Dr Nicola ; Gianotti
email: nicola.gianotti@hsr.it

San Ospedale

Raffaele

Laura ; Galli

Valeria Micheli

Ospedale L Sacco

Maurizio Zazzi

Valeria Ghisetti

Stefano Bonora

Paola Meraviglia

Paola Corsi

Bianca Bruzzone

Stefano Menzo

Simona Di Giambenedetto

Andrea De Luca

Gaetano Filice

Giovanni Penco

Antonella Castagna

Cristina Rossi

Federica Poletti

; Verbania -Virologia

No pol mutation is associated independently with the lack of immune recovery in patients infected with HIV and failing antiretroviral therapy

Keywords: CD4 T-lymphocytes, drug resistance, resistance to protease inhibitors, resistance to nucleoside reverse transcriptase inhibitors, resistance to non-nucleoside reverse transcriptase inhibitors

Drug resistance mutations at baseline by antiretroviral class in patients showing immune recovery (IR) or lack of immune recovery (LIR). By univariable

Follow-up characteristics

Median years of follow-up (Q1, Q3)

3.1 (3.1, 3.3) 3.1 (3.1, 3.3) 3.1 (3.1, 3.2) 0.021
Median 3-year HIV-RNA, log 10 copies/ml (Q1, Q3) 2.5 (1.7, 4.1) 3.6 (1.9, 4.5) 1.7 (1.7, 3) <0.0001

Median change in HIV-RNA, log 10 copies/ml (Q1, Q3) -1.2 (-2.1, -0.1) -0.5 (-1.4, 0.4) -1.7 (-2.4, -0.7) <0.0001

Median viremia detectability ratio (Q1, Q3) 0.7 (0.4, 1) 0.9 (0.

F o r P e e r R e v i e w

INTRODUCTION

Extensive antiretroviral resistance is associated with a blunted virological response to subsequent regimens; in particular, there is a gradient of response depending on the number of active drugs, with greater efficacy being found in the patients who receive more active drugs [START_REF] Hicks | Durable efficacy of tipranavir-ritonavir in combination with an optimised background regimen of antiretroviral drugs for treatment-experienced HIV-1-infected patients at 48 weeks in the Randomized Evaluation of Strategic Intervention in multi-drug reSistant resistant patients with Tipranavir (RESIST) studies: an analysis of combined data from two randomised open-label trials[END_REF][START_REF] Clotet | Efficacy and safety of darunavir-ritonavir at week 48 in treatment-experienced patients with HIV-1 infection in POWER 1 and 2: a pooled subgroup analysis of data from two randomised trials[END_REF][START_REF] Lazzarin | Efficacy and safety of TMC125 (etravirine) in treatmentexperienced HIV-1-infected patients in DUET-2: 24-week results from a randomised, double-blind, placebo-controlled trial[END_REF]Madruga et al., 2007;[START_REF] Fätkenheuer | Subgroup analyses of maraviroc in previously treated R5 HIV-1 infection[END_REF][START_REF] Cooper | Subgroup and resistance analyses of raltegravir for resistant HIV-1 infection[END_REF].

It has been shown that extensive resistance to nucleoside reverse transcriptase inhibitors (NRTIs), non-NRTIs (NNRTIs), and protease inhibitors (PIs) increases the risk of progression to AIDS or death [START_REF] Ormaasen | An algorithm-based genotypic resistance score is associated with clinical outcome in HIV-1-infected adults on antiretroviral therapy[END_REF][START_REF] Hogg | Emergence of drug resistance is associated with an increased risk of death among patients first starting HAART[END_REF]Zaccarelli et al., 2007;[START_REF] Lohse | Genotypic drug resistance and long-term mortality in patients with triple-class antiretroviral drug failure[END_REF], and that there are associations between clinical progression and single pol mutations [START_REF] De Luca | Three-year clinical outcomes of resistance genotyping and expert advice: extended follow-up of the Argenta trial[END_REF]Zaccarelli et al., 2007). One possible reason for these findings is that extensive drug resistance and some mutations in particular hamper the virological efficacy of subsequent regimens, thus leading to a weaker immune response and increasing eventually the rate of disease progression.

However, this view has been challenged by one study [START_REF] Lohse | Genotypic drug resistance and long-term mortality in patients with triple-class antiretroviral drug failure[END_REF] that found mortality to be independent of CD4+ values at the time of death.

The relationship between the degree of drug resistance (or the number of active drugs)

and the extent of immune recovery after the failure of an antiretroviral regimen has not been investigated, and it is not known whether specific pol mutations can hinder longterm immune recovery regardless of the virological efficacy of new antiretroviral regimens after drug failure.

This study was aimed at identifying the predictors of a lack of immune recovery in this clinical setting and at investigating whether specific pol mutations or their combinations may be associated independently with hindered immune reconstitution.

Statistical analysis

The continuous variables were described by means of median values and quartiles (Q1, Q3) or frequencies (%), as appropriate.

The study outcome was immune recovery, stratified as immune recovery or a lack of immune recovery after at least 3 years of follow-up. All of the available CD4+ values, obtained between baseline and the end of follow-up, were retained and used in the univariable linear regression to estimate the mean change (slope) in CD4+ values within each study patient. Lack of immune recovery was defined as a mean change in CD4+ count during follow-up of ≤0 (slope ≤0); conversely, immune recovery was defined as a mean change in CD4+ count during follow-up of >0 (slope >0). Univariable linear regression was also applied to HIV RNA values.

The viremia detectability ratio was calculated as the number of HIV RNA values of >50 copies/mL divided by the number of HIV-RNA values measured during follow-up.

The characteristics of the subjects with lack of immune recovery or immune recovery were compared using Wilcoxon's rank-sum test and the chi-square test.

Logistic regression was used for the univariable and multivariable analyses to study the associations between the risk (odds ratio) of lack of immune recovery and the demographic, clinical, and genotypic parameters. Univariable logistic regression was used for all of the clinical and demographic characteristics considered in the study and All of the statistical tests were two-sided at the 5% level and were made using SAS Software (release 9.1; SAS Institute).

RESULTS

A total of 826 patients fulfilled all of the inclusion criteria and entered the study. Table I shows their baseline demographic and clinical characteristics in detail, grouped on the basis of their immune recovery status (lack of immune recovery vs. immune recovery).

The drugs used most frequently for genotyping were lamivudine (797; 96.5%), zidovudine (782; 94.7%), stavudine (723; 87.5%), didanosine (657; 79.5%), indinavir (560; 67.8%), saquinavir (479; 58.0%), and nelfinavir (403; 48.8%). Tenofovir was used more frequently among patients who showed immune recovery in the next three years of follow-up (p = 0.02); statistically significant differences were not observed with regard to other drugs.

Figure 1 illustrates the proportion of patients with individual pol mutations (excluding those observed in fewer than 5%, which were not considered in the analysis). Fortyseven patients (5.7%) had no pol mutations at baseline. During follow-up, there was no statistically significant difference in the drugs prescribed, except for atazanavir, which was included more frequently (p = 0.002) in the regimens received by patients with immune recovery.

After 3.13 years of follow-up, the overall mean (SE) slope in CD4+ cell count was 7.90

(1.85) cells per year of follow-up (lack of immune recovery: -33.2 (1.67); immune recovery: 42.1 (1.97); p < 0.0001), and the mean (SE) slope in HIV RNA levels was -0.18 (0.01) log 10 copies/ml per year (lack of immune recovery: -0.06 (0.02); immune recovery: -0.28 (0.01); p < 0.0001). Overall, 375 patients (45.4%) showed lack of immune recovery. The median (Q1, Q3) CD4+ count at the end of follow-up was 377 (232, 561) cells/µL (lack of immune recovery: 322 (177, 503); immune recovery: 411 (277, 613); p < 0.0001). The virological outcome at the end of follow-up is shown in Table I.

Table II shows the results of the univariable and multivariable logistic regression. By multivariable analysis, the risk of lack of immune recovery increased with increasing baseline CD4+ cell count (OR = 1.104 per 50-cell increase, 95% CI = 1.069-1.142, p < 0.0001) and increasing viremia detectability ratio during follow-up (OR = 1.145 per 0.1 increase, 95% CI = 1.093-1.202, p < 0.0001). In addition, the odds of lack of immune recovery decreased with decreasing calendar year of genotyping (overall effect: p = 0.0007).

None of the resistance mutations included in the multivariable model resulted in an increase or decrease in the odds of lack of immune recovery.

DISCUSSION

If specific pol mutations were able to hinder immune recovery in HIV-infected patients failing antiretroviral therapy, it would be wise (when possible) to avoid the use of drugs selecting preferentially these mutations. The present analysis did not detect any pol mutation that predicted a long-term lack of immune recovery regardless of virological response to the regimens that the patients received after GRT: this implies that, in terms of the risk of lack of immune recovery, there should be no reason to prefer one PI rather than another or one RTI rather than another.

The results of the present study also suggest that, in patients harbouring drug-resistant HIV variants, disease progression is not just a direct consequence of hindered immune recovery following subsequent regimens or of the pol mutations selected at failure, but the result of a complex interplay between various factors (including virological and immunological factors as well as adherence to treatment after GRT).

Immune recovery after treatment failure was predicted independently by the ability of the new regimen(s) to suppress viremia, baseline CD4+ cell counts, and the year of HIV genotyping. These factors seem thus to be more important than individual mutations selected by the failing regimen in driving immune recovery during the next years of therapy.

A gradient of immune responses relating to the degree of virological response was observed previously [START_REF] Wood | Full suppression of viral load is needed to achieve an optimal CD4 cell count response among patients on triple drug antiretroviral therapy[END_REF]. The viremia detectability ratio has been used

to measure virological responses to the regimens prescribed after GRT because it was believed that, unlike point observations, this could provide detailed information concerning the cumulative exposure to HIV throughout the follow-up. A positive association between the viremia detectability ratio and lack of immune recovery is also responses are related directly in treatment-experienced patients [START_REF] Lalezari | Enfuvirtide, an HIV-1 fusion inhibitor, for drug-resistant HIV infection in North and South America[END_REF][START_REF] Lazzarin | Efficacy of enfuvirtide in patients infected with drugresistant HIV-1 in Europe and Australia[END_REF][START_REF] Hicks | Durable efficacy of tipranavir-ritonavir in combination with an optimised background regimen of antiretroviral drugs for treatment-experienced HIV-1-infected patients at 48 weeks in the Randomized Evaluation of Strategic Intervention in multi-drug reSistant resistant patients with Tipranavir (RESIST) studies: an analysis of combined data from two randomised open-label trials[END_REF][START_REF] Clotet | Efficacy and safety of darunavir-ritonavir at week 48 in treatment-experienced patients with HIV-1 infection in POWER 1 and 2: a pooled subgroup analysis of data from two randomised trials[END_REF][START_REF] Lazzarin | Efficacy and safety of TMC125 (etravirine) in treatmentexperienced HIV-1-infected patients in DUET-2: 24-week results from a randomised, double-blind, placebo-controlled trial[END_REF][START_REF] Fätkenheuer | Subgroup analyses of maraviroc in previously treated R5 HIV-1 infection[END_REF]Gulick et al., 2008;[START_REF] Steigbigel | Raltegravir with optimized background therapy for resistant HIV-1 infection[END_REF]. Furthermore, these findings also underline the importance of pursuing the goal of HIV RNA values of <50 copies/mL also in this clinical setting [START_REF] Hammer | Antiretroviral treatment of adult HIV infection: 2008 recommendations of the International AIDS Society-USA panel[END_REF].

