Can it be said that proteins determine phenotypic traits?

Can it be said that proteins determine phenotypic traits?

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I am not ignoring the function of genes, or the gene-environment interactions. What I want to know is if behind every "observable characteristic" we can find a protein -or a group of proteins- which is responsible for that trait. The idea comes from a school curriculum, and it is expressed as follows:

In this unit the core topic is the central role of proteins as executors of fundamental biological functions and thus as responsible of an organism phenotype…

Thank you in advance.

A general note on phenotype

I think that the meaning of phenotype has become more complicated over time compared to what it was when it was first coined. As you said, in simple words, a phenotype is an observable characteristic of an organism. However, for Mendel, an observable characteristic would have been only an obviously gross change in the morphology whereas with the current state of technology we can observe the assembly of macromolecules inside a cell. We can also study the physiology of an organism/tissue/cell in real time using various sensors. We can also observe the gene expression profile using different technologies (with different levels of sensitivity and resolution). The question now is, can you call such a characteristic (for e.g. an assembly of some complex in a cell observed by cryo-EM) a phenotype? In other words "what is an observable"?

Coming back to your question:

Can it be said that proteins determine phenotypic traits?


Are they the sole determinants of phenotypic traits?

No. A phenotype is usually determined by many factors. Many genes are involved, which may code for proteins as well as functional non-coding RNAs. However, if you make a comparison, proteins seem to be more versatile than ncRNAs (also most of them are involved in direct action). So you may say that proteins are the primary determinants of most phenotypic traits, but I would generally advise that such generalizations should be used cautiously. I'll give a counter example in favour of RNAs. If there is a mutation in a tRNA or a rRNA, you'll see a great effect on the phenotype.

Yep, that is correct in general. However, sometimes proteins indirectly determine phenotype. For example, the skin colour is dependent on melanin (not a protein) but the melanin producing cells work because of proteins. Further, melanin production is also controlled by proteins. Thus, skin colour is not DIRECTLY determined by proteins but because proteins produce the pigment melanin, proteins are ultimately the reason behind our skin colour phenotype.

Brain Basics: Genes At Work In The Brain

Genes do more than just determine the color of our eyes or whether we are tall or short. Genes are at the center of everything that makes us human.

Genes are responsible for producing the proteins that run everything in our bodies. Some proteins are visible, such as the ones that compose our hair and skin. Others work out of sight, coordinating our basic biological functions.

For the most part, every cell in our body contains exactly the same genes, but inside individual cells some genes are active while others are not. When genes are active, they are capable of producing proteins. This process is called gene expression. When genes are inactive, they are silent or inaccessible for protein production.

At least a third of the approximately 20,000 different genes that make up the human genome are active (expressed) primarily in the brain. This is the highest proportion of genes expressed in any part of the body. These genes influence the development and function of the brain, and ultimately control how we move, think, feel, and behave. Combined with the effects of our environment, changes in these genes can also determine whether we are at risk for a particular disease and if we are, the course it might follow.

This brochure is an introduction to genes, how they work in the brain, and how genomic research is helping lead to new therapies for neurological disorders.


Human ageing is associated with a number of changes in how the body and its organs function 1 . Among visible signs of ageing are greying of hair, changes in posture and loss of skin elasticity 2,3 . Less noticeable signs include hearing loss, increase in blood pressure or sarcopenia 4 . On the molecular level, ageing is associated with numerous processes, such as telomere length reduction, changes in metabolic and gene-transcription profiles and an altered DNA-methylation pattern 5,6,7,8,9,10 . In addition to chronological time, lifestyle factors such as smoking or stress can affect both the pattern of DNA-methylation 11 and telomere length 12 and thereby the aging of an individual. Ageing and lifestyle are the strongest known risk factors for many common non-communicable diseases, hence, lifestyle factors or molecular markers have been used as 5-year mortality predictors 13,14 . Additionally, specific food-items have been associated with lowered all cause mortality 15 . Various predictor models have been developed using measures of facial morphology 16 , physical fitness and physiology 12,17 , telomere length 18 and methylation pattern 6 to predict ones chronological age. Remarkably, some models are able to predict chronological age with correlation coefficients (R 2 ) to actual age up to 0.75 and even above 0.90, when based on DNA-methylation status over 353 or 71 CpG-sites 6,19 . Comparisons of the actual chronological age with the predicted age, sometimes denoted the biological age, can be used as an indicator of health status, monitor the effect of lifestyle changes and even aid in the decision on treatment strategies for cancer patients 16,20 . To date, no current models have explored the potential of using the plasma protein profile for age prediction. Furthermore, while lifestyle factors such as stress have been shown to affect the rate of cellular ageing 12 , to the best of our knowledge, no studies have examined the effect of a wide range of lifestyle factors, including smoking or dietary habits, on the predicted age. We have previously characterized abundance levels of 144 circulating plasma proteins using the proximity extension assay (PEA) and have found over 40% of investigated proteins to be significantly correlated with one or more of the following factors, age, weight, length and hip circumference 10,21 . We therefore reasoned that the plasma protein profile might also be predictive of these traits. Here we demonstrate for the first time that the profile of circulating plasma proteins can be used to accurately predict chronological age, as well as anthropometrical measures such as height, weight and hip circumference. Moreover, we used the plasma protein-based model to identify lifestyle choices that accelerate or decelerate the predicted age. The protein analysis method used has previously been applied to dried blood spot material 22 . Interestingly, the ability to accurately predict anthropometrical characteristics from a dried blood spot sample could potentially be applicable in forensic investigations.

