Genetic Variation in the Social Environment Contributes to Health and Disease

PLoS Genetics

Volumn 13, Issue 1, 2017, Pages

  Baud, Amelie ^a   Mulligan, Megan K ^b   Casale, Francesco Paolo ^a   Ingels, Jesse F ^b   Bohl, Casey J ^b   Callebert, Jacques ^c   Launay, Jean Marie ^c   Krohn, Jon ^d   Legarra, Andres ^e   Williams, Robert W ^b   Stegle, Oliver ^a  

^a EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^b UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^c HÔPITAL LARIBOISIÈRE   (France)

^d UNIVERSITY OF OXFORD   (United Kingdom)

^e UMR1388 GenPhySE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL EXPERIMENT; ARTICLE; BODY WEIGHT; C57BL 6 MOUSE; DBA 2 MOUSE; GENE EXPRESSION; GENETIC TRAIT; GENETIC VARIATION; GENOTYPE; HEALTH; HERITABILITY; IMMUNITY; MOUSE; NONHUMAN; PHENOTYPE; PHENOTYPIC VARIATION; PREFRONTAL CORTEX; SOCIAL ENVIRONMENT; WOUND HEALING; ANIMAL; C57BL MOUSE; DBA MOUSE; GENETIC PREDISPOSITION; GENETICS; GENOTYPE ENVIRONMENT INTERACTION; QUANTITATIVE TRAIT;

ANIMALS; BODY WEIGHT; GENE-ENVIRONMENT INTERACTION; GENETIC PREDISPOSITION TO DISEASE; GENETIC VARIATION; GENOTYPE; IMMUNITY; MICE; MICE, INBRED C57BL; MICE, INBRED DBA; QUANTITATIVE TRAIT, HERITABLE; SOCIAL ENVIRONMENT; WOUND HEALING;

EID: 85011339604     PISSN: 15537390     EISSN: 15537404     Source Type: Journal    
DOI: 10.1371/journal.pgen.1006498     Document Type: Article

References (77)

