Skip to main content
SpringerLink
Log in

The Genotype–Phenotype Maps of Systems Biology and Quantitative Genetics: Distinct and Complementary

  • Chapter
  • First Online:
Book cover Evolutionary Systems Biology

Part of the book series: Advances in Experimental Medicine and Biology ((volume 751))

Abstract

The processes by which genetic variation in complex traits is generated and maintained in populations has for a long time been treated in abstract and statistical terms. As a consequence, quantitative genetics has provided limited insights into our understanding of the molecular bases of quantitative trait variation. With the developing technological and conceptual tools of systems biology, cellular and molecular processes are being described in greater detail. While we have a good description of how signaling and other molecular networks are organized in the cell, we still do not know how genetic variation affects these pathways, because systems and molecular biology usually ignore the type and extent of genetic variation found in natural populations. Here we discuss the quantitative genetics and systems biology approaches for the study of complex trait architecture and discuss why these two disciplines would synergize with each other to answer questions that neither of the two could answer alone.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    A single genotype can sometimes give rise to multiple phenotypic values depending on environmental conditions or random factors such as developmental and gene expression noise.

  2. 2.

    It is increasingly clear that copy number differences are pervasive within populations. How duplications or deletions are handled within quantitative genetics depends upon how the genotype space is set up and conceptualized. Traditionally the edges of a genotype space (see Fig. 17.3) represent mutations between different alleles at a locus where each locus is a single copy. However, these genotype spaces could be used to represent movement between copy number variants. The “allele swapping” represented by an edge would not be point mutation or small indels but would instead be duplications or deletions. In this case the “allele” would be the copy number of the gene.

  3. 3.

    Usually, the genetic component of a phenotype for a genotype that is predicted by a quantitative genetic model is called the genotypic value. In these examples we do not have any environmental effect and so the phenotypic landscape is also the landscape of genotypic values. For consistency with the systems biology section, we will talk in terms of phenotypes instead of genotypic values.

References

  1. Lynch M, Sung W, Morris K, Coffey N, Landry CR, Dopman EB, Dickinson WJ, Okamoto K, Kulkarni S, Hartl DL, Thomas WK (2008) A genome-wide view of the spectrum of spontaneous mutations in yeast. Proc Natl Acad Sci USA 105 (27):9272–9277. doi:0803466105 [pii] 10.1073/pnas.0803466105

    Article  PubMed  CAS  Google Scholar 

  2. Haag-Liautard C, Dorris M, Maside X, Macaskill S, Halligan DL, Houle D, Charlesworth B, Keightley PD (2007) Direct estimation of per nucleotide and genomic deleterious mutation rates in Drosophila. Nature 445(7123):82–85. doi:nature05388 [pii] 10.1038/nature05388

    Article  PubMed  CAS  Google Scholar 

  3. Sawyer SA, Parsch J, Zhang Z, Hartl DL (2007) Prevalence of positive selection among nearly neutral amino acid replacements in Drosophila. Proc Natl Acad Sci USA 104(16):6504–6510. doi:0701572104 [pii] 10.1073/pnas.0701572104

    Article  PubMed  CAS  Google Scholar 

  4. Mackay TF, Stone EA, Ayroles JF (2009) The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 10(8):565–577. doi:nrg2612 [pii] 10.1038/nrg2612

    Article  PubMed  CAS  Google Scholar 

  5. Ohya Y, Sese J, Yukawa M, Sano F, Nakatani Y, Saito TL, Saka A, Fukuda T, Ishihara S, Oka S, Suzuki G, Watanabe M, Hirata A, Ohtani M, Sawai H, Fraysse N, Latgé J-P, François JM, Aebi M, Tanaka S, Muramatsu S, Araki H, Sonoike K, Nogami S, Morishita S (2005) High-dimensional and large-scale phenotyping of yeast mutants. Proc Nat Acad Sci USA 102(52):19015–19020

    Article  PubMed  CAS  Google Scholar 

  6. Nogami S, Ohya Y, Yvert Gl (2007) Genetic complexity and quantitative trait loci mapping of yeast morphological traits. PLoS Genet 3(2):e31–e31

