Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for “polygenic epistasis”

English version

Autoři: Christoph D. Rau ^aff001; Natalia M. Gonzales ^aff002; Joshua S. Bloom ^aff003; Danny Park ^aff004; Julien Ayroles ^aff005; Abraham A. Palmer ^aff006; Aldons J. Lusis ^aff003; Noah Zaitlen ^aff007
Působiště autorů: Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America ^aff001; Department of Human Genetics, University of Chicago, Chicago, IL, United States of America ^aff002; Department of Human Genetics, UCLA, Los Angeles, CA, United States of America ^aff003; Department of Medicine, UCSF, San Francisco, CA, United States of America ^aff004; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America ^aff005; Department of Psychiatry, and Institute for Genomic Medicine, UCSD, San Diego, CA, United States of America ^aff006; Department of Neurology, UCLA, Los Angeles, CA, United States of America ^aff007
Vyšlo v časopise: Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for “polygenic epistasis”. PLoS Genet 16(10): e32767. doi:10.1371/journal.pgen.1009165
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1009165

Souhrn

Background

The majority of quantitative genetic models used to map complex traits assume that alleles have similar effects across all individuals. Significant evidence suggests, however, that epistatic interactions modulate the impact of many alleles. Nevertheless, identifying epistatic interactions remains computationally and statistically challenging. In this work, we address some of these challenges by developing a statistical test for polygenic epistasis that determines whether the effect of an allele is altered by the global genetic ancestry proportion from distinct progenitors.

Results

We applied our method to data from mice and yeast. For the mice, we observed 49 significant genotype-by-ancestry interaction associations across 14 phenotypes as well as over 1,400 Bonferroni-corrected genotype-by-ancestry interaction associations for mouse gene expression data. For the yeast, we observed 92 significant genotype-by-ancestry interactions across 38 phenotypes. Given this evidence of epistasis, we test for and observe evidence of rapid selection pressure on ancestry specific polymorphisms within one of the cohorts, consistent with epistatic selection.

Conclusions

Unlike our prior work in human populations, we observe widespread evidence of ancestry-modified SNP effects, perhaps reflecting the greater divergence present in crosses using mice and yeast.

Klíčová slova:

Epistasis – Genetic loci – Inbred strains – Mammalian genomics – Mouse models – Phenotypes – Single nucleotide polymorphisms – Yeast

Zdroje

1. Polderman TJC, Benyamin B, de Leeuw C a, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. Nature Publishing Group; 2015;47 : 702–709. doi: 10.1038/ng.3285 25985137

2. Hill WG, Goddard ME, Visscher PM. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. Public Library of Science; 2008;4:e1000008. doi: 10.1371/journal.pgen.1000008 18454194

3. Fish AE, Capra JA, Bush WS. Are Interactions between cis-Regulatory Variants Evidence for Biological Epistasis or Statistical Artifacts? Am J Hum Genet. Elsevier; 2016;99 : 817–830. doi: 10.1016/j.ajhg.2016.07.022 27640306

4. Mäki-Tanila A, Hill WG. Influence of gene interaction on complex trait variation with multilocus models. Genetics. Genetics Society of America; 2014;198 : 355–67. doi: 10.1534/genetics.114.165282 24990992

5. Huang W, Richards S, Carbone MA, Zhu D, Anholt RRH, Ayroles JF, et al. Epistasis dominates the genetic architecture of Drosophila quantitative traits. Proc Natl Acad Sci U S A. 2012; doi: 10.1073/pnas.1213423109 22949659

6. Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C, Tan G, et al. A global genetic interaction network maps a wiring diagram of cellular function. Science (80-). 2016; doi: 10.1126/science.aaf1420 27708008

7. Tyler AL, Donahue LR, Churchill GA, Carter GW. Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross. PLoS Genet. Public Library of Science; 2016;12:e1005805. doi: 10.1371/journal.pgen.1005805 26828925

8. Varón-González C, Navarro N. Epistasis regulates the developmental stability of the mouse craniofacial shape. Heredity (Edinb). Nature Publishing Group; 2018; 1. doi: 10.1038/s41437-018-0140-8 30209292

9. Hemani G, Shakhbazov K, Westra H-J, Esko T, Henders AK, McRae AF, et al. Detection and replication of epistasis influencing transcription in humans. Nature. Nature Publishing Group; 2014;508 : 249–53. doi: 10.1038/nature13005 24572353

