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The influence of recombination on human genetic diversity.

Spencer CC, Deloukas P, Hunt S, Mullikin J, Myers S, Silverman B, Donnelly P, Bentley D, McVean G - PLoS Genet. (2006)

Bottom Line: In humans, the rate of recombination, as measured on the megabase scale, is positively associated with the level of genetic variation, as measured at the genic scale.Broad-scale association between recombination and diversity is explained through covariance of both factors with base composition.To our knowledge, these results are the first evidence of a direct and local influence of recombination hotspots on genetic variation and the fate of individual mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of Oxford, Oxford, United Kingdom.

ABSTRACT
In humans, the rate of recombination, as measured on the megabase scale, is positively associated with the level of genetic variation, as measured at the genic scale. Despite considerable debate, it is not clear whether these factors are causally linked or, if they are, whether this is driven by the repeated action of adaptive evolution or molecular processes such as double-strand break formation and mismatch repair. We introduce three innovations to the analysis of recombination and diversity: fine-scale genetic maps estimated from genotype experiments that identify recombination hotspots at the kilobase scale, analysis of an entire human chromosome, and the use of wavelet techniques to identify correlations acting at different scales. We show that recombination influences genetic diversity only at the level of recombination hotspots. Hotspots are also associated with local increases in GC content and the relative frequency of GC-increasing mutations but have no effect on substitution rates. Broad-scale association between recombination and diversity is explained through covariance of both factors with base composition. To our knowledge, these results are the first evidence of a direct and local influence of recombination hotspots on genetic variation and the fate of individual mutations. However, that hotspots have no influence on substitution rates suggests that they are too ephemeral on an evolutionary time scale to have a strong influence on broader scale patterns of base composition and long-term molecular evolution.

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Related in: MedlinePlus

Wavelet Transformation of Genome Annotations(A) To illustrate the purpose of wavelet transformation, we show the original traces and continuous wavelet transformations using the derivative of Gaussian wavelet basis for gene content and divergence over a 2-Mb stretch of Chromosome 20. Colours indicate the magnitude (blue = low, red = high, white = zero) of the wavelet coefficients at each scale and location, with each level being normalised to have equal variance.(B) Analysis of the correlation between the smoothed and detailed coefficients at each scale (see Text S2). The height of each bar is the value of the correlation coefficient and the boxes are the contributions from broader scales (top is the broadest scale), with colour intensity related to the magnitude of the effect (blue is negative, red is positive) and size proportional to the fraction of variance explained by a given level. The correlation between divergence and constraint in the original signal (−0.0823) can be decomposed into positive contributions from correlations between detail coefficients at broad scales and negative contributions from correlations between detail coefficients at fine scales.
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pgen-0020148-g001: Wavelet Transformation of Genome Annotations(A) To illustrate the purpose of wavelet transformation, we show the original traces and continuous wavelet transformations using the derivative of Gaussian wavelet basis for gene content and divergence over a 2-Mb stretch of Chromosome 20. Colours indicate the magnitude (blue = low, red = high, white = zero) of the wavelet coefficients at each scale and location, with each level being normalised to have equal variance.(B) Analysis of the correlation between the smoothed and detailed coefficients at each scale (see Text S2). The height of each bar is the value of the correlation coefficient and the boxes are the contributions from broader scales (top is the broadest scale), with colour intensity related to the magnitude of the effect (blue is negative, red is positive) and size proportional to the fraction of variance explained by a given level. The correlation between divergence and constraint in the original signal (−0.0823) can be decomposed into positive contributions from correlations between detail coefficients at broad scales and negative contributions from correlations between detail coefficients at fine scales.

Mentions: To illustrate these points, consider the relationship between gene content and divergence. Figure 1A shows the original signals and their wavelet decompositions over a 2-Mb region of the short arm (here a continuous wavelet decomposition is used merely for visual clarity; all analyses are carried out on discrete wavelet transformations). There is clearly both fine-scale and broad-scale variation in both signals. Correlation of the signals smoothed over successively broader scales over the long arm of Chromosome 20 (Figure 1B) shows that gene content and diversity are positively correlated when calculated in windows of 1 to 16 Mb but negatively correlated if calculated in smaller windows. Indeed, if the signals are computed in windows of 1 Mb there is no apparent correlation. Analysis of the detail coefficients explains this unusual behaviour. Over fine scales the detail coefficients show negative correlation, while at broad scales there are weak, but positive correlations. The correlation between the smoothed coefficients at any scale can be decomposed into a weighted sum of the correlations between the detailed coefficients at broader scales (see Text S2) [23]. Consequently, the detail coefficient correlations predict the behaviour of the smoothed coefficient correlations but critically also enable the separation of factors acting at different scales.