    Article  PubMed  Google Scholar 

  7. Provine WB (2001) The origins of theoretical population genetics. University of Chicago Press, Chicago

    Google Scholar 

  8. Cheverud JM (2006) Genetic architecture of quantitative variation. In: Wolf JB, Fox CW (eds) Evolutionary genetics: concepts and case studies. Oxford University Press, Oxford, pp 288–309

    Google Scholar 

  9. Fox CW, Wolf JB (2006) Evolutionary genetics: concepts and case studies. Oxford University Press, Oxford

    Google Scholar 

  10. Roff DA (1997) Evolutionary quantitative genetics. Chapman & Hall, New York

    Book  Google Scholar 

  11. Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121(1):185–199

    PubMed  CAS  Google Scholar 

  12. Giurumescu CA, Sternberg PW, Asthagiri AR (2009) Predicting phenotypic diversity and the underlying quantitative molecular transitions. PLoS Comput Biol 5(4):e1000354-e1000354

    Article  PubMed  Google Scholar 

  13. Rendel JM (1967) Canalisation and gene control. Logos Press, New York

    Google Scholar 

  14. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Longman, New York

    Google Scholar 

  15. Wagner GP, Laubichler MD, Bagheri-Chaichian H (1998) Genetic measurement of theory of epistatic effects. Genetica 102–103(1–6):569–580

    Article  PubMed  Google Scholar 

  16. Cheverud JM, Routman EJ (1995) Epistasis and its contribution to genetic variance components. Genetics 139(3):1455–1461

    PubMed  CAS  Google Scholar 

  17. Gjuvsland AB, Plahte E, Ådnøy T, Omholt SW (2010) Allele interaction – single locus genetics meets regulatory biology. PLoS ONE 5(2):e9379–e9379

    Article  PubMed  Google Scholar 

  18. Hansen TF, Wagner GnP (2001) Modeling genetic architecture: a multilinear theory of gene interaction. Theor Popul Biol 59(1):61–86

    Article  PubMed  CAS  Google Scholar 

  19. Rice SH (2004) Evolutionary theory: mathematical and conceptual foundations. Sinauer Associates, Sunderland

    Google Scholar 

  20. Bagheri HC, Wagner GnP (2004) Evolution of dominance in metabolic pathways. Genetics 168(3):1713–1735

    Article  PubMed  CAS  Google Scholar 

  21. Wagner A (2008) Neutralism and selectionism: a network-based reconciliation. Nat Rev Genet 9(12):965–974. doi:nrg2473 [pii] 10.1038/nrg2473

    Article  PubMed  CAS  Google Scholar 

  22. Amsterdam A, Nissen RM, Sun Z, Swindell EC, Farrington S, Hopkins N (2004) Identification of 315 genes essential for early zebrafish development. Proc Natl Acad Sci USA 101(35):12792–12797. doi:10.1073/pnas.0403929101 0403929101 [pii]

    Article  PubMed  CAS  Google Scholar 

  23. Dudley AM, Janse DM, Tanay A, Shamir R, Church GM (2005) A global view of pleiotropy and phenotypically derived gene function in yeast. Mol Syst Biol 1:2005 0001. doi:msb4100004 [pii] 10.1038/msb4100004

    Google Scholar 

  24. Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED, Sevier CS, Ding H, Koh JL, Toufighi K, Mostafavi S, Prinz J, St Onge RP, VanderSluis B, Makhnevych T, Vizeacoumar FJ, Alizadeh S, Bahr S, Brost RL, Chen Y, Cokol M, Deshpande R, Li Z, Lin ZY, Liang W, Marback M, Paw J, San Luis BJ, Shuteriqi E, Tong AH, van Dyk N, Wallace IM, Whitney JA, Weirauch MT, Zhong G, Zhu H, Houry WA, Brudno M, Ragibizadeh S, Papp B, Pal C, Roth FP, Giaever G, Nislow C, Troyanskaya OG, Bussey H, Bader GD, Gingras AC, Morris QD, Kim PM, Kaiser CA, Myers CL, Andrews BJ, Boone C (2010) The genetic landscape of a cell. Science 327(5964):425–431. doi:327/5964/425 [pii] 10.1126/science.1180823

    Article  PubMed  CAS  Google Scholar 

  25. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402(6761 Suppl):C47–C52. doi:10.1038/35011540