10. Park DS, Eskin I, Kang EY, Gamazon ER, Eng C, Gignoux CR, et al. An ancestry-based approach for detecting interactions. Genet Epidemiol. Wiley-Blackwell; 2018;42 : 49–63. doi: 10.1002/gepi.22087 29114909

11. Sittig LJ, Carbonetto P, Engel KA, Krauss KS, Barrios-Camacho CM, Palmer AA. Genetic Background Limits Generalizability of Genotype-Phenotype Relationships. Neuron. Cell Press; 2016;91 : 1253–1259. doi: 10.1016/j.neuron.2016.08.013 27618673

12. Lusis AJ, Seldin MM, Allayee H, Bennett BJ, Civelek M, Davis RC, et al. The Hybrid Mouse Diversity Panel: A Resource for Systems Genetics Analyses of Metabolic and Cardiovascular Traits. J Lipid Res. 2016;58 : 7250–7. doi: 10.1194/jlr.R066944 27099397

13. Philip VM, Sokoloff G, Ackert-Bicknell CL, Striz M, Branstetter L, Beckmann MA, et al. Genetic analysis in the Collaborative Cross breeding population. Genome Res. 2011; doi: 10.1101/gr.113886.110 21734011

14. Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J, et al. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nature Genetics. 2004. doi: 10.1038/ng1104-1133 15514660

15. Andreux PA, Williams EG, Koutnikova H, Houtkooper RH, Champy MF, Henry H, et al. Systems genetics of metabolism: The use of the BXD murine reference panel for multiscalar integration of traits. Cell. 2012; doi: 10.1016/j.cell.2012.08.012 22939713

16. Solberg Woods LC, Palmer AA. Using Heterogeneous Stocks for Fine-Mapping Genetically Complex Traits. Methods in Molecular Biology. Humana Press Inc.; 2019. pp. 233–247. doi: 10.1007/978-1-4939-9581-3_11 31228160

17. King EG, Merkes CM, McNeil CL, Hoofer SR, Sen S, Broman KW, et al. Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Res. 2012; doi: 10.1101/gr.134031.111 22496517

18. Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, et al. Efficient control of population structure in model organism association mapping. Genetics. 2008;178 : 1709–1723. doi: 10.1534/genetics.107.080101 18385116

19. Gonzales NM, Seo J, Hernandez Cordero AI, St. Pierre CL, Gregory JS, Distler MG, et al. Genome wide association analysis in a mouse advanced intercross line. Nat Commun. 2018; doi: 10.1038/s41467-018-07642-8 30514929

20. Bloom JS, Boocock J, Treusch S, Sadhu MJ, Day L, Oates-Barker H, et al. Rare variants contribute disproportionately to quantitative trait variation in yeast. Elife. 2019; doi: 10.7554/eLife.49212 31647408

21. Jha SK, Rauniyar K, Jeltsch M. Key molecules in lymphatic development, function, and identification. Ann Anat—Anat Anzeiger. 2018;219 : 25–34. doi: 10.1016/j.aanat.2018.05.003 29842991

22. Afratis NA, Selman M, Pardo A, Sagi I. Emerging insights into the role of matrix metalloproteases as therapeutic targets in fibrosis. Matrix Biol. 2018;68–69 : 167–179. doi: 10.1016/j.matbio.2018.02.007 29428229

23. Cerdà-Costa N, Gomis-Rüth FX. Architecture and function of metallopeptidase catalytic domains. Protein Sci. 2014;23 : 123–44. Available: http://www.ncbi.nlm.nih.gov/pubmed/24596965 doi: 10.1002/pro.2400 24596965

24. Bennett BJ, Farber CR, Orozco L, Kang HM, Ghazalpour A, Siemers N, et al. A high-resolution association mapping panel for the dissection of complex traits in mice. Genome Res. 2010;20 : 281–290. doi: 10.1101/gr.099234.109 20054062

25. Sul JH, Bilow M, Yang W-Y, Kostem E, Furlotte N, He D, et al. Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models. Schork NJ, editor. PLOS Genet. 2016;12:e1005849. doi: 10.1371/journal.pgen.1005849 26943367

