Author Archives: sahil

Prominent Strain-Condition Interactions in Yeast Transcripts

Gene-environment interactions occur when the effect of a genetic variant differs in multiple environments. Representing an intermediate between the “nature and nurture” sides of genetic variation, these interactions are important contributors to the development of complex phenotypes. There have been many studies performed on humans and model organisms that have attempted to observe the effects of these interactions, most of them involving the use of techniques such as genome-wide association studies (GWAS) and candidate gene association studies (CGAS). These methods, however, have proven ineffective in elucidating the molecular mechanisms of gene-environment interactions.

Expression quantitative trait loci (eQTL) mapping is a powerful extension of standard quantitative mapping techniques that has shown promise in studying these interactions. As its name suggests, eQTL mapping involves the association of variances in gene expression and genetic polymorphisms. With modern technology, thousands of genetic transcripts can be simultaneously measured under varying genetic and environmental conditions. This allows for the application of eQTL mapping techniques when observing difficult-to-study phenomena such as gene-environment interactions.

Based on existing conclusions surrounding gene-environment interactions in different organisms, Smith and Kruglyak intended to characterize the overall genetic architecture of the gene-environment interactions in yeast. They particularly focused on the metabolism of two sugar sources: glucose and ethanol. These conditions provide an interesting environmental contrast, as yeast metabolizes both compounds through reverse metabolic pathways (fermentation for glucose and anaerobic respiration for ethanol). The researchers also narrowed down the genetic component by focusing on two particular strains: BY and RM. Due to the molecular differences between the conditions and the strains tested, most of the measured variance was hypothesized to be due to strain and condition effects. However, based on the literature evidence for gene-environment interactions, they also expected to observe a significant contribution of this component in phenotypic variance.

The two parental strains BY and RM, as well as 109 segregant strains derived from a cross between the parental strains, were grown in glucose and ethanol. Each strain was expression profiled by using DNA microarrays to hybridize mRNA that was extracted from the cells. A hybridization standard was created by mixing equal amounts of mRNA from both parents grown in both conditions.

Smith and Kruglyak’s experiments produced two major conclusions. First, from their analyses of variance (ANOVA), they determined that yeast transcripts are influenced by genetic components, environmental components, and by the interactions between both of these components (Figure 1). The presence of all three components demonstrates that even though genetic and environmental contributions to the phenotypic variance are dominant, the interactions between the two are still significant. Another major conclusion from this experiment was the characterization of local and distant linkages. Local linkages were described as being more stable and less dependent on the environment. They also usually affected both environmental conditions in the same manner. On the other hand, distant linkages were described as being more volatile and environmentally-dependent. In addition, distant linkages usually affected only one condition. In general, the majority of the interactions between genes and the environment occurred at distant linkages.

Figure 1. The relative proportion of strain-condition, strain, and condition variance for all transcripts where these three components accounted for more than 50% of total variance. Insets illustrate two-factor plots for representative transcripts. The averages of the BY and RM strains are in orange and purple, respectively, with error bars indicating standard deviation.

Figure 1. The relative proportion of strain-condition, strain, and condition variance for all transcripts where these three components accounted for more than 50% of total variance. Insets illustrate two-factor plots for representative transcripts. The averages of the BY and RM strains are in orange and purple, respectively, with error bars indicating standard deviation.

The results of this experiment highlight the importance of considering gene-environment interactions when performing linkage studies, as ignoring this component produces bias towards certain loci and compromises the data’s validity. More specifically, even though gene-environment interactions “play a dominant role in a minority of traits” (Smith & Kruglyak, 2008), these traits have the potential to play a significant role in determining the overall phenotypic variance. This is particularly relevant for creating lifestyle choices in relation to human diseases that have already been proven to possess a significant contribution from these interactions, like in the cases of heart disease, depression, and cancer.


Smith, E. N., & Kruglyak, L. (2008). Gene-Environment Interaction in Yeast Gene Expression. PLoS Biology, 6(4), 0812-0824.

