FADS) gene cluster have been associated with the homeostasis model assessment of insulin sensitivity (HOMA-IS) and serum fatty acid composition. Objective: To investigate whether common genetic variations in the FADS gene cluster influence fasting glucose (FG) and fasting insulin (FI) responses following a 6-week n-3 polyunsaturated fatty acids (PUFA) supplementation. Methods: 210 subjects completed a 2-week run-in period followed by a 6-week supplementation with 5 g/d of fish oil (providing 1.9 g–2.2 g of EPA + 1.1 g of DHA). Genotyping of 18 SNPs of the FADS gene cluster covering 90% of all common genetic variations  was performed. Results: Carriers of the minor allele for rs482548 (FADS2) had increased plasma FG levels after the n-3 PUFA supplementation in a model adjusted for FG levels at baseline, age, sex, and BMI. A significant genotype*supplementation interaction effect on FG levels was observed for rs482548 (p = 0.008). For FI levels, a genotype effect was observed with one SNP (rs174456). For HOMA-IS, several genotype*supplementation interaction effects were observed for rs7394871, rs174602, rs174570, rs7482316 and rs482548 . Conclusion: Results suggest that SNPs in the FADS gene cluster may modulate plasma FG, FI and HOMA-IS levels in response to n-3 PUFA supplementation.Changes in desaturase activity are associated with insulin sensitivity and may be associated with type 2 diabetes mellitus (T2DM). Polymorphisms (SNPs) in the fatty acid desaturase ( Type 2 diabetes mellitus (T2DM) and cardiovascular diseases are major public health concerns worldwide and especially in North America where 40 millions people are going to be affected by 2030 . Adults n-3) polyunsaturated fatty acids (PUFA) are of particular interest because of their roles in improving the plasma lipid profile, especially by reducing plasma TG [N-3 PUFAs represent a reasonable therapeutic strategy to improve the dyslipidemic profile in individuals with T2DM [n-3 PUFA supplementation is often associated with a significant but marginal increase in fasting plasma glucose (FG) concentrations [n-3 PUFA supplementation with doses ranging from 3 to 18 g/d of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) [It is therefore important to study T2DM risk factors. The degree of obesity, the presence of the metabolic syndrome, family history of diabetes, impaired glucose tolerance, a low physical activity level, high blood levels of triglycerides (TG), high-density lipoprotein cholesterol (HDL-c) levels under 0.91 mmol/L as well as certain ethnic groups are well-known T2DM risk factors [ith T2DM . On the trations . A revieid (DHA) .FADS) gene cluster [FADS1) and fatty acid desaturase-2 (FADS2) genes encode respectively for two desaturases: δ-5 desaturase (D5D) and δ-6 desaturase (D6D) [n-3 PUFA and omega-6 (n-6) PUFA pathways, have been associated with differences in fatty acid composition of plasma, adipose tissue, and membrane fluidity [Changes in desaturase activity are associated with insulin sensitivity, which can be estimated using the homeostasis model assessment (HOMA-IS) , and mig cluster . The fatse (D6D) . The D5Dfluidity . In genefluidity .FADS gene cluster, mostly FADS1 and FADS2 that encode the desaturases in response to n-3 PUFA. By giving high doses of n-3 PUFA above 3 g/d, the n-3 PUFA pathway is promoted at the expense of the n-6 pathway which would potentially reduce the n-6:n-3 ratio by increasing D5D and D6D ADA and generate improvements in the metabolic profile of individuals.Prospective studies have shown a strong inverse relationship between D5D and T2DM risk and a strong positive relation with D6D, using the desaturase indexes that estimate aggregate desaturase activity (ADA) using products to precursors ratios ,12. Howen-3 PUFA supplementation were influenced by common genetic variations in the FADS gene cluster. We hypothesized that SNPs within the FADS gene cluster are associated with glycemic controls parameters after an n-3 PUFA supplementation.The objective of the present study was to investigate whether the FG, the fasting insulin (FI), and the HOMA-IS responses to a 6-week n-3 PUFA supplements for at least 6 months prior to the study. However, only 210 subjects completed the intervention protocol and glycemic control parameters pre- and/or post-supplementation are missing for two participants [A total of 254 subjects from the greater Quebec City metropolitan area were recruited between September 2009 and December 2011 through advertisements in local newspapers as well as by electronic messages sent to university students/employees. Subjects had to be aged between 18 and 50 years with a BMI between 25 and 40. Subjects were excluded from the study if they had taken icipants . Statisticipants .Eating Well with Canada’s Food Guide [n-3 PUFA during the study period. Subjects were also asked to limit their alcohol intakes to no more than two drinks per week. Subjects were not allowed to take n-3 PUFA supplements, including those of vegetable sources and to take vitamins or natural health products during the protocol.Subjects followed a run-in period of two weeks during which a trained registered dietitian gave individual dietary instructions. Recommendations were drawn from the od Guide . All subn-3 PUFA capsules  covering the following six weeks period. They had to take five capsules of fish oil per day, which gave them a total of 3–3.3 g of n-3 PUFA (1.9–2.2 g EPA and 1.1 g DHA) per day. Compliance was measured by bottles returning and by calculating the number of remaining capsules in the bottles at the end of the supplementation. Subjects had to report any deviations that may have occurred during the protocol. They also had to write their alcohol and fish consumption on a log sheet. Before each phase of the study, subjects received written and verbal dietary instructions by a registered dietitian.After the run-in period, each participant received a bottle containing et al. [Detailed methods were previously presented in Cormier et al. . n-3 PUFA supplementation [2).Body weight, height, and waist girth were measured according to the procedures recommended by the Airlie Conference and were taken before the run-in period, as well as pre- and post-entation . BMI wasBlood samples were collected from an antecubital vein into vacutainer tubes containing EDTA after 12 h overnight fast and 48 h alcohol abstinence. Blood samples were taken to identify and exclude individuals with metabolic disorders such as diabetes (FG > 7.0 mmol/L), hypertension (>140 mmHG/>90 mmHG), hypo/hyperthyroidism (based on thyroid-stimulating hormone (TSH) levels) or severe dyslipidemias .n-3 PUFA supplementation period. Plasma was separated by centrifugation  and samples were aliquoted and frozen for subsequent analyses. FI was measured by radioimmunoassay with polyethylene glycol separation [ [Afterwards, selected participants had blood samples taken at prior and after the paration . FG concparation . HOMA-ISration [ . Plasma ration [ . Plasma ration [ . The higration [ . Low-denration [ . Apoliporation [ . FADS1, FADS2 and FADS3 were identified using the International HapMap Project SNP database, based on the National Center for Biotechnology Information (NCBI) B36 assembly Data Rel 28. phase II + III, build 126. The FADS gene cluster is made of three genes that are located very close to each other among chromosome 11. Because of the head-to head orientation of FADS1 and FADS2 and the tail-to-tail orientation of FADS2 and FADS3, we added 500 kilo-base pairs (kbp) downstream of FADS1 and 2,500 kbp upstream of FADS3 to cover the promoter region. Intergenic areas were also covered. Tagger procedure in Haploview V4.2 was used to determine tag SNPs (tSNPs) using a minor allele frequency (MAF) ≥3% and pairwise tagging (r2 ≥ 0.8). Subsequently, we examined linkage disequilibrium (LD) out of the 19 SNPs covering all common variations in the FADS gene cluster area, using the LD Plot procedure in Haploview V4.2. Since the majority of the SNPs within the FADS gene cluster are in high LD (r2 ≥ 0.8) with each other, 19 SNPs were sufficient to cover 96% of the entire area. The SIGMA GenElute Gel Extraction Kit  has been used to extract genomic DNA. Selected SNPs of the FADS gene cluster  have been genotyped using validated primers and TaqMan probes . DNA was mixed with TaqMan Universal PCR Master Mix , with a gene-specific primer and with probe mixture  in a final volume of 10 μL. Genotypes were determined using a 7500 RT-PCR System and analyzed using ABI Prism SDS version 2.0.5 . The Exonic Splicing Enhancer (ESE) finder Webbased program was used to determine the potential effect of variants of the FADS gene cluster on pre-mRNA splicing. All tests were run under default threshold values [SNPs in p ≤ 0.01).All genotype distributions were tested for any deviation from Hardy-Weinberg equilibrium (HWE) using the ALLELE procedure in SAS Genetics v9.3 . All SNPs were in HWE except one: rs7935946. Significance testing for LD coefficient D was obtained using a chi-square test, likelihood ratio and Fisher exact test , we decided to present the results without correction for multiple testing and using a p-value ≤0.05. Statistical significance was defined as p ≤ 0.05. All other statistical analyses were carried out using SAS v9.3 . Normal distribution of outcome variables was evaluated looking at the box-plot, and also skewness and kurtosis ranges for normal distribution. When needed, outcome variables non-normally distributed were log10-transformed. A linear regression using the stepwise bidirectional elimination approach was used to assess which SNPs could explain part of the glycemic control parameters’ variance where the effects of the 18 SNPs that were in HWE and the effects of age, sex, and BMI were included in the statistical model. The MIXED procedure was used to test for the effects of the genotype, the supplementation and the genotype by supplementation interaction for each SNP on FG, FI and HOMA-IS levels when age, sex and BMI were included in the model. The repeated statement was used to indicate the within subjects (repeated) variables. Genotypic groups were assessed as three groups expressed as Major Allele Homozygotes (11), Heterozygotes (12) and Minor Allele Homozygotes (22). For some SNPs, Heterozygotes and Minor Allele Homozygotes were grouped if the genotypic frequency of the Minor Allele Homozygotes was under 5%. Since polymorphisms tested in complex diseases rarely account for a large amount of variance, characterized by very low 2 in both men and women. Gender differences are evident with respect to weight, TC/HDL-C ratio, HDL-C, TG and CRP levels. In both men and women, the n-3 PUFA supplementation was associated with a decrease of plasma TG levels as well as with an increase of FG levels [Baseline characteristics of study participants are presented in G levels .n-3 PUFA supplementation slightly increased FG in the present study  [FADS gene cluster may modulate glycemic control parameters’ response after the 6-week supplementation period. A previous paper from our group reported that All SNPs were in HWE except one: rs7935946. This SNP was not considered for further analysis. Therefore, associations with 18 SNPs were tested in statistical analyses. The percentage of gene coverage with these 18 SNPs was of 90%. FADS gene cluster, all SNPs were included in a linear regression model with values post-n-3 PUFA supplementation as the dependent variable, adjusted for baseline values , age, sex and BMI. With the use of the stepwise bidirectional selection method, 2 SNPs were associated with FG levels: rs482548  and rs498793 ; 1 SNP was associated with FI levels: rs174602 ; and 2 SNPs were associated with HOMA-IS: rs174602  and rs482548 (0.90% p = 0.05) (Data not shown).To validate the presence of associations between glycemic control parameters and SNPs of the FADS gene polymorphisms and the n-3 PUFA supplementation on plasma FG, FI and HOMA-IS levels, a general linear model adjusted for sex, age and BMI was used in order to verify whether the genotype, the supplementation or the interaction (genotype by supplementation) were associated with plasma levels of glycemic control parameters. As shown in FADS gene cluster) meaning that the supplementation had an independent effect on plasma FG levels, as expected. One SNP (rs482548) was associated with plasma FG levels meaning that the genotype for that particular polymorphism may modulate the plasma FG during the intervention. A significant genotype by supplementation interaction effect on FG levels was observed for rs482548 (p = 0.008). For FI levels, a genotype effect was observed with one SNP (rs174456). For HOMA-IS, several genotype by supplementation interaction effects were observed for rs7394871, rs174602, rs174570, rs7482316 and rs482548   . Table 3n-3 PUFA supplementation may exert beneficial effects on cardiovascular risk on that specific population. Connor et al. showed that n-3 PUFA intake (6 g/d of EPA + DHA for 6 months), along with oral therapy for T2DM, can lower TG concentrations, with no adverse effects on glycemic control [n-3 PUFA supplementation may increase slightly FG levels. As previously reported by our research group, FG increased by 2.44% ± 49.55% (p = 0.0004) after the 6-week n-3 PUFA supplementation [n-3 PUFA supplementation. Because FG is part of the HOMA-IS formula and correlates negatively with this model, it is believed that the high inter-individual variability may also be seen in HOMA-IS values. As shown in n-3 PUFA supplementation.Insulin resistance is a condition in which glucose intakes by sensitive tissues decreased. Therefore, insulin resistance and insulin sensitivity are strong predictors of T2DM . Individ control . In addin-3 PUFA supplementation could be due to different dietary factors such as a diet rich in saturated and trans fats, a reduced enzymatic activity of D5D and D6D, a low dietary contribution of C20-C22 PUFAs or high amount of 18:2 n-6 (linoleic acid) in the diet [The fatty acid profile of membrane phospholipids and the activity of desaturases are strongly linked to the incidence of T2DM . The larthe diet . Increasthe diet ,30. Remathe diet ,32. DefeFADS1 and FADS2, may influence glucose metabolism [n-3 and n-6 PUFA metabolism, creating a competition where the n-6 PUFA pathway is often favored [n-3:n-6 due to the enzymatic activity of desaturases involved in PUFA metabolism might alter glucose metabolism [The mechanism by which insulin resistance increases cardiovascular risk is unclear. However, the emerging literature demonstrates that genetic factors may be involved ,34. D5D tabolism . The D6Dtabolism . These d favored . An unbaet al. has identified many loci implicated in T2DM, including SNPs in FADS1, which were associated with FG [FADS2 gene, but as the study covered 90% of all common genetic variations of the FADS gene cluster, some SNPs may be in LD with other SNPs found on FADS1 or FADS3 that may be potentially functional  and rs2727270  were associated with insulin resistance [A study by Brenner has shown that insulin influences the activity of D5D and D6D . Accordisistance .2 = 1.0) with FG levels [n-3 PUFA supplementation (p = 0.002) and a trend was also observed for TG levels post-n-3 PUFA supplementation (p = 0.07) [n-3 supplementation.Many studies have detected a significant relation of the genotype rs174547  . Out of Regarding the possible effect of variants of rs482548, analysis of the scores obtained with ESE revealed that no variants markedly alter the binding capacity of putative ESE elements, either by reducing their values or by creating new binding site for SR proteins.n-3 were administered to participants. Additionally, it would have been interesting to use an oral glucose tolerance test (OGTT) and to measure insulin secretion by dosing C-peptide. The actual study does not allow to go further in the analyses of glycemic control parameters, suggesting that more studies are needed to better understand the impact of the FADS gene cluster polymorphisms on T2DM risk. Also, the short run-in period (2 weeks) may not be sufficient to take into account high basal levels of n-3 PUFAs in phospholipids membranes. Compliance was assessed from the return of containers and capsules (87%) to determine the quantity consumed and by measuring membrane phospholipid composition. A key strength of the study is that it was an interventional study design and that high doses of FADS gene cluster are associated with glycemic control parameters, mainly with HOMA-IS, in response to an intervention with high doses of n-3 PUFA. These SNPs are of particular interest because of their potential effects on glucose metabolism. Dietary n-3 supplements could be used as add-ons to a well-balanced diet and can be part of a healthy lifestyle in treating T2DM associated dyslipidemia.In conclusion, the importance of desaturases, which are the key enzymes in the PUFAs metabolism in the development of T2DM, deserves special attention. Our results show that several SNPs involved in the