The M184V mutation was not associated with immune recovery: one hypothesis it that this mutation slows the CD4+ cell loss as compared to treatment interruption [START_REF] Castagna | Lamivudine monotherapy in HIV-1-infected patients harbouring a lamivudine-resistant virus: a randomized pilot study (E-184V study)[END_REF], but it does not necessarily favour immune recovery when antiretroviral therapy is continued [START_REF] Fox | A randomized trial to evaluate continuation versus discontinuation of lamivudine in individuals failing a lamivudine-containing regimen: the COLATE trial[END_REF].

Year of genotyping was another independent predictor of lack of immune recovery: this likely reflects the improved ability of interpreting genotypic drug-resistance testing and constructing more effective new regimens after treatment failure in more recent years.

In addition to those typical of all retrospective analyses, the present study has some specific limitations. Patients who were lost to follow-up or who died during the 3-year follow-up period were excluded from the analysis. It is possible that they had the worst immune response, but this analysis was aimed at studying the association between pol mutations and long-term immune responses rather than the rate of clinical progression.

Another limitation is the fact that data concerning adherence to therapy before and after study entry were not uniformly collected: poor adherence is a relevant predictor of lack of immune recovery [START_REF] Gazzola | The absence of CD4+ T cell count recovery despite receipt of virologically suppressive highly active antiretroviral therapy: clinical risk, immunological gaps, and therapeutic options[END_REF], and so it is likely that patients with lower viremia detectability ratios were those more adherent to treatment.

In conclusion, no pol mutation was associated with a lack of immune recovery in viremic patients who underwent GRT and were followed up for at least three years,

  undertaken to determine whether specific pol mutations hinder long-term immune recovery regardless of virological response. In total, 826 patients with >50 HIV RNA copies/mL, who underwent genotypic resistance testing between 1 January 2000 and 31 December 2003 after >3 years of antiretroviral treatment, and were followed up for >3 years after genotypic resistance testing, were analyzed retrospectively. The outcome of the study was the lack of immune recovery after >3 years of follow-up, defined as a slope by linear regression <0. The viremia detectability ratio was defined as the number of HIV RNA values of >50 copies/mL divided by the number of HIV RNA measurements during follow-up. Logistic regression was used for univariable and multivariable analysis. Median (Q1, Q3) values at baseline were the following: age 40 (37, 45) years, years on antiretroviral therapy 4.45 (3.65, 5.47), HIV RNA 3.91 (3.39, 4.53) log 10 copies/mL, CD4+ T-cell 358 (211, 524)/µL. After 3.13 years of follow-up, 375 patients (45.4%) showed a lack of immune recovery. The risk of lack of immune recovery increased independently with increasing baseline CD4+ counts (OR = 1.104 per 50-cell increase, 95% CI = 1.069-1.142, p < 0.0001), increasing viremia detectability ratio during follow-up (OR = 1.145 per 0.1-unit increase, 95% CI = 1.093-1.202, p < 0.0001), and with earlier calendar years of resistance testing (overall effect: p = 0.0007). In conclusion, no pol mutation is associated independently with the lack of immune recovery. KEY WORDS: CD4+ T-lymphocytes; drug resistance; resistance to protease inhibitors; resistance to nucleoside reverse transcriptase inhibitors; resistance to nonnucleoside reverse transcriptase inhibitors.

  Cohort Analysis (ARCA) database is a public database that has been developed as a means of investigating resistance to antiretroviral drugs, and contains data concerning the patients' treatment, longitudinally collected CD4+ cell counts, plasma HIV-RNA measurements, and HIV sequences. The data are provided by the individual centres, and descriptive statistics can be consulted in the public access area of the database at www.hivarca.net.For the purposes of this retrospective study, we considered all of the HIV-infected patients in the database with >50 HIV RNA copies/ml while on antiretroviral treatment (ART), who underwent genotypic resistance testing (GRT) between 1 January 2000 and 31 December 2003 after at least three years of ART, and who were followed up for at least three years after GRT. The date of the GRT was considered the study baseline, and baseline values were those recorded on the date nearest to the GRT, provided that this was within the 120 days preceding it; the end-of-follow-up values were those closest to three years after GRT.HIV genotypes were assessed at each centre on the basis of local procedures and checked for consistency upon sequence uploading into the ARCA database, which stores the sequences and automatically extracts the mutations with respect to HIV-1 consensus B using a built-in local alignment script based on ClustalW. PI mutations were defined as major or minor according to the IAS list(Johnson et al., 2009), and thymidine analogue mutations (TAMs) were profiled as previously described[START_REF] De Luca | Three-year clinical outcomes of resistance genotyping and expert advice: extended follow-up of the Argenta trial[END_REF]. Genotypic sensitivity scores (GSSs) were calculated as the number of active drugs according to the ANRS version 18 algorithm //www.hivfrenchresistance.org/) from among zidovudine, didanosine, stavudine, lamivudine, abacavir, tenofovir, emtricitabine, nevirapine, efavirenz, etravirine, ritonavir, indinavir, nelfinavir, saquinavir/ritonavir, amprenavir/ritonavir, lopinavir/ritonavir, atazanavir/ritonavir, tipranavir/ritonavir, and darunavir/ritonavir.

  pol mutations observed in at least 5% of the patients. The multivariable model included only those mutations and patient characteristics associated with the lack of immune recovery in the univariable logistic regression at a level of p < 0.2, with the exception of age and gender, which were retained as fixed covariates. The odds ratios (OR) and corresponding 95% confidence intervals were calculated to estimate the risk of lack of immune recovery.

  results of clinical trials showing that virological and immune

Figure 1 .

 1 FIGURE TITLES AND LEGENDS

Table I .

 I Demographic and clinical characteristics of the study subjects

	Presence of at least 1 TAM, n (%)
				Overall 614 (74.3)	Lack of immune 284 (75.7)	Immune 330 (73.2)	0.424
	TAMs, n (%)			(n = 826)	recovery	recovery	P value
		None	212 (25.7)	(n = 375) 91 (24.3)	(n = 451) 121 (26.8)
		Pattern = 1	361 (43.7)	175 (46.7)	186 (41.2)	0.411
	Demographic and clinical characteristics at baseline Other 253 (30.6)	109 (29.1)	144 (31.9)
	Gender, n (%) Presence of at least 1 NNRTI mutation, Male	577 (69.8)	271 (72.3)	306 (67.9)
	n (%)	Female		249 (30.2) 444 (53.8)	104 (27.7) 202 (53.9)	145 (32.1) 242 (53.7)	0.171 0.999
	Median age at baseline, years (Q1, Q3) AIDS diagnosis before baseline, n (%) Median number of major PI mutations F (Q1, Q3) o r Presence of at least 1 PI mutation, n	40 (37, 45) 24 (2.9) 1 (0, 2)	40 (36, 45) 9 (2.4) 1 (0, 2)	40 (37, 44) 15 (3.3) 1 (0, 2)	0.707 0.411 0.029
	Median duration of antiretroviral (%)	P 460 (55.7)	222 (59.2)	238 (52.8)	0.068
	treatment, years (Q1, Q3) Median PI genotypic sensitivity score	4.5 (3.7, 5.5) e	4.4 (3.6, 5.4)	4.5 (3.7, 5.6)	0.623
	Median baseline CD4+ count, cells/mm 3 (Q1, Q3)	358 (211, 524) e r	418 (257, 593)	310 (195, 454) <0.0001
	Median baseline HIV-RNA, log 10		R
	copies/ml (Q1, Q3)			3.9 (3.4, 4.5) e 3.9 (3.3, 4.6)	3.9 (3.4, 4.5)	0.552
	Genotype characteristics at baseline	v i e
	Year of genotyping, n (%)	2000	107 (3.0)	62 (16.5) w	45 (10.0)
		2001	175 (21.2)	88 (23.5)	87 (19.3)	0.003
		2002	157 (19.0)	72 (19.2)	85 (18.9)
		2003	387 (46.9)	153 (40.8)	234 (51.9)
	Median total number of mutations (Q1,		7 (4, 10)	7 (4, 11)	7 (4, 10)	0.0001
	Q3)
	Presence of at least 1 NRTI mutation, n
	(%)			711 (86.1)	328 (87.5)	383 (84.9)	0.314

Table II .

 II Univariable and multivariable logistic regression: unadjusted and adjusted odds ratios for lack of immune recovery.

			Unadjusted	95% confidence		Adjusted	95% confidence
			OR	limits	P value	OR	limits	P value
	Gender	F vs. M	0.810	0.599-1.092	0.169	0.826	0.589-1.156	0.267
	Age (per 5-year increase) Baseline CD4+ (per 50-cell increase) Viremia detectability ratio (per 0.1 increase) Year of genotyping F o r	0.999 1.086 1.129	0.919-1.087 1.054-1.119 1.083-1.179	0.988 <0.0001 <0.0001 0.003	1.009 1.104 1.145	0.990-1.029 1.069-1.142 1.093-1.202	0.344 <0.0001 <0.0001 0.0007
	2000 vs. 2003	2.107	1.368-3.268	0.015	2.619	1.611-4.300	0.002
	2001 vs. 2003	1.547	1.080-2.218	0.049	1.684	1.130-2.515	0.039
	2002 vs. 2003	1.295	0.890-1.883	0.462	1.215	0.805-1.829	0.129
	PI GSS (per unit increase)		0.925	0.856-0.998	0.046	0.990	0.884-1.110	0.868
	Treatment interruption during follow-up
		Yes vs. no	1.258	0.890-1.776	0.194	1.337	0.917-1.952	0.131
	Change in any antiretroviral drug in the 3 months
	following GRT
		Yes vs. no	0.798	0.890-1.776	0.194	0.974	0.717-1.323	0.865
	Use of a new drug after GRT
		Yes vs. no	0.742	0.486-1.124	0.163	0.714	0.430-1.175	0.188

P e e r R e v i e w

	L10I
	Yes vs. no	1.238	0.922-1.661	0.157	0.981	0.668-1.438	0.922
	I54V
	Yes vs. no	1.600	1.124-2.288	0.009	1.366	0.826-2.264	0.224
	V82A
	Yes vs. no	1.629	1.143-2.331	0.007	1.148	0.712-1.855	0.572
	T69D
	Yes vs. no	1.379	0.855-2.232	0.187	1.195	0.713-2.007	0.498
	A98G
	Yes vs. no	1.490	0.854-2.625	0.161	1.517	0.838-2.768	0.170
	M184V
	Yes vs. no	1.261	0.959-1.661	0.097	1.311	0.972-1.770	0.076
	GSS: genotypic sensitivity score; GRT: genotypic resistance testing; PI: protease inhibitor.

