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Genome evolution by matrix algorithms: cellular automata approach to population genetics.

Qiu S, McSweeny A, Choulet S, Saha-Mandal A, Fedorova L, Fedorov A - Genome Biol Evol (2014)

Bottom Line: Computer modeling with GEMA has demonstrated that the number of meiotic recombination events per gamete is among the most crucial factors influencing population fitness.In humans, these recombinations create a gamete genome consisting on an average of 48 pieces of corresponding parental chromosomes.Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (40 per individual) even when the number of mutations with deleterious effects is up to ten times more abundant than those with beneficial effects.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics and Proteomics/Genomics, University of Toledo.

ABSTRACT
Mammalian genomes are replete with millions of polymorphic sites, among which those genetic variants that are colocated on the same chromosome and exist close to one another form blocks of closely linked mutations known as haplotypes. The linkage within haplotypes is constantly disrupted due to meiotic recombination events. Whole ensembles of such numerous haplotypes are subjected to evolutionary pressure, where mutations influence each other and should be considered as a whole entity-a gigantic matrix, unique for each individual genome. This idea was implemented into a computational approach, named Genome Evolution by Matrix Algorithms (GEMA) to model genomic changes taking into account all mutations in a population. GEMA has been tested for modeling of entire human chromosomes. The program can precisely mimic real biological processes that have influence on genome evolution such as: 1) Authentic arrangements of genes and functional genomic elements, 2) frequencies of various types of mutations in different nucleotide contexts, and 3) nonrandom distribution of meiotic recombination events along chromosomes. Computer modeling with GEMA has demonstrated that the number of meiotic recombination events per gamete is among the most crucial factors influencing population fitness. In humans, these recombinations create a gamete genome consisting on an average of 48 pieces of corresponding parental chromosomes. Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (40 per individual) even when the number of mutations with deleterious effects is up to ten times more abundant than those with beneficial effects.

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Related in: MedlinePlus

Exemplification of results from GEMA_r1.pl and GEMA_r01.java, illustrating evolutionary computations for 50 virtual individuals, each of whose genome is represented by human chromosome 22. (A) and (B) The change of relative fitness of individuals in population with respect to time (generations). In this modeling, we defined the distribution of mutations as a decay curve of selection coefficient (s), where 88% of mutations have negative s values and only 12% have positive s values (see fig. 3A). We do not normalize selection coefficient values, so the illustrated fitness of individuals is presented in relative units. Negative values of relative fitness show a decline in organism adaptability, whereas positive values indicate improvement. In these computational experiments, genes were assigned codominance mode (h = 0.5). In (A), how different numbers of offspring per individual (α = 3, 5, 8, or 10 offspring) influence the relative fitness, under the same recombination rate (r = 1) is demonstratd. In (B), how different numbers of recombination events per gamete (r = 1, 10, 20, or 48) affect the relative fitness whereas the number of offspring remained constant (α = 5) is demonstrated. (C) and (D) illustrate the dynamics of number of SNPs in the population. (C) Variations in the number of SNPs with respect to generations for four different values of novel mutations per gamete (µ = 2, 8, 20, or 30). In (D), smoothed number of SNPs (by taking averages for extended number of generations) in addition to emphasizing that under specific conditions (e.g., recessive genes in which the dominance mode h is close to 1) there may be considerable and long-lasting spikes in the number of SNPs when recombination rate is low (r ≤ 1) is demonstrated.
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evu075-F2: Exemplification of results from GEMA_r1.pl and GEMA_r01.java, illustrating evolutionary computations for 50 virtual individuals, each of whose genome is represented by human chromosome 22. (A) and (B) The change of relative fitness of individuals in population with respect to time (generations). In this modeling, we defined the distribution of mutations as a decay curve of selection coefficient (s), where 88% of mutations have negative s values and only 12% have positive s values (see fig. 3A). We do not normalize selection coefficient values, so the illustrated fitness of individuals is presented in relative units. Negative values of relative fitness show a decline in organism adaptability, whereas positive values indicate improvement. In these computational experiments, genes were assigned codominance mode (h = 0.5). In (A), how different numbers of offspring per individual (α = 3, 5, 8, or 10 offspring) influence the relative fitness, under the same recombination rate (r = 1) is demonstratd. In (B), how different numbers of recombination events per gamete (r = 1, 10, 20, or 48) affect the relative fitness whereas the number of offspring remained constant (α = 5) is demonstrated. (C) and (D) illustrate the dynamics of number of SNPs in the population. (C) Variations in the number of SNPs with respect to generations for four different values of novel mutations per gamete (µ = 2, 8, 20, or 30). In (D), smoothed number of SNPs (by taking averages for extended number of generations) in addition to emphasizing that under specific conditions (e.g., recessive genes in which the dominance mode h is close to 1) there may be considerable and long-lasting spikes in the number of SNPs when recombination rate is low (r ≤ 1) is demonstrated.