26. Ghazalpour A, Rau CDCD, Farber CRCR, Bennett BJBJ, Orozco LDLD, Van Nas A, et al. Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits. Mamm Genome. 2012;23 : 680–92. doi: 10.1007/s00335-012-9411-5 22892838

27. Rau CD, Wang J, Avetisyan R, Romay MC, Martin L, Ren S, et al. Mapping genetic contributions to cardiac pathology induced by beta-adrenergic stimulation in mice. Circ Cardiovasc Genet. 2015;8. doi: 10.1161/CIRCGENETICS.113.000732 25480693

28. Parks BWW, Sallam T, Mehrabian M, Psychogios N, Hui STT, Norheim F, et al. Genetic Architecture of Insulin Resistance in the Mouse. Cell Metab. 2015;21 : 334–346. doi: 10.1016/j.cmet.2015.01.002 25651185

29. Rau CD, Wang J, Avetisyan R, Romay MC, Martin L, Ren S, et al. Mapping genetic contributions to cardiac pathology induced by beta-adrenergic stimulation in mice. Circ Cardiovasc Genet. 2015;8 : 40–49. doi: 10.1161/CIRCGENETICS.113.000732 25480693

30. Wang JJ-C, Rau C, Avetisyan R, Ren S, Romay MCMC, Stolin G, et al. Genetic Dissection of Cardiac Remodeling in an Isoproterenol-Induced Heart Failure Mouse Model. PLoS Genet. 2016;12 : 1–30. doi: 10.1371/journal.pgen.1006038 27385019

31. Gibbs GM, Scanlon MJ, Swarbrick J, Curtis S, Gallant E, Dulhunty AF, et al. The cysteine-rich secretory protein domain of Tpx-1 is related to ion channel toxins and regulates ryanodine receptor Ca2+ signaling. J Biol Chem. American Society for Biochemistry and Molecular Biology; 2006;281 : 4156–63. doi: 10.1074/jbc.M506849200 16339766

32. Zhang S, Liu X, Bawa-Khalfe T, Lu L-S, Lyu YL, Liu LF, et al. Identification of the molecular basis of doxorubicin-induced cardiotoxicity. Nat Med. 2012;18 : 1639–1642. doi: 10.1038/nm.2919 23104132

33. Albino-Sanchez M, Vazquez-Hernandez J, Ocadiz-Delgado R, Serafin-Higuera N, León-Galicia I, Garcia-Villa E, et al. Decreased RARβ expression induces abundant inflammation and cervical precancerous lesions. Exp Cell Res. 2016;346 : 40–52. doi: 10.1016/j.yexcr.2016.05.010 27207583

34. Wyler DJ. Fibrosin, a Novel Fibrogenic Protein: Discovery, Cloning and Implications for Fibrotic Disorders. Int Arch Allergy Immunol. 1996;111 : 326–329. doi: 10.1159/000237388 8957104

35. White GR, Varley JM, Heighway J. Genomic structure and expression profile of LPHH1, a 7TM gene variably expressed in breast cancer cell lines. Biochim Biophys Acta. 2000;1491 : 75–92. Available: http://www.ncbi.nlm.nih.gov/pubmed/10760572 doi: 10.1016/s0167-4781(00)00020-8 10760572

36. Rau CD, Romay MC, Tuteryan M, Wang JJ-C, Santolini M, Ren S, et al. Systems Genetics Approach Identifies Gene Pathways and Adamts2 as Drivers of Isoproterenol-Induced Cardiac Hypertrophy and Cardiomyopathy in Mice. Cell Syst. Elsevier Inc.; 2017;4 : 121–128.e4. doi: 10.1016/j.cels.2016.10.016 27866946

37. Lara-Pezzi E, Gómez-Salinero J, Gatto A, García-Pavía P. The Alternative Heart: Impact of Alternative Splicing in Heart Disease. J Cardiovasc Transl Res. 2013;6 : 945–955. doi: 10.1007/s12265-013-9482-z 23775418

38. Schumer M, Brandvain Y. Determining epistatic selection in admixed populations. Mol Ecol. 2016; doi: 10.1111/mec.13641 27061282

39. Ehrenreich IM. Epistasis: Searching for interacting genetic variants using crosses. Genetics. 2017. doi: 10.1534/genetics.117.203059 28592494

40. Corbett-Detig RB, Zhou J, Clark AG, Hartl DL, Ayroles JF. Genetic incompatibilities are widespread within species. Nature. 2013; doi: 10.1038/nature12678 24196712