John Wiley & SonsJournal of Medical Virology

Acknowledgements

Nicola Gianotti conceived and designed the study, collected and updated data, and wrote the first draft of the manuscript.

Laura Galli designed the study, performed the statistical analyses and interpretations, and wrote the first draft of the manuscript.

(Nicola Gianotti and Laura Galli contributed equally to the writing of this article.)

Maurizio Zazzi designed the study, collected and updated data, and contributed to the writing of the manuscript.

Valeria Ghisetti collected and updated data.

Stefano Bonora collected and updated data.

Valeria Micheli collected and updated data.

Paola Meraviglia collected and updated data Paola Corsi collected and updated data.

Bianca Bruzzone collected and updated data.

Stefano Menzo collected and updated data.

Simona Di Giambenedetto collected and updated data.


Anamaria Necsulea

Laurent Duret
email: laurent.duret@univ-lyon1.fr

Alexandra Popa

David N Cooper

Peter Stentson

Dominique Mouchiroud

Christian Gautier

Anamaria Necşulea

Peter D Stenson

Meiotic recombination

Keywords: human disease-associated mutations, meiotic recombination, GCbiased gene conversion, human polymorphisms, derived allele frequencies, non-synonymous mutations human disease-associated mutations, meiotic recombination, GC-biased gene conversion, non-synonymous mutations, polymorphisms, derived allele frequencies

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

The majority of disease-causing mutations (DMs) detected in human populations are very recent, having only been transmitted over a few generations at most [START_REF] Slatkin | Estimating allele age[END_REF]. A substantial fraction of DMs nevertheless correspond to more ancient mutations that have persisted for a large number of generations. Several non-exclusive hypotheses have been proposed to explain why such detrimental mutations could have escaped negative selection.

First, detrimental mutations that have a limited impact on reproductive success (e.g. mutations causing late-onset diseases) can spread simply by genetic drift [START_REF] Kryukov | Most rare missense alleles are deleterious in humans: implications for complex disease and association studies[END_REF] Second, some DMs confer a selective advantage upon heterozygotes (overdominance) [START_REF] Dean | Balanced polymorphism selected by genetic versus infectious human disease[END_REF]. Third, some DMs may have attained a high population frequency in the past because they were once advantageous under environmental conditions that no longer pertain [Di [START_REF] Rienzo | An evolutionary framework for common diseases: the ancestral-susceptibility model[END_REF]. Finally, some DMs may occur at high frequency because of a high de novo mutation rate or a germ-line selective advantage [START_REF] Choi | A germ-line-selective advantage rather than an increased mutation rate can explain some unexpectedly common human disease mutations[END_REF].

Population genetic models indicate that in addition to genetic drift and natural selection, there is a third process that can contribute to the spreading of mutations within a population: biased gene conversion (BGC). Gene conversion occurs during homologous recombination and involves the non-reciprocal transfer of sequence information between the two recombining DNA molecules. This process is said to be biased if one of the two DNA molecules involved is more likely than the other to be the donor. Gene conversion can affect paralogous sequences duplicated in the genome or different alleles at a given locus [START_REF] Chen | Gene conversion: mechanisms, evolution and human disease[END_REF]. In the case of allelic gene conversion, BGC leads to an excess of the 'favored' allele in the pool of gametes and hence tends to increase the frequency of this allele in the population.

Theoretical analyses have shown that, as with selection, BGC can increase the probability of fixation of the favored allele [START_REF] Nagylaki | Evolution of a finite population under gene conversion[END_REF]. Although the theoretical consequences of the BGC process have been known for some time, the potential practical importance of this phenomenon has remained largely unstudied.

Recently, the analysis of polymorphism and nucleotide substitution patterns in primates has provided firm evidence for BGC acting genome-wide, favoring GC alleles over AT alleles (for a review, see Duret and Galtier, 2009a). Indeed, this process of GC-biased gene conversion (gBGC) appears to be the major determinant of the evolution of base composition at silent sites (non-coding regions, synonymous codon positions) in primate genomes [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. Further, there is now good evidence that gBGC has impacted upon the evolution of functional sequences, both in regulatory non-coding sequences [Duret and Galtier, 2009b;[START_REF] Galtier | Adaptation or biased gene conversion? Extending the null hypothesis of molecular evolution[END_REF] and in protein-coding exons [START_REF] Berglund | Hotspots of biased nucleotide substitutions in human genes[END_REF]Galtier, et al., 2009]. Importantly, these results indicate that, in our species' evolutionary past, gBGC is likely to have hampered the action of purifying selection and led to the fixation of deleterious mutations.

Here we have sought to determine whether gBGC influences the frequency of deleterious non-synonymous polymorphisms in extant human populations. To this end, we investigated the segregation patterns of AT→GC and GC→AT single nucleotide polymorphisms (SNPs) according to the local recombination rate. We also analyzed different classes of nonsynonymous SNPs, predicted to be deleterious or known to be involved in genetic disease, using synonymous and non-coding SNPs as a neutral control. All classes of SNPs were found to display the hallmarks of the gBGC process. Further, we provide evidence that these segregation patterns cannot be explained by ascertainment bias in SNP detection, artifacts in SNP orientation, or other biological processes such as natural selection. In support of these observations, we present simulations to illustrate the conditions under which gBGC can extend the persistence of deleterious mutations in finite populations. We conclude that gBGC has not only had a substantial impact on human evolution but is also highly relevant to human health and disease.

Material and methods

Single nucleotide polymorphism data. To determine the frequency of SNPs in human populations, we used the data gathered in the HapMap Project phase III, release 27 [START_REF] Frazer | A second generation human haplotype map of over 3.1 million SNPs[END_REF]. We analyzed data from four HapMap populations: YRI (Yoruba in Ibadan, Nigeria), JPT (Japanese in Tokyo), CHB (Han Chinese in Beijing) and CEU (Utah residents with ancestry from northern and western Europe) and we grouped the CHB and JPT samples into a single set. We analyzed only SNPs that were polymorphic in the unrelated individuals genotyped in each sample (3,566,377 total, Supp. Table S1). Ensembl annotations [START_REF] Hubbard | Ensembl 2009[END_REF] were used to determine the positions of SNPs with respect to transcripts and coding sequences. Four classes of polymorphism were retained for analysis: intergenic, intronic, protein-coding synonymous and protein-coding non-synonymous.

As a complement, we used an independent polymorphism dataset comprising 39,440 autosomal SNPs, found exclusively in coding sequences, at both synonymous and nonsynonymous positions [START_REF] Lohmueller | Proportionally more deleterious genetic variation in European than in African populations[END_REF]. These SNPs were determined by direct exon sequencing in 10,150 transcripts, for two population samples (hereafter termed AFR and CAU): 15 African-American individuals (30,718 SNPs) and 20 European-American individuals [START_REF] Lohmueller | Proportionally more deleterious genetic variation in European than in African populations[END_REF]514 SNPs,Supp. Table S2).

Inference of ancestral and derived alleles. We determined the ancestral and derived states of human polymorphisms using human-chimpanzee whole-genome alignments, obtained from the UCSC Genome Browser [START_REF] Rhead | The UCSC Genome Browser database: update 2010[END_REF] through Galaxy [START_REF] Giardine | Galaxy: a platform for interactive large-scale genome analysis[END_REF]. To infer the most likely ancestral and derived alleles for each SNP, we used a maximum likelihood approach that takes into account the hypermutability of CpG dinucleotides [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. Starting from whole-genome alignments of the human and chimpanzee sequences, we constructed triple alignments that included two sequences for the human population, corresponding to the two alleles observed for each SNP. The allocation of alleles to the two human sequences was performed randomly. We then inferred the ancestral sequence for the human population, thereby obtaining for each genomic position a probability distribution for the identity of the ancestral nucleotide. The ancestral nucleotide was randomly drawn according to these four probabilities. In our analysis, we included only SNPs with a constant 5'-3' context (i.e. positions with two neighboring SNPs were removed, and we required that the human and chimpanzee nucleotides should be identical).

To confirm that this first approach had not been misled by ancestral 'misinference' issues, we also used a second approach, developed by Hernandez, et al. [2007a], which corrects the spectrum of derived allele frequencies, obtained by parsimonious reasoning, using a contextdependent model of sequence evolution (software kindly provided by Ryan D. Hernandez).

We only considered SNPs found within a constant 5'-3' context, as defined above. As indicated by the authors, we further restricted our dataset to positions where the chimpanzee nucleotide corresponded to one of the two alleles observed in the human population. The context-dependent site frequency spectrum obtained by maximum parsimony was then corrected using the model proposed by Hernandez, et al. [2007a] As noted previously [START_REF] Gibbs | Evolutionary and biomedical insights from the rhesus macaque genome[END_REF] for disease-associated mutations, the diseaseassociated allele sometimes represents the ancestral state; here, we focused exclusively on SNPs for which the derived allele was associated with the disease. The number of genotyped chromosomes varies widely between individual SNPs. The correction method developed by Hernandez et al. [2007a] requires the derived allele frequency spectrum to be constructed employing the same number of chromosomes for all SNPs. To fulfill this requirement, we applied the following procedure (as proposed by Hernandez et al. [2007b]): we computed the minimum number of sampled chromosomes (n min ) for a given SNP dataset and then estimated the derived allele frequencies for a dataset reduced to n min chromosomes. For a SNP that was originally present in n out of m sampled chromosomes, the probability that it will be present at a frequency i in the reduced sample is given by the hypergeometric distribution:

SNP sampling and derived allele frequency spectrum

C n i × C m-n n min -i C m n min
, where C u v is the number of choices of v elements among u. Using this formula, we can generate the expected derived allele frequency spectrum in a subsample of n min chromosomes. Note that this procedure was applied independently for each class of SNPs analyzed here (intergenic, intronic, synonymous SNPs etc.). The n min values for each SNPs sample and for each region are given in Supp.

Table S5.

Recombination rates and hotspots. The positions of 34,136 recombination hotspots were taken from HapMap release 21 [START_REF] Myers | A fine-scale map of recombination rates and hotspots across the human genome[END_REF], and converted from hg17 to hg18 assembly coordinates using the liftover utility from the UCSC Genome Browser [START_REF] Rhead | The UCSC Genome Browser database: update 2010[END_REF]. We also computed the regional recombination rates in 10kb sliding windows for autosomal mutations using the genetic maps provided by [START_REF] Frazer | A second generation human haplotype map of over 3.1 million SNPs[END_REF], release 36.

