A high resolution map of segmental DNA copy number variation in the mouse genome

Graubert, Timothy A.; Cahan, Patrick; Edwin, Deepa; Selzer, Rebecca R.; Richmond, Todd; Eis, Peggy; Shannon, William D.; Li, Xia; McLeod, Howard L.; Cheverud, James M.; Ley, Timothy J.

doi:10.1371/journal.pgen.0030003.eor

Cited by 29 publications

(37 citation statements)

References 23 publications

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“…Because positive selection increases the rate of evolution, but does not induce any longlasting polymorphisms, the McDonald-Kreitman test (20) would indicate positive selection if such CNVs are rare. Although currently available data on rat (21) and mouse (22,23) seem to be consistent with this, analysis of a larger number of wild-type rat and mouse genotypes is necessary. If piRNAs are indeed involved in transposon silencing, it is natural to assume that selection for cluster acquisitions is caused by an arms race between expanding families of mammalian transposons and piRNA clusters.…”

Section: Discussionmentioning

confidence: 73%

Rapid repetitive element-mediated expansion of piRNA clusters in mammalian evolution

Assis

Kondrashov

2009

Proc. Natl. Acad. Sci. U.S.A.

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Piwi-interacting RNAs (piRNAs) are Ϸ30 nucleotide noncoding RNAs that may be involved in transposon silencing in mammalian germline cells. Most piRNA sequences are found in a small number of genomic regions referred to as clusters, which range from 1 to hundreds of kilobases. We studied the evolution of 140 rodent piRNA clusters, 103 of which do not overlap protein-coding genes. Phylogenetic analysis revealed that 14 clusters were acquired after rat-mouse divergence and another 44 after rodent-primate divergence. Most clusters originated in a process analogous to the duplication of protein-coding genes by ectopic recombination, via insertions of long sequences that were mediated by flanking chromosome-specific repetitive elements (REs). Source sequences for such insertions are often located on the same chromosomes and also harbor clusters. The rate of piRNA cluster expansion is higher than that of any known gene family and, in contrast to other large gene families, there was not a single cluster loss. These observations suggest that piRNA cluster expansion is driven by positive selection, perhaps caused by the need to silence the ever-expanding repertoire of mammalian transposons.arms race ͉ molecular evolution ͉ small RNA ͉ positive selection E ukaryotic genomes contain a variety of small noncoding RNAs, including microRNAs (miRNAs), repeat-associated small interfering RNAs (rasiRNAs), small interfering RNAs (siRNAs), and Piwi-interacting RNAs (piRNAs). miRNAs regulate the expression of protein-coding genes, rasiRNAs are involved in transposon silencing, and siRNAs play a dual role in silencing genes and transposons (1). Because of a number of similarities between rasiRNAs in Drosophila and piRNAs in mammals, rasiRNAs are considered to be a subclass of piRNAs. Thus, mammalian piRNAs are hypothesized to also be involved in transposon silencing, although they may perform other functions as well (2). Some noncoding RNAs, in particular miRNAs, evolve very slowly (3). In contrast, small-scale evolution of piRNA sequences proceeds at a rate typical of nonfunctional genomic regions (4). Here, we consider the large-scale evolution of mammalian piRNA clusters. ResultsRecent Acquisition of Many piRNA Clusters. Thus far, 140 rat and mouse piRNA clusters have been described, each of which is most likely transcribed as a unit and subsequently processed into mature piRNAs (2, 4-8). We studied the evolution of each of these clusters within their genomic contexts (Table S1). For this purpose, we obtained regions that included 2 flanking protein-coding genes on either side of a cluster and constructed pairwise alignments of orthologous rat, mouse, human, dog, and cow regions. Thirty-seven clusters overlap protein-coding genes, often spanning several exons and introns. All of these clusters are ancestral, being present in rat, mouse, and human, which is not surprising because protein-coding genes are generally conserved. Among the remaining 103 clusters, each of which is contained within an intergenic region, only 43 are ancestr...

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Section: Discussionmentioning

confidence: 73%

Rapid repetitive element-mediated expansion of piRNA clusters in mammalian evolution

Assis

Kondrashov

2009

Proc. Natl. Acad. Sci. U.S.A.

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“…5 online). The 100 kb interval size selected for our analysis may result in intervals that span transition zones between regions with different subspecific origin, some intervals may contain smaller segments of a different subspecific origin embedded within them and segmental duplications and copy number polymorphisms [38][39][40] may lead to unusual patterns in the assignment of ancestry. Although the subspecific assignment for each classical strain and MOLF is well supported by bootstrap replicates (85/100 replicates support for 94.5% of intervals and by 99/100 replicates in 86.3% of intervals) the ancestral origin may be incorrectly inferred in some intervals due to the limitations of the data and methodology.…”