    Article  PubMed  CAS  Google Scholar 

  26. Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8(6):450–461

    Article  PubMed  CAS  Google Scholar 

  27. Coudreuse D, Nurse P (2010) Driving the cell cycle with a minimal CDK control network. Nature 468(7327):1074–1079. doi:nature09543 [pii] 10.1038/nature09543

    Article  PubMed  CAS  Google Scholar 

  28. O’Shaughnessy EC, Palani S, Collins JJ, Sarkar CA (2011) Tunable signal processing in synthetic MAP kinase cascades. Cell 144(1):119–131. doi:S0092-8674(10)01432-7 [pii] 10.1016/j.cell.2010.12.014

    Article  PubMed  Google Scholar 

  29. Lim WA (2010) Designing customized cell signalling circuits. Nat Rev Mol Cell Biol 11(6):393–403. doi:nrm2904 [pii] 10.1038/nrm2904

    Article  PubMed  CAS  Google Scholar 

  30. Tarassov K, Messier V, Landry CR, Radinovic S, Serna Molina MM, Shames I, Malitskaya Y, Vogel J, Bussey H, Michnick SW (2008) An in vivo map of the yeast protein interactome. Science 320(5882):1465–1470. doi:1153878 [pii] 10.1126/science.1153878

    Article  PubMed  CAS  Google Scholar 

  31. Krogan NJ, Cagney G, Yu H, Zhong G, Guo X, Ignatchenko A, Li J, Pu S, Datta N, Tikuisis AP, Punna T, Peregrin-Alvarez JM, Shales M, Zhang X, Davey M, Robinson MD, Paccanaro A, Bray JE, Sheung A, Beattie B, Richards DP, Canadien V, Lalev A, Mena F, Wong P, Starostine A, Canete MM, Vlasblom J, Wu S, Orsi C, Collins SR, Chandran S, Haw R, Rilstone JJ, Gandi K, Thompson NJ, Musso G, St Onge P, Ghanny S, Lam MH, Butland G, Altaf-Ul AM, Kanaya S, Shilatifard A, O’Shea E, Weissman JS, Ingles CJ, Hughes TR, Parkinson J, Gerstein M, Wodak SJ, Emili A, Greenblatt JF (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440(7084):637–643. doi:nature04670 [pii] 10.1038/nature04670

    Article  PubMed  CAS  Google Scholar 

  32. Collins SR, Miller KM, Maas NL, Roguev A, Fillingham J, Chu CS, Schuldiner M, Gebbia M, Recht J, Shales M, Ding H, Xu H, Han J, Ingvarsdottir K, Cheng B, Andrews B, Boone C, Berger SL, Hieter P, Zhang Z, Brown GW, Ingles CJ, Emili A, Allis CD, Toczyski DP, Weissman JS, Greenblatt JF, Krogan NJ (2007) Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446(7137):806–810. doi:nature05649 [pii] 10.1038/nature05649

    Article  PubMed  CAS  Google Scholar 

  33. Collins SR, Schuldiner M, Krogan NJ, Weissman JS (2006) A strategy for extracting and analyzing large-scale quantitative epistatic interaction data. Genome Biol 7(7):R63–R63

    Article  PubMed  Google Scholar 

  34. Segre D, Deluna A, Church GM, Kishony R (2005) Modular epistasis in yeast metabolism. Nat Genet 37(1):77–83. doi:ng1489 [pii] 10.1038/ng1489

    PubMed  CAS  Google Scholar 

  35. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–1555. doi:10.1126/science.1073374 297/5586/1551 [pii]

    Article  PubMed  CAS  Google Scholar 

  36. Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34(2):166–176. doi:10.1038/ng1165 ng1165 [pii]

    Article  PubMed  CAS  Google Scholar 

  37. Fraser HB, Plotkin JB (2007) Using protein complexes to predict phenotypic effects of gene mutation. Genome Biol 8(11):R252. doi:gb-2007-8-11-r252 [pii] 10.1186/gb-2007-8-11-r252

    Article  PubMed  Google Scholar 

  38. DeLuna A, Vetsigian K, Shoresh N, Hegreness M, Colon-Gonzalez M, Chao S, Kishony R (2008) Exposing the fitness contribution of duplicated genes. Nat Genet 40(5):676–681. doi:ng.123 [pii] 10.1038/ng.123