Disease-associated mutations. We extracted 45,751 disease-associated mutations occurring in protein-coding sequences from HGMD release 2008.3 [START_REF] Stenson | The Human Gene Mutation Database: 2008 update[END_REF]. Using annotations from the Ensembl database [START_REF] Hubbard | Ensembl 2009[END_REF] HGMD mutations are allocated to four distinct classes with respect to their association with disease: DM, mutations regarded as being a direct cause of disease; DP, polymorphisms exhibiting a significant statistical association with disease but without additional functional evidence supporting their involvement; DFP, disease-associated polymorphisms with additional functional evidence supporting their direct involvement; FP, polymorphisms reported to affect the structure, function or expression of the gene (or gene product), but with no known disease association (Supp. Table S3).

PolyPhen predictions. To predict which non-synonymous SNPs present in HapMap are potentially damaging for protein structure and function, we used PolyPhen predictions for dbSNP build 126 [START_REF] Sunyaev | Prediction of deleterious human alleles[END_REF]. For the exon sequencing dataset, we used the PolyPhen predictions provided by the authors [Lohmueller et al., 2008] (Supp. Table S4). We focused on the SNPs predicted to be "probably damaging", for which the derived allele has been shown to be the deleterious allele in 99% of cases [Lohmueller et al., 2008].

Definition of recombination classes.

To define regions of high and low recombination, we sorted each SNP dataset according to the minimum distance to a recombination hotspot, and then divided the dataset into three equal-sized classes. Only the first and the third classes were compared in order to maximize the crossover rate difference between the high and low recombination regions. This procedure was applied independently for each genomic region Development Core Team, 2008]. To test the effect of gBGC, we compared the mean derived allele frequencies (DAF) for AT→GC and GC→AT mutations. Given that the distribution of DAF is non-Gaussian, we used a randomization procedure to test the statistical significance of the mean difference (d = mean(AT→GC)-mean(GC→AT)). To do this, we randomized the direction of AT→GC and GC→AT SNPs and compared the observed d value with those obtained from 1000 randomized datasets. We computed a p-value corresponding to the proportion of simulated datasets for which the d value was higher than that observed in the real dataset; our test was thus one-tailed.

We also analyzed the difference in mean DAF between the two mutation classes (d) for regions of high and low recombination. To test if the difference in d (∆d) between the two recombination classes was statistically significant, we developed a randomization procedure:

we drew randomly two sets of sites (from all possible SNPs in a given genomic region), equal in size to the original low recombination and high recombination classes, and computed ∆d for the simulated dataset. A one-tailed p-value was computed by comparing the observed ∆d value with 1000 simulated datasets.

Simulation of the impact of gBGC on the derived allele frequency spectrum

We used simulations to determine the expected distribution of derived allele frequencies (DAF) at loci that are subject to mutation, negative selection and biased gene conversion. The initial population was homozygous and finite following a Fisher-Wright probabilistic model

f SS * = ω SS ω z SS ; f SW * = ω SW ω z SW ; f WW * = ω WW ω z WW alleles n + 1: p S = f SS * + 1 2 f SW * p W = 1-p S
where f represents the frequency of individuals at generation n, g the frequency of gametes at generation n, and f * the frequency of individuals at generation n+1. Here we only considered mutations that are both deleterious and recessive. We termed s the selection coefficient, so that the fitness of individuals homozygous for the mutant allele is ω = 1s. Thus, for the simulations of the fate of a newly-arisen S W → mutation in a WW population, we haveω SS = ω and ω SW = ω WW = 1 whereas for the simulations of the fate of a newly-arisen W S → mutation in an SS population, we haveω SS = ω SW = 1 and ω WW = ω .

Simulations were run in populations of size N e =10,000 with a mutation rate of 10 -8 mutations per base-pair per individual per generation, using different combinations of gBGC coefficient (δ=0, δ=0.00013 and δ=0.0013) and selection coefficient (s=0, s=10 -4 , s=10 -3 and s=10 -2 ).

Supporting Information

The dataset used in this publication is freely available at the following website: ftp://pbil.univ-lyon1.fr/pub/datasets/Necsulea2010

Results

gBGC hallmarks are observed for deleterious SNPs

To investigate whether gBGC affects the segregation of deleterious mutations in human populations, we studied the spectrum of derived allele frequencies (DAFs) of nonsynonymous SNPs as a function of the local recombination rate across human chromosomes.

We first analyzed the HapMap dataset of human SNPs, which provides frequencies of each allele in different human populations [START_REF] Frazer | A second generation human haplotype map of over 3.1 million SNPs[END_REF]. We inferred the ancestral and derived alleles for SNPs by means of a maximum likelihood approach that incorporates CpG hypermutability [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF], using the chimpanzee genome as an outgroup.

Three distinct subsets of non-synonymous polymorphisms were investigated: 1) all HapMap non-synonymous SNPs; 2) HapMap non-synonymous mutations for which the impact on the function of the protein was predicted by PolyPhen [START_REF] Sunyaev | Prediction of deleterious human alleles[END_REF] to be 'probably damaging'; and 3) HapMap SNPs corresponding to disease-associated non-synonymous mutations reported in the HGMD database [START_REF] Stenson | The Human Gene Mutation Database: 2008 update[END_REF]. We further split the HGMD dataset in order to analyze specifically those inherited mutations which are considered to be a direct cause of disease (DM), thereby excluding those mutations that have only been associated statistically with disease (Supp. Table S3). As a control, we also analyzed SNPs at silent sites, for which evidence of gBGC has already been reported [START_REF] Galtier | GC-content evolution in mammalian genomes: the biased gene conversion hypothesis[END_REF]Spencer, et al., 2006;[START_REF] Webster | Fixation biases affecting human SNPs[END_REF]. As expected, DAFs were found to be negatively correlated with the strength of purifying selection: SNPs in non-coding regions or at synonymous codon positions exhibited the highest mean DAFs, whereas the lowest mean DAFs were observed for mutations that are known to be involved in genetic disease or that were predicted by PolyPhen to be deleterious (Fig. 1, Supp. Tables S8-S10).

The gBGC model makes two firm predictions: first, in regions of high recombination, the spectrum of derived allele frequencies (DAFs) for SNPs is expected to be skewed, with higher 13 frequencies for AT→GC than for GC→AT mutations; second, this skewing is expected to be weaker in genomic regions characterized by a lower recombination rate. To test these predictions, we classified SNPs into groups of high and low recombination on the basis of their physical distance to the nearest recombination hotspot [START_REF] Myers | A fine-scale map of recombination rates and hotspots across the human genome[END_REF]; similar results were obtained when the recombination classes were computed on the basis of the average crossover rate in fixed-size sliding windows (not shown). We found that in regions of high recombination, AT→GC mutations segregated at higher frequencies than GC→AT mutations (Fig. 2, Supp. Tables S8-S10, Supp. Fig. S2-S4). This difference was statistically significant in all HapMap samples, both for silent SNPs and for the three sets of nonsynonymous SNPs (Table 1). This pattern was evident even within the DM subset. For this class, the tests remained significant in only one of the HapMap samples. Nevertheless, given that our observations for the more abundant classes of mutations (silent sites, nonsynonymous SNPs) were always in agreement with the gBGC hypothesis, and significantly so, the uncertainty related to the DM class is most likely only a consequence of the reduced sample size. As predicted by the gBGC model, the difference between the mean AT→GC and GC→AT frequencies is much stronger for SNPs located in regions of high recombination rate as compared to SNPs located in regions of low recombination rate (Fig. 1, Table 1, Supp.

Tables S13-S15). Thus, all classes of SNPs exhibit the hallmarks of the gBGC process, not only the silent sites but also the three subsets of non-synonymous sites.

Control for variations in selective pressure on non-synonymous mutations

We observed that at non-synonymous sites, GC→AT mutations segregate at lower frequency than AT→GC mutations. One potential explanation for this observation is that AT→GC nonsynonymous mutations might be, on average, less deleterious than GC→AT non-synonymous mutations. To test this hypothesis, we compared AT→GC and GC→AT SNPs that lead to the In total, there are 10 amino-acid changes that can be caused both by AT→GC and GC→AT mutations. For each of the three populations, we performed pairwise comparisons of the mean DAF of AT→GC and GC→AT SNPs causing the same amino-acid changes: in 23 out of 30 comparisons, the AT→GC SNP had the highest mean DAF (Supp. Table S19). For example, the mean DAF of Q→H non-synonymous SNPs in the CEU population is 0.19 when it results from an AT→GC mutation, compared to 0.16 when it results from a GC→AT mutation.

Conversely, the mean DAF of the reverse amino-acid change (H→Q) is 0.35 when it results from an AT→GC mutation, compared to 0.23 when it results from a GC→AT mutation.

Thus, the mean DAF varies according to the direction of the GC-content change (AT→GC vs.

GC→AT), independently of the nature of the amino-acid change. Hence, the observed differences in mean DAF between AT→GC and GC→AT non-synonymous SNPs cannot be attributed to differences in selective pressure on the corresponding amino-acid changes.

Control for SNP ascertainment bias and ancestral misidentification

The HapMap dataset is known to be biased towards high frequency polymorphisms, and this representation bias can confound some population genetic analyses [START_REF] Clark | Ascertainment bias in studies of human genome-wide polymorphism[END_REF]. There is however no a priori reason why this ascertainment bias should differentially affect AT→GC-and GC→AT-derived allele frequencies. This notwithstanding, to ensure that our observations were not affected by this intrinsic bias in HapMap data, we repeated our analysis on an independent polymorphism dataset that was acquired through direct exon re-sequencing in two human populations [Lohmueller et al., 2008], and which should therefore be free of ascertainment bias. Our conclusions remained unchanged with the re-sequencing dataset: in regions of high recombination, AT→GC mutations segregated at higher frequencies than GC→AT mutations and this excess was higher than in regions of low recombination. This pattern was observed in both populations, not only for the synonymous sites but also for the 1, Supp. Tables S11-S12, S16-S17, Supp. Fig.

S5-S6

). We may therefore conclude that the observed skewing of derived allele frequencies was not simply a consequence of ascertainment bias. It may be noted that the pattern appears to be stronger with the HapMap dataset as compared to the re-sequencing dataset (Table 1).

By means of simulations, we showed that this difference is due to the fact that the HapMap SNP sampling strategy provides greater power to detect gBGC (see Supporting Information).

One other potential artifact that had to be considered and assessed was the possibility that the observed gBGC-like pattern stemmed from ancestral 'misinference' [Hernandez et al., 2007a]: when the mutational pattern is biased towards AT, and most notably in the case of strong context dependence (such as CpG dinucleotide mutational hotspots in mammalian genomes), maximum parsimony tends to incorrectly ascribe directionality for GC→AT mutations, yielding an apparent excess of high-frequency AT→GC SNPs [Hernandez et al., 2007a]. Nevertheless, we are confident that this artifact has not influenced our results for the following reasons. First, instead of using parsimony-based reasoning, we determined SNP directionality using a maximum-likelihood approach that takes CpG hypermutability into account [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. Second, our conclusions were unchanged when CpG sites were excluded (Supp. Table S7). Third, we repeated our analyses using the context-dependent model proposed by Hernandez and colleagues [Hernandez et al., 2007a] to correct for potential ancestral allele misidentification. With this method, the results remained in agreement with our previous observations (Supp. Table S6). Finally, it should be highlighted that the difference between the mean DAFs of AT→GC and GC→AT mutation was found to be much stronger in regions of high recombination (Fig. 1). This observation, which is consistent with the gBGC model, cannot be explained by an ancestral misinference artifact.

