View source for Envirome

{{Short description|Total set of environmental factors affecting an organism}}
{{Redirect|Enviromics|environmental omics (the omics of eDNA)|Environmental DNA}}
"Envirome" is a concept that relates the core of environmental conditions with the successful biological performance of living beings. This concept was created in [[genetic epidemiology]], in which an '''envirome''' is defined as the total set of environmental factors, both present, and past, that affect the state, and in particular the disease state, of an organism.<ref>{{cite web|title=-Omes and -omics glossary & taxonomy|url=http://www.genomicglossaries.com/content/omes.asp|publisher=Cambridge Healthtech Institute|accessdate=25 April 2013}}</ref> The study of the envirome and its effects is termed '''enviromics'''. The term was first coined in the field of psychiatric epidemiology by J.C. Anthony in 1995.<ref name=Gad2008>{{cite book|last=Shayne Cox Gad|title=Preclinical Development Handbook: Toxicology|year=2008|publisher=John Wiley & Sons|isbn=9780470249048|page=778}}</ref><ref name=Cooper2001>{{cite journal|last=Cooper|first=Brian|title=Nature, nurture and mental disorder: old concepts in the new millennium|journal=The British Journal of Psychiatry|year=2001|volume=178|issue=suppl. 40|pages=91–102|doi=10.1192/bjp.178.40.s91|pmid=11315233|doi-access=free}}</ref> More recently, use of the term has been extended to the cellular domain, where ''cell functional enviromics'' studies both the genome and envirome from a systems biology perspective.<ref name=Teixeira2011>{{cite journal|last1=Teixeira|first1=Ana P|last2=ML Dias |first2=João |last3=Carinhas |first3=Nuno |last4=Sousa |first4=Marcos |last5=J Clemente |first5=João |last6=E Cunha |first6=António |last7=von Stosch |first7=Moritz |last8=M Alves |first8=Paula |last9=JT Carrondo |first9=Manuel |last10=Oliveira |first10=Rui |title=Cell functional enviromics: Unravelling the function of environmental factors|journal=BMC Systems Biology|year=2011|volume=5|pages=92–108|url= |doi=10.1186/1752-0509-5-92|pmc=3118353 |pmid=21645360 |doi-access=free }}</ref> In plants, '''enviromics''' is directly related to complex [[ecophysiology]], in which the wide [[Biophysical environment|environment]] of the plants, into an [[omics]] scale, can be dissected and understood as a mosaic of possible growing factors and the balance of diverse resources available.<ref name=Resende2021>{{cite journal|last1=Resende|first1=Rafael T.|last2=Piepho|first2=Hans-Peter|last3=Rosa|first3=Guilherme|last4=Silva-Junior|first4=Orzenil B.|last5=Resende|first5=Marcos Deon V.|last6=Grattapaglia|first6=Dario|title=Enviromics in breeding: applications and perspectives on envirotypic-assisted selection|journal=Theoretical and Applied Genetics|year=2021|volume=134|issue=4 |pages=95–112|url= https://rdcu.be/cI2gB|doi=10.1007/s00122-016-2691-5|doi-access=free|pmid=26932121 |pmc=4799247}}</ref><ref name="ReferenceA">Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., and Fritsche-Neto, R. (2021b). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3 Genes|Genomes|Genetics. doi:10.1093/g3journal/jkab040.</ref> In [[ecology]], this concept can be related to the [[Shelford's law of tolerance]].<ref name="Costa-Neto, G. 2021">Costa-Neto, G., Crossa, J., and Fritsche-Neto, R. (2021a). Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. Frontiers in Plant Science 12. doi:10.3389/fpls.2021.717552.</ref> The ''enviromics'' (study of the enviromes) is conceived as a pillar of the Modern [[Plant Breeding]],<ref>Crossa, J., Fritsche-Neto, R., Montesinos-lopez, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-lopez, A., et al. (2021). The Modern Plant Breeding Triangle : Optimizing the Use of Genomics , Phenomics , and Enviromics Data. Frontiers in plant science 12, 1–6. doi:10.3389/fpls.2021.651480.</ref> capable to connect the design and development of breeding goals concealing it with the agronomic targets for a [[climate-smart agriculture]].<ref>Cooper, M., and Messina, C. D. (2021). Can We Harness “Enviromics” to Accelerate Crop Improvement by Integrating Breeding and Agronomy? Frontiers in Plant Science 12. doi:10.3389/fpls.2021.735143.</ref> It also has the ability to bridge the knowledge gaps between the different levels of [[systems biology]] and [[phenomics]] in the context of [[Gene–environment interaction]].<ref>Costa-Neto, G., and Fritsche-Neto, R. (2021). Enviromics: bridging different sources of data, building one framework. Crop Breeding and Applied Biotechnology 21, https://cbab.sbmp.org.br/2021/07/06/article-enviromics-bridging-different-sources-of-data-building-one-framework/</ref>