Phenotypic variation along environmental gradients has been documented among and within many species, and in some cases, genetic variation has been shown to be associated with these gradients. Bayenv is a relatively new method developed to detect patterns of polymorphisms associated with environmental gradients. Using a Bayesian Markov Chain Monte Carlo (MCMC) approach, Bayenv evaluates whether a linear model relating population allele frequencies to environmental variables is more probable than a null model based on observed frequencies of neutral markers. Although this method has been used to detect environmental adaptation in a number of species, including humans, plants, fish, and mosquitoes, stability between independent runs of this MCMC algorithm has not been characterized. In this paper, we explore the variability of results between runs and the factors contributing to it.p value tails were surprisingly low. Enrichments of genic versus non-genic SNPs in the empirical tails were more robust than the empirical p values; however, the significance of the enrichments for some environmental variables still varied among runs, contradicting previously published conclusions. Runs with a greater number of MCMC iterations slightly reduced run-to-run variability, and excluding the Siberian populations did not have a large effect on the stability of the runs.Independent runs of the Bayenv program were carried out using genome-wide single-nucleotide polymorphism (SNP) data from samples from 60 worldwide human populations following previous applications of the Bayenv method. To assess factors contributing to the method's stability, we used varying numbers of MCMC iterations and also analyzed a second modified data set that excluded two Siberian populations with extreme climate variables. Between any two runs, correlations between Bayes factors and the overlap of SNPs in the empirical Because of high run-to-run variability, we advise against making conclusions about genome-wide patterns of adaptation based on only one run of the Bayenv algorithm and recommend caution in interpreting previous studies that have used only one run. Moving forward, we suggest carrying out multiple independent runs of Bayenv and averaging Bayes factors between runs to produce more stable and reliable results. With these modifications, future discoveries of environmental adaptation within species using the Bayenv method will be more accurate, interpretable, and easily compared between studies. Phenotypic variation along environmental gradients, such as limb length in animals increasing with colder climates, animal Various methods have been developed to determine the contribution of environmental variation to allelic or genotypic frequency patterns. Early theory in population genetics was used to derive statistics that allowed variation in allele frequencies to distinguish selection from drift-24. AddiCoop et al. developeGiven a set of populations with associated environmental variables and genome-wide single-nucleotide polymorphism (SNP) allele frequencies, a null model is first estimated to describe how allele frequencies covary across populations. Sample allele frequencies are drawn from a set of underlying population frequencies, which are assumed to be distributed according to a multivariate normal distribution around a transformed global allele frequency with a variance-covariance matrix representing the genetic structure of the populations. These transformed frequencies are not constrained between 0 and 1. The prior on the variance-covariance matrix is an inverse Wishart distribution, and Markov chain Monte Carlo (MCMC) computation is used to explore the posterior distribution of the covariance matrix, given the ancestral allele frequency and population allele frequencies.After the covariance of allele frequencies between populations is estimated, the posterior probability of an alternative model for an individual SNP is determined. The alternative model predicts that the transformed population allele frequencies at a SNP are normally distributed around the ancestral allele frequency according to a linear model:θ is the transformed allele frequency, α is the ancestral allele frequency, Y is the environmental variable, and Ω is the variance-covariance matrix that was estimated in the first step of this procedure.where For each SNP, the Bayes factor (BF) is the posterior probability of the data at that SNP under the alternative model, integrated over all parameters, then divided by the posterior probability of the data under the null model, integrated over all parameters of the null model. Using a single run of the MCMC under the null distribution and importance sampling, the Bayes factors for multiple environmental variables are estimated quickly for each of the SNPs assayed . This mThe Bayenv method has been applied to many species including humans-44, planHancock et al. applied Although the Bayenv method has been applied to different species and environmental variables and has produced some results agreeing with other analytical methods, the stability of the results across multiple MCMC runs is not known. It is also unknown how the results may be affected by the inclusion of specific populations from extreme environments, which could potentially introduce bias. In thisDuring the writing of this manuscript, an updated version of this program, Bayenv 2.0, was relFirst, we analyzed the full data set of 60 populations and assessed correlations with 11 climate variables using 100,000 MCMC iterations . We comWe found that Bayenv produced slightly different results in independent runs of the program. For a given climate variable, pairwise correlations of the logarithms of Bayes factors (BF's) were computed for all SNPs from each run, and correlations were averaged across all pairs of runs mentioned above  of association with minimum winter temperature as reported in Hancock et al.[10BF = -0.32, -0.50, and -0.48 and empirical p values = 0.40, 0.57, and 0.44) and Hancock2 . Some of this variability could be due to the limited burn-in in Hancock1 (see ‘Methods’ section). Other SNPs, such as rs2075756 in TRIP6, show more consistency. Blair1 obtained a log10BF of 4.0 and empirical p value of 7.9 × 10-4, while Hancock2 reported a log10BF of 1.4 and empirical p value of 3.2 × 10-3 for this SNP. This SNP shows a strong signal with absolute latitude, the variable for which we saw consistently strong signals of genic/non-genic enrichment among SNPs with low empirical p values (see below).There was also variation among the results at individual SNPs. For example, SNPs in the CORIN gene region  gave strong signals  varied across runs and differed from Hancock's publisheThe absolute latitude and winter relative humidity variables were almost always significant, with the exception of the 0.005 tail in Blair5; the summer precipitation variable was never significant. All three of these findings agreed with Hancock et al.'s published results. HoweverIn order to determine whether the observed variability between runs was due to the MCMC not converging, the number of MCMC iterations was increased fivefold to 500,000. Five independent runs were performed, each with different seeds, but with the same full data set of 60 populations. All pairs of the five runs with 500,000 iterations were compared . This p values between Hancock2 and non-Siberian runs   between pairs of our runs, between our runs and the results of Hancock et al., and betp values. Hancock et al.