1. Moore AJ, Brodie ED, IIIWolf JB, Interacting phenotypes and the evolutionary process: I. Direct and indirect genetic effects of social interactions. Evolution; international journal of organic evolution. 1997:1352–62.
2. Bergsma R, Kanis E, Knol EF, Bijma P, The contribution of social effects to heritable variation in finishing traits of domestic pigs (Sus scrofa). Genetics. 2008;178(3):1559–70. Epub 2008/02/05. PubMed Central PMCID: PMC2391867. doi: 10.1534/genetics.107.08423618245326
3. Wolf JB, Wade MJ, What are maternal effects (and what are they not)?Philosophical Transactions of the Royal Society B: Biological Sciences. 2009;364(1520):1107–15.
4. Wilson A, Coltman D, Pemberton J, Overall A, Byrne K, Kruuk L, Maternal genetic effects set the potential for evolution in a free‐living vertebrate population. Journal of evolutionary biology. 2005;18(2):405–14. doi: 10.1111/j.1420-9101.2004.00824.x15715846
5. de Albuquerque G, Estimates of direct and maternal genetic effects for weights from birth to 600 days of age in Nelore cattle. Journal of Animal breeding and Genetics. 2001;118(2):83–92.
6. Department of Biological Sciences University of South Carolina Timothy A. Mousseau Associate Professor C, University CWFAPF. Maternal Effects As Adaptations: Oxford University Press, USA; 1998.
7. Strandberg E, Jacobsson J, Saetre P, Direct genetic, maternal and litter effects on behaviour in German shepherd dogs in Sweden. Livestock Production Science. 2005;93(1):33–42.
8. Champagne FA, Meaney MJ, Stress during gestation alters postpartum maternal care and the development of the offspring in a rodent model. Biological psychiatry. 2006;59(12):1227–35. doi: 10.1016/j.biopsych.2005.10.01616457784
9. Brinker T, Bijma P, Visscher J, Rodenburg TB, Ellen ED, Plumage condition in laying hens: genetic parameters for direct and indirect effects in two purebred layer lines. Genetics, selection, evolution: GSE. 2014;46:33. Epub 2014/06/03. PubMed Central PMCID: PMC4073196. doi: 10.1186/1297-9686-46-3324885199
10. Peeters K, Eppink TT, Ellen ED, Visscher J, Bijma P, Indirect genetic effects for survival in domestic chickens (Gallus gallus) are magnified in crossbred genotypes and show a parent-of-origin effect. Genetics. 2012;192(2):705–13. doi: 10.1534/genetics.112.14255422851648
11. Duijvesteijn N, Knol E, Bijma P, Direct and associative effects for androstenone and genetic correlations with backfat and growth in entire male pigs. Journal of animal science. 2012;90(8):2465–75. doi: 10.2527/jas.2011-462522367075
12. Arango J, Misztal I, Tsuruta S, Culbertson M, Herring W, Estimation of variance components including competitive effects of Large White growing gilts. Journal of animal science. 2005;83(6):1241–6. 15890801
13. Khaw H, Indirect genetic effects for harvest weight in Nile tilapia (Oreochromis niloticus). Age. 2014;6330(166):58.97.
14. Petfield D, Chenoweth SF, Rundle HD, Blows MW, Genetic variance in female condition predicts indirect genetic variance in male sexual display traits. Proceedings of the National Academy of Sciences. 2005;102(17):6045–50.
15. Alemu SW, Bijma P, Møller SH, Janss L, Berg P, Indirect genetic effects contribute substantially to heritable variation in aggression-related traits in group-housed mink (Neovison vison). Genet Sel Evol. 2014;46:30. doi: 10.1186/1297-9686-46-3024884874
16. Wilson A, Morrissey M, Adams M, Walling C, Guinness F, Pemberton J, et al. Indirect genetics effects and evolutionary constraint: an analysis of social dominance in red deer, Cervus elaphus. Journal of evolutionary biology. 2011;24(4):772–83. doi: 10.1111/j.1420-9101.2010.02212.x21288272
17. Sartori C, Mantovani R, Indirect genetic effects and the genetic bases of social dominance: evidence from cattle. Heredity. 2013;110(1):3–9. doi: 10.1038/hdy.2012.5622968222
18. Peirce JL, Lu L, Gu J, Silver LM, Williams RW, A new set of BXD recombinant inbred lines from advanced intercross populations in mice. BMC genetics. 2004;5(1):7.
19. Bredy TW, Wu H, Crego C, Zellhoefer J, Sun YE, Barad M, Histone modifications around individual BDNF gene promoters in prefrontal cortex are associated with extinction of conditioned fear. Learning & memory. 2007;14(4):268–76.
20. Caldji C, Diorio J, Anisman H, Meaney MJ, Maternal behavior regulates benzodiazepine/GABAA receptor subunit expression in brain regions associated with fear in BALB/c and C57BL/6 mice. Neuropsychopharmacology. 2004;29(7):1344–52. doi: 10.1038/sj.npp.130043615085086
21. Kerns RT, Ravindranathan A, Hassan S, Cage MP, York T, Sikela JM, et al. Ethanol-responsive brain region expression networks: implications for behavioral responses to acute ethanol in DBA/2J versus C57BL/6J mice. The Journal of neuroscience. 2005;25(9):2255–66. doi: 10.1523/JNEUROSCI.4372-04.200515745951
22. Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO, et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature genetics. 2006;38(8):879–87. doi: 10.1038/ng184016832355
23. Huang G-J, Smith AL, Gray DH, Cosgrove C, Singer BH, Edwards A, et al. A genetic and functional relationship between T cells and cellular proliferation in the adult hippocampus. 2010.
24. Huang G-J, Shifman S, Valdar W, Johannesson M, Yalcin B, Taylor MS, et al. High resolution mapping of expression QTLs in heterogeneous stock mice in multiple tissues. Genome research. 2009;19(6):1133–40. doi: 10.1101/gr.088120.10819376938
25. Bijma P, Muir WM, Ellen ED, Wolf JB, Van Arendonk JA, Multilevel selection 2: estimating the genetic parameters determining inheritance and response to selection. Genetics. 2007;175(1):289–99. doi: 10.1534/genetics.106.06272917110493
26. Lippert C, Casale FP, Rakitsch B, Stegle O, LIMIX: genetic analysis of multiple traits. bioRxiv. 2014.
27. Casale FP, Rakitsch B, Lippert C, Stegle O, Efficient set tests for the genetic analysis of correlated traits. Nature methods. 2015.
28. Bijma P, Estimating indirect genetic effects: precision of estimates and optimum designs. Genetics. 2010;186(3):1013–28. doi: 10.1534/genetics.110.12049320713688
29. Gardner M, Steinberg L, Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: an experimental study. Developmental psychology. 2005;41(4):625–35. doi: 10.1037/0012-1649.41.4.62516060809
30. Groh DR, Jason LA, Keys CB, Social network variables in alcoholics anonymous: a literature review. Clinical psychology review. 2008;28(3):430–50. PubMed Central PMCID: PMC2289871. doi: 10.1016/j.cpr.2007.07.01417719158
31. Haeffel GJ, Hames JL, Cognitive Vulnerability to Depression Can Be Contagious. Clinical Psychological Science. 2014;2(1):75–85.
32. Kumpulainen K, Psychiatric conditions associated with bullying. International journal of adolescent medicine and health. 2008;20(2):121–32. 18714551
33. Russek LG, Schwartz GE, Feelings of parental caring predict health status in midlife: A 35-year follow-up of the Harvard Mastery of Stress Study. J Behav Med. 1997;20(1):1–13. 9058175
34. Hutson J, Niall M, Evans D, Fowler R. Effect of salivary glands on wound contraction in mice. 1979.
35. Detillion CE, Craft TK, Glasper ER, Prendergast BJ, DeVries AC, Social facilitation of wound healing. Psychoneuroendocrinology. 2004;29(8):1004–11. doi: 10.1016/j.psyneuen.2003.10.00315219651
36. Ebrecht M, Hextall J, Kirtley L-G, Taylor A, Dyson M, Weinman J, Perceived stress and cortisol levels predict speed of wound healing in healthy male adults. Psychoneuroendocrinology. 2004;29(6):798–809. doi: 10.1016/S0306-4530(03)00144-615110929
37. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53. Epub 2009/10/09. PubMed Central PMCID: PMC2831613. doi: 10.1038/nature0849419812666
38. Eaves L, A model for sibling effects in man. Heredity. 1976;36(2):205–14. 1063734
39. Silventoinen K, Sammalisto S, Perola M, Boomsma DI, Cornes BK, Davis C, et al. Heritability of adult body height: a comparative study of twin cohorts in eight countries. Twin research. 2003;6(05):399–408.
40. Hyttinen V, Kaprio J, Kinnunen L, Koskenvuo M, Tuomilehto J, Genetic Liability of Type 1 Diabetes and the Onset Age Among 22,650 Young Finnish Twin Pairs A Nationwide Follow-Up Study. Diabetes. 2003;52(4):1052–5. 12663480
41. Wirdefeldt K, Gatz M, Reynolds CA, Prescott CA, Pedersen NL, Heritability of Parkinson disease in Swedish twins: a longitudinal study. Neurobiology of aging. 2011;32(10):1923. e1–. e8.
42. Conley D, Siegal ML, Domingue BW, Harris KM, McQueen MB, Boardman JD, Testing the key assumption of heritability estimates based on genome-wide genetic relatedness. Journal of human genetics. 2014;59(6):342–5. doi: 10.1038/jhg.2014.1424599117
43. Muir WM, Incorporation of competitive effects in forest tree or animal breeding programs. Genetics. 2005;170(3):1247–59. doi: 10.1534/genetics.104.03595615911590
44. Wolf JB, Brodie ED, IIICheverud JM, Moore AJ, Wade MJ, Evolutionary consequences of indirect genetic effects. Trends in ecology & evolution. 1998;13(2):64–9.
45. Dawkins R, The Extended Phenotype: The Long Reach of the Gene: Oxford University Press; 1999.
46. Kema IP, Schellings AM, Hoppenbrouwers CJ, Rutgers HM, de Vries EG, Muskiet FA, High performance liquid chromatographic profiling of tryptophan and related indoles in body fluids and tissues of carcinoid patients. Clinica chimica acta; international journal of clinical chemistry. 1993;221(1–2):143–58. Epub 1993/11/30. 7512001
47. Gamache P, Ryan E, Svendsen C, Murayama K, Acworth IN, Simultaneous measurement of monoamines, metabolites and amino acids in brain tissue and microdialysis perfusates. Journal of chromatography. 1993;614(2):213–20. Epub 1993/05/05. 7686176
48. Box GE, Cox DR, An analysis of transformations. Journal of the Royal Statistical Society Series B (Methodological). 1964:211–52.