==Envirome classification in humans==

While there can be both positive and negative effects of the envirome on the organism, negative effects are often emphasized in discussing disease. A typology of envirome health hazards suggested by McDowall is natural physico-chemical, man-made physico-chemical, biological/organic, natural or man-made, macrosocial, micro- or psychosocial.<ref name=Cooper2001 /> One approach to classifying the envirome is to organize the factors based on their likely disease-specific causality such as cardiovascular disease <ref>{{Cite journal|last1=Riggs|first1=Daniel W.|last2=Yeager|first2=Ray A.|last3=Bhatnagar|first3=Aruni|date=2018-04-27|title=Defining the Human Envirome: An Omics Approach for Assessing the Environmental Risk of Cardiovascular Disease|url=http://circres.ahajournals.org/content/122/9/1259|journal=Circulation Research|language=en|volume=122|issue=9|pages=1259–1275|doi=10.1161/CIRCRESAHA.117.311230|issn=0009-7330|pmid=29700071|pmc=6398443}}</ref>

The time-scale of the envirome hazard is another possible dimension of classification; an envirome hazard are said to be a sudden change (such as a disaster), a rapid environmental change, or a slow change or a static situation.<ref name=Cooper2001 /> In [[twin studies]], envirome influences are often decomposed into shared environmental factors, common to both twins and non-shared environmental factors that differ between the twins.<ref name="Neiderhiser2018">{{cite journal|last1=Neiderhiser|first1=Jenae M.|title=Understanding the roles of genome and envirome: methods in genetic epidemiology|journal=British Journal of Psychiatry|volume=178|issue=S40|year=2018|pages=s12–s17|issn=0007-1250|doi=10.1192/bjp.178.40.s12|pmid=11315218|doi-access=free}}</ref>