41. Srivastava A, Morgan AP, Najarian ML, Sarsani VK, Sigmon JS, Shorter JR, et al. Genomes of the mouse collaborative cross. Genetics. 2017; doi: 10.1534/genetics.116.198838 28592495

42. Wang Y, Alla V, Goody D, Gupta SK, Spitschak A, Wolkenhauer O, et al. Epigenetic factor EPC1 is a master regulator of DNA damage response by interacting with E2F1 to silence death and activate metastasis-related gene signatures. Nucleic Acids Res. 2016;44 : 117–133. doi: 10.1093/nar/gkv885 26350215

43. Kee HJ, Kim J-R, Nam K-I, Park HY, Shin S, Kim JC, et al. Enhancer of Polycomb1, a Novel Homeodomain Only Protein-binding Partner, Induces Skeletal Muscle Differentiation. J Biol Chem. 2007;282 : 7700–7709. doi: 10.1074/jbc.M611198200 17192267

44. Combarros O, Cortina-Borja M, Smith AD, Lehmann DJ. Epistasis in sporadic Alzheimer’s disease. Neurobiol Aging. 2009;30 : 1333–1349. doi: 10.1016/j.neurobiolaging.2007.11.027 18206267

45. Badano JL, Leitch CC, Ansley SJ, May-Simera H, Lawson S, Lewis RA, et al. Dissection of epistasis in oligogenic Bardet–Biedl syndrome. Nature. Nature Publishing Group; 2006;439 : 326–330. doi: 10.1038/nature04370 16327777

46. Mackay TF, Moore JH. Why epistasis is important for tackling complex human disease genetics. Genome Med. BioMed Central; 2014;6 : 125. doi: 10.1186/gm561 25031624

47. Buchner DA, Nadeau JH. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res. Cold Spring Harbor Laboratory Press; 2015;25 : 775–91. doi: 10.1101/gr.187450.114 25953951

48. Ackerman KG, Huang H, Grasemann H, Puma C, Singer JB, Hill AE, et al. Interacting genetic loci cause airway hyperresponsiveness. Physiol Genomics. 2005;21 : 105–111. doi: 10.1152/physiolgenomics.00267.2004 15657107

49. Street VA, Kujawa SG, Manichaikul A, Broman KW, Kallman JC, Shilling DJ, et al. Resistance to Noise-Induced Hearing Loss in 129S6 and MOLF Mice: Identification of Independent, Overlapping, and Interacting Chromosomal Regions. J Assoc Res Otolaryngol. Springer US; 2014;15 : 721–738. doi: 10.1007/s10162-014-0472-x 24952082

50. Fuchs SBA, Lieder I, Stelzer G, Mazor Y, Buzhor E, Kaplan S, et al. GeneAnalytics: An Integrative Gene Set Analysis Tool for Next Generation Sequencing, RNAseq and Microarray Data. Omi A J Integr Biol. 2016; doi: 10.1089/omi.2015.0168 26983021

Článek Comparing DNA replication programs reveals large timing shifts at centromeres of endocycling cells in maize roots

Článek A single Ho-induced double-strand break at the MAT locus is lethal in Candida glabrata

Článek A Rad51-independent pathway promotes single-strand template repair in gene editing

Článek Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits

Článek C. elegans CLASP/CLS-2 negatively regulates membrane ingression throughout the oocyte cortex and is required for polar body extrusion

Článek The O-GlcNAc transferase OGT is a conserved and essential regulator of the cellular and organismal response to hypertonic stress

Článek Drosophila phosphatidylinositol-4 kinase fwd promotes mitochondrial fission and can suppress Pink1/parkin phenotypes

Článek A new domestic cat genome assembly based on long sequence reads empowers feline genomic medicine and identifies a novel gene for dwarfism

Článek Chromosome separation during Drosophila male meiosis I requires separase-mediated cleavage of the homolog conjunction protein UNO

Článek How noncrossover homologs are conjoined and segregated in Drosophila male meiosis I: Stable but reversible homolog linkers require a novel Separase target protein

Článek Predominance of positive epistasis among drug resistance-associated mutations in HIV-1 protease

Článek Human ABCB1 with an ABCB11-like degenerate nucleotide binding site maintains transport activity by avoiding nucleotide occlusion