Indeed, the pattern of substitution is more biased toward AT in regions of low recombination as compared to regions of high recombination [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. Thus, an artifactual

Simulation of the impact of gBGC in a finite population

To investigate the impact of gBGC on the fate of deleterious mutations (AT→GC or GC→AT), we performed simulations in a finite population (effective population size N e =10,000), considering recessive mutations subject to different selection coefficients (s) and gBGC coefficients (δ; see methods). The population-scale gBGC coefficient (N e δ) in the human genome was estimated by [START_REF] Spencer | The influence of recombination on human genetic diversity[END_REF] by analyzing the DAF spectra of noncoding SNPs. In genomic regions of high recombination (defined as the top 20% of the genome with the highest recombination rate; average crossover rate= 2.5 cM/Mb) their estimate was N e δ=0.325. Given that, in the human genome, recombination is essentially confined to hotspots (typically less than 2 kb long) with an average crossover rate of about 40 cM/Mb [START_REF] Myers | The distribution and causes of meiotic recombination in the human genome[END_REF], it is expected that the gBGC coefficient should be about 16 times higher in these hotspots. Recombination hotspots vary in intensity [START_REF] Myers | The distribution and causes of meiotic recombination in the human genome[END_REF]. We therefore considered two values for the population-scale gBGC coefficient: N e δ=1.3 (for a moderate recombination hotspot) and N e δ=13 (for a more intense recombination hotspot).

With gBGC parameters corresponding to those of a moderate human recombination hotspot, the impact of gBGC on the DAF spectrum was clearly detectable for both nearly-neutral (|N e s| = 1) and mildly deleterious mutations (|N e s| = 10): compared to a situation without gBGC

(N e δ=0), AT→GC segregate at higher frequency, whereas GC→AT segregate at lower frequency (Fig. 3). For the more intense recombination hotspots, the impact of gBGC on the DAF spectrum was detectable even for highly deleterious mutations (|N e s| = 100). synonymous SNPs that we analyzed, 6% were located within 2kb of the center of a recombination hotspot. Thus, only a limited fraction of SNPs is expected to be affected by gBGC. This explains why the skewing observed in real data (Fig. 2) is intermediate between the patterns obtained in simulations corresponding to moderate hotspots (N e δ=1.3) or to the absence of gBGC (N e δ=0) (Fig. 3). Thus, the pattern observed with real data appears to be compatible with the hypothesis that the skew in the DAF spectrum is due to gBGC affecting deleterious mutations in recombination hotspots. It should be noted that the location of recombination hotspots is extremely dynamic [START_REF] Baudat | PRDM9 is a major determinant of meiotic recombination hotspots in humans and mice[END_REF][START_REF] Myers | Drive against hotspot motifs in primates implicates the PRDM9 gene in meiotic recombination[END_REF]], which suggests that the fraction of SNPs that are at some time affected by gBGC, might be larger than that estimated above. To obtain a more realistic estimation of the expected DAF spectra, it would be necessary to take into account not only the intensity recombination hotspots but also their dynamics.

Discussion

We have shown that all functional classes of SNPs, including non-synoynmous SNPs known to be implicated in human disease, and non-synonymous SNPs predicted to be damaging for protein structure and function, exhibit the hallmarks of gBGC: the derived allele frequency of AT→GC mutations is higher than that of GC→AT mutations, and this is more promounced in regions characterized by high recombination rates. Importantly, we demonstrated that the observed excess of high-frequency SNPs in regions of high recombination does not result from sampling biases nor from artifacts of SNP directionality determination.

Is gBGC the only possible explanation for these observations? One alternative hypothesis to explain the fact that non-synonymous GC→AT mutations segregate at lower frequency than AT→GC mutations is that GC→AT mutations could be more deleterious that the AT→GC mutations. For instance, it has been recently shown that GC→AT mutations at hypermutable CpG sites within coding regions are under stronger purifying selection than other nonsynonymous mutations [START_REF] Schmidt | Hypermutable non-synonymous sites are under stronger negative selection[END_REF]. Several observations however argue against this hypothesis. First, we note that our conclusions remained unchanged when SNPs occurring within a CpG context were excluded (Supp. Table S7). Second, comparison of GC→AT and AT→GC mutations causing the same amino-acid changes confirmed that the higher mean DAF of the latter cannot be attributed to a weaker impact on the encoded protein. , 2008]. Hence, the most parsimonious explanation for our findings is that both silent sites and non-synonymous sites are subject to gBGC.

Taken together, the data presented are consistent with the hypothesis that biased gene conversion is responsible for the excess of AT→GC SNPs segregating at high frequency in regions of high recombination. This result has important implications for human health because it indicates that recombination, via gBGC, leads to an increase in the frequency of disease-causing AT→GC mutations in human populations. It should be stressed that the impact of gBGC on deleterious mutations is not always negative. Indeed, a majority (58.7%) of known DMs correspond to GC→AT mutations. Thus, for a majority of DMs, gBGC acts in such a way as to limit their probability of spreading. However, the price to pay for this positive influence of gBGC is that it can lead to an increase in the frequency of diseasecausing AT→GC mutations in human populations. We speculate that the genes most likely to be influenced by this effect will be those that are AT-rich (i.e. for which there are more opportunities for AT→GC mutations) and which coincide with recombination hotspots: an additional argument for these hotspots being an Achilles' heel of the human genome [Duret and Galtier, 2009b;[START_REF] Galtier | Adaptation or biased gene conversion? Extending the null hypothesis of molecular evolution[END_REF]. The effect of sampling bias on the power of gBGC detection To search for a potential effect of gBGC, we measured the difference between the mean AT -→ GC and GC -→ AT frequencies (denoted d) in regions of low or high recombination. We performed this analysis on two independent SNP datasets: the HapMap SNP dataset [START_REF] Frazer | A second generation human haplotype map of over 3.1 million SNPs[END_REF], and the SNP dataset from Lohmueller et al. (2008), which was obtained by direct exon resequencing. Both datasets revealed the hallmarks of gBGC (Table 1): i) in regions of high recombination, d is positive; ii) d increases with the recombination rate. This pattern is observed not only for the silent sites but also for the three classes of non-synonymous sites (Table 1).

We noted however that for the two subsets of non-synonymous sites under the strongest purifying selection (DM and probably damaging mutations), the parameter d was lower in the re-sequencing dataset than in the HapMap dataset, and was no longer significantly different from zero (Table 1). We suspected that this difference might be a consequence of the differences between the SNP sampling strategies that were used to prepare the two datasets. On the one hand, the HapMap dataset prioritized using validated SNPs in order to focus resources on common (rather than rare or false positive) candidate SNPs from the public databases [START_REF] Frazer | A second generation human haplotype map of over 3.1 million SNPs[END_REF]. This strategy would omit very rare alleles. On the other hand, the SNP dataset from Lohmueller et al. (2008) was obtained by direct exon resequencing, and therefore contains no a priori bias in allele frequencies. To assess the impact of these two sampling strategies on the detection of biased gene conversion, we performed theoretical simulations.

First we simulated populations (N =10 4 ) whose genome is subject to mutation, mild negative selection (ω=0.9999) and medium gBGC (δ=0.00013), as described in the main text (Methods).

We recorded the allele frequencies at each polymorphic locus (SNP) in these initial populations.

Simulations were performed on a large number of loci, so as to obtain a total of more than 10,000 SNPs within each initial population. We sampled N c chromosomes from these initial To simulate the re-sequencing strategy used by Lohmueller et al. (2008), we randomly drew SNPs in the initial population, and then determined their DAF in the sample of N c chromosomes. SNPs for which only one allele was present in the sample (i.e. that were not detected as polymorphic) were discarded. This random sampling of SNPs was repeated until a dataset of 100 W→S and 100 S→W SNPs was obtained. Then we calculated the statistic d, which is defined as the difference between the mean DAFs of W→S SNPs and of S→W SNPs. To obtain the distribution of the d statistic, this procedure was repeated until at least 60 independent SNP datasets were obtained (Supplementary Table S18).

We used the same procedure to simulate the HapMap strategy, except that rare SNPs (i.e SNPs whose DAF in the initial population was lower or higher than given thresholds) were excluded. The threshold values for rare SNPs were chosen in order to have 95% of DAFs in the final sample within the interval [0.03, 0.97].

In the initial simulated population, the derived allele frequency spectrum shows the hallmark of gBGC: DAFs are significantly higher for W→S SNPs than for S→W SNPs (Figure 3). This excess of high-frequency W→S SNPs (i.e. a positive d statistic) was detected with both sampling strategies. However, independently of the number of sampled chromosomes (N c =120 or N c =40), the d statistic obtained by the re-sequencing strategy was on average lower than that obtained with the HapMap strategy (Supplementary Figure S1. For N c = 40 -Resequencing: d=0.1129; HapMap: d=0.146, Wilcoxon rank sum test: p=1e -4 . For N c = 120 -Resequencing: d=0.0893; HapMap: d=0.1318, Wilcoxon rank sum test: p=0.). This indicates that all else being equal, the HapMap SNP sampling strategy has a greater power to detect the effect of gBGC than the re-sequencing strategy. This can be explained by the fact that the impact of gBGC is more readily detectable on the upper part of the DAF spectrum. Table S19: Mean values for AT→GC and GC→AT non-synonymous mutations, for those amino-acid changes that can result from both classes of mutations. The values in parantheses represent the sample sizes. Dark green: the difference between mean values was statistically significant (randomization, p-value <0.05), and the mean was higher for AT→GC mutations. Light green: the difference between mean values was not statistically significant (randomization test, p-value >=0.05), and the mean was higher for AT→GC mutations. Light red: the difference between mean values was not statistically significant (randomization test, p-value >=0.05), and the mean was lower for AT→GC mutations. No cases were found where the mean is significantly lower for AT→GC mutations. Human Mutation

  intronic, coding synonymous etc.) and for each HGMD and PolyPhen subset of SNPs.Statistical analyses.All statistical analyses were performed with the R environment [R

  acid replacement and hence are expected to have the exact same fitness impact.

  non-synonymous sites (Table

  DAFs caused by ancestral misinference would be expected to be stronger in regions of low recombination, in contradistinction to our own observations (Fig.1).

  only a small fraction of the genome: among the non-

Formatted

  Moreover, this hypothesis that AT→GC mutations are relatively less deleterious cannot explain why their mean DAF increases with the recombination rate. Finally, we have shown that the DAF pattern is consistent over all classes of SNP, including those located in intergenic and intronic regions, which may be presumed to be largely free of selective pressure. It has been previously demonstrated that the relationship between recombination and the evolution of GC-content in non-coding regions is the consequence of gBGC and not selection[Duret and

Figure 1 .

 1 Figure legends

Figure 2 .