A formal theory of the selfish gene

Population genetics

The proper basis of evolutionary biology is population genetics, so any formal theory of selfish genes must make reference to the genetics of populations. Here, we develop the dynamical aspects of the theory using standard population genetic principles and assumptions. We assume a very large, but finite, population of gene positions. These are places where physical portions of genetic material – hereafter, ‘genes’– occur. For example, if we consider two individuals, each containing three diploid cells with four loci per haploid genome, we have 2 × 3 × 2 × 4 = 48 gene positions. For simplicity, we assume that all gene positions are equivalent (genes do not belong to separate ‘classes’ Grafen, 2006b ) and that each is occupied by a single gene. Put another way, there are no intrinsic differences between gene positions, aside from the alleles that occupy them. We assume that only a finite number of allelic variants are possible. We assume discrete (although potentially overlapping) generations and also that the number of gene positions remains fixed at N. We assign every gene position (and hence also its occupant gene) in a focal generation a unique index iI, and for the purpose of computing population statistics, we give every gene position an equal weighting 1/N. Thus, for any quantity x that varies across genes positions, the arithmetic average of this quantity over the population is EI(x) = ∑Ixi/N. Notation is summarized in Table 2.

Evolutionary model Optimization programme
With and without social interactions:
Number of genes/gene positions N N Number of agents
Gene/gene-position index i i Agent index
Set of gene/gene-position indices I I Set of agent indices
Reproductive outcome ω
Set of all possible outcomes Ω
Probability of outcome ω q ω
Fitness of gene i under outcome ω w i ω
Expected fitness of gene i w i = ∑Ωq ω wi ω
Genic value of gene i g i
Average genic value of gene i’s descendants under outcome ω g i′ ω = gi + Δgi ω
Allelic state of gene i a i
Set of alleles A
Genotype function (a)
Phenotype of gene i πi s i Strategy of agent i
Set of all phenotypes P S Strategy set
Phenotype function (a)
Without social interactions:
Fitness function (π) (s) Objective function
With social interactions:
Unordered list of all phenotypes in the population Π Context parameter
Role index j
Set of all role indices J
Phenotype of role-j social partner of gene i πij
Ordered list of phenotypes belonging to gene i’s social set
Fitness function in context Π (Π) = B(Π) + ∑JAj,jΠ) + N(Π)
Baseline fitness in context Π B(Π) = 1
Additive effect of role-j social partner phenotype π upon personal fitness in context Π Aj,jΠ)
Nonadditive effect of social partner phenotypes upon personal fitness in context Π N(Π)
Coefficient of relatedness between focal gene and its role-j social partner r j = covI(gj,g)/covI(g,g)
Inclusive fitness in context Π (πΠ) = B(Π) + ∑JA(π,jΠ)rj (s℘) Objective function in context ℘

The fitness of gene i in the current generation is defined as the number of gene positions in the subsequent generation that receive their genetic material from gene position i. This captures both the physical survival of genetic material between generations and also the synthesis of new genetic material (replication). We allow for stochastic fitness effects: owing to finite gene positions and finite allelic variants, there are a finite number of possible states in which the next generation can manifest. We assign each possible outcome a unique index ω ∈ Ω, and the probability of this outcome is q ω . The fitness of gene i under outcome ω is wi ω , and the average of this quantity over uncertainty is wi = ∑Ωq ω wi ω . Owing to the assumption of fixed population size, the average fitness of all genes is EI(w ω ) = EI(w) = 1.

