Envirome

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. One approach to classifying the envirome is to organize the factors based on their likely disease-specific causality such as cardiovascular disease {{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}}

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. 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.{{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}}

In plants, the enviromics term was probably first scientifically mentioned by Xu,{{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}} in his iconic article on Envirotyping, and also comprehensively described by Resende et al.,{{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}} which is the field of applied data science that integrates databases of environmental factors into quantitative genetics. 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, 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. The combination of those two factors has started the spring of enviromics-aided breeding in recent years. Recently, Costa-Neto et al. 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.

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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.{{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 }} The Critical Environmental Regressor through Informed Search (CERIS {{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 }}{{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 }}{{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 }}{{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 }}) uses whole-season environmental variants to identify major explicit environmental conditions that contribute to performance, similar to how 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.

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 interaction. Genotype-environment correlation occurs because, for example, children both inherit genes from their parents and live under the influence of their parents. 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. 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 $\backslash epsilon\; 4$ allele.{{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 }}

Some researchers see envirome as a renaming of the already well-established nurture component of the nature-nurture dichotomy in explaining psychological behavior. 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.{{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}}

• Environmental epidemiology
• Epidemiology
• Exposome
• Molecular epidemiology
• Ecophysiology
• Quantitative Genetics
• Phenomics

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Category:Genetic epidemiology