The influence of recombination on human genetic diversity.

Spencer CC, Deloukas P, Hunt S, Mullikin J, Myers S, Silverman B, Donnelly P, Bentley D, McVean G - PLoS Genet. (2006)

Wavelet Transformation of Genome Annotations(A) To illustrate the purpose of wavelet transformation, we show the original traces and continuous wavelet transformations using the derivative of Gaussian wavelet basis for gene content and divergence over a 2-Mb stretch of Chromosome 20. Colours indicate the magnitude (blue = low, red = high, white = zero) of the wavelet coefficients at each scale and location, with each level being normalised to have equal variance.(B) Analysis of the correlation between the smoothed and detailed coefficients at each scale (see Text S2). The height of each bar is the value of the correlation coefficient and the boxes are the contributions from broader scales (top is the broadest scale), with colour intensity related to the magnitude of the effect (blue is negative, red is positive) and size proportional to the fraction of variance explained by a given level. The correlation between divergence and constraint in the original signal (−0.0823) can be decomposed into positive contributions from correlations between detail coefficients at broad scales and negative contributions from correlations between detail coefficients at fine scales.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1575889&req=5

pgen-0020148-g001: Wavelet Transformation of Genome Annotations(A) To illustrate the purpose of wavelet transformation, we show the original traces and continuous wavelet transformations using the derivative of Gaussian wavelet basis for gene content and divergence over a 2-Mb stretch of Chromosome 20. Colours indicate the magnitude (blue = low, red = high, white = zero) of the wavelet coefficients at each scale and location, with each level being normalised to have equal variance.(B) Analysis of the correlation between the smoothed and detailed coefficients at each scale (see Text S2). The height of each bar is the value of the correlation coefficient and the boxes are the contributions from broader scales (top is the broadest scale), with colour intensity related to the magnitude of the effect (blue is negative, red is positive) and size proportional to the fraction of variance explained by a given level. The correlation between divergence and constraint in the original signal (−0.0823) can be decomposed into positive contributions from correlations between detail coefficients at broad scales and negative contributions from correlations between detail coefficients at fine scales.
Mentions: To illustrate these points, consider the relationship between gene content and divergence. Figure 1A shows the original signals and their wavelet decompositions over a 2-Mb region of the short arm (here a continuous wavelet decomposition is used merely for visual clarity; all analyses are carried out on discrete wavelet transformations). There is clearly both fine-scale and broad-scale variation in both signals. Correlation of the signals smoothed over successively broader scales over the long arm of Chromosome 20 (Figure 1B) shows that gene content and diversity are positively correlated when calculated in windows of 1 to 16 Mb but negatively correlated if calculated in smaller windows. Indeed, if the signals are computed in windows of 1 Mb there is no apparent correlation. Analysis of the detail coefficients explains this unusual behaviour. Over fine scales the detail coefficients show negative correlation, while at broad scales there are weak, but positive correlations. The correlation between the smoothed coefficients at any scale can be decomposed into a weighted sum of the correlations between the detailed coefficients at broader scales (see Text S2) [23]. Consequently, the detail coefficient correlations predict the behaviour of the smoothed coefficient correlations but critically also enable the separation of factors acting at different scales.

Bottom Line: In humans, the rate of recombination, as measured on the megabase scale, is positively associated with the level of genetic variation, as measured at the genic scale.Broad-scale association between recombination and diversity is explained through covariance of both factors with base composition.To our knowledge, these results are the first evidence of a direct and local influence of recombination hotspots on genetic variation and the fate of individual mutations.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of Oxford, Oxford, United Kingdom.

ABSTRACT
In humans, the rate of recombination, as measured on the megabase scale, is positively associated with the level of genetic variation, as measured at the genic scale. Despite considerable debate, it is not clear whether these factors are causally linked or, if they are, whether this is driven by the repeated action of adaptive evolution or molecular processes such as double-strand break formation and mismatch repair. We introduce three innovations to the analysis of recombination and diversity: fine-scale genetic maps estimated from genotype experiments that identify recombination hotspots at the kilobase scale, analysis of an entire human chromosome, and the use of wavelet techniques to identify correlations acting at different scales. We show that recombination influences genetic diversity only at the level of recombination hotspots. Hotspots are also associated with local increases in GC content and the relative frequency of GC-increasing mutations but have no effect on substitution rates. Broad-scale association between recombination and diversity is explained through covariance of both factors with base composition. To our knowledge, these results are the first evidence of a direct and local influence of recombination hotspots on genetic variation and the fate of individual mutations. However, that hotspots have no influence on substitution rates suggests that they are too ephemeral on an evolutionary time scale to have a strong influence on broader scale patterns of base composition and long-term molecular evolution.

Show MeSH
Related in: MedlinePlus