We assign every gene an ‘allele’aiA, according to its nucleic acid sequence, where A is the set of all possible alleles. Next, we assign every gene a numerical ‘genic value’ according to its allele, i.e. gi = (ai), where is the ‘genotype function’. The assignment of genic values to alleles is arbitrary. For example, we may choose to assign a focal allele a genic value of 1 and all other alleles a genic value of 0, such that the average genic value corresponds to the population frequency of the focal allele. Alternatively, the assignment of genic values to alleles might be with reference to their phenotypic effects (i.e. average effects Fisher, 1918 ). We denote the average genic value of the descendants of gene i in the next generation (under outcome ω) as gi′ ω = gi + Δgi ω . Change in genic value between parent and offspring genes (Δgi ω ≠ 0) may occur, owing to factors such as spontaneous mutation (i.e. change in a) or generational differences in the average effect of an allele (i.e. changes to Fisher, 1941 ).

Genic selection

The first term on the RHS of eqn 1 is the covariance, taken across all gene positions in the population, of a gene’s fitness and its genic value, and this defines the action of ‘selection’ at the gene level. The second term on the RHS of eqn 1 is the expectation, taken across all gene positions in the population, of the product of a gene’s fitness and the difference between its own genic value and that of its offspring, and this defines the ‘transmission’ effect. Transmission includes factors such as mutation (i.e. change in allelic variant) and change in the average effect of a gene between generations, and there is no reason to suspect that it is negligible (to be discussed later in this article).

This formalism for genic selection, mediated by fitness differences between genes, is analogous to expressions for natural selection, mediated by fitness differences between individual organisms ( Price, 1970 see also Robertson, 1966, 1968 ). However, gene fitness may be literally different from organism fitness: it represents the gene’s success in gaining gene positions in the subsequent generation, which may or may not involve the improvement of the carrier organism’s reproductive success (or, indeed, fitness effects at the group level). For example, the formalism allows for gene conversion, transposition and meiotic drive: mechanisms that potentially incur fitness costs for the organism ( Burt & Trivers, 2006 ) and that are traditionally regarded as accruing to the ‘transmission’ component of evolutionary change ( Price, 1970 ).

Adaptation and the optimization program

The optimization program (4) describes an (implicit) agent with an agenda and an instrument to be employed in the pursuit of its agenda. Specifically, the agent has a set of strategies S available to it (i.e. ways in which it may wield the instrument), and each strategy sS assigned a corresponding real value by the objective function (s), according to how well the strategy performs in the pursuit of the agenda (the larger the value, the closer the agent is to having realized its objective).

Thus, the notion of purpose is expressed as a maximization problem: the agent seeks the strategy that will maximize its objective function ( von Neumann & Morgenstern, 1944 ). This leads to a formal definition of an optimum. An optimal strategy is one that (i) belongs to the strategy set and (ii) when given as input to the objective function, returns an output that is equal to or greater than that of all other strategies in the set. Formally, an optimal strategy is s* where (s*) ≥ (s) ∀sS. Conversely, a suboptimal strategy is one that (i) belongs to the strategy set and (ii) returns a lower output from the objective function than at least one other member of the set. Formally, a suboptimal strategy is s° where ∃sS:(s°) < (s). Importantly, although the optimization program provides a formal definition for optimality, it does not imply that optimality obtains – i.e. it sets a maximization problem, which may or may not be solved by the agent. This formalization therefore captures two important ideas about adaptation that were emphasized by Paley (1802) : biological adaptations show contrivance with respect to some end, but this does not imply perfection, or even optimality within constraints ( Parker & Maynard Smith, 1990 Gardner, 2009 ).

Gene as fitness-maximizing agent

The traditional view of adaptation at the level of the individual ( Darwin, 1859 Hamilton, 1964, 1970, 1996 Ch. 2) has been formalized by identifying the agent described in an optimization program with an individual organism ( Grafen, 2002, 2006a, 2007, 2009 ). Here, we are interested in forming a ‘gene as maximizing agent’ analogy, i.e. the idea that the gene is a purposeful agent with an agenda. Hence, the first step in forming the analogy is to identify the gene as the agent, which we do by assigning every agent the index i of the corresponding gene.

Second, we choose a phenotype, associated with the gene, to be the agent’s instrument. Note that an instrument must be under the sole control of the corresponding agent. Hence, in order for the phenotype to fulfil this role, we must consider only phenotypes that are fully determined by the allele encoded by the corresponding gene. We denote the phenotype associated with this gene position by πi = (ai), where is the ‘phenotype function’ that relates allele to phenotype. Because there are a finite number of possible alleles, there are a finite set of possible phenotypes, which we denote P. With this notation in place, we may identify the phenotype as the gene’s strategy, i.e. πisi, and the set of possible phenotypes as the strategy set, i.e. PS.

