mass spectrometric metadata. Data Integration In The Life Sciences, Proceedings 224 239

43. LukovitsI

LinertW

2001 A topological account of chirality. Journal of Chemical Information and Computer Sciences 41 1517 1520

44. LynchM

WalshB

1998 Genetics and analysis of quantitative traits. Sunderland, Massachusetts Sinauer Associates, Inc

45. RiesebergLH

WidmerA

ArntzAM

BurkeJM

2003 The genetic architecture necessary for transgressive segregation is common in both natural and domesticated populations. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 358 1141 1147

46. de BakkerPIW

YelenskyR

Pe'erI

GabrielSB

DalyMJ

2005 Efficiency and power in genetic association studies. Nature Genetics 37 1217 1223

47. BeavisWD

1998 QTL analyses: power, precision, and accuracy.

PatersonAH

Molecular Dissection of Complex Traits New York, N.Y. CRC Press 145 162

48. BeavisWD

The power and deceit of QTL experiments: lessons from comparitive QTL studies; 1994; Washington, DC. American Seed Trade Association. 250 266

49. NordborgM

BorevitzJO

BergelsonJ

BerryCC

ChoryJ

2002 The extent of linkage disequilibrium in Arabidopsis thaliana. Nature Genetics 30 190 193

50. NordborgM

HuTT

IshinoY

JhaveriJ

ToomajianC

2005 The Pattern of Polymorphism in Arabidopsis thaliana. PLoS Biol 3 e196 doi:10.1371/journal.pbio.0030196

51. NordborgM

WeigelD

2008 Next-generation genetics in plants. Nature 456 720 723

52. ZhaoKY

AranzanaMJ

KimS

ListerC

ShindoC

2007 An Arabidopsis example of association mapping in structured samples. PLoS Genet 3 e4 doi:10.1371/journal.pgen.0030004

53. FisherRA

1918 The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinb 52 399 433

54. FernieAR

SchauerN

2009 Metabolomics-assisted breeding: a viable option for crop improvement? Trends in Genetics 25 39 48

55. BucklerES

HollandJB

BradburyPJ

AcharyaCB

BrownPJ

2009 The Genetic Architecture of Maize Flowering Time. Science 325 714 718

56. SzalmaSJ

BucklerES

SnookME

McMullenMD

2005 Association analysis of candidate genes for maysin and chlorogenic acid accumulation in maize silks. Theoretical And Applied Genetics 110 1324 1333

57. ByrnePF

McMullenMD

SnookME

MusketTA

TheuriJM

1996 Quantitative trait loci and metabolic pathways: Genetic control of the concentration of maysin, a corn earworm resistance factor, in maize silks. Proceedings of the National Academy of Sciences of the United States of America 93 8820 8825

58. WilliamsRBH

ChanEKF

CowleyMJ

LittlePFR

2007 The influence of genetic variation on gene expression. Genome Research 17 1707 1716

59. ChanEKF

RoweHC

KliebensteinDJ

2009 Understanding the Evolution of Defense Metabolites in Arabidopsis thaliana Using Genome-Wide Association Mapping. Genetics. genetics.109.108522

60. ClarkRM

SchweikertG

ToomajianC

OssowskiS

ZellerG

2007 Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana. Science 317 338 342

61. KeurentjesJJB

FuJY

de VosCHR

LommenA

HallRD

2006 The genetics of plant metabolism. Nature Genetics 38 842 849

62. DoergeRW

ChurchillGA

1996 Permutation tests for multiple loci affecting a quantitative character. Genetics 142 285 294

63. KliebensteinDJ

GershenzonJ

Mitchell-OldsT

2001 Comparative quantitative trait loci mapping of aliphatic, indolic and benzylic glucosinolate production in Arabidopsis thaliana leaves and seeds. Genetics 159 359 370

64. McMullenMD

ByrnePF

SnookME

WisemanBR

LeeEA

1998 Quantitative trait loci and metabolic pathways. Proceedings Of The National Academy Of Sciences Of The United States Of America 95 1996 2000

65. ByrnePF

McMullenMD

WisemanBR

SnookME

MusketTA

1998 Maize silk maysin concentration and corn earworm antibiosis: QTLs and genetic mechanisms. Crop Science 38 461 471

66. MackayTFC

2001 The genetic architecture of quantitative traits. Annual Review Of Genetics 35 303 339

67. GhazalpourADS

KangH

FarberC

WenP-Z

BrozellA

2008 High-Resolution Mapping of Gene Expression Using Association in an Outbred Mouse Stock. PLoS Genet 4 e1000149 doi:10.1371/journal.pgen.1000149

68. SpencerCCA

SuZ

DonnellyP

MarchiniJ

2009 Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip. PLoS Genet 5 e1000477 doi:10.1371/journal.pgen.1000477

69. KliebensteinDJ

2008 A role for gene duplication and natural variation of gene expression in the evolution of metabolism. PLoS ONE 3 e1838 doi:10.1371/journal.pone.0001838

70. MartinsAM

CamachoD

ShumanJ

ShaW

MendesP

2004 A systems biology study of two distinct growth phases of Saccharomyces cerevisiae cultures. Current Genomics 5 649 663

71. WeckwerthW

WenzelK

FiehnO

2004 Process for the integrated extraction identification, and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks. Proteomics 4 78 83

72. ElowitzMB

LevineAJ

SiggiaED

SwainPS

2002 Stochastic gene expression in a single cell. Science 297 1183 1186

73. RaserJM

O'SheaEK

2004 Control of stochasticity in eukaryotic gene expression. Science 304 1811 1814

74. StrimmerK

2008 A unified approach to false discovery rate estimation. Bmc Bioinformatics 9 303

75. StrimmerK

2008 fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24 1461 1462

76. SchaferJ

StrimmerK

2005 A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol 4 Article32

77. CsardiG

NepuszT

2006 The igraph software package for complex network research. InterJournal Complex Systems 1695

78. BorevitzJO

HazenSP

MichaelTP

MorrisGP

BaxterIR

2007 Genome-wide patterns of single-feature polymorphism in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America 104 12057 12062

79. KangHM

ZaitlenNA

WadeCM

KirbyA

HeckermanD

2008 Efficient control of population structure in model organism association mapping. Genetics 178 1709 1723

80. StoreyJD

2002 A direct approach to false discovery rates. Journal of the Royal Statistical Society Series B-Statistical Methodology 64 479 498

81. DabneyA

StoreyJD

2009 qvalue: Q-value estimation for false discovery rate control. R package version 1.18.0. ed

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2010 Číslo 11
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#