Mentions: Several examples of GEMA computations are shown in figure 2. These graphs illustrate the modeled dynamics for the influx of mutations, 12% of which have positive selection coefficient (s > 0) whereas the rest 88% have a negative effect (s < 0). The distribution of mutations by s parameter has been modeled according to a decay curve, shown in the figure 3A. When the number of meiotic recombination events was low (r = 1, recombinations per gamete) and the rate of mutations was approximated to the one naturally observed for humans (µ = 20, mutations per gamete), the relative fitness of individuals declined with generations. Yet, a higher degree of purifying selection pressure (corresponding to a larger number of offspring per individual—α-parameter) caused the decline of fitness to be less sudden with respect to increasing number of generations (see fig. 2A). These parameters are thoroughly explained in the User Guide for GEMA (in supplementary file S1, Supplementary Material online, p. 6–9) and also in the GEMA web site (http://bpg.utoledo.edu/∼afedorov/lab/GEMA.html, last accessed April 17, 2014) including GEMA_video_presentation.m4v, GEMA.java pseudocode, and other supplementary materials, Supplementary Material online.Fig. 1.—


Genome evolution by matrix algorithms: cellular automata approach to population genetics.

Qiu S, McSweeny A, Choulet S, Saha-Mandal A, Fedorova L, Fedorov A - Genome Biol Evol (2014)

Exemplification of results from GEMA_r1.pl and GEMA_r01.java, illustrating evolutionary computations for 50 virtual individuals, each of whose genome is represented by human chromosome 22. (A) and (B) The change of relative fitness of individuals in population with respect to time (generations). In this modeling, we defined the distribution of mutations as a decay curve of selection coefficient (s), where 88% of mutations have negative s values and only 12% have positive s values (see fig. 3A). We do not normalize selection coefficient values, so the illustrated fitness of individuals is presented in relative units. Negative values of relative fitness show a decline in organism adaptability, whereas positive values indicate improvement. In these computational experiments, genes were assigned codominance mode (h = 0.5). In (A), how different numbers of offspring per individual (α = 3, 5, 8, or 10 offspring) influence the relative fitness, under the same recombination rate (r = 1) is demonstratd. In (B), how different numbers of recombination events per gamete (r = 1, 10, 20, or 48) affect the relative fitness whereas the number of offspring remained constant (α = 5) is demonstrated. (C) and (D) illustrate the dynamics of number of SNPs in the population. (C) Variations in the number of SNPs with respect to generations for four different values of novel mutations per gamete (µ = 2, 8, 20, or 30). In (D), smoothed number of SNPs (by taking averages for extended number of generations) in addition to emphasizing that under specific conditions (e.g., recessive genes in which the dominance mode h is close to 1) there may be considerable and long-lasting spikes in the number of SNPs when recombination rate is low (r ≤ 1) is demonstrated.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4007542&req=5