This provides a formal statement of what we mean when we say that a gene is responsible for a phenotype and that the function of the phenotype is to maximize the gene’s fitness. It provides a rigorous basis for forming statements about the optimality of phenotypes. In particular, an optimal phenotype is one that (i) belongs to the set of possible phenotypes and (ii) achieves an expected fitness that is equal to or greater than that of any other phenotype in the set. Formally, an optimal phenotype is π* where (π*) ≥ (π) ∀πP. Conversely, a suboptimal phenotype is one that (i) belongs to the set of possible phenotypes and (ii) achieves lower expected fitness than at least one other member of the set. Formally, a suboptimal phenotype is π° where ∃π ∈ P:(π°) < (π). Importantly, although the gene as maximizing agent analogy (6) provides a formal definition for phenotype optimality, it does not imply that optimality obtains – i.e. it defines the function of the phenotype, without stating that the gene achieves maximum fitness.

Formal justification for the selfish gene

We have developed an evolutionary model that describes how selection acting upon genes drives genetic change of populations – summarized by dynamical equation (3). We have also developed a formal account of what it means to say that a gene is striving to maximize its fitness – summarized by optimization program (6). Here, we seek formal justification for the selfish-gene view, by establishing mathematical correspondences between the equations of motion (genetic change) and the calculus of purpose (genes maximizing their fitness). The translation of purpose into dynamics reveals that the optimization view recovers the results of a population-genetic analysis. The translation of dynamics into purpose captures the way in which genic selection gives rise to the emergence of apparently selfish genes.

We derive six correspondences between our dynamical and purposeful accounts of genic adaptation (Table 3 derivations given in Appendix). Correspondences I–V translate gene-optimization scenarios into population-genetic results. Specifically, if all agents described in the optimization view are behaving optimally, then this corresponds to a scenario in the population-genetic model where there is no selection with respect to any genic value (no ‘scope for selection’ I), and where no allele in the permitted set can be introduced to the population, which will increase in frequency from rarity under the action of genic selection (no ‘potential for positive selection’ II) if all agents are suboptimal, but equally so, then there is no scope for selection (III) but there is potential for positive selection, i.e. there exists an allele that, if introduced into the population, is expected to increase in frequency from rarity, under the action of genic selection (IV) and if agents vary in their optimality, then there is scope for selection, and the selective change in the average of every genic value and in every allele frequency is given by its covariance, across all gene positions, with relative attained maximand (i.e. relative fitness V). Arising from these five correspondences is a further correspondence (VI), translating in the opposite direction, which states that if there is neither scope for selection nor potential for positive selection, then all agents must be behaving optimally.

Numeral Correspondence
I If all agents are optimal, there is no scope for selection (no expected change in the average of any genic value)
II If all agents are optimal, there is no potential for positive selection (no introduced allele can increase from rarity due to selection)
III If all agents are suboptimal, but equally so, there is no scope for selection (no expected change in the average of any genic value)
IV If all agents are suboptimal, but equally so, there is potential for positive selection (there exists an allele that, if introduced, can increase from rarity due to selection)
V If agents vary in their optimality, there is scope for selection, and change in the average of all genic values, and in all gene frequencies, is given by their covariance with relative attained maximand
VI If there is neither scope for selection nor potential for positive selection, all agents are optimal

These correspondences are analogous to those derived by Grafen (2002, 2006a) , for the ‘individual as maximizing agent’ analogy, that justify seeing organisms as economic agents, and by Gardner & Grafen (2009) , for the ‘clonal group as maximizing agent’ analogy, that do the same for colonies of clonal organisms. We have shown that genes also formally qualify as adaptive agents, insofar as our simplified biological model can be regarded as painting an accurate picture of the real world. One major limitation of the model is that it assumes that genes do not impact upon each other’s fitness, aside from a density-dependent scaling effect. The next section relaxes this assumption.

Materials and Methods

Plant materials and growth conditions

The t483 mutant (japonica cv. Nipponbare) was obtained from an EMS (Ethyl methanesulfonate)-mutagenized population. Nipponbare was used as a WT line for phenotypic observation and gene expression analysis. All materials for crossing and analysis were grown in the experimental field at the Chinese Academy of Agricultural Sciences, Beijing and Sanya.

For germination analysis, rice seeds were soaked in tap-water at 37°C in the dark for three days. The soaked seeds were incubated at 28°C with 12 h of light and 12 h of darkness for eight days, then seed germination rate was measured by counting only those seeds with shoots longer than 2 cm. For gibberellin treatment, mutant and WT seeds were surface sterilized in 2.5% NaClO, soaked with tap-water in sterile Petri dishes at 37°C in the dark for one day. Seeds were then soaked in different concentrations of gibberellin acid solution and incubated at 28°C with 12 h of light and 12 h of darkness.

