Chapter 3: Nucleic variation analysis of a Drosophila melanogaster natural population




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(Parts of this chapter results and methods are published in Mackay et al. 2012, with unpublished data)
CHAPTER 3:

Nucleic variation analysis of a Drosophila melanogaster natural population

A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype–phenotype map were based on incomplete genotypic information. Here is descrived the population genomic analysis of the Drosophila melanogaster Genetic Reference Panel (DGRP) that reveal reduced polymorphism in centromeric autosomal regions and the X chromosome (in correlation with recombination), evidence for positive and negative selection, and rapid evolution of the X chromosome.



3.1 Matherial and Methods

We used the DGRP Illumina sequence data and genome sequences from Drosophila simulans and Drosophila yakuba (Clark et al. 2007) to perform genome-wide analyses of polymorphism and divergence, assess the association of these parameters with genomic features and the recombination landscape.

3.1.1 Diversity measures & Linkage disequilibrium

We computed various diversity measures for the whole genome, by chromosome arm (X, 2L, 2R, 3L, 3R), by chromosome region (three regions of equal size in Mb — telomeric, middle and centromeric) and in 50-kbp non-overlapping windows.

Diversity was estimated as the number of segregating sites (S) (Nei 1987), the total minimum number of mutations (η) (Tajima 1996), the number of singletons, nucleotide diversity (π) (Nei 1987), Watterson’s estimator of nucleotide diversity per site (θ) (Nei 1987, Tajima 1993) , and the Jukes-Cantor corrected divergence per site (k) (Jukes & Cantor 1969). Linkage disequilibrium was estimated as the number of haplotypes (h) and haplotype diversity (Hd) (Nei 1987), Fu’s Fs statistic (Fu 1997), D (Lewontin & Kojima 1960), the absolute value of D (|D|), D’ (Lewontin 1964), the absolute value of D’ (|D’|), and r2 (Hill & Robertson 1966, Kelly 1997). The different Ds and the r2 estimates were computed by averaging over all comparisons of polymorphic sites in a window. Several neutrality tests were applied to the data: Fu & Li’s D and F statistics (Fu & Li 1993), Fay & Wu’s H statistic (Fay & Wu 2000) and Tajima’s D statistic (Tajima 1989).

These measures tests were estimated by implementing PDA 2 (Casillas & Barbadilla 2006), VariScan 2 (Hutter et al. 2006), The R statistical package and custom Perl scripts. The number of lines analyzed in each window was fixed at 140, which minimized the loss of sites in each window while accounting for the bias introduced by the clustering of polymorphic sites with ambiguous nucleotides. In addition, windows in which 50% or more sites were ambiguous or unaligned were excluded.


3.1.2 Recombination estimates

The recombination calculator of Fiston-Lavier (Fiston-Lavier et al. 2010) was used to estimate the recombination rate in centiMorgans per megabase (cM/Mb) in windows along each chromosome arm or by gene. The rate at the center of each interval was used. The calculator is based on Marey maps (Marais et al. 2001), where the genetic (cM) and physical (Mb) positions of 644 genes were fitted to a third-order polynomial curve for each chromosome arm, and the recombination rate estimated as the derivative of the curve (Fiston-Lavier et al. 2010).


3.1.3 Natural selection

Adaptive mutations and weakly deleterious selection act in opposite directions on the MKT, so if both selection events are occurring, they will be underestimated. To take adaptive and slightly deleterious mutation mutually into account, a method that incorporates site frequency spectrum (SFS) data to the framework of the McDonald & Kreitman (McDonald & Kreitman 1991) test (MKT), the Integrative-MKT developed by David Castellano (Castellano et al. unpublished, Mackay et al. 2012) was developed during the DGRP freeze 1.0. To estimate selection along chromosomes, the integrative-MKT was implemented in a software written in java.


The Integrative-MKT allows estimating five different regimes of selection acting on new mutations for any given region of the genome: the fraction of strongly deleterious (d), weakly deleterious (b) and neutral sites (f) for the given region. It has also been implemented the calculation of the proportion of adaptive fixations (α) (Smith & Eyre-Walker 2002), the Direction of Selection (DoS) (Stoletzki & Eyre-Walker 2011) and the ratio ωα (Gossman et al. 2012). To do the analysis with different functional classes, each position in the genome has been annotated in the following categories: non-coding, small introns (<= 100 bp), long introns (> 100 bp), UTRs and synonymous and non-synonymous coding positions. In the case of a 2-fold position, it was only classified either synonymous or non-synonymous only in the simple cases and not in the complex ones (Kumar et al. 2001).
The normal Multi-Fasta alignment files were converted into a new vertical Multi-Fasta format (vFasta), where each position was a row and each individual genome was a column. As we had a fixed number of genomes, this implied a fixed number of characters per row in the vFasta file, so random access memory techniques could be applied to speed-up the reading of the alignment.

3.2 POLYMORPHISM AND DIVERGENCE IN THE CHROMOSOME ARMS OF D.MELANOGASTER

Averaged over the entire genome, π = 0.0056 and θ = 0.0067, similar to previous estimates from North American populations (Sackton et al. 2009, Andolfatto & Przeworski et al. 2001). Average polymorphism on the X chromosome (πX = 0.0040) is reduced relative to the autosomes (πA = 0.0060) (X/A ratio = 0.67, Wilcoxon test P = 0), even after correcting for the X/A effective population size (X4/3 = 0.0054, Wilcoxon test P < 0.00002; Table 3.1). Autosomal nucleotide diversity is reduced on average 2.4-fold in centromeric regions relative to non-centromeric regions, and at the telomeres (Fig. 3.1a), whereas diversity is relatively constant along the X chromosome.



