Interplay of Interlocus Gene Conversion and Crossover in Segmental Duplications Under a Neutral Scenario

Hartasánchez, Diego A.; Vallès-Codina, Oriol; Brasó-Vives, Marina; Navarro, Arcadi

doi:10.1534/g3.114.012435

Cited by 15 publications

(16 citation statements)

References 63 publications

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“…Gel-based methods provided the basis for the barcode hypothesis and claims that MUPs are highly polymorphic24 – and therefore, the hypothesis that MUPs mediate individual odour and genetic kin recognition (inbreeding avoidance)234613 also needs to be re-evaluated. The low individual variation of Mup genes and linked microsatellites suggest that these genes are evolving under purifying selection26, perhaps through a selective sweep in this large region40, and the high homology of different Mup loci may be generated by concerted evolution (gene conversion)4142. It is unclear why selection favours low individual variation and high homology of MUP loci, and studies are needed to investigate possible effects on individual survival43, as well as reproductive success (through chemical communication).…”

Section: Discussionmentioning

confidence: 99%

Diversity of major urinary proteins (MUPs) in wild house mice

Thoß

Enk

et al. 2016

Sci Rep

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Major urinary proteins (MUPs) are often suggested to be highly polymorphic, and thereby provide unique chemical signatures used for individual and genetic kin recognition; however, studies on MUP variability have been lacking. We surveyed populations of wild house mice (Mus musculus musculus), and examined variation of MUP genes and proteins. We sequenced several Mup genes (9 to 11 loci) and unexpectedly found no inter-individual variation. We also found that microsatellite markers inside the MUP cluster show remarkably low levels of allelic diversity, and significantly lower than the diversity of markers flanking the cluster or other markers in the genome. We found low individual variation in the number and types of MUP proteins using a shotgun proteomic approach, even among mice with variable MUP electrophoretic profiles. We identified gel bands and spots using high-resolution mass spectrometry and discovered that gel-based methods do not separate MUP proteins, and therefore do not provide measures of MUP diversity, as generally assumed. The low diversity and high homology of Mup genes are likely maintained by purifying selection and gene conversion, and our results indicate that the type of selection on MUPs and their adaptive functions need to be re-evaluated.

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Section: Discussionmentioning

confidence: 99%

Diversity of major urinary proteins (MUPs) in wild house mice

Thoß

Enk

et al. 2016

Sci Rep

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“…IGC reduces LD to an extent that increases with the rate of IGC in that region [53]. Recombination hotspots within a region undergoing IGC are also theorized to further reduce LD, but only in the hotspot region [53]. However, P1/P2 and P3 have lower than average recombination rates in A. thaliana [60].…”

Section: Resultsmentioning

confidence: 99%

“…Maximum parsimony trees of the coding sequence excluding the LRR and trees of the LRR alone were constructed to contrast the evolution of these portions of the RPP8 sequence. IGC can also be detected through an analysis of linkage disequilibrium (LD), which is reduced as IGC increases [53]. LD values within and between loci were determined in R and plotted using the R package ‘LDheatmap’ (v099.5).…”

Section: Methodsmentioning

confidence: 99%

“…SeDuS is a forward-in-time simulator designed to investigate the interplay of interlocus gene conversion and crossovers in segmental duplications under a neutral scenario [48,53]. Here, we modified this program, kindly provided by the authors, to simulate polymorphism in a three-copy gene family with realistic parameters for RPP8 , with two closely linked duplicates (here copies one and two) and a distant third duplicate (here copy three), undergoing gene conversion at unequal rates.…”

Section: Methodsmentioning

confidence: 99%

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Population genetics of the highly polymorphicRPP8gene family

MacQueen

Tian

Chang

et al. 2019

Preprint

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Plant NLR resistance genes provide some of the most extreme examples of polymorphism 15 in eukaryotic genomes, rivalling even the vertebrate major histocompatibility complex.16 Surprisingly, this is also true in Arabidopsis thaliana, a predominantly selfing species with low 17 heterozygosity. Here, we investigate how gene duplication and intergenic exchange contribute to 18 this extraordinary variation. RPP8 is a three-locus system that is configured chromosomally as either 19 a direct-repeat tandem duplication or as a single copy locus, plus a locus 2 Mb distant. We 20 sequenced 48 RPP8 alleles from 37 accessions of A. thaliana and 12 RPP8 alleles from A. lyrata to 21 investigate the patterns of interlocus shared variation. The tandem duplicates display fixed 22 differences and share less variation with each other than either shares with the distant paralog. A 23 high level of shared polymorphism among alleles at one of the tandem duplicates, the single-copy 24 locus and the distal locus, must involve both classical crossing over and intergenic gene conversion.25 Despite these polymorphism-enhancing mechanisms, the observed nucleotide diversity could not 26 be replicated under neutral forward-in-time simulations. Only by adding balancing selection to the 27 simulations do they approach level of polymorphism observed at RPP8. In this NLR gene triad, 28 genetic architecture, gene function and selection all combine to generate diversity.29

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“…During meiosis, we use the single-copy crossover model, assuming all crossovers occurred between CNVs (Hartasanchez et al 2014).…”

Section: Simulation Of Cnvsmentioning

confidence: 99%

A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification

Chung

Kang

2018

Preprint

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An integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, there is no simulation tool that simulates multiple types of omics data and models their relationships with disease status. Without such a tool, it is difficult to evaluate the multi-omics analysis methods on the same scale and to estimate the sample size or power when planning a new multi-omics disease study. We developed a multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., SNPs and copy number variations), epigenomics (i.e., whole-genome bisulphite sequencing), transcriptomics (i.e., RNA-seq), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as meQTLs (SNPs influencing methylation), eQTLs (SNPs influencing gene expression), and eQTM (methylation influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model, and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and run time. Our results 3 demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and a random forest-based method showed the highest prediction accuracy when the multi-omics data were properly normalized.

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Interplay of Interlocus Gene Conversion and Crossover in Segmental Duplications Under a Neutral Scenario

Cited by 15 publications

References 63 publications

Diversity of major urinary proteins (MUPs) in wild house mice

Diversity of major urinary proteins (MUPs) in wild house mice

Population genetics of the highly polymorphicRPP8gene family

A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification

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