Chlorophyll content measurement and transmission electron microscopy analysis

Chlorophyll contents were measured using a spectrophotometer according to the method of Arnon [18] with minor modifications. Briefly, equal weights of freshly collected second top leaves from two-week-old seedlings were marinated in 95% ethanol for 48 h in darkness. For thorough chlorophyll extraction tubes were periodically inverted five times. Residual plant debris was removed by centrifugation. The supernatants were used to measure chlorophyll content by a DU 800 UV/Vis spectrophotometer (Beckman Coulter) at 665, 649 and 470 nm.

For transmission electron microscopy analysis, leaf samples from two-week-old plants growing in paddy field were first fixed in 2% glutaraldehyde solution and then transferred into 1% OsO4 for two days. After fixation, samples were stained with uranyl acetate and dehydrated in an ethanol series, and then embedded in Spurr’s medium before ultrathin sectioning. Samples were stained with uranyl acetate again and observed with a transmission electron microscope (Hitachi H-7650, Japan).

Map-based cloning of the mutated gene in t483

To map the mutated gene, t483 was crossed with cv. 93–11 (indica). The plants with the mutant phenotype in F2 population were selected for a genetic linkage analysis. Molecular markers distributed throughout the rice genome were chosen for preliminary mapping [19, 20]. Additional Insertion-deletion (IN) markers for fine mapping were developed according to the DNA sequence difference between japonica and indica. PCR procedure was as following: 95°C for 5 min, followed by 35 cycles of 95°C for 30 s, annealing for 30 s, extension 72°C for 30 s, and a final extension at 72°C for 5 min.

Generation of transgenic rice plants

Because the genomic sequence of CHR729 was very large, containing 13,848 bp (not including promoter sequence), it was difficult to construct a complementary transformation plasmid. We therefore used a pCUbi1390-ΔFAD2 RNAi vector to generate a CHR729-RNAi construct [21]. The specific region of CHR729 used for the RNAi construct was identified by alignment with the basic local alignment search tool ( A 346 bp specific fragment of the CHR729 gene was amplified with primer pairs RNAi-SF/ RNAi-SR and RNAi-AF/ RNAi-AF, then cloned into the pCUbi1390-ΔFAD2 vector as described by Mao et al. [22]. The RNAi construct was introduced into wild type Nipponbare by Agrobacterium tumefaciens-mediated transformation as previous report and empty pCUbi1390 vector was also introduced as a control [23].

Quantitative real-time PCR analysis

Total RNA was extracted from various tissues using RNA Prep Pure Plant Kit (Tiangen Co., Beijing), and was reverse transcribed using a SuperScript II Kit (TaKaRa), according to the user’s manual. qRT-PCR (quantitative real-time PCR) analyses were performed using the 7900 HT Fast Real-Time PCR System (ABI). UBIQUITIN gene (Os03g0234200) was chosen as a reference gene. Reactions containing SYBR premix (TaKaRa) were carried out in final volumes of 20 μL with 2 pmol of the appropriate primers. The 2 —ΔΔCT method was used to calculate relative levels of gene expression [24].

Subcellular localization of CHR729

To determine the subcellular localization, the CHR729 cDNA fragment was amplified (primer pair GFP-F/GFP-R) and ligated into pA7-GFP vector (p35S-CHR729-GFP). As a control, the cDNA of a previously characterized nuclear protein, OsMADS3, was fused to the mCherry gene (p35S-OsMADS3-mCherry) [25]. Protoplasts were isolated from rice seedlings, and co-transfected with the p35S-CHR729-GFP and p35S-OsMADS3-mCherry vectors. The transformed protoplasts were incubated at 28°C in darkness for 16 h before detection. Fluorescence of GFP was observed using a confocal laser scanning microscope (Leica TCS SP5).


Two-week-old whole WT and t483 seedlings were immediately frozen in liquid nitrogen and stored at -80°C. Material from five different plots was pooled together. Total RNA was extracted using a RNA Prep Pure Plant Kit (Tiangen Co., Beijing), and treated with RNase-free DNase I (NEB, Ipswich, MA, USA) to remove any genomic DNA contamination. For each sample, at least 10 μg of total RNA was used for illumina HiSeq2000 sequencing conducted at Beijing Novo Co. (Beijing). After sequencing, the raw reads were first purified by trimming adapter sequences and removing low-quality sequencing date. The clean reads were mapped to the reference genome of japonica cv. Nipponbare using SOAP2 software [26]. Genes differentially expressed (DEGs) between t483 and WT were identified using DEGseq R package (1.12.0 TNLIST, Beijing). P-values were adjusted using the Benjamini and Hochberg method [27]. Corrected p-values of 0.001 and log2 (fold change) of ±1 were set as the threshold to determine significant differential expression.