 2 Figure 2. Derived allele frequency spectra for the HapMap YRI sample, for different genomic regions and classes of non-synonymous SNPs. The data presented here relate only to the high recombination class. Dark gray: AT→GC mutations, light gray: GC→AT mutations.

Figure 3 .Formatted

 3 Figure 3. Derived allele frequency spectrum obtained through simulations with different parameter sets. Represented in light gray are the distributions of derived allele frequencies for GC→AT alleles, and in dark gray, those of AT→GC alleles. The population-scaled selection coefficient (Nes) and the population-scaled biased gene conversion parameter (Ne δ) is indicated for each graph.

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Mean derived allele frequencies for AT→GC and GC→AT alleles in regions of high and low recombination, for the HapMap YRI sample, for different genomic regions and classes of nonsynonymous SNPs. Dark gray: AT→GC, light gray: GC→AT mutations. Solid bars: low recombination, hatched bars: high recombination. Probably damaging: HapMap non-synonymous SNPs predicted by Polyphen to be probably damaging. HGMD: entire HGMD dataset. DM: inherited mutations known to be a direct cause of disease (HGMD mutations minus those that have only been associated statistically with disease). 210x144mm (600 x 600 DPI)

  populations. Two values were tested for N c : 120 (60 pairs of chromosomes in a sample, similar to the number of chromosomes in the HapMap samples), 40 (20 pairs of chromosomes in a Necsulea et al., Human Mutation sample, similar to the Lohmueller et al. (2008) sample). In order to test the influence of sampling alone, both strategies were compared for the same value of N c .

Figure S1 :

 S1 Figure S1: The distribution of the statistics d in the case of an initial spectrum frequencies obtained with values of δ = 0.00013 and ω = 0.9999. Left panel: simulations for N c = 40; right panel: for N c = 120. Gray bars: the histogram of d in the case of HapMap sampling strategy. Hatched bars: the histogram of resequencing sampling strategy. The p-value corresponds to the Wilcoxon rank sum test, for a comparison of medians of the two distributions. The mean value of d is also given in the case of HapMap (dH) and resequencing (dR) sampling strategies.

  Table of the number of classes each of size 100 polymorphic loci and the range of initial frequencies for the HapMap biased sampling strategy. These variables depend on the values of gBGC (δ) and the fitness of the derived allele (ω) which were used to obtain the initial distribution of derived alleles, as well as the number of chromosomes analyzed.

  ∆d values for different GC-contents. Each set of SNPs was divided into two equal-size classes, according to the GC-content in 100bp flanking regions. The regions of low and high recombination are defined based on the distance from SNPs to recombination hotspots. ∆d values for different CpG contents. Each set of SNPs was divided into two equal-size classes, according to the CpG observed/expected ratio in 100bp flanking regions. The regions of low and high recombination are defined based on the distance from

  release 49, we were able to

	map unambiguously onto the human genome the positions of 43,953 disease-associated
	mutations. 193 mutations were synonymous and hence were excluded -here we only analyzed
	non-synonymous mutations (34,814 missense and 8,946 nonsense).
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	P
	e
	e r
	R
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	w
	8

  SW and ω WW . The mean fitness value in the population isω : ω = z SS ω SS + z SW ω SW + z WW ω WW where z denotes the zygotic frequencies. u and has positive values when gBGC occurs. The frequency of the S allele is denoted p and hence the frequency of allele W is 1-p. The model describes the transition from one generation, n, to the next, n+1, admitting panmixia, with the

	with multinomial sampling, ensuring a constant population size over time. The evolution of
	the derived allele frequency was simulated independently for each locus. Each simulation was
	performed for over 20,000 generations, at the end of which, the DAF of the derived allele was
	calculated.				Deleted: (
					Formatted: Font: Times
					New Roman
	The alleles that can segregate at each locus belong to one of two classes: S(trong) (G or C) or	Field Code Changed
	W(eak) (A or T). The fitness of genotypes SS, SW and WW are denoted respectively	Formatted: Font: Times New Roman
	ω SS ,ω For individuals that are heterozygous at a given locus (SW), we termed u the probability of F conversion W S → and v the probability of conversion S W → . The gene conversion bias at o r P e this site is measured throughδ = v following equations: e r adults n : f SS ; f SW ; f WW ;	Deleted: ) Deleted: e one Deleted: i Deleted: classes
	gametes n :	g S =	2 f SS + (1+ δ) f SW 2		R g W = 1-g S
	zygotes n + 1: adults n + 1:	z SS = g S 2 ;	z SW = 2g S g W ;	e z WW = g W 2 v i e
					w
					Deleted: i
					Deleted:
					10

Table legends ¶Table 1 :

 legends1 Summary table for the BGC hallmarks for the HapMap and resequencing SNP datasets. The difference in mean derived allele frequencies between AT→GC and GC→AT SNPs is denoted by d. d H is the value of d in regions of high recombination. ∆d represents the difference in d between the high and low recombination regions.

	Page 25 of 61	Human Mutation
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	Page 27 of 61			Human Mutation
	Dataset	Population	Intergenic	Introns	Synonymous	Nonsynonymous	HGMD		DM	Probably damaging
			d H	∆d	d H	∆d	d H	∆d	d H	∆d	d H	∆d	d H	∆d	d H	∆d
		CEU	0.03	0.013	0.03	0.022	0.08	0.043	0.09	0.016	0.09	0.052 0.07 0.031 0.08	0.055
	HapMap Resequencing	CHB+JPT YRI AFR CAU F 0.03 0.03 o r P 0.013 0.016 F o r P e e r R e v i e w 0.03 0.02 0.08 0.052 0.11 0.015 0.03 0.028 0.07 0.033 0.07 0.034 0.1 0.073 0.06 0.05 0.1 0.092 0.05 0.035 e	0.09 0.12 0.1 0.07	0.026 0.11 0.062 0.14 0.076 0.1 0.056 0.07 0.09 0.05 0.052 0.02 0.098 -0.01 0.039 0.04	0.086 0.042 0.046 0.026
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				John Wiley & Sons, Inc.

Table 1 :

 1 Summary table for the BGC hallmarks for the HapMap and resequencing SNP datasets. The difference in mean derived allele frequencies between AT→GC and GC→AT SNPs is denoted by d. d H is the value of d in regions of high recombination. ∆d represents the difference in d between the high and low recombination regions. Bold font: values are positive and significantly different from zero, with a p-value < 0.05. Italic font: values are positive but not significantly different from zero. Normal font: values are negative but not significantly different from zero. No cases were found where d H or ∆d were significantly lower than zero.

Table S1 :

 S1 SNP dataset from HapMap release 27. 5' and 3' UTR exons are excluded from our dataset. Note that the total sample size is given here and that further restrictions are applied when computing the DAF spectrum: constant 5' -3' context (i.e. positions with two neighboring SNPs were removed, and we required that the human and chimpanzee nucleotides should be identical) and non-ambiguous ancestral allele prediction.

	Necsulea et al., Human Mutation
	3 Supplementary Tables
	Type	CEU	CHB+JPT	YRI	All samples
	Intergenic	1,705,876 1,621,044 1,903,673	2,151,095
	Introns	1,044,507	998,219	1,168,601	1,329,002
	Synonymous	15,313	14,545	17,155	20,095
	Non-synonymous	18,251	18,467	18,594	24,609
	F o r F o r F o r F o r F o r Other 31,011 F r o Total 2,814,958 2,681,680 3,142,116 29,405 34,093	41,576 3,566,377
	P P P P P P
		e e e e e e
		e r e r e r e r e r e r
			R R R R R R
		Type	e e e e e e AFR	CAU
	v i e w v i e w v i e w v i e w v i e w 17,011 11,931 v i Non-synonymous 13,707 10,583 Synonymous e w Total 30,718 22,514

Table S2 :

 S2 SNP dataset fromLohmueller et al., 2008. Note that the total sample size is given here and that further restrictions are applied when computing the DAF spectrum: constant 5' -3' context, (i.e. positions with two neighboring SNPs were removed, and we required that the human and chimpanzee nucleotides should be identical) and non-ambiguous ancestral allele prediction.

	Type	All HGMD	CEU	CHB+JPT	YRI	AFR	CAU
	DM 41,949 (33,096) 225 (221) 213 (207) 215 (210) 163 (163) 142 (142)
	DP	837 (801)	492 (477) 441 (425) 412 (399) 241 (241) 274 (274)
	FP	923 (866)	168 (162) 140 (135) 150 (145)	71 (71)	73 (73)
	DFP	51 (51)	36 (36)	32 (32)	31 (31)	23 (23)	22 (22)
	Total 43,760 (34,814) 921 (896) 826 (799) 808 (785) 498 (498) 511 (511)
		F o r
			P
			e
			e r
				R
				e
				v i e
					w

Table S3 :

 S3 Number of non-synonymous disease-associated mutations in HGMD and found within our SNP datasets. The numbers in parantheses represent the number of missense mutations (the remaining ones are nonsense mutations). Note that this table includes mutations for which the disease-associated allele is ancestral, although in the derived allele frequencies spectra we include only positions for which the disease-associated allele is derived.

	Type	CEU CHB+JPT YRI	AFR	CAU
	Benign	12,338	12,468	12,681 9,698	7,366
	Possibly damaging 2,566	2,688	2,597	2,366	1,829
	Probably damaging 1,591	1,721	1,518	1,361	1,168
	Total	16,495	16,877	16,796 13,425 10,363

Table S4 :

 S4 PolyPhen predictions for the non-synonymous SNPs in our dataset.

	Type	CEU CHB+JPT YRI AFR CAU
	Intergenic	56	88	60
	Intron	56	78	52
	Synonymous	90	84	92	18	18
	Non-synonymous	92	92	90	18	18
	HGMD	100	144	100	18	18
	DM	100	148	106	18	18
	Probably damaging	98	148	94	18	18

Table S5 :

 S5 The minimum number of genotyped chromosomes for each SNP dataset.