    Article  PubMed  CAS  Google Scholar 

  39. Sopko R, Huang D, Preston N, Chua G, Papp B, Kafadar K, Snyder M, Oliver SG, Cyert M, Hughes TR, Boone C, Andrews B (2006) Mapping pathways and phenotypes by systematic gene overexpression. Mol Cell 21(3):319–330. doi:S1097-2765(05)01853-8 [pii] 10.1016/j.molcel.2005.12.011

    Article  PubMed  CAS  Google Scholar 

  40. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K, Andre B, Bangham R, Benito R, Boeke JD, Bussey H, Chu AM, Connelly C, Davis K, Dietrich F, Dow SW, El Bakkoury M, Foury F, Friend SH, Gentalen E, Giaever G, Hegemann JH, Jones T, Laub M, Liao H, Liebundguth N, Lockhart DJ, Lucau-Danila A, Lussier M, M’Rabet N, Menard P, Mittmann M, Pai C, Rebischung C, Revuelta JL, Riles L, Roberts CJ, Ross-MacDonald P, Scherens B, Snyder M, Sookhai-Mahadeo S, Storms RK, Veronneau S, Voet M, Volckaert G, Ward TR, Wysocki R, Yen GS, Yu K, Zimmermann K, Philippsen P, Johnston M, Davis RW (1999) Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285(5429):901–906. doi:7737 [pii]

    Google Scholar 

  41. Dowell RD, Ryan O, Jansen A, Cheung D, Agarwala S, Danford T, Bernstein DA, Rolfe PA, Heisler LE, Chin B, Nislow C, Giaever G, Phillips PC, Fink GR, Gifford DK, Boone C (2010) Genotype to phenotype: a complex problem. Science 328(5977):469. doi:328/5977/469 [pii] 10.1126/science.1189015

    Article  PubMed  CAS  Google Scholar 

  42. Brown KM, Landry CR, Hartl DL, Cavalieri D (2008) Cascading transcriptional effects of a naturally occurring frameshift mutation in Saccharomyces cerevisiae. Mol Ecol 17(12):2985–2997. doi:MEC3765 [pii] 10.1111/j.1365-294X.2008.03765.x

    Article  PubMed  CAS  Google Scholar 

  43. Friedman A, Perrimon N (2007) Genetic screening for signal transduction in the era of network biology. Cell 128(2):225–231. doi:S0092-8674(07)00063-3 [pii] 10.1016/j.cell.2007.01.007

    Article  PubMed  CAS  Google Scholar 

  44. Breitkreutz A, Choi H, Sharom JR, Boucher L, Neduva V, Larsen B, Lin ZY, Breitkreutz BJ, Stark C, Liu G, Ahn J, Dewar-Darch D, Reguly T, Tang X, Almeida R, Qin ZS, Pawson T, Gingras AC, Nesvizhskii AI, Tyers M (2010) A global protein kinase and phosphatase interaction network in yeast. Science 328(5981):1043–1046. doi:328/5981/1043 [pii] 10.1126/science.1176495

    Article  PubMed  CAS  Google Scholar 

  45. Levy ED, Landry CR, Michnick SW (2010) Cell signaling. Signaling through cooperation. Science 328(5981):983–984. doi:328/5981/983 [pii] 10.1126/science.1190993

    CAS  Google Scholar 

  46. Bodenmiller B, Wanka S, Kraft C, Urban J, Campbell D, Pedrioli PG, Gerrits B, Picotti P, Lam H, Vitek O, Brusniak MY, Roschitzki B, Zhang C, Shokat KM, Schlapbach R, Colman-Lerner A, Nolan GP, Nesvizhskii AI, Peter M, Loewith R, von Mering C, Aebersold R (2010) Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci Signal 3(153):rs4. doi:3/153/rs4 [pii] 10.1126/scisignal.2001182

    Google Scholar 

  47. Landry CR, Lemos B, Rifkin SA, Dickinson WJ, Hartl DL (2007) genetic properties influencing the evolvability of gene expression. Science 317(5834):118–121

    Article  PubMed  CAS  Google Scholar 

  48. Zhu J, Zhang B, Smith EN, Drees B, Brem RB, Kruglyak L, Bumgarner RE, Schadt EE (2008) Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 40(7):854–861. doi:ng.167 [pii] 10.1038/ng.167