Gene ontology (GO) analysis was performed using the open-source MAS3 database ( A threshold of a two-fold change in gene expression levels and a FDR of <0.05 were used to identify DEGs. The p-values and FDRs of DEGs were calculated as previously reported [28].

Determination of endogenous GAs levels

500 mg of the plant material powder was extracted with 5 mL of 90% aqueous methanol (MeOH). Simultaneously 2 ng of each D-labelled GA compound was added to the extracting solvents as internal standards for GAs content measurement. MAX and MCX Cartridges (6 mL, 500 mg) were purchased from Waters Corporation (Milford, MA, USA). The MAX cartridge was activated and equilibrated with 10 mL MeOH, water, 5% NH4OH, 90% MeOH in turn, while MCX with 10 mL MeOH, water and 90% MeOH. After the two columns were connected with an adapter, the crude extracts were subjected to the tandem cartridges. Then the MAX cartridge was disconnected and washed with 5% NH4OH in 5% MeOH, MeOH in sequence. At last GAs were eluted with 2% FA in 90% MeOH. After dried with N2 stream, the eluent was reconstructed with 200 μL 80% MeOH and subjected to UPLC-MS/MS analysis. GAs analysis was performed on a quadrupole linear ion trap hybrid mass spectrometer (QTRAP 5500, AB SCIEX, Foster City, CA) equipped with an electrospray ionization source coupled with a UPLC (Waters, Milford, MA, USA). Five microliters of each sample were injected onto a BEH C18 column (100 mm*2.1 mm, 1.7 μm). The inlet method was set as follows: mobile phase A: 0.05% acetic acid in water, B: acetonitrile. Gradient: 0–17 min, 3% B to 65% B 17–18.5 min, 65% B to 90% B 18.5–19.5min, 90% B 19.5–21 min, 90% B to 3% B 21–22.5 min, 3% B. GAs were detected in negative multiple reaction monitoring (MRM) mode. Each GA compound was quantified with a MRM transition and qualified with another one. The source parameters were set as: IS voltage -4500 V, TEM 600°C, GS1 45, GS2 55 and curtain gas 28.

Several Current Representations of the Connection between Genotype and Phenotype Implicitly Dismiss the Differential View

We argued above that the differential view should always be kept in mind when thinking about the connection between genotypes and phenotypes. GWAS, which represent the most popular method to detect genomic loci that are associated with complex traits in populations, are based on the analysis of differences (Visscher et al., 2012). Nevertheless, in current research the differential view is sometimes implicitly dismissed. When multiple factors are observed to influence phenotypic traits (Figure 1B), the differential view is considered as too simplistic and researchers often prefer to focus back on phenotypes of single individuals, without explicitly relating them to a phenotypic reference.

In most current articles, the problem of connecting the genotype to the phenotype is framed in terms of genotype and phenotype maps. The first GP map was introduced by Richard Lewontin in his book “The genetic basis of evolutionary change” (Lewontin, 1974a Figure 2A). He indicated the average genotype of a population as a point in the space of all possible genotypes (G space) and the average phenotype of the same population as a corresponding point in the space of all possible phenotypes (P space). The evolutionary process was thus decomposed into four steps: (1) the average phenotype is derived from the development of the distinct genotypes in various environments (2) migration, mating, and natural selection acts in P space to change the average phenotype of the initial population into the average phenotype of the individuals which will have progeny (3) the identity of successful parents determines which genotypes are preserved and (4) genetic processes such as mutation and recombination modify position in G space.

FIGURE 2. Three current graphical representations of GP maps. (A) The early version of the GP map proposed by Lewontin (1974a). (B) A GP map where each point represents a single individual (Houle et al., 2010 Gjuvsland et al., 2013 Salazar-Ciudad and Marín-Riera, 2013). (C) The relationships between traits and genes, as depicted by Wagner (1996). See text for details.

In another common graphical representation (Figure 2B), a point in the G space and its corresponding point in the P space correspond to the genotype and the phenotype of a single individual (Fontana, 2002 Landry and Rifkin, 2012). Under such a representation, the abstract object that we defined above as the GP relationship would correspond to a “move” in genotype space associated with a “move” in phenotype space (or, better, a sum of several “moves” in genotype, and phenotype spaces because several distinct genomes can carry the two alternative alleles of a given GP relationship). In a third representation put forward by Wagner (1996 Figure 2C), individual genes are connected to individual traits.