	Probably damaging		d H ∆d		0.08 0.055	0.14 0.086	0.07 0.042	0.02 0.046	0.04 0.026	0.08 0.038	0.13 0.06	0.07 0.022	0.04 0.026	0.06 0.031
	DM		∆d		0.031	0.062	0.056	0.052	0.039	-0.016	0.041	0.006	0.047	0.042
			d H		0.07	0.11	0.1	0.05	-0.01	0.02	0.08	0.04	0.05	-0.02
	Intergenic Introns Synonymous Non-synonymous HGMD	Population	d H ∆d d H ∆d d H ∆d d H ∆d d H ∆d	Maximum likelihood method (Duret and Arndt, 2008)	CEU 0.03 0.013 0.03 0.022 0.08 0.043 0.09 0.016 0.09 0.052	F o r P e e r R e v i e w CHB+JPT 0.03 0.013 0.03 0.02 0.08 0.052 0.11 0.015 0.09 YRI 0.03 0.016 0.03 0.028 0.07 0.033 0.07 0.034 0.12 AFR 0.1 0.073 0.06 0.05 0.1 CAU 0.1 0.092 0.05 0.035 0.07 CEU 0.04 0.012 0.05 0.019 0.06 0.02 0.07 0.005 YRI AFR CAU CHB+JPT 0.05 0.013 0.05 0.018 0.07 0.034 0.09 0.003 0.05 0.014 0.06 0.023 0.05 0.011 0.05 0.022 0.06 0.048 0.04 0.027 0.06 0.061 0.03 0.011 SFS correction with context-dependent model (Hernandez et al., 2007) 0.07 0.08 0.1 0.09 0.06 0.026 0.076 0.09 0.098 0.034 0.034 0.071 0.074 0.06
		Dataset				HapMap		Resequencing			HapMap		Resequencing

Table S6 :

 S6 Summary table for the BGC hallmarks. d H represents the difference in mean derived allele frequencies between AT→GC and GC→AT SNPs, for high recombination regions. ∆d represents the difference in d between high and low recombination regions. The recombination classes were computed as a function of the distance to recombination hotspots. Dark green: d and ∆d are positive, with a p-value < 0.05. Light green: d and ∆d are positive, with a p-value ≥ 0.05. Light red: d and ∆d are negative, with a p-value >= 0.05. No cases were found where d and ∆d are negative, with a p-value < 0.05.

	Probably damaging		d H ∆d		0.04 0.058	0.08 0.074	0.03 0.019	0.05 0.036	0.05 0.051	0.03 0.04	0.06 0.045	0.04 0.04	0.07 0.047	0.06 0.052
	DM		∆d		-0.038	0.001	-0.132	-0.037	-0.002	-0.041	0.012	-0.149	-0.036	-0.001
			d H		0.03	0.12	-0.04	0.1	0.01	0.03	0.13	-0.06	0.1	0.02
	Intergenic Introns Synonymous Non-synonymous HGMD	Population	d H ∆d d H ∆d d H ∆d d H ∆d d H ∆d	Maximum likelihood method (Duret and Arndt, 2008)	F o r P e e r R e v i e w CEU 0.02 CHB+JPT 0.03 YRI 0.02 AFR CAU CEU 0.03 YRI AFR CAU CHB+JPT 0.03 0.04 0.012 0.03 0.018 0.05 0.032 0.09 0.017 0.12 0.07 0.012 0.02 0.016 0.05 0.031 0.11 0.009 0.14 0.067 0.013 0.02 0.024 0.03 0.013 0.05 0.016 0.1 0.056 0.05 0.059 0.03 0.023 0.11 0.043 0.06 0.067 0.04 0.047 0.12 0.102 0.011 0.03 0.015 0.04 0.013 0.08 0.015 0.13 0.012 0.03 0.014 0.04 0.017 0.09 0 0.13 0.012 0.04 0.019 0.02 -0.008 0.05 0.014 0.08 0.04 0.042 0.04 0.013 0.11 0.04 0.049 0.04 0.033 0.11 SFS correction with context-dependent model (Hernandez et al., 2007) 0.057 0.079 0.043 0.035 0.047
		Dataset				HapMap		Resequencing			HapMap		Resequencing

Table S7 :

 S7 Summary table for the BGC hallmarks. d H represents the difference in mean derived allele frequencies between AT→GC and GC→AT SNPs, for high recombination regions. ∆d represents the difference in d between high and low recombination regions. The recombination classes were computed as a function of the distance to recombination hotspots. Only SNPs found within a non-CpG context (i.e. the 5' neighbor is different from C and the 3' neighbor is different from G) were considered here. Dark green: d and ∆d are positive, with a p-value < 0.05. Light green: d and ∆d are positive, with a p-value ≥ 0.05. Light red: d and ∆d are negative, with a p-value >= 0.05. No cases were found where d and ∆d are negative, with a p-value < 0.05. AT → GC Mean AT → GC Nb. GC → AT Mean GC → AT d

	Necsulea et al., Human Mutation
	Region Nb. H P-value	Maximum likelihood method (Duret and Arndt, 2008)	Intergenic 203038 0.39 242795 0.36 0.03 0 / 1000	Intron 127478 0.39 150521 0.36 0.03 0 / 1000	F o r P e e r R e v i e w Synonymous 1124 0.42 3440 0.34 Non-synonymous 1492 0.35 3467 0.26 PolyPhen probably damaging 111 0.24 272 0.15 HGMD 58 0.25 141 0.16 DM 13 0.15 45 0.09 Intergenic Intron Synonymous Non-synonymous HGMD PolyPhen probably damaging SFS correction with context-dependent model (Hernandez et al., 2007) 214565 0.39 217374 0.35 134207 0.39 134843 0.34 1113 0.39 3245 0.34 1443 0.33 3061 0.27 108 0.24 232 0.16 59 0.25 130 0.17 0.08 0 / 1000 0.09 0 / 1000 0.08 0 / 1000 0.09 6 / 1000 0.07 105 / 1000 0.04 0 / 1000 0.05 0 / 1000 0.06 0 / 1000 0.07 0 / 1000 0.08 5 / 1000 0.07 8 / 1000	DM 12 0.11 39 0.09 0.02 354 / 1000

Table S8 :

 S8 Derived allele frequencies for AT → GC and GC → AT SNPs, for the CEU sample from HapMap, for high recombination regions. The recombination classes were defined based on the distance to recombination hotspots. AT → GC Mean AT → GC Nb. GC → AT Mean GC → AT d

	Necsulea et al., Human Mutation
	Region Nb. H P-value	Maximum likelihood method (Duret and Arndt, 2008)	Intergenic 193158 0.4 230503 0.37 0.03 0 / 1000	Intron 121829 0.4 143749 0.37 0.03 0 / 1000	F o r P e e r R e v i e w Synonymous 1055 0.43 3255 0.35 Non-synonymous 1440 0.36 3563 0.25 PolyPhen probably damaging 110 0.28 307 0.14 HGMD 52 0.27 126 0.18 DM 16 0.21 39 0.1 Intergenic Intron Synonymous Non-synonymous HGMD PolyPhen probably damaging SFS correction with context-dependent model (Hernandez et al., 2007) 205085 0.4 207474 0.36 129054 0.4 129683 0.35 1054 0.41 3121 0.35 1406 0.34 3204 0.25 105 0.28 264 0.15 52 0.27 115 0.19 0.08 0 / 1000 0.11 0 / 1000 0.14 0 / 1000 0.09 9 / 1000 0.11 22 / 1000 0.05 0 / 1000 0.05 0 / 1000 0.07 0 / 1000 0.09 0 / 1000 0.13 0 / 1000 0.08 13 / 1000	DM 14 0.18 36 0.1 0.08 86 / 1000

Table S9 :

 S9 Derived allele frequencies for AT → GC and GC → AT SNPs, for the CHB+JPT sample from HapMap, for high recombination regions. The recombination classes were defined based on the distance to recombination hotspots. AT → GC Mean AT → GC Nb. GC → AT Mean GC → AT d

	Necsulea et al., Human Mutation
	Region Nb. H P-value	Maximum likelihood method (Duret and Arndt, 2008)	Intergenic 226907 0.34 271411 0.31 0.03 0 / 1000	Intron 142683 0.34 168833 0.3 0.03 0 / 1000	F o r P e e r R e v i e w Synonymous 1281 0.36 3837 0.29 Non-synonymous 1563 0.31 3412 0.24 PolyPhen probably damaging 111 0.23 240 0.16 HGMD 52 0.27 117 0.15 DM 14 0.22 38 0.11 Intergenic Intron Synonymous Non-synonymous HGMD PolyPhen probably damaging SFS correction with context-dependent model (Hernandez et al., 2007) 240916 0.33 244249 0.28 150974 0.33 151760 0.27 1298 0.33 3648 0.28 1558 0.29 3063 0.24 113 0.23 202 0.16 55 0.27 104 0.16 0.07 0 / 1000 0.07 0 / 1000 0.07 1 / 1000 0.12 0 / 1000 0.1 65 / 1000 0.05 0 / 1000 0.06 0 / 1000 0.05 0 / 1000 0.05 0 / 1000 0.07 3 / 1000 0.1 2 / 1000	DM 14 0.16 32 0.12 0.04 358 / 1000

Table S10 :

 S10 Derived allele frequencies for AT → GC and GC → AT SNPs, for the YRI sample from HapMap, for high recombination regions. The recombination classes were defined based on the distance to recombination hotspots. AT → GC Mean AT → GC Nb. GC → AT Mean GC → AT d

	Necsulea et al., Human Mutation
	Region Nb. H P-value	Maximum likelihood method (Duret and Arndt, 2008)	Synonymous 1157 0.3 4016 0.21 0.1 0 / 1000	Non-synonymous 1086 0.24 2583 0.18 0.06 0 / 1000	F o r P e e r R e v i e w PolyPhen probably damaging 118 0.16 255 0.13 HGMD 32 0.23 76 0.13 DM 8 0.14 32 0.09 Synonymous Non-synonymous HGMD DM PolyPhen probably damaging SFS correction with context-dependent model (Hernandez et al., 2007) 1026 0.28 3363 0.22 955 0.23 2072 0.19 104 0.16 187 0.12 30 0.25 65 0.15 8 0.16 26 0.12 0.02 159 / 1000 0.1 9 / 1000 0.05 100 / 1000 0.06 0 / 1000 0.04 0 / 1000 0.04 17 / 1000 0.09 13 / 1000 0.05 266 / 1000

Table S11 :

 S11 Derived allele frequencies for AT → GC and GC → AT SNPs, for the AFR sample from Lohmueller et al., 2008, for high recombination regions. The recombination classes were defined based on the distance to recombination hotspots. AT → GC Mean AT → GC Nb. GC → AT Mean GC → AT

	Necsulea et al., Human Mutation
	Region Nb. d H P-value	Maximum likelihood method (Duret and Arndt, 2008)	Synonymous 805 0.36 2818 0.26 0.1 0 / 1000	Non-synonymous 825 0.25 2034 0.21 0.05 0 / 1000	F o r P e e r R e v i e w PolyPhen probably damaging 89 0.15 223 0.11 HGMD 34 0.23 79 0.15 DM 8 0.1 25 0.11 Synonymous Non-synonymous HGMD DM PolyPhen probably damaging SFS correction with context-dependent model (Hernandez et al., 2007) 675 0.35 2252 0.29 662 0.27 1493 0.24 66 0.2 149 0.14 31 0.26 62 0.2 8 0.11 17 0.13 0.04 69 / 1000 0.07 33 / 1000 -0.01 635 / 1000 0.06 0 / 1000 0.03 0 / 1000 0.06 6 / 1000 0.06 96 / 1000 -0.02 797 / 1000

Table S12 :

 S12 Derived allele frequencies for AT → GC and GC → AT SNPs, for the CAU sample from Lohmueller et al., 2008, for high recombination regions. The recombination classes were defined based on the distance to recombination hotspots.