==Envirome classification in plants==

In plants, the enviromics term was probably first scientifically mentioned by Xu,<ref name=Xu2016>{{cite journal|last1=Xu|first1=Yunbi|title=Envirotyping for deciphering environmental impacts on crop plants|journal=Theoretical and Applied Genetics|year=2016|volume=129|issue=4 |pages=653–673|url= https://rdcu.be/cI2gE|doi=10.1007/s00122-016-2691-5|doi-access=free|pmid=26932121 |pmc=4799247}}</ref> in his iconic article on '''Envirotyping''', and also comprehensively described by Resende et al.,<ref name=Resende2019>{{cite journal|last1=Resende|first1=Rafael T.|last2=Piepho|first2=Hans-Peter|last3=Silva-Junior|first3=Orzenil B.|last4=Resende|first4=Marcos Deon V.|last5=Grattapaglia|first5=Dario|title=Enviromics in breeding: applications and perspectives on envirotypic-assisted selection|journal=bioRxiv|year=2019|pages=1–23|doi=10.1101/726513 |s2cid=201196743 |url=https://www.biorxiv.org/content/10.1101/726513v1.full}}</ref> which is the field of applied [[data science]] that integrates databases of environmental factors into [[quantitative genetics]].<ref name="Costa-Neto, G. 2021"/> Then, it can leverage an important plant ecophysiology knowledge capable to bridge the gaps about how the environment acts across different levels of the systems biology ([[genes]], [[Transcript (biology)|transcripts]], [[proteins]], and [[metabolites]]). Consequently, it can boost the ability to better understand/model the [[phenotypic plasticity]] of the main agronomic traits under diverse growing conditions. The plant breeding community has experienced reduced costs for acquiring environmental sensors (e.g., [[weather stations]]) to be installed in the field trials while increasing the reliability and resolution of the [[remote sensing]] techniques.<ref name="ReferenceA"/> The combination of those two factors has started the spring of enviromics-aided breeding in recent years. Recently, Costa-Neto et al. <ref name="Costa-Neto, G. 2021"/> introduced the concept of '''enviromic-aided genomic prediction''' involving the use of [[adaptation]] typologies to process the raw environmental data into a reliable descriptor of the environmental diversity. This data is then used for training accurate GxE [[prediction]] models, mostly involving [[molecular breeding]] protocols in [[agriculture]] and [[forestry]] improvement.
[[File:SFig2A.jpg|thumb|416x416px|Genetic methods can be applied to elucidate the phenotypic variations caused by enviromics.<ref name=":0" />]]
[[Phenotypic plasticity]], the ability of an organism to express different traits in response to internal and external environmental factors, is influenced by both genetic and environmental factors. Similar to how genetics approaches have been used to identify and predict performance based on genetic markers, the contribution of environmental factors to phenotypic plasticity can be systematically analyzed and predicted.<ref name=":0">{{Cite journal |last1=Li |first1=Xianran |last2=Guo |first2=Tingting |last3=Bai |first3=Guihua |last4=Zhang |first4=Zhiwu |last5=See |first5=Deven |last6=Marshall |first6=Juliet |last7=Garland-Campbell |first7=Kimberly A. |last8=Yu |first8=Jianming |date=2022-02-07 |title=Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions |journal=Molecular Plant |volume=15 |issue=2 |pages=203–206 |doi=10.1016/j.molp.2022.01.001 |issn=1752-9867 |pmid=34999020|doi-access=free }}</ref> The Critical Environmental Regressor through Informed Search (CERIS <ref>{{Cite journal |last1=Li |first1=Xianran |last2=Guo |first2=Tingting |last3=Wang |first3=Jinyu |last4=Bekele |first4=Wubishet A. |last5=Sukumaran |first5=Sivakumar |last6=Vanous |first6=Adam E. |last7=McNellie |first7=James P. |last8=Tibbs-Cortes |first8=Laura E. |last9=Lopes |first9=Marta S. |last10=Lamkey |first10=Kendall R. |last11=Westgate |first11=Mark E. |last12=McKay |first12=John K. |last13=Archontoulis |first13=Sotirios V. |last14=Reynolds |first14=Matthew P. |last15=Tinker |first15=Nicholas A. |date=2021-06-07 |title=An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops |journal=Molecular Plant |language=en |volume=14 |issue=6 |pages=874–887 |doi=10.1016/j.molp.2021.03.010 |issn=1674-2052|doi-access=free |pmid=33713844 }}</ref><ref>{{Cite journal |last1=Guo |first1=Tingting |last2=Mu |first2=Qi |last3=Wang |first3=Jinyu |last4=Vanous |first4=Adam E. |last5=Onogi |first5=Akio |last6=Iwata |first6=Hiroyoshi |last7=Li |first7=Xianran |last8=Yu |first8=Jianming |date=2020-05-01 |title=Dynamic effects of interacting genes underlying rice flowering-time phenotypic plasticity and global adaptation |url=https://genome.cshlp.org/content/30/5/673 |journal=Genome Research |language=en |volume=30 |issue=5 |pages=673–683 |doi=10.1101/gr.255703.119 |issn=1088-9051 |pmid=32299830|pmc=7263186 }}</ref><ref>{{Cite journal |last1=Li |first1=Xin |last2=Guo |first2=Tingting |last3=Mu |first3=Qi |last4=Li |first4=Xianran |last5=Yu |first5=Jianming |date=2018-06-26 |title=Genomic and environmental determinants and their interplay underlying phenotypic plasticity |journal=Proceedings of the National Academy of Sciences |language=en |volume=115 |issue=26 |pages=6679–6684 |doi=10.1073/pnas.1718326115 |issn=0027-8424 |pmc=6042117 |pmid=29891664 |doi-access=free |bibcode=2018PNAS..115.6679L }}</ref><ref>{{Cite journal |last1=Mu |first1=Qi |last2=Guo |first2=Tingting |last3=Li |first3=Xianran |last4=Yu |first4=Jianming |title=Phenotypic plasticity in plant height shaped by interaction between genetic loci and diurnal temperature range |journal=New Phytologist |date=2022 |language=en |volume=233 |issue=4 |pages=1768–1779 |doi=10.1111/nph.17904 |issn=0028-646X|doi-access=free |pmid=34870847 }}</ref>) uses whole-season environmental variants to identify major explicit environmental conditions that contribute to performance, similar to how [[Qtl|QTL]]/ [[GWAS]] analysis identifies major genes from genome-wide markers. Enviromic prediction can be used to predict how an organism will perform under new growth conditions based on analysis of the whole-season environmental variants, akin to how genomic prediction is used to predict the performance of new genotypes.