Although these three graphical representations of GP maps may facilitate our understanding of certain aspects of biology, in all of them the GP relationship and the differential view are not easy to grasp. It is quite perplexing that the first person to draw such a GP map was Richard Lewontin, an eloquent advocate of the differential view (see for example his preface to Oyama, 2000, a masterpiece of persuasion). Because these graphics focus on individual rather than differential objects, we believe that these three representations implicitly incite us to go back to the more intuitive idea of one genotype associated with one phenotype. Losing sight of the differential view might also come from the molecular biology perspective, where proteins are viewed as having causal effects on their own, such as phosphorylation of a substrate or binding to a DNA sequence. Because of the two entangled definitions of the gene, either as encoding a protein, or as causing a phenotypic change (Griffiths and Stotz, 2013), it is easy to move from a differential view to a non-differential view of the GP relationship.

In summary, many current mental representations of the connection between genotype and phenotype implicitly dismiss the differential view. We will now show that the differential view is compatible with the fact that phenotypic traits are influenced by a complex combination of multiple factors and that we can find a relevant schematic representation of GP relationships.


Both the host genotype and gut microbiota of an animal play significant roles in shaping key phenotypes of aquacultural relevance, including growth metabolism and immune functions.

Traditional approaches to improve production have relied on selecting for direct genotype–phenotype correlations or on directly modulating gut microbiome communities.

The hologenome theory argues that the genomes of host organisms and their associated microbial communities are subject to biological interactions and cannot be viewed independently.

The gut microbiota can be viewed as a collection of genotypes contributing to holobiont phenotypes linked to the host genotype, and any attempts to modify the gut microbiota can only be successful in the context of the host genotype ‘environment’.

A hologenomic approach to aquaculture has potential to improve growth, health, and sustainable production.

Aquaculture will play an essential role in feeding a growing human population, but several biological challenges impede sustainable growth of production. Emerging evidence across all areas of life has revealed the importance of the intimate biological interactions between animals and their associated gut microbiota. Based on challenges in aquaculture, we leverage current knowledge in molecular biology and host microbiota interactions to propose an applied holo-omic framework that integrates molecular data including genomes, transcriptomes, epigenomes, proteomes, and metabolomes for analyzing fish and their gut microbiota as interconnected and coregulated systems. With an eye towards aquaculture, we discuss the feasibility and potential of our holo-omic framework to improve growth, health, and sustainability in any area of food production, including livestock and agriculture.

DNA Is Not a Blueprint: How Genes Really Work

Sequencing of a fetal genome from parental samples demonstrates how we have advanced in genetic analyses, but the title of a June 6 article in The New York Times, "DNA Blueprint for Fetus Built Using Tests of Parents," gives me pause. While the content does reflect a few interviews where researchers caution against overemphasizing what DNA sequences can tell us, the majority of the public reading the headline will see, yet again, an oversimplified and potentially damaging version of what we actually know about genetics.

Genes play an important role in our development and functioning, not as directors but as parts of a complex system. "Blueprints" is a poor way to describe genes. It is misleading to talk about genes as doing things by themselves. There are very few instances of direct gene-to-trait scenarios, even in well known "genetic" disorders. Traits emerge from the interactions of genes and a range of developmental and environmental influences, and similar DNA sequences often produce slightly different outcomes. Our DNA influences who we are, but not in a linear or easily described manner. (See here for more.)

DNA contains basic information that, when combined with the appropriate organic structures (in the egg) and context (the mother's uterus), will facilitate the growth of a single cell (the combined sperm and egg) into a multibillion-cell person. Note that I say "facilitate," not "determine." The DNA is not the blueprint of life rather, it contains many of the basic codes and signals for the development of an organism. At its core DNA contains the basic information needed to assemble molecules called "proteins," which are the building blocks of our bodies, and it also acts to regulate how and where different proteins are made and used.

Genes contain information, but the actual relationship between genes and our bodies and behavior is complicated. Chemical interactions inside our cells, interactions between cells, and developmental processes above the level of DNA occur throughout the life span. Most one-gene-to-one-trait analogies are unrealistic. For example, although your hands are composed of numerous proteins that emerge from information in your DNA, hands themselves are not the product of a "hand gene." Hands are the product of a developmental program in which DNA plays an important, but not exclusive, role.

Think of genes as having many types of relationships with traits. Single genes can affect single molecules, groups of genes may work together to produce effects, and one gene can even have many effects on a number of different traits and/or systems. Most genes have many of these patterns at the same time. In all cases the same gene can produce slightly different proteins in different individuals.

Multiple factors influence the development of an organism. These include chemical and physical patterns, internal and external influences, and physical constraints on shape and size, in addition to the information carried in the genes. To make things even more complex, starting with the successful joining of sperm and egg, epigenetic (outside the DNA) processes also affect development. Changes in temperature, fluctuating chemical environments, and mistakes in chemical cues in addition to variations in DNA produce slightly different outcomes.

There is little evidence to support any one-to-one relationship between genes and behavior. However, DNA does influence our physical structures (brain, eyes, mouth, hands, and so on), and because behavior is exhibited via these structures, all behavior has some genetic component.