    Article  PubMed  CAS  Google Scholar 

  49. Jelier R, Semple JI, Garcia-Verdugo R, Lehner B (2011) Predicting phenotypic variation in yeast from individual genome sequences. Nat Genet 43(12):1270–1274. doi:10.1038/ng.1007 ng.1007 [pii]

    Article  PubMed  CAS  Google Scholar 

  50. Brem RB, Yvert Gl, Clinton R, Kruglyak L (2002) Genetic dissection of transcriptional regulation in budding yeast. Science 296(5568):752–755

    Article  PubMed  CAS  Google Scholar 

  51. Ansel J, Bottin H, Rodriguez-Beltran C, Damon C, Nagarajan M, Fehrmann S, Francois J, Yvert G (2008) Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genet 4(4):e1000049. doi:10.1371/journal.pgen.1000049

    Article  PubMed  Google Scholar 

  52. Zheng W, Zhao H, Mancera E, Steinmetz LM, Snyder M (2010) Genetic analysis of variation in transcription factor binding in yeast. Nature 464(7292):1187–1191. doi:nature08934 [pii] 10.1038/nature08934

    Article  PubMed  CAS  Google Scholar 

  53. Ehrenreich IM, Gerke JP, Kruglyak L (2009) Genetic dissection of complex traits in yeast: insights from studies of gene expression and other phenotypes in the BYxRM cross. Cold Spring Harb Symp Quant Biol 74:145–153. doi:sqb.2009.74.013 [pii] 10.1101/sqb.2009.74.013

    Article  PubMed  CAS  Google Scholar 

  54. Malleshaiah MK, Shahrezaei V, Swain PS, Michnick SW (2010) The scaffold protein Ste5 directly controls a switch-like mating decision in yeast. Nature 465(7294):101–105

    Article  PubMed  CAS  Google Scholar 

  55. Freschi L, Courcelles M, Thibault P, Michnick SW, Landry CR (2011) Phosphorylation network rewiring by gene duplication. Mol Syst Biol 7:504. doi:10.1038/msb.2011.43 msb201143 [pii]

    Article  PubMed  Google Scholar 

  56. Foss EJ, Radulovic D, Shaffer SA, Goodlett DR, Kruglyak L, Bedalov A (2011) Genetic variation shapes protein networks mainly through non-transcriptional mechanisms. PLoS Biol 9(9):e1001144–e1001144

    Article  PubMed  CAS  Google Scholar 

  57. Tirosh I, Reikhav S, Sigal N, Assia Y, Barkai N (2010) Chromatin regulators as capacitors of inter-species variations in gene expression. Mol Syst Biol 6:435. doi:10.1038/msb.2010.84

    PubMed  CAS  Google Scholar 

  58. Landry CR, Rifkin SA (2010) Chromatin regulators shape the genotype-phenotype map. Mol Syst Biol 6:434. doi:msb201097 [pii] 10.1038/msb.2010.97

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank two anonymous reviewers for their comments. CRL’s and SAR’s research on evolutionary systems biology is funded by the Human Frontier Science Program (RGY0073/2010)

Author information

Authors and Affiliations

  1. Institut de Biologie Intégrative et des Systèmes (IBIS), Département de biologie, PROTEO, Université Laval, Quebec City, QC, G1V 0A6, Canada

    Christian R. Landry

  2. Ecology, Behavior, and Evolution, Division of Biology, University of California, San Diego, CA, USA

    Scott A. Rifkin

Authors
  1. Christian R. Landry
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott A. Rifkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Christian R. Landry or Scott A. Rifkin .

Editor information

Editors and Affiliations

  1. University of Exeter, Prince of Wales Road, Exeter, EX4 4SB, Devon, United Kingdom

    Orkun S. Soyer

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Landry, C.R., Rifkin, S.A. (2012). The Genotype–Phenotype Maps of Systems Biology and Quantitative Genetics: Distinct and Complementary. In: Soyer, O. (eds) Evolutionary Systems Biology. Advances in Experimental Medicine and Biology, vol 751. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3567-9_17

Download citation

Publish with us

Policies and ethics

Search

Navigation