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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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Distributions of mutations by user-assumed selection coefficients (s-values), which were used for modeling analysis. (A) A continuous distribution of mutations by s that can range from −20 to +20 depending on their deleterious (negative s values) or beneficial (positive s values) effects. This curve represents 88% deleterious and 12% beneficial mutations. (B) A discrete distribution of mutations characterized predominantly by neutral mutations occurring at a frequency of 90% within the population, whereas the remaining 10% is characterized by deleterious and beneficial mutations occurring in a ratio of 9:1. (C) illustrates another discrete distribution for mutations, where the ratio of deleterious to beneficial mutations occurs again in the ratio of 9:1. However, this model is characterized by a preponderance of mutations with deleterious effects (81%). Neutral mutations in this case comprise 10% and beneficial—9% of overall nucleotide changes occurring within the population.
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evu075-F3: Distributions of mutations by user-assumed selection coefficients (s-values), which were used for modeling analysis. (A) A continuous distribution of mutations by s that can range from −20 to +20 depending on their deleterious (negative s values) or beneficial (positive s values) effects. This curve represents 88% deleterious and 12% beneficial mutations. (B) A discrete distribution of mutations characterized predominantly by neutral mutations occurring at a frequency of 90% within the population, whereas the remaining 10% is characterized by deleterious and beneficial mutations occurring in a ratio of 9:1. (C) illustrates another discrete distribution for mutations, where the ratio of deleterious to beneficial mutations occurs again in the ratio of 9:1. However, this model is characterized by a preponderance of mutations with deleterious effects (81%). Neutral mutations in this case comprise 10% and beneficial—9% of overall nucleotide changes occurring within the population.

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)

Distributions of mutations by user-assumed selection coefficients (s-values), which were used for modeling analysis. (A) A continuous distribution of mutations by s that can range from −20 to +20 depending on their deleterious (negative s values) or beneficial (positive s values) effects. This curve represents 88% deleterious and 12% beneficial mutations. (B) A discrete distribution of mutations characterized predominantly by neutral mutations occurring at a frequency of 90% within the population, whereas the remaining 10% is characterized by deleterious and beneficial mutations occurring in a ratio of 9:1. (C) illustrates another discrete distribution for mutations, where the ratio of deleterious to beneficial mutations occurs again in the ratio of 9:1. However, this model is characterized by a preponderance of mutations with deleterious effects (81%). Neutral mutations in this case comprise 10% and beneficial—9% of overall nucleotide changes occurring within the population.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4007542&req=5

evu075-F3: Distributions of mutations by user-assumed selection coefficients (s-values), which were used for modeling analysis. (A) A continuous distribution of mutations by s that can range from −20 to +20 depending on their deleterious (negative s values) or beneficial (positive s values) effects. This curve represents 88% deleterious and 12% beneficial mutations. (B) A discrete distribution of mutations characterized predominantly by neutral mutations occurring at a frequency of 90% within the population, whereas the remaining 10% is characterized by deleterious and beneficial mutations occurring in a ratio of 9:1. (C) illustrates another discrete distribution for mutations, where the ratio of deleterious to beneficial mutations occurs again in the ratio of 9:1. However, this model is characterized by a preponderance of mutations with deleterious effects (81%). Neutral mutations in this case comprise 10% and beneficial—9% of overall nucleotide changes occurring within the population.
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