For example, you are reading this blog using your eyes (optical tissue, muscles, nerves) and maybe your hands (muscle, bones, tendons) to scan the letters and words on the page. You are also using your brain (a set of neurons, vascular tissues, and various hormones that connects all the organs in your body and mediates among them) to process the meaning. All of these elements have a genetic component. However, you are reading the words, a behavior that must be taught to you, and you are reading them in English, something else that must be taught to you. Do reading and using the English language have a genetic component? Yes, the neurons, eyes, muscles, and other parts of the body used in reading are composed of molecules initially coded for by DNA. Are there genes for reading in English? No, the specific language that someone reads is an experiential factor, as languages are parts of cultural systems. Can aspects of our genetic complement impact our ability to acquire specific reading skills? Possibly. Structural differences in the eyes, motor connectivity, and even hormone pathways in the brain might impact the pace and pattern of reading acquisition.

There is a very complex set of relationships between our bodies and behavior on the one hand, and DNA, development, and environment on the other. This relationship is not linear, nor can it be easily described as a simple equation. We should not use simple models or labels such as "blueprints," "building blocks," or "code of life" to describe DNA and genes. Rather, the DNA is an integral component of life itself, and understanding the function of genetic material is critical to understanding evolution and the functioning of organisms. But an understanding of genetics is by no means the complete picture.

For a better understanding of these topics, have a look at these sources:

Fox-Keller, E. The Mirage of a Space Between Nature and Nurture. Duke University Press (2010).

Fuentes, A. Race, Monogamy and Other Lies They Told You: Busting Myths About Human Nature. University of California Press (2012).

Online Class: Biology 101

Biology, the science of life, concentrates on the structure, function, distribution, adaptation, interactions, origins and evolution of living organisms, a grouping which encapsulates both plants and animals. Biology 101 will encompass the principles of biology that include the structure and function of the cell to the complexities of current Ecological issues. Topics will include: Methods of Biological Studies, Chemistry Fundamentals, Cell Biology, The Study of Genetics (Heredity and Molecular), Origin and Principles of Evolution, the Dynamics of Populations, Ecosystem Structures and other Ecology Topics.

This course is an excellent learning resource for the college or pre-college student. This logical and easy to understand class is divided into self-paced lessons, complimented with well-written text, diagrams, critical-thinking assignments and end of lesson review exams. This course is a wonderful, stand-alone resource covering all concepts in biology, and can also be used as a comprehensive review or a personalized tutor.

By definition, biology is the science of life or living matter in all its many forms and phenomena. It takes a look at issues like the origins of life, growth and development, reproduction and structure.

Biology is further broken down into many different specialized fields, like microbiology, cellular biology, etc.

Its also served as the arena for a great deal of controversy and debate such as Darwinism vs. Intelligent Design and the like.

Unfortunately, the "science" of it has the potential to scare some people away. Admittedly, being such a huge branch of science, it has the potential to be intimidating. However, if you can break biology down to its basics, and use that as a solid foundation on which to build upon, you'll never have to cower in confusion again!

Enrollment's always open and the materials and lessons are available 24/7.

Biology 101 is an ideal course for college and pre-college students looking for a logical and easy to understand class in a self-paced learning environment. This course is additionally strengthened by integrating clear, well written text, a host of diagrams and pictures, critical thinking assignments and comprehensive lesson reviews and exams for each lesson.

Of course, high school and college students aren't the only people who stand to benefit from this course. Successful completion doesn't require any previous medical or scientific background. In fact, anyone over the age of 13 years is encouraged to enroll.

You'll begin with an introduction to the science of biology and progress through the related chemistry, studying cells, taking a look at the science of genetics and the principles of evolution, plant life, animal life and the delicate system of ecology.

Even better, you won't have to spend an arm and a leg on textbooks or other materials. This class was designed as a stand-alone platform which covers all the major concepts of biology. Everything that you'll need in order to succeed is provided for you immediately upon enrollment. So, not only will you save time in the self-paced environment, you'll also be able to save money as well.

Grades are awarded based on your performance on each of the lessons multiple choice and true or false quizzes (which are worth between 20 and 25 points each). A few lessons will also include a short assignment that is based on lesson facts and opinions that were expressed. You'll need to maintain an average grade of 70% throughout the class in order for successful course completion.

By the time the class is over, you'll have gained a solid and clear understanding of the very science of our existence. You will be well equipped to further your studies in other scientific arenas as well.

While traditional classroom settings demand that you wait for a new quarter or semester to enroll, those delays are moot here. Class enrollment is open and available 24 hours a day, 7 days a week, so there is no reason you can't act now to gain a competitive edge at school or in the workplace.


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