[p values were seen to be no more stable than the Bayes factors themselves, as correlation coefficients of the empirical p values were in the same range as the correlation coefficients of the Bayes factors  of two runs was found to be low, we recognize that this slightly overestimates run-to-run variability. For example, 24% of the 0.001 tail of Hancock2 overlapped with the 0.001 tail of Blair1; that proportion increased to 83% with the 0.005 tail of Blair1 .Despite this trend, a SNP's significance and relative ranking nearly always vary from one run of Bayenv to another. SNPs inferred to be significant using some pre-defined p values. However, the significances of climate variables including summer maximum temperature, summer relative humidity, summer solar radiation, winter minimum temperature, winter precipitation rate, and winter solar radiation varied widely from run to run . Additionally, individual SNPs that show large Bayes factors in at least one run could be rerun with more MCMC iterations to determine a Bayes factor value to which all iterations converge. Unfortunately, this process would not be as useful for a genome-wide analysis, as a large set of SNPs is necessary for the calculation of the empirical p values and the genic/non-genic enrichments.It can be difficult to disentangle stability and specificity, and we recognize that some of the run-to-run variability of this method may be due to less strong signals of environmental adaptation . However, as every run may be different, it is still advisable to carry out multiple runs when making biological conclusions, especially when the signal tested may not be strong to begin with. Averaging Bayes factors of two or more independent runs before calculating the empirical p values across runs. We also suggest averaging Bayes factors from independent runs to produce more stable results. With these modifications, future discoveries of environmental adaptation within species using the Bayenv method will be more accurate, interpretable, and easily compared between studies.We conclude that, in using the Bayenv method, relying on the relative rankings of SNPs calculated from only one run of the program may lead to biological conclusions that might not have been reached with another independent run of the program. Neither increasing the number of MCMC iterations nor removing outlying populations completely eliminated this variability. Thus, we suggest carrying out several independent runs of the Bayenv analysis to assess variability of the Bayes factors and empirical http://genapps2.uchicago.edu:8081/dbcline/main.jsp) were analyzed. The data set included samples from 52 HGDP populations plus Vasekela !Kung, Amhara, Naukan Yup’ik, Maritime Chukchee, Luhya, Maasai, Tuscans, and Gujarati. These were the same populations used by Hancock et al.[Allele frequency data on 623,318 SNPs from samples of 60 human populations from the freq_data file (61pops_climate_freqs.txt) on the dbCline website , were generated using the Bayenv program . The prFive independent runs of the Bayenv program were carried out using the full data set of 60 populations and 11 climate variables. As with the covariance matrices, SNPs were analyzed with their corresponding ascertainment panel. Each run used a different random seed but the same input data set and 100,000 MCMC iterations. Bayes factors produced from the three ascertainment panels were combined into one file for each run. These runs are referred to as Blair1 - Blair5  .Five independent runs of the Bayenv program were carried out using a modified data set and 150,000 MCMC iterations. In this modified data set, all 11 climate variables were included, but the Siberian populations (Naukan Yup’ik and Maritime Chukchee) were excluded . For calculations of genic/non-genic enrichment (described below), we performed the analysis on both Hancock1 and Hancock2. In general, our analysis of the Hancock1 data set gave similar results to those published by Hancock et al.[Bayes factors and empirical k et al. were obtk et al. publicatk et al..p values by binning SNPs based on their global allele frequency (across 60 populations). For each SNP, a transformed rank statistic was computed by comparing the computed Bayes factor for the SNP with the Bayes factors from other SNPs in the same global allele frequency bin. For this process, all SNPs were analyzed together, and no distinction was made for SNPs belonging to different ascertainment panels.As in Hancock et al., we calcTo calculate genic/non-genic enrichments, a SNP was defined as genic if it was within 10 kb of a gene and non-genic if it was greater than 50 kb from a gene . Then, p values were calculated. For each climate variable, the Pearson correlations of the log of the Bayes factors and the Pearson correlations of the empirical p values were computed for each pair of runs compared  were calculated.  Finally, significances of genic/non-genic enrichment values were calculated for each run, as described above, and compared.Each run produces a Bayes factor for each climate variable for each SNP. From those, the empirical BF: Bayes factor; MCMC: Markov Chain Monte Carlo; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.LB participated in the design of the study and performed the statistical analysis. JG and MF participated in its design and coordination. All authors read and approved the final manuscript.


Waxy gene, represents the key determinant of rice cooking properties. In occidental countries high AAC rice represents the most requested market class but the availability of molecular markers allowing specific selection of high AAC varieties is limited.Apparent Amylose Content (AAC), regulated by the japonica ssp. and 2 indica ssp.) characterized by AAC values from glutinous to 26%. The analyses highlighted the presence of several different allelic patterns identifiable by a few molecular markers, and two of them, i.e., the SNPs at intron1 and exon 6, were able to explain a maximum of 79.5% of AAC variation. However, the available molecular markers haplotypes did not provide tools for predicting accessions with AAC higher than 24.5%. To identify additional polymorphisms, the re-sequencing of the Waxy gene and 1kbp of the putative upstream regulatory region was performed in 21 genotypes representing all the AAC classes identified. Several previously un-characterized SNPs were identified and four of them were used to develop dCAPS markers.In this study, the effectiveness of available molecular markers in predicting AAC was evaluated in a collection of 127 rice accessions  ratio. In particular, the SNP at the first intron, which identifies the Wxa and Wxb alleles, was associated with differences in the width of the grain, the L/W ratio and the length of the kernel, most likely as a result of human selection.The addition of the SNPs newly identified slightly increased the AAC explained variation and allowed the identification of a haplotype almost unequivocally associated to AAC higher than 24.5%. Haplotypes at the  Oryza sativa L.) is the most important staple food for over half of the world population. Rice quality is primarily influenced by starch which is composed of two polysaccharides: amylose and amylopectin. The percentage of amylose on total starch, measured as Apparent Amylose Content (AAC), is the key determinant of rice cooking properties. High amylose varieties, like risotto varieties, cook dry with firm and separate grain; while low amylose cultivars (cvs.) are tender, glossy and cohesive after cooking. Suggested classification of amylose content identified classes as waxy (0–5%), very low (5–12%), low (12–20%), intermediate (20–25%), and high (25–33%), even considering that commercially rice is classified by amylose content as either low (less than 20% amylose), medium (21–25%) and high (26–33%) (Juliano Rice ( Juliano .Granule-Bound Starch Synthase (GBSS) alleles compared to AAC assays since it avoids complications such as modifier genes, cytoplasmic factors, as well as environmental effects such as differences in temperature during grain development  gene which encodes the Granule-Bound Starch Synthase I (GBSSI) enzyme and the level of grain amylose is directly associated to the amount of GBSSI in the endosperm  at the 5′ splice site of the first 1,124 bp long intron, localized 1,164 bp upstream the start codon (haplotype AGTT instead of AGGT). Most of the waxy and low AAC cvs. screened so far carry this polymorphism which results, for Wxb, in the reduction of pre-mRNA splicing efficiency and promotion of alternative splicing at cryptic sites in exon 1, leading to a decreased production of functional enzymes and causing the glutinous and low amylose phenotypes  of Waxy exon 1 . One example in rice is the identification of 24 SNPs and one InDel mutation obtained through the comparison of the Starch Synthase II (SSII) gene sequence in 30 rice genotypes. The polymorphisms were then utilized for marker-assisted backcross breeding programs  was analyzed for AAC leading to the identification of the AAC classes low, intermediate and high. Effectiveness of available markers diagnostic for Apparent Amylose Content was verified and a low predictability within the high AAC class was observed. To discover new molecular markers associated to the different AACs, twenty-one genotypes representing each AAC class were selected and subjected to the re-sequencing of the waxy locus. New SNPs were identified in four high AAC accessions and used to develop new SNP-based molecular markers. Moreover, un-expected associations between grain shape characters and polymorphisms associated to the waxy locus were identified and analyzed.In this study a collection of 127 rice accessions  and CT21 for Bomba (22.84% AAC). The most