Mutation and the long game of genomic evolution

Genomic evolution results from the accumulation of mutations within a population over time. Although most mutations are neutral or even deleterious, occasionally beneficial mutations arise and become fixated within a gene pool. These mutations produce phenotypes of greater fitness than the preceding generation, and are more adapted to survive their environment.

The relationship between genome evolution and adaptation is quite complex, as not all mutations are beneficial. While neutralists argue that genetic drift is the main factor of interest, in which it causes the accumulation of neutral mutations at a roughly constant rate, selectionists argue that the rate of beneficial and deleterious mutations depends on the environment, as well as population size/structure. These rates may not follow discernible patterns, at least when seen over long periods of time, and have been found to undergo “events” of sudden, rapid growth or decline.2 Complex organismal features can suddenly appear within a population, due to random mutations and selection.3 In some cases, the phenotype of greatest fitness may even be overtaken by one of lesser fitness.4

The advent of efficient genome sequencing techniques in E. coli has made it possible to observe the genomic evolution and adaptation relationship. A 2009 study looked at how this genomic evolution occurs, and at what rates. It also assessed whether genetic drift or selection was the main cause of genomic evolution, hypothesizing genetic drift as the main driver for mutation.

The study sequenced the genomes of E. coli bacteria at generations 2k, 5k, 10k, 15k, 20k and 40k. The E. coli were evolved by propagating 12 populations at 37oC for 6,000 days and by transferring 0.2 mL of culture into 9.9 mL of fresh medium each day. Mutations were identified using NimbleGen to conduct comparative sequencing with microarrays.

The study found that the E. coli clones had more non-synonymous mutations than synonymous ones. It was also found that mutation often occurred in the same genes across populations. It was found that the mutations found in the 2k and 15k generations were also found in subsequent generations. The study found that nearly all of the mutations provided a fitness advantage. All four of these findings contradict what would be expected in the populations given the genetic drift hypothesis; in all four cases, the opposite was expected to occur.

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Figure 1: The mutations found in E.coli in various generations. (Ref. 1)

The study also proposes ecological pressures that drive mutation and genomic adaptation rates. An experiment with yeast showed that adaptation measured in each episode of selection was greater than when adaptation was measured from start to finish, although this was not supported with in the E. coli populations from the study. The study also proposed clonal interference as an alternative explanation for the negation of the neutral genetic drift hypothesis. Here, beneficial mutations would be outcompeted for success by mutation with an even greater benefit. Some mutations might have negative side-effects, but these would also create additional opportunity for beneficial mutations, albeit on a smaller scale.1

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Figure 2: The relative fitness and number of mutations graphed from generation 0 to generation 20k. Inlay: The number of mutations up to generation 40k. (Ref. 1)


Finally, the paper looks at synonymous changes. The hypermutable phenotype results in a higher amount of transversion mutations, which are more likely to cause nonsynonymous mutations than other types of mutations. This was reflected in the data, as a lower fraction of mutations in the 40K genome were synonymous than would have been expected due to random chance. The number of synonymous mutations was used to estimate the mutation rate after the emergence of the hypermutable phenotype, which was found to be about 70 times as large as the previous mutation rate.

The study provides a glimpse into the complex world of quantitative genetics. While the results of this study contradicted the hypothesis of genetic drift as the primary driver of genomic adaptation, this is hardly the only interpretation of or cause for adaptation.


  1. Barrick, Jeffrey E., et. al. “Genome evolution and adaptation in a long-term experiment with Escherichia coli.” Nature 461 (2009): 1243-1247. doi:10.1038/nature080480.
  2. Lenski, RE, and M. Travisano. “Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations.” Proc. Natl. Acad. Sci. USA 91 (1994): 6808-6814. Accessed March 24, 2015.
  3. Lenski, RE, Charles Ofria, Robert T. Pennock, and Christoph Adami. “The evolutionary origin of complex features.” Nature 423 (2003): 139-144. Accessed March 24, 2015.
  4. Wilke, Claus O., Jia Lan Wang, Charles Ofria, Richard E. Lenski, and Christoph Adami. “Evolution of digital organisms at high mutation rates leads to survival of the flattest.” Nature 412 (2001): 331-333. Accessed March 24, 2015. doi:10.1038/35085569