	Necsulea et al., Human Mutation
	Low recombination High recombination ∆d P-value	Region AT → GC GC → AT d AT → GC GC → AT d	Maximum likelihood method (Duret and Arndt, 2008)	F o r P e e r R e v i e w Intergenic 0.38 0.36 0.01 0.39 0.36 0.03 0.013 0 / 1000 Intron 0.37 0.36 0.01 0.39 0.36 0.03 0.022 0 / 1000 Synonymous 0.37 0.33 0.04 0.42 0.34 0.08 0.043 1 / 1000 Non-synonymous 0.3 0.22 0.07 0.35 0.26 0.09 0.016 122 / 1000 PolyPhen probably damaging 0.16 0.13 0.03 0.24 0.15 0.08 0.055 95 / 1000 HGMD 0.16 0.13 0.03 0.25 0.16 0.09 0.052 120 / 1000 DM 0.09 0.05 0.04 0.15 0.09 0.07 0.031 347 / 1000 Intergenic 0.38 0.34 0.03 0.39 0.35 Intron 0.36 0.34 0.03 0.39 0.34 Synonymous 0.36 0.32 0.04 0.39 0.34 Non-synonymous 0.29 0.23 0.06 0.33 0.27 SFS correction with context-dependent model (Hernandez et al., 2007) 0.04 0.012 0 / 1000 0.05 0.019 0 / 1000 0.06 0.02 80 / 1000 0.07 0.005 358 / 1000	PolyPhen probably damaging 0.17 0.13 0.04 0.24 0.16 0.08 0.038 163 / 1000	HGMD 0.19 0.15 0.04 0.25 0.17 0.07 0.034 191 / 1000	DM 0.1 0.06 0.03 0.11 0.09 0.02 -0.016 609 / 1000

Table S13 :

 S13 Derived allele frequencies for AT → GC and GC → AT SNPs, for the CEU sample from HapMap, for regions of low and high recombination. The regions of low and high recombination were defined based on the distance from SNPs to recombination hotspots.

	Necsulea et al., Human Mutation
	Low recombination High recombination ∆d P-value	Region AT → GC GC → AT d AT → GC GC → AT d	Maximum likelihood method (Duret and Arndt, 2008)	F o r P e e r R e v i e w Intergenic 0.39 0.37 0.02 0.4 0.37 0.03 0.013 0 / 1000 Intron 0.38 0.37 0.01 0.4 0.37 0.03 0.02 0 / 1000 Synonymous 0.37 0.34 0.03 0.43 0.35 0.08 0.052 0 / 1000 Non-synonymous 0.31 0.22 0.1 0.36 0.25 0.11 0.015 145 / 1000 PolyPhen probably damaging 0.17 0.12 0.06 0.28 0.14 0.14 0.086 23 / 1000 HGMD 0.22 0.15 0.06 0.27 0.18 0.09 0.026 319 / 1000 DM 0.1 0.05 0.05 0.21 0.1 0.11 0.062 319 / 1000 Intergenic 0.39 0.35 0.03 0.4 0.36 Intron 0.38 0.35 0.03 0.4 0.35 Synonymous 0.36 0.33 0.03 0.41 0.35 Non-synonymous 0.31 0.22 0.09 0.34 0.25 SFS correction with context-dependent model (Hernandez et al., 2007) 0.05 0.013 0 / 1000 0.05 0.018 0 / 1000 0.07 0.034 5 / 1000 0.09 0.003 420 / 1000	PolyPhen probably damaging 0.19 0.12 0.07 0.28 0.15 0.13 0.06 80 / 1000	HGMD 0.22 0.17 0.05 0.27 0.19 0.08 0.034 287 / 1000	DM 0.1 0.06 0.04 0.18 0.1 0.08 0.041 226 / 1000

Table S14 :

 S14 Derived allele frequencies for AT → GC and GC → AT SNPs, for the CHB+JPT sample from HapMap, for regions of low and high recombination. The regions of low and high recombination were defined based on the distance from SNPs to recombination hotspots.

	Necsulea et al., Human Mutation
	Low recombination High recombination ∆d P-value	Region AT → GC GC → AT d AT → GC GC → AT d	Maximum likelihood method (Duret and Arndt, 2008)	F o r P e e r R e v i e w Intergenic 0.32 0.31 0.01 0.34 0.31 0.03 0.016 0 / 1000 Intron 0.3 0.3 0 0.34 0.3 0.03 0.028 0 / 1000 Synonymous 0.31 0.27 0.04 0.36 0.29 0.07 0.033 4 / 1000 Non-synonymous 0.26 0.22 0.03 0.31 0.24 0.07 0.034 1 / 1000 PolyPhen probably damaging 0.16 0.13 0.03 0.23 0.16 0.07 0.042 142 / 1000 HGMD 0.16 0.12 0.04 0.27 0.15 0.12 0.076 74 / 1000 DM 0.13 0.08 0.05 0.22 0.11 0.1 0.056 259 / 1000 Intergenic 0.31 0.27 0.04 0.33 0.28 Intron 0.3 0.26 0.03 0.33 0.27 Synonymous 0.29 0.25 0.04 0.33 0.28 Non-synonymous 0.25 0.22 0.03 0.29 0.24 SFS correction with context-dependent model (Hernandez et al., 2007) 0.05 0.014 0 / 1000 0.06 0.023 0 / 1000 0.05 0.011 190 / 1000 0.05 0.022 28 / 1000	PolyPhen probably damaging 0.18 0.13 0.04 0.23 0.16 0.07 0.022 250 / 1000	HGMD 0.18 0.14 0.03 0.27 0.16 0.1 0.071 70 / 1000	DM 0.14 0.1 0.03 0.16 0.12 0.04 0.006 437 / 1000
				20

Table S15 :

 S15 Derived allele frequencies for AT → GC and GC → AT SNPs, for the YRI sample from HapMap, for regions of low and high recombination. The regions of low and high recombination were defined based on the distance from SNPs to recombination hotspots.

	Necsulea et al., Human Mutation
	Low recombination High recombination ∆d P-value	Region AT → GC GC → AT d AT → GC GC → AT d	Maximum likelihood method (Duret and Arndt, 2008)	Synonymous 0.24 0.21 0.02 0.3 0.21 0.1 0.073 0 / 1000	F o r P e e r R e v i e w Non-synonymous 0.18 0.17 0.01 0.24 0.18 0.06 0.05 0 / 1000 PolyPhen probably damaging 0.1 0.12 -0.02 0.16 0.13 0.02 0.046 64 / 1000 HGMD 0.16 0.15 0.01 0.23 0.13 0.1 0.09 77 / 1000 DM 0.16 0.16 0 0.14 0.09 0.05 0.052 401 / 1000 Synonymous 0.23 0.21 0.02 0.28 0.22 Non-synonymous 0.19 0.17 0.01 0.23 0.19 HGMD DM PolyPhen probably damaging 0.12 0.11 0.01 0.16 0.12 0.18 0.16 0.02 0.25 0.15 0.19 0.19 0 0.16 0.12 SFS correction with context-dependent model (Hernandez et al., 2007) 0.06 0.048 0 / 1000 0.04 0.027 10 / 1000 0.04 0.026 152 / 1000 0.09 0.074 105 / 1000 0.05 0.047 414 / 1000

Table S16 :

 S16 Derived allele frequencies for AT → GC and GC → AT SNPs, for the AFR sample fromLohmueller et al., 2008, for regions of low and high recombination. The regions of low and high recombination were defined based on the distance from SNPs to recombination hotspots.

	Necsulea et al., Human Mutation
	Low recombination High recombination ∆d P-value	Region AT → GC GC → AT d AT → GC GC → AT d	Maximum likelihood method (Duret and Arndt, 2008)	Synonymous 0.28 0.27 0.01 0.36 0.26 0.1 0.092 0 / 1000	F o r P e e r R e v i e w Non-synonymous 0.2 0.19 0.01 0.25 0.21 0.05 0.035 12 / 1000 PolyPhen probably damaging 0.1 0.09 0.01 0.15 0.11 0.04 0.026 229 / 1000 HGMD 0.12 0.15 -0.02 0.23 0.15 0.07 0.098 57 / 1000 DM 0.06 0.1 -0.05 0.1 0.11 -0.01 0.039 294 / 1000 Synonymous 0.29 0.29 0 0.35 0.29 Non-synonymous 0.24 0.21 0.02 0.27 0.24 HGMD DM PolyPhen probably damaging 0.15 0.12 0.03 0.2 0.14 0.19 0.19 0 0.26 0.2 0.08 0.14 -0.06 0.11 0.13 SFS correction with context-dependent model (Hernandez et al., 2007) 0.06 0.061 0 / 1000 0.03 0.011 253 / 1000 0.06 0.031 193 / 1000 0.06 0.06 151 / 1000 -0.02 0.042 256 / 1000

Table S17 :

 S17 Derived allele frequencies for AT → GC and GC → AT SNPs, for the CAU sample from Lohmueller et al., 2008, for regions of low and high recombination. The regions of low and high recombination were defined based on the distance from SNPs to recombination hotspots.

John Wiley & Sons, Inc.Human Mutation

Acknowledgements

This work was supported by the Centre National de la Recherche Scientifique and by the Agence Nationale de la Recherche (ANR-08-GENO-003-01). All computations were performed at the IN2P3 Computing Center. We thank Nicolas Galtier, Sylvain Glémin, Alex Kondrashov and one anonymous referee for their helpful comments, Rasmus Nielsen and Dara Torgersson for providing us with SNP data and Ryan D. Hernandez for providing the software used to correct the DAF spectrum.

Supplementary Material

Meiotic recombination favors the spreading of deleterious mutations in human populations Anamaria Necşulea 1 , Alexandra Popa 1 , David N. Cooper 2 , Peter D. Figure S2: Derived allele frequencies spectra obtained for the HapMap CEU sample, for high recombination regions (as defined by the distance to recombination hotspots). The ancestral and derived alleles were determined using a maximum likelihood method that takes into account CpG hypermutability [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. m represents the mean derived allele frequency, and N is the number of SNPs in each category. Figure S3: Derived allele frequencies spectra obtained for the HapMap CHB+JPT sample, for high recombination regions (as defined by the distance to recombination hotspots). The ancestral and derived alleles were determined using a maximum likelihood method that takes into account CpG hypermutability [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. m represents the mean derived allele frequency, and N is the number of SNPs in each category. Figure S4: Derived allele frequencies spectra obtained for the HapMap YRI sample, for high recombination regions (as defined by the distance to recombination hotspots). The ancestral and derived alleles were determined using a maximum likelihood method that takes into account CpG hypermutability [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. m represents the mean derived allele frequency, and N is the number of SNPs in each category. The ancestral and derived alleles were determined using a maximum likelihood method that takes into account CpG hypermutability [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. m represents the mean derived allele frequency, and N is the number of SNPs in each category. The ancestral and derived alleles were determined using a maximum likelihood method that takes into account CpG hypermutability [START_REF] Duret | The impact of recombination on nucleotide substitutions in the human genome[END_REF]. m represents the mean derived allele frequency, and N is the number of SNPs in each category.

Synonymous sites

Derived allele frequency