Divergence is more uniform (coefficient of variation (CV)k = 0.2841) across chromosome arms than is polymorphism (CVπ = 0.4265). The peaks of divergence near the centromeres could be attributable to the reduced quality of alignments in these regions. Patterns of divergence are similar regardless of the outgroup species used (Fig. 3.1b).

Table 3.1 Estimates of nucleotide polymorphism (π), Watterson’s θ, and divergence (k) for the whole genome, for each chromosome arm and for regions within arms (based on 50kbp non-overlapping windows). Each chromosome arm has been divided in 3 regions of equal size (in Mb): centromere, middle and telomere. Outgroup species for divergence estimates are D. simulans (dsim) and D. yakuba (dyak).












π







Ѳ







k dsim




k dyak




N




mean

median

Sd




mean

median

sd




mean

median

sd




mean

median

sd

ALL

2383




0.0056

0.0059

0.0024




0.0067

0.0073

0.0023




0.0620

0.0555

0.0317




0.1283

0.1198

0.0447

2L

461




0.0068

0.0070

0.0022




0.0080

0.0092

0.0021




0.0592

0.0542

0.0238




0.1279

0.1209

0.0389

2R

423




0.0061

0.0064

0.0024




0.0071

0.0074

0.0022




0.0660

0.0538

0.0535




0.1318

0.1163

0.0649

3L

491




0.0061

0.0068

0.0028




0.0074

0.0081

0.0025




0.0597

0.0538

0.0327




0.1267

0.1173

0.0504

3R

559




0.0051

0.0053

0.0022




0.0063

0.0065

0.0021




0.0546

0.0527

0.0142




0.1183

0.1131

0.0334

X

449




0.0040

0.0043

0.0014




0.0049

0.0053

0.0012




0.0729

0.0693

0.0216




0.1403

0.1382

0.0264

Autosomes

1934




0.0060

0.0064

0.0025




0.0072

0.0078

0.0023




0.0594

0.0536

0.0331




0.1256

0.1164

0.0475

(4/3)X

449




0.0054

0.0057

0.0018




0.0066

0.0070

0.0016



























Figure 3.1: Pattern of polymorphism, divergence, α and recombination rate along chromosome arms in non-overlapping 50-kbp windows.

a, Nucleotide polymorphism (π). The solid curves give the recombination rate (cM Mb−1). b, Divergence (k) for D. simulans (light green) and D. yakuba (dark green). c, Polymorphism to divergence ratio (Pol/Div), estimated as 1 − [(π0-fold/π4-fold)/(k0-fold/k4-fold)]. An excess of 0-fold divergence relative to polymorphism (k0-fold/k4-fold) > (π0-fold/π4-fold) is interpreted as adaptive fixation whereas an excess of 0-fold polymorphism relative to divergence (π0-fold/π4-fold) > (k0-fold/k4-fold) indicates that weakly deleterious or nearly neutral mutations are segregating in the population.

3.3 RECOMBINATION LANDSCAPE

Evolutionary models of hitchhiking and background selection (Begun & Aquadro 1992, Charlesworth et al. 1993) predict a positive correlation between polymorphism and recombination rate. This expectation is realized in regions where recombination is less than 2 cM Mb−1 (Spearman’s ρ = 0.471, P = 0), but recombination and polymorphism are independent in regions where recombination exceeds 2 cM Mb−1 (Spearman’s ρ = −0.0044, P = 0.987) (Fig. 3.2a). The average rate of recombination of the X chromosome (2.9 cM Mb−1) is greater than that of autosomes (2.1 cM Mb−1), which may account for the low overall X-linked correlation between recombination rate and π. The lack of correlation between recombination and divergence (Table 3.2) excludes mutation associated with recombination as the cause of the correlation.


Table 3.2. Spearman and Pearson correlations between nucleotide diversity (π) / divergence (k) and recombination for the whole genome, for each chromosome arm and for regions within arms on 50 kbp non-overlapping windows. Each chromosome arm has been divided in 3 regions of equal size (in Mb): centromere, middle and telomere. P-values < 0.05 are boldfaced.









π versus recombination

kdyak versus recombination







Spearman ρ

p-value




Pearson r

p-value




Spearman ρ

p-value




Pearson r

p-value

2L




0.6599

0.0000




0.6952

0.0000




-0.2755

0.0000




-0.3816

0.0000

2R




0.6529

0.0000




0.7598

0.0000




-0.4402

0.0000




-0.5243

0.0000

3L




0.6629

0.0000




0.7745

0.0000




-0.4844

0.0000




-0.4100

0.0000

3R




0.5123

0.0000




0.5660

0.0000




-0.3328

0.0000




-0.3895

0.0000

X




0.1080

0.0239




0.2081

0.0000




-0.1784

0.0002




-0.3204

0.0000


3.4 MAPPING SELECTION ACROSS THE GENOME

We can observe that the footprint of natural selection is pervasive all along the D. melanogaster genome, although the type of selection is different considering the genomic region or the functional class. In autosomes, selection seems to be weaker in the regions near the centromeres (Figure 3.2a), while in the X chromosome this pattern cannot be seen. This suggests a greater efficiency of natural selection in the X chromosome (Figure 3.2b).

Also, different functional classes show different types of selection. Non-synonymous sites present greater evidence of negative selection than other classes, followed by UTRs. Small introns display quite a different pattern when compared with the other classes, with an overall excess of weakly deleterious positions (Figure 3.2).


  1. Chromosome 3R





  1. Chromosome X


Figure 3.2. Fraction of alleles segregating under different selection regimes by site class in non-overlapping 100 kbp windows for (a) the autosome 3R and the (b) X chromosomes. The selection regimes are strongly deleterious (d, in red), weakly deleterious (b, in yellow) and neutral (f, in green).

(Chapter bibliography, to be removed later)

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