==Genotype-environment correlation and interaction==

The effect of an envirome on an organism can be potentially modulated by its genetic makeup, i.e., its genome. The two main ways genes and environment may interact is through [[genotype-environment correlation]] and [[genotype-environment interaction|interaction]].<ref name=Neiderhiser2018/> Genotype-environment correlation occurs because, for example, children both inherit genes from their parents and live under the influence of their parents.<ref name=Neiderhiser2018/>  In the context of genetic epidemiology, interaction refers to the genes and the environment both participating in a causal way that departs from a simple additive model of the effects.<ref name=Cooper2001 /> An example of a genotype-environment interaction is the increased risk of getting [[Alzheimer's disease]] following a head injury in persons carrying the APOE <math>\epsilon 4</math> allele.<ref>{{cite journal|last=Nicoll|first=J.A. |author2=Roberts, G. W. |author3=Graham, D. I.|title=Amyloid beta-protein, APOE genotype and head injury (review)|journal=Annals of the New York Academy of Sciences|year=1996|volume=777|pages=271–275|doi=10.1111/j.1749-6632.1996.tb34431.x|pmid=8624097 |s2cid=84564798 }}</ref>

==Criticism in human health==

Some researchers see ''envirome'' as a renaming of the already well-established ''nurture'' component of the [[nature-nurture dichotomy]] in explaining psychological behavior.<ref name=Cooper2001 /> [[Steven Rose]] has argued that in [[psychiatry]], it is time to abandon the genome-envirome dichotomy altogether in favor of an integrative view of a person's life course.<ref>{{cite journal|last=Rose|first=S.|title=Moving on from old dichotomies: beyond nature ^ nurture towards a lifeline perspective|journal=British Journal of Psychiatry|year=2001|volume=178|issue=suppl. 40|pages=s3–s7|doi=10.1192/bjp.178.40.s3|pmid=11315221|url=http://bjp.rcpsych.org/content/bjprcpsych/178/40/s3.full.pdf|doi-access=free}}</ref>

==See also==
* [[Environmental epidemiology]]
* [[Epidemiology]]
* [[Exposome]]
* [[Molecular epidemiology]]
* [[Ecophysiology]]
* [[Quantitative Genetics]]
* [[Phenomics]]

== References==
{{Reflist|30em}}

[[Category:Genetic epidemiology]]