frequent allele, CT18 identified in 67 accessions, represents a wide range of AAC (14.92-21.56%). Similar results were observed for CT17 and CT19 which were associated to heterogeneous AAC intervals ranging from 15.55% to 23.27%. Considering non-glutinous genotypes, the RM190 microsatellite explained the 74.9% of the AAC variation in our collection (Table The germplasm collection of 127 rice accessions (Table For the SNP in the splicing site of the leader intron intron 1), results confirming previous investigations were observed  in exon 2 responsible for Waxy gene inactivation was present; literature data in fact provide AAC values for this cv. ranging from 0.8 to 1% . Similarly, the CT14 allele associated with the CG and AG allelic patterns shared similar AAC (ranging from 23.2 to 23.8%). Among the CT20 group, the most frequent allelic pattern for high AAC class was CG, which identifies an AAC range from 22 to 24%, while in combination with AG frequently identified accessions with more than 25% AAC. CT9, CT10 and CT11 associated to the allelic pattern AG were typical of genotypes with more than 24% of AAC.The allelic variation at the RM190 locus together with intron 1/exon 6 SNPs data allowed the identification of allelic patterns associated to different AAC classes. In particular, even considering that no association of CTDespite the fact that the level of variation in AAC explained by our results is in agreement to the one recently evaluated in an Italian rice collection  and dbSNP (http://www.ncbi.nlm.nih.gov/sites/entrez?db=snp&TAbCmd=Limits) databases. Additionally to these SNPs polymorphisms, a 4 bp tandem repeat that was identified in rice cvs. showing high level of Wx transcripts  (Antoni and Gigante Vercelli), +1,801 (exon 9), +2,282 (intron 10) and +2,806 (intron 12) (Alinano C and Zhen Shang 47) and used to screen the 127 accessions Figure ; Table 120 with A (exon 6) and G  was always associated to an AAC higher than 24.5%  was associated with differences in the width of the grain, the length to width ratio (L/W), and the length of the kernel. The SNP at the sixth intron showed no association with these traits by itself, but it slightly increased the overall explained variance once the SNP at the first intron was considered. This suggests that the latter SNP was the actual responsible of the association, whereas the SNP at the intron 6 has only an ancillary effect. Even the CTn showed a significant association, but its explanatory capability of the variance of grain biometric parameters was lower than that of the SNP at the first intron, thus that, given it also has no direct effect on the functionality of GBSSI, its association is most probably indirect and most likely ascribable to the phylogenetic association of some CTn haplotypes with the two versions of the SNP at the first intron.In rice, traits related to grain size, shape and cooking properties have a large impact on market appreciation and play a pivotal role in the adoption of new varieties Webb . It is tversus AAC for the overall genotype set used in this work, the positive correlation  is immediately apparent - this SNP offers a sharp distinction between genotypes with AAC ≤ 21% (Wxb) and AAC ≥ 21% (Wxa); (b)- in our set of genotypes, there seems to be a break for AAC around 21.5%, which, therefore, appears to be a more precise threshold value for discriminating between Wxa/Wxb allelic versions, even though a few genotypes spill over, and small differences in AAC can actually occur because of the method of assay  are skewed towards low L/W ratios, and, vice-versa, the frequencies of high amylose genotypes (haplotype Wxa ) are skewed towards high L/W ratios. It is indeed the presence of these skewed distributions that generates the positive correlation between L/W ratio and AAC, and, then, the association between L/W ratio and the SNP at the first intron.The positive correlation between AAC and L/W ratio, and the negative one with the width of the grain were further analysed Figure . In factWxa haplotype, which, in turn, is linked to the up-mentioned presence of two clearly distinct modes in grain width . It is however not a necessary correlation, since some genotypes with high AAC and short cycle were present as well  to low and intermediate AAC  and one in the promoter region . Moreover, two accessions with high AAC, Zhen Shang 47 and Alinano C, showed the highest sequence variation with respect to Nipponbare that included a non-conservative SNP in the coding region of exon 9, causing a Pro to Ser substitution in the GBSSI protein. As this mutation implicates the substitution of an apolar amino acid to a polar one it should be possible that it increases the activity of the GBSSI enzyme, even if its effect should be small as the SNP does not affect the AAC variation significantly (Tab. 3).Antoni and Gigante Vercelli accessions carried the SNP in exon 6 typical of the 20 RM190 alleles in combination with the A haplotype for exon 6 and the G haplotype for SNP −1,514 was always associated to an AAC higher than 24.5% thus providing an efficient tool for selecting high AAC rice accessions.New dCAPS markers were developed exploiting the SNPs in intron 1 at position −1,514 (identified in Antoni and Gigante Vercelli), exon 9, intron 10 and intron 12 (identified in Zhen Shang 47 and Alinano C). After screening of the 127 accessions with these molecular markers it was observed that the combination of the CTWxa and Wxb haplotypes, and many candidate genes are available  and the width of the grain, the length to width ratio (L/W), and the length of the kernel is owed to the skewing of genotype frequencies towards a bold grain in Wxa rice and towards a slender grain in Wxb ones , of which 125 were japonica and 2 indica. The Italian rice varieties were selected because of their past and present relevance for breeding programs, and their contribution to rice production in the last decades. The 87 foreign accessions were selected as being relevant to Italian rice breeding, or considered reference varieties at the international level.An Italian Each rice accession was grown in triplicate field trials for two years. Maturity time of each field trials was recorded when at least 50% of the plants was ready for harvesting. Data was represented by the mean value of three replicates and indicates the days from sowing to panicles maturation. Harvested rice seeds were used for amylose quantification and biometric parameters evaluations. According to the protocol of Williams et al. , with moGBSSI gene re-sequencing.To obtain genomic DNA from the rice accessions, seeds were germinated in petri dishes at 30°C and one-week old seedlings were transplanted and grown in a greenhouse until three leaf stages; leaves were therefore collected, frozen in liquid nitrogen and store at −80°C. Genomic DNA was extracted on plates using the Wizard® Magnetic 96 DNA Plant System (Promega) according to manufacturer’s instructions. The CTAB DNA extraction method (Doyle and Doyle The RM190 CT repeat was assayed using the M13-tailed forward primer RM-190 F (CACGACGTTGTAAAACGACCTTTGTCTATCTCAAGACAC) and the reverse primer RM-190R (TTGCAGATGTTCTTCCTGATG)  in a total volume of 10 μl at 37°C over night. The samples were run on 2% agarose gel. Each digested sample was compared alongside with its not-digested cognate control.PCRs for dCAPS analyses of the G/T polymorphism in intron 1 were carried out in 20 μl using GoTaq DNA Polymerase (Promega) supplemented with 5% DMSO and with 20 ng of genomic DNA, 1 μM of GBSS-W2F, 1 μM of GBSS-W2R . Primers and restriction enzymes for dCAPS assay are listed in Additional file To detect the SNPs at position −1,514, +1,801, +2,282 and +2,806 by dCAPS analysis, mismatched forward primers were designed by dCAPS Finder 2.0 software  and blasted against the rice genomic sequence on the Gramene website (http://www.gramene.org) to ensure the specificity for the GBSSI gene.For the allele mining of the http://multalin.toulouse.inra.fr/multalin/). All data were analysed with the Systat 12 software . The relationships between numerical variables  were evaluated by Pearson correlation coefficients and regression analysis. The associations between numerical variables (AAC) and categorical variables (marker haplotypes) were analysed according to the General Linear Model (GLM) procedure.The RM190 microsatellite was analyzed using the GeneMapper software (Applied Biosystem). Sequence assembly was assessed with the ContigExpress tool of Vector NTI Software (Invitrogen) using the Nipponbare genomic sequence as reference. Sequence comparison was carried out by MultAlin software (The authors declare that they have no competing interests.CB carried out the molecular genetic studies, the sequence alignment and drafted the manuscript; DC, RP, PB, SU, GO, FD participated to molecular genetic studies, the sequence alignment and determination of Apparent Amylose Content; AG participated in data analysis and helped to draft the manuscript; EL, LC participated in the design of the study and helped to draft the manuscript; GV designed the study and helped to draft the manuscript. All authors read and approved the final manuscript.Markers obtained from the newly discovered SNPs. For each marker, primers, SNP position, restriction enzyme and origin of the restricted amplicon are indicated. The mutated base is in bold and underlined.Click here for filePrimer combinations used to amplify the Waxy alleles and internal primers used for amplicons sequencing.Click here for file