49. Venables W, Ripley B, Modern applied statistics with S. Fourth edition ed: Springer; 2002.
50. Keane TM, Goodstadt L, Danecek P, White MA, Wong K, Yalcin B, et al. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature. 2011;477(7364):289–94. Epub 2011/09/17. PubMed Central PMCID: PMC3276836. doi: 10.1038/nature1041321921910
51. Munger SC, Raghupathy N, Choi K, Simons AK, Gatti DM, Hinerfeld DA, et al. RNA-Seq alignment to individualized genomes improves transcript abundance estimates in multiparent populations. Genetics. 2014;198(1):59–73. Epub 2014/09/23. PubMed Central PMCID: PMC4174954. doi: 10.1534/genetics.114.16588625236449
52. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL, TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome biology. 2013;14(4):R36. Epub 2013/04/27. PubMed Central PMCID: PMC4053844. doi: 10.1186/gb-2013-14-4-r3623618408
53. Anders S, Pyl PT, Huber W, HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. Epub 2014/09/28. PubMed Central PMCID: PMC4287950. doi: 10.1093/bioinformatics/btu63825260700
54. Love MI, Huber W, Anders S, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014;15(12):550. Epub 2014/12/18. PubMed Central PMCID: PMC4302049. doi: 10.1186/s13059-014-0550-825516281
55. Stegle O, Parts L, Piipari M, Winn J, Durbin R, Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nature protocols. 2012;7(3):500–7. doi: 10.1038/nprot.2011.45722343431
56. Lassmann T, Hayashizaki Y, Daub CO, SAMStat: monitoring biases in next generation sequencing data. Bioinformatics. 2011;27(1):130–1. Epub 2010/11/20. PubMed Central PMCID: PMC3008642. doi: 10.1093/bioinformatics/btq61421088025
57. Andrews S. FastQC: A quality control tool for high throughput sequence data. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
58. Storey JD, The positive false discovery rate: a Bayesian interpretation and the q-value. Annals of statistics. 2003:2013–35.
59. Bass JDSwcfAJ DAaRD. qvalue: Q-value estimation for false discovery rate control. R package version 220. 2015.
60. Alexa A, Rahnenfuhrer J, topGO: enrichment analysis for gene ontology. R package version. 2010;2(0).
61. Alexa A, Rahnenführer J, Lengauer T, Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22(13):1600–7. doi: 10.1093/bioinformatics/btl14016606683
62. Demarest K, Koyner J, McCaughran J, JrCipp L, Hitzemann R, Further characterization and high-resolution mapping of quantitative trait loci for ethanol-induced locomotor activity. Behavior genetics. 2001;31(1):79–91. 11529277
63. Legarra A, Aguilar I, Misztal I, A relationship matrix including full pedigree and genomic information. J Dairy Sci. 2009;92(9):4656–63. doi: 10.3168/jds.2009-206119700729
64. Christensen OF, Lund MS, Genomic prediction when some animals are not genotyped. Genetics Selection Evolution. 2010;42(1):1.
65. Aguilar I, Misztal I, Johnson D, Legarra A, Tsuruta S, Lawlor T, Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science. 2010;93(2):743–52. doi: 10.3168/jds.2009-273020105546
66. Vitezica ZG, Aguilar I, Misztal I, Legarra A, Bias in genomic predictions for populations under selection. Genet Res (Camb). 2011;93(5):357–66.
67. Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G, Single-step methods for genomic evaluation in pigs. Animal. 2012;6(10):1565–71. doi: 10.1017/S175173111200074222717310
68. VanRaden P, Efficient methods to compute genomic predictions. Journal of dairy science. 2008;91(11):4414–23. doi: 10.3168/jds.2007-098018946147
69. Aguilar I, Misztal I, Legarra A, Tsuruta S, Efficient computation of the genomic relationship matrix and other matrices used in single‐step evaluation. Journal of Animal Breeding and Genetics. 2011;128(6):422–8. doi: 10.1111/j.1439-0388.2010.00912.x22059575
70. Krohn J. Genes contributing to variation in fear-related behaviour: Oxford, UK; 2013.
71. Du P, Kibbe WA, Lin SM, lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547–8. Epub 2008/05/10. doi: 10.1093/bioinformatics/btn22418467348
72. Barbosa-Morais NL, Dunning MJ, Samarajiwa SA, Darot JF, Ritchie ME, Lynch AG, et al. A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic acids research. 2010;38(3):e17. Epub 2009/11/20. PubMed Central PMCID: PMC2817484. doi: 10.1093/nar/gkp94219923232
73. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids research. 1997;25(17):3389–402. Epub 1997/09/01. PubMed Central PMCID: PMC146917. 9254694
74. Johnson WE, Li C, Rabinovic A, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27. doi: 10.1093/biostatistics/kxj03716632515
75. Kang HM, Sul JH, Service SK, Zaitlen NA, Kong S-y, Freimer NB, et al. Variance component model to account for sample structure in genome-wide association studies. Nature genetics. 2010;42(4):348–54. doi: 10.1038/ng.54820208533
76. Searle SR, Matrix algebra useful for statistics: Wiley; 1982.
77. Murphy KP, Machine Learning: A Probabilistic Perspective: The MIT Press; 2012. 1096 p.