evu075-F2: Exemplification of results from GEMA_r1.pl and GEMA_r01.java, illustrating evolutionary computations for 50 virtual individuals, each of whose genome is represented by human chromosome 22. (A) and (B) The change of relative fitness of individuals in population with respect to time (generations). In this modeling, we defined the distribution of mutations as a decay curve of selection coefficient (s), where 88% of mutations have negative s values and only 12% have positive s values (see fig. 3A). We do not normalize selection coefficient values, so the illustrated fitness of individuals is presented in relative units. Negative values of relative fitness show a decline in organism adaptability, whereas positive values indicate improvement. In these computational experiments, genes were assigned codominance mode (h = 0.5). In (A), how different numbers of offspring per individual (α = 3, 5, 8, or 10 offspring) influence the relative fitness, under the same recombination rate (r = 1) is demonstratd. In (B), how different numbers of recombination events per gamete (r = 1, 10, 20, or 48) affect the relative fitness whereas the number of offspring remained constant (α = 5) is demonstrated. (C) and (D) illustrate the dynamics of number of SNPs in the population. (C) Variations in the number of SNPs with respect to generations for four different values of novel mutations per gamete (µ = 2, 8, 20, or 30). In (D), smoothed number of SNPs (by taking averages for extended number of generations) in addition to emphasizing that under specific conditions (e.g., recessive genes in which the dominance mode h is close to 1) there may be considerable and long-lasting spikes in the number of SNPs when recombination rate is low (r ≤ 1) is demonstrated.
Mentions: Several examples of GEMA computations are shown in figure 2. These graphs illustrate the modeled dynamics for the influx of mutations, 12% of which have positive selection coefficient (s > 0) whereas the rest 88% have a negative effect (s < 0). The distribution of mutations by s parameter has been modeled according to a decay curve, shown in the figure 3A. When the number of meiotic recombination events was low (r = 1, recombinations per gamete) and the rate of mutations was approximated to the one naturally observed for humans (µ = 20, mutations per gamete), the relative fitness of individuals declined with generations. Yet, a higher degree of purifying selection pressure (corresponding to a larger number of offspring per individual—α-parameter) caused the decline of fitness to be less sudden with respect to increasing number of generations (see fig. 2A). These parameters are thoroughly explained in the User Guide for GEMA (in supplementary file S1, Supplementary Material online, p. 6–9) and also in the GEMA web site (http://bpg.utoledo.edu/∼afedorov/lab/GEMA.html, last accessed April 17, 2014) including GEMA_video_presentation.m4v, GEMA.java pseudocode, and other supplementary materials, Supplementary Material online.Fig. 1.—

Bottom Line: Computer modeling with GEMA has demonstrated that the number of meiotic recombination events per gamete is among the most crucial factors influencing population fitness.In humans, these recombinations create a gamete genome consisting on an average of 48 pieces of corresponding parental chromosomes.Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (40 per individual) even when the number of mutations with deleterious effects is up to ten times more abundant than those with beneficial effects.

View Article: PubMed Central - PubMed

Affiliation: Program in Bioinformatics and Proteomics/Genomics, University of Toledo.

ABSTRACT
Mammalian genomes are replete with millions of polymorphic sites, among which those genetic variants that are colocated on the same chromosome and exist close to one another form blocks of closely linked mutations known as haplotypes. The linkage within haplotypes is constantly disrupted due to meiotic recombination events. Whole ensembles of such numerous haplotypes are subjected to evolutionary pressure, where mutations influence each other and should be considered as a whole entity-a gigantic matrix, unique for each individual genome. This idea was implemented into a computational approach, named Genome Evolution by Matrix Algorithms (GEMA) to model genomic changes taking into account all mutations in a population. GEMA has been tested for modeling of entire human chromosomes. The program can precisely mimic real biological processes that have influence on genome evolution such as: 1) Authentic arrangements of genes and functional genomic elements, 2) frequencies of various types of mutations in different nucleotide contexts, and 3) nonrandom distribution of meiotic recombination events along chromosomes. Computer modeling with GEMA has demonstrated that the number of meiotic recombination events per gamete is among the most crucial factors influencing population fitness. In humans, these recombinations create a gamete genome consisting on an average of 48 pieces of corresponding parental chromosomes. Such highly mosaic gamete structure allows preserving fitness of population under the intense influx of novel mutations (40 per individual) even when the number of mutations with deleterious effects is up to ten times more abundant than those with beneficial effects.

Show MeSH
Related in: MedlinePlus