Wolbachia bacteria influence arthropod hosts in multiple ways. They are mostly recognized for their manipulations of host reproduction, yet, more recent studies demonstrate that Wolbachia also impact host behavior, metabolic pathways and immunity. Besides their biological and evolutionary roles, Wolbachia are new potential biological control agents for pest and vector management. Importantly, Wolbachia-based control strategies require controlled symbiont transfer between host species and predictable outcomes of novel Wolbachia-host associations. Theoretically, this artificial horizontal transfer could inflict genetic changes within transferred Wolbachia populations. This could be facilitated through de novo mutations in the novel recipient host or changes of haplotype frequencies of polymorphic Wolbachia populations when transferred from donor to recipient hosts. Here we show that Wolbachia resident in the European cherry fruit fly, Rhagoletis cerasi, exhibit ancestral and cryptic sequence polymorphism in three symbiont genes, which are exposed upon microinjection into the new hosts Drosophila simulans and Ceratitis capitata. Our analyses of Wolbachia in microinjected D. simulans over 150 generations after microinjection uncovered infections with multiple Wolbachia strains in trans-infected lines that had previously been typed as single infections. This confirms the persistence of low-titer Wolbachia strains in microinjection experiments that had previously escaped standard detection techniques. Our study demonstrates that infections by multiple Wolbachia strains can shift in prevalence after artificial host transfer driven by either stochastic or selective processes. Trans-infection of Wolbachia can claim fitness costs in new hosts and we speculate that these costs may have driven the shifts of Wolbachia strains that we saw in our model system.The common endosymbiotic  Wolbachia pipientis manipulates reproductive biology of many arthropod species in order to warrant its own transmission in host populations  as the most common  Wolbachia strains are transferable between diverse host systems and insect genera, (ii) the transferred strains are capable of inducing the expected phenotype such as CI, life shortening or resistance against pathogens, and (iii) transferred infection and their expected phenotypes persist stably in the novel host. So far, several authors have reported successful transfer of Wolbachia by microinjection from donor to recipient hosts within the same insect order, followed by confirmation of their phenotype in the novel host  induced de novo structural rearrangements, and/or (ii) enhanced sequence polymorphism within the symbiont genome post microinjection. The potential appearance of additional Wolbachia subvariants, hereby designated haplotypes, that are distinctive from the reference sequence of the symbiont in its native host . For the first host-transfer experiment, embryos of Wolbachia-free D. simulans  in 2002 . Wolbachia-free D. simulans Nouméa TC and D. melanogaster w1118 (Wolneg) were used as negative controls, Wolbachia-infected DSR  were used as positive control. All Drosophila lines were kept on standard medium at 24°C.Fly stocks of thirteen  and elevthirteen . Rhagole - wCer5 . Based os in 200 . From thg mating . This se1 and G2 . For the in 2002 . This reta lines  and one Wolbachia depletion, embryos of R. cerasi were transferred to larval medium containing a final concentration of 0.02, 0.025 and 0.05% (w/v) tetracycline and incubated at 24°C until reaching the third instar (L3). Antibiotic larval media and R. cerasi individuals were kindly provided by K. Köppler from the Center for Agricultural Technology Augustenberg (LTZ), Stuttgart, Germany. D. simulans RC20 and RC50 were placed on standard Drosophila diet containing 0.03% tetracycline for two consecutive generations before the presence of Wolbachia was tested.For Wolbachia depletion assays, DNA was extracted from tetracycline-treated adults of trans-infected D. simulans RC lines using the Puregene DNA Purification Kit (Qiagen). DNA was stored at −20°C until use.For PCR, cloning and sequencing, high quality genomic DNA was extracted from individual pupae or adults using the Puregene DNA Purification Kit. For Southern blots, genomic DNA was extracted from individual adult flies and processed following the protocol from Wolbachia MLST genes gatB , coxA  and ftsZ  in donor and recipient hosts . To selectively target wCer1, we used a strain-specific primer set described in Escherichia coli cells. Clones containing the insert were Sanger sequenced at the University of Chicago Cancer Research Center (UCCRC-DSF). Sequences were analyzed using ApE plasmid editor v1.10.4 (M. Wayne Davis), CodonCode Aligner Version 2.0.3 (CodonCode Corporation) and the BLAST algorithm (www.ncbi.nlm.nih.gov).We analyzed the frequencies of SNPs in the three nt hosts . GeneralD. simulans recipients, we analyzed SNP frequencies for each line separately  plus for the pool of all trans-infected lines (RC) in order to maximize sample size. The automated base-calling in CodonCode Aligner software detected SNPs in many clones from single individuals. In order to verify their authenticity, we visually inspected all SNPs in the corresponding chromatograms from both forward and reverse reads. All ambiguous SNPs were excluded from the final data set. Confirmed SNPs were then divided into two groups: recurrent SNPs and true singletons. Recurrent SNPs refer to nucleotide positions that were detected in independent clones in either different lines of new hosts, in R. cerasi clones only, or in both systems. Unique SNPs that were found in single clones only, but appeared reliable in the sequence chromatogram, were classified as singletons.For Taq DNA polymerase used for all assays. We did not use a proof-reading enzyme as the Promega Go Taq® Flexi DNA Polymerase used in all experiments is one of the best non-proof-reading high quality and high performance polymerases on the market (see Promega notes available at www.promega.com). Based on re-PCR and re-sequencing of known coxA, ftsZ, and gatB fragments inserted into the pTZ57R/T cloning vector from independent batches of this polymerase (data not shown), we calculated the following error base lines: 0 SNPs in 4.44 kb of coxA (10×444 bp), 0 SNPs in 2.90 kb of ftsZ (10×290 bp), and 2 SNPs in 6.00 kb of gatB (14×429 bp). Compared to the published mean estimate for standard non-proof-reading Taq DNA polymerases of 0.21 SNPs/kb .Anticipating that PCR accuracy was strongly impacted by the performance of the enzyme polymerase, we first determined the error base line of the  SNPs/kb , our ProWolbachia marker sets: Insertion Sequence elements (IS) and Variable Number of Tandem Repeats (VNTRs). 0.5–1 µg genomic DNA from single flies was digested with 6U HindIII  for 4 hrs at 37°C. After high-resolution vertical gel electrophoresis  and exposed to high sensitivity X-ray films . Probe primers were designed with respect to the annotated genome of wMel of D. melanogaster  in the wMel genome of D. melanogaster were chosen . HindIII-digested DNA of the lambda phage  was utilized as size marker. Size of fragments was determined with respect to this standard, allowing the comparison of number and size of fragments between donor R. cerasi and new hosts D. simulans and C. capitata.We determined the structural integrity of the bacterial chromosome in the novel hosts via RFLP-analysis with highly dynamic phoresis , gels wee chosen . VNTR pr in wMel  and VNTRD. simulans. Analysis was performed between G168 and G182 post microinjection and followed the fecundity assay by Drosophila line were screened. Fecundity status of ovaries was estimated according to number of mature eggs in the ovary: 0 eggs = class I; 1–2 eggs = class II; 3–9 eggs = class III; 10 and more eggs = class IV. Only eggs at stage 14 of oogenesis  were used. SNP frequencies were analyzed using χ2 with Yates Correction (2×2 contingency table); two tailed P-values indicated significant differences between values when <0.05. To detect potential traits of positive or negative selection, synonymous to non-synonymous substitutions per site were calculated using SNAP  provided at http://www.hiv.lanl.gov. This program uses the Nei-Gojobori corrected path counting method that adjusts for counts via Jukes-Cantor plus the weighting of pathways from one codon to another according to an equi-probable model for each possible codon-to-codon path . Although IS and VNTRs had earlier been reported as hypervariable entities of Wolbachia genomes, we did not detect any structural re-arrangements for the five tested loci in wCer2 of the novel hosts .We analyzed the genome synteny of artificially transferred Taq polymerase used in our lab. Consequently, we analyzed SNP frequencies in three Wolbachia loci  in trans-infected D. simulans and C. capitata. For gatB, we sequenced and analyzed 29.1 kb: 12×404 bp from each of RC20, RC21, RC33, RC45, RC50 of D. simulans, plus 12×404 bp from WolMed88.6 of C. capitata (Table S1). In total, 38 SNPs randomly dispersed along the 404 bp gatB gene fragment  with an average SNP-frequency of 1.64 SNPs/kb  with an average SNP-frequency of 0.78 SNPs/kb .Prior to all sequencing experiments, we determined the base line error rate during polymerase chain reaction of the fragment Table 1 wCer1 in three lines of microinjected D. simulans, although this strain had previously been considered as lost from the microinjected lines between G1 and G2  either represent ancestral, hidden sequence polymorphism, i.e., multiple Wolbachia haplotypes already present in the donor host, or (ii) have arisen de novo following microinjection. To test this hypothesis, we compared SNP frequencies in the novel hosts with the frequencies in the donor of the Wolbachia strains. Since original donor specimens for the microinjection experiments into D. simulans and C. capitata were not kept as voucher material, we sequenced gatB, coxA, and ftsZ from a broader representative range of other R. cerasi populations across Europe instead.Regarding the heterogeneity detected in Wolbachia SNP frequencies from R. cerasi samples collected all over Europe . As shown in gatB of wCer2 from the donor R. cerasi was 1.01/kb (9 variable positions in 8.9 kb). Comparing these data with those of the novel hosts, we did not observe an increase in SNP-frequencies per kb of wCer2 gatB in the recipients . For gatB of wCer1, we sequenced 9.7 kb consisting of a 24×404 bp amplicons data set and calculated an average SNP-frequency for gatB of wCer1 in R. cerasi that was not statistically significant from wCer2 in R. cerasi , but the small sample set  impeded statistical testing and thus did not allow a reliable comparison between both wCer 1 and wCer2.Since we unexpectedly detected coxA locus, we sequenced 5.7 kb of wCer1 (13×444 bp amplicons), and 888 bp of wCer2. Average SNP-frequencies for wCer1 and wCer2 infections were rather low  and 10 kb of wCer2 (21×478 bp amplicons). At this locus SNP frequencies of the two Wolbachia strains were quite similar with 1.14 SNPs/kb and 1.10 SNPs/kb for wCer1 and wCer2, respectively (ftsZ of wCer1 resulted in a transversion from guanine to thymine in the first position of the consensus triplet GGA (Gly), thus introducing the stop codon TGA to the sequence. We detected this stop codon twice and independently in ftsZ of wCer1.For the ectively Table 2CWolbachia housekeeping genes was highly unexpected and hence considered with extreme caution. However, similar to the ochre mutation in gatB of wCer2 in D. simulans and C. capitata (see 3.2), we found additional nonsense mutations in coxA and ftsZ of wCer1 Wolbachia . Finally we also uncovered an ochre mutation in coxA of wCer1 in recipient line RC20 but as a singleton only . Except for RC20, also the other new hosts exhibited reduced number of mature eggs (ranging from 52.5 to 60% in class IV) pointing towards a fitness cost effect related to the new symbiont infection. Interestingly, in RC20 percentage of class IV ovaries is higher than in all other new hosts and even slightly higher then in the control (85% vs. 80%). This might represent a positive correlation of fecundity with Wolbachia infection in this special case, but would require further testing for verification  via Southern hybridization, and thus covered at least 30 loci dispersed in the wCer2 genome. We did, however, not detect any transpositions in these loci. Recent studies demonstrated that Wolbachia carry a high percentage of functional transposable elements that can display transpositional activity during short term Wolbachia evolution  but this might not be highly representative since we tested a very small sample size. In the recipient hosts D. simulans and C. capitata, we determined SNP-frequencies in wCer2 that were not accelerated compared to R. cerasi (see gatB of one of the recipients (RC20), at the time when wCer2 was unambiguously present). We calculated the ratio of the number of non-synonymous substitutions per non-synonymous site (dN) to the number of synonymous substitutions per synonymous site (dS) for gatB of wCer2, a general indicator for selective pressure acting on protein-coding genes. This ratio for gatB, in R. cerasi and RC lines (Table S3) corroborated our finding that SNP-frequencies did not change upon arrival of Wolbachia in the new host systems.We determined SNP frequencies for rasi see Table 2.wCer2, we found overall low SNP-frequencies in gatB, coxA, and ftsZ of wCer1 in the native host R. cerasi, ranging from 0.5 to 1.1 SNP/kb. For the trans-infected hosts, we only analyzed the frequency of gatB and coxA, not for ftsZ. Both of these data sets are rather small and thus still inconclusive. Unexpectedly, we revealed cryptic double infections with wCer1, but this Wolbachia strain persisted in lower densities than the predominant wCer2. Since the focus of this study was from its onset primarily on wCer2 and not wCer1, a more extensive analysis of wCer1 sequences deriving from the trans-infected host systems was not carried out. Our coxA sequence data, however, did not indicate an increased wCer1 SNP-frequency in the de novo hosts. In particular, in wCer1-gatB, we did not find a statistically significant increase in the SNP frequency within RC compared to R. cerasi.Similar to gatB of wCer2 were either present in the native and/or in the trans-infected hosts. This finding raises the question whether the observed polymorphism represented de novo mutation events or ancestral cryptic infection polymorphism. Wolbachia haplotypes might be generated by point mutations in outer membrane proteins. In our study 68% of the SNPs revealed in wCer2 of gatB (26/38) were not detected in the donor of the infection but exclusively in the trans-infected hosts, suggesting de novo mutation events. Our PCR, cloning and Sanger sequencing based approach resulted in relatively low sample size, so that this study is not sensitive enough to rule out the existence of rare haplotypes in the donor host. Deep-sequencing strategies of donor and recipient with much higher coverage will be of pivotal interest to finally uncover potential de novo mutation rates of the endosymbiont upon artificial host switch. An alternative reason for our failure of detecting shared SNPs in both donor and recipients is genetic drift. Through drift effects, allele frequencies change and this may result in the loss of certain haplotypes followed by consequent reduction of genetic variation within a population. As a third explanation, selection for certain, beneficial haplotypes can be envisaged. This would lead to subsequent fixation of these haplotypes and loss of others.As shown in gatB of wCer2 . SNP-11, SNP-93 and SNP-186 provide three additional cases for the existence of distinctive gatB haplotypes of the wCer2 infection since all three were repeatedly isolated from hosts that were independently microinjected . We revealed, however, that three out of 38 SNPs (8%) detected in gatB of wCer2 introduced novel pre-mature stop codons caused by in-frame ochre mutations, i.e. a transition of cytosine to thymine in the first position of a CAA triplet. SNP-226 was found uniquely in transinfected RC20, whereas SNP-253 was found in both recipient hosts independently, i.e. D. simulans (line RC50) and C. capitata (line WolMed88.6). Novel stop codons were not restricted to wCer2 since we also traced them in coxA and ftsZ of wCer1. SNP-22 in coxA of wCer1 was found uniquely in line RC20. In contrast, SNP-45 in ftsZ of wCer1 seemed to be of ancestral origin, occurring in Sicilian R. cerasi populations only.We tested if SNP frequency in Wolbachia genes and even complete genomes are being transferred onto insect host chromosomes . It can hence not yet be explained why unexpectedly high frequencies of nonsense mutations occurred in two Wolbachia housekeeping genes of both Wolbachia strains. We propose, however, several ideas that might explain the compensation of these mutations. First, the ‘codon capture model’ allows a bacterial codon that has fallen to low frequencies to be reassigned without triggering fitness implications . So far, we do not know how stably these mutant haplotypes are maintained within the wCer population but an ongoing deep sequencing project of the wCer2 genome will allow us to screen, in detail, for the presence and maintenance of these mutations.In total, we found two types of nonsense mutations in recipient and donor hosts, ochre and opal. In the recipient systems, we revealed two in-frame ochre mutations in  E. coli , and allrevisiae . We perfications . In the Wolbachia strains can coexist within single host individuals e.g. ants . Beaumont and colleagues recently reported on the experimental evolution of bet hedging strategies in bacterial populations . Hence we might observe a correlation between female fecundity and Wolbachia infection in RC20. We have tracked a switch in wCer2/wCer1 prevalence in RC20 that most likely occurred between generations 150 and 167. It might be possible that this change of female fecundity is correlated with a Wolbachia strain switch in this line. Although we have no direct evidence yet, we speculate that wCer2 might be negatively correlated with female fecundity in this special case of RC20. This line was reported as mono-infected with wCer2 in earlier passages (wCer1&2 double-infected (this study), and eventually the wCer1 infection has outcompeted wCer2, since the latter strain was no longer traceable by PCR in later generations  and G168 Click here for additional data file.Figure S1(TIF)Click here for additional data file.Figure S2(TIF)Click here for additional data file.Table S1(DOCX)Click here for additional data file.Table S2(DOCX)Click here for additional data file.Table S3(DOC)Click here for additional data file.Table S4(DOCX)Click here for additional data file.


Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.We aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA.A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration.We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms. Detecting causal single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) has been focusing on measuring the statistical power of single SNPs, which have a relatively small effect on predicting disease susceptibility and ignore prior biological information about the target disease. Especially in complex diseases such as type 2 diabetes (T2D), the effect of each single SNP is too small to explain the disease association significantly.To enhance the statistical power, we propose considering combinations of SNPs. Yang et al. discovered that estimates of variance explained by genome-wide SNPs are unbiased with the proportion of SNPs used to estimate genetic relationships in human height . AlthougTo rank SNPs and find SNP combinations, various methods are applied: Bayes factors , logistiBasically, RF is known to have a relatively low risk of overfitting compared to other machine learning algorithms . HoweverFrom T2D GWA studies, approximately 40 causal individual SNPs have been identified . HoweverTo detect T2D causal SNP combinations, Ban et al. tried to find SNP combinations from a dataset containing 408 SNPs in 462 T2D cases and 456 controls using SVM. From p-values that were less than 0.6, a SNP combination with 14 SNPs was selected using a p-value-based filtering method, and the prediction accuracy was 0.653 . Ban et Recently, we presented T2D causal SNP combinations from an optimal SNP dataset and find the biological meaning of the detected causal SNP combination . Our pre-4. After QC, 409,656 SNPs remained. For single SNP association analysis, Cochran-Armitage trend test statistics is applied using PLINK 1.07 (http://pngu.mgh.harvard.edu/purcell/plink/) [T2D association of single SNPs is analyzed by measuring the statistical power of individual markers. The WTCCC  GWAS dat/plink/) .2 > 0.8 within 1 Mb. After LD pruning, 42,798 SNPs were selected for the SNP combination analysis.To reduce the number of variables, LD pruning is used on 409,656 SNPs after the single SNP association analysis. Reducing the number of variables has advantages for reducing the computational complexity and avoiding the possibility of overfitting. In addition, to map SNP combinations at the gene level and the functional module level, LD pruning is an effective solution to avoid overestimation of a specific gene or a functional module containing multiple SNPs. The most significant SNP is selected among the SNPs in LD with rTo reduce the computational complexity, the proposed method finds an effective threshold of the significance of SNPs by comparing the error rates of the SNP combinations from the Bonferroni threshold, the p-value rank-based threshold and the p-value range-based threshold criteria. Based on the Bonferroni threshold criteria, r is defined as the number of SNPs within the corrected p-value threshold that is determined as 0.05 divided by the total number of SNPs. The Bonferroni thresholds r, 2r, 5r, and 10r are applied to select SNP datasets. Based on the p-value rank-based threshold criteria, 500 SNP sets that are generated from the SNPs of p-value ranks 1-500, which are calculated using a cumulative approach, are fully tested to find the patterns of error rates. Furthermore, the p-value range-based threshold criteria are applied with p-value cutoffs of 0.01, 0.05 and 0.1 to 1.0 based on a cumulative approach. The error rates of SNP combinations from the SNP datasets with various threshold criteria are compared to find the optimal SNP dataset.The RF algorithm is a combinational classifier that contains multiple classification trees to aggregate them into one classifier. Each classification tree is generated from a bootstrapped sample set, and the Gini index is measured for splitting. RF is selected to find SNP combinations because of its effective performance in ranking the causal SNPs. In addition, RF can detect SNPs with small statistical power because separate models are automatically fit to subsets of data from early splits in the tree . To findFor the optimal parameter settings of varSelRF for finding a SNP combination from the WTCCC T2D dataset, various values are applied to mtryFactor (the multiplication factor to decide the number of variables for the splitting), ntree (the number of trees for the first forest), ntreeIterat , and vars.drop.frac (the drop fraction of variables at each iteration). The error rates are not significantly affected by ntree and ntreIterat when ntree is changed from 500 to 10,000 and ntreeIterat is changed from 200 to 4,000. However, the error rates decrease as the values of vars.drop.frac decrease from 0.35 to 0.2, even though the computation time is greatly increased. Furthermore, mtryFactor values from 0 to 13 are tested, and the error rates are smaller than 0.12 for mtryFactor values between 0.75 and 2. Therefore, the default values of arguments from varSelRF are accepted for the SNP combination analysis: mtryFactor = 1, ntree = 5,000, ntreeIterat = 2,000, and vars.drop.frac = 0.2.T2D genes are collected to find the biological meanings of SNP combinations by using gene level mapping. T2D genes are collected from public disease gene databases such as OMIM , KEGG 2, and GADTo discover the biological meaning and disease mechanism of a SNP combination that consisted of SNPs from diverse genes, an expanded gene set enrichment analysis (GSEA) is applied to test the disease association of functionally related genes . ExpandeVarious functional modules are collected for a better understanding of the biological functions of a SNP combination Table . First, We compare the prediction error rates of SNP combinations and SNP sets from an optimal SNP dataset and from a functional module-based filtration