Section: Ancestry Of Classical Strainsmentioning

confidence: 99%

On the subspecific origin of the laboratory mouse

et al. 2007

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The genome of the laboratory mouse is thought to be a mosaic of regions with distinct subspecific origins. We have developed a high-resolution map of the origin of the laboratory mouse by generating 25,400 phylogenetic trees in 100 kb intervals spanning the genome. On average 92% of the genome is of M. m. domesticus origin and the distribution of diversity is strikingly non random among the chromosomes. There are large regions of extremely low diversity, representing blind spots for studies of natural variation and complex traits, as well as hot spots of diversity. In contrast with the mosaic model we found that the majority of the genome has intermediate levels of variation of intrasubspecific origin. Finally, the wild-derived mouse strains that are supposed to represent different mouse subspecies show substantial intersubspecific introgression. This has serious implications for evolutionary studies that assume these are pure representatives of a given subspecies.Laboratory mice, the most popular model organism in mammalian genetics 1,2 , were derived from wild mice belonging to the Mus musculus species by an intricate process that included the generation of "fancy" mice in both Asia and Europe and a complex web of relationships among inbred strains 3 . Early studies demonstrated that the mitochondria and the Y chromosome present in many classical laboratory strains were derived from different subspecies, M. m. domesticus for the mitochondria and M. m. musculus for the Y chromosome 4,5 . Furthermore, the Y chromosome was introduced in the laboratory mouse through M. m. molossinus males 6 . Based on these findings, it was proposed that the genomes of inbred strains were a mosaic of regions with different subspecific origin 7 . Recently, the fine structure of such mosaic variation has been described 8 . This study reported that strain-to-strain comparisons revealed regions with extremely high variation spanning one third of the genome and regions with extremely low variation covering the remaining two thirds of the genome. This distinctively bimodal distribution was assumed to represent regions with the same and different subspecific origin. This mosaic model has been the driving concept behind mouse association mapping studies and haplotype analysis [9][10][11][12] . However, the origin of a given region of a laboratory strain could not be directly assigned to a subspecies due to the lack of reference sequences for the three major mouse subspecies. Subsequent studies raised questions regarding the haplotype structure 11,13 , the effect of ascertainment biases in subspecific assignment [14][15][16] and the contributions of intersubspecific

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“…There are many methods for detecting SNPs (11)(12)(13)(14) and structural variants (SVs) (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), including NGS, which can capture nearly all DNA polymorphisms (26)(27)(28). This approach has been widely used to analyze markers in crop species such as rice (29), genes associated with diseases (6,26), and meiotic recombination in yeast and plants (30,31).…”

mentioning

confidence: 99%

“…Genomes usually contain repetitive sequences that can differ in copy number between individuals (26)(27)(28)31); therefore, resequencing analyses must account for chromosomal context to avoid mistaking highly similar paralogous sequences for polymorphisms. Here, we use recently published datasets to describe several DNA sequence features that can be mistaken as allelic (32,33) and describe a strategy for differentiating between repetitive sequences and polymorphic alleles.…”

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confidence: 99%

Detection of genomic variations and DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping

Chen

Copenhaver

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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DNA polymorphisms are important markers in genetic analyses and are increasingly detected by using genome resequencing. However, the presence of repetitive sequences and structural variants can lead to false positives in the identification of polymorphic alleles. Here, we describe an analysis strategy that minimizes false positives in allelic detection and present analyses of recently published resequencing data from Arabidopsis meiotic products and individual humans. Our analysis enables the accurate detection of sequencing errors, small insertions and deletions (indels), and structural variants, including large reciprocal indels and copy number variants, from comparisons between the resequenced and reference genomes. We offer an alternative interpretation of the sequencing data of meiotic products, including the number and type of recombination events, to illustrate the potential for mistakes in single-nucleotide polymorphism calling. Using these examples, we propose that the detection of DNA polymorphisms using resequencing data needs to account for nonallelic homologous sequences. Fig. 1 A and B). They can have phenotypic consequences and also serve as molecular markers for genetic analyses, facilitating linkage and association studies of genetic diseases, and other traits in humans (4-6), animals, plants, (7)(8)(9)(10) and other organisms. Using DNA polymorphisms for modern genetic applications requires low-error, high-throughput analytical strategies. Here, we illustrate the use of short-read nextgeneration sequencing (NGS) data to detect DNA polymorphisms in the context of whole-genome analysis of meiotic products.There are many methods for detecting SNPs (11-14) and structural variants (SVs) (15-25), including NGS, which can capture nearly all DNA polymorphisms (26-28). This approach has been widely used to analyze markers in crop species such as rice (29), genes associated with diseases (6, 26), and meiotic recombination in yeast and plants (30,31). However, accurate identification of DNA polymorphisms can be challenging, in part because short-read sequencing data have limited information for inferring chromosomal context.Genomes usually contain repetitive sequences that can differ in copy number between individuals (26-28, 31); therefore, resequencing analyses must account for chromosomal context to avoid mistaking highly similar paralogous sequences for polymorphisms. Here, we use recently published datasets to describe several DNA sequence features that can be mistaken as allelic (32, 33) and describe a strategy for differentiating between repetitive sequences and polymorphic alleles. We illustrate the effectiveness of these analyses by examining the reported polymorphisms from the published datasets.Meiotic recombination is initiated by DNA double-strand breaks (DSBs) catalyzed by the topoisomerase-like SPORULATION 11 (SPO11). DSBs are repaired as either crossovers (COs) between chromosomes (Fig. 1C), or noncrossovers (NCOs). Both COs and NCOs can be accompanied by gene conversion (GC) events, whic...

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A high resolution map of segmental DNA copy number variation in the mouse genome

Cited by 29 publications

References 23 publications

Rapid repetitive element-mediated expansion of piRNA clusters in mammalian evolution

Rapid repetitive element-mediated expansion of piRNA clusters in mammalian evolution

On the subspecific origin of the laboratory mouse

Detection of genomic variations and DNA polymorphisms and impact on analysis of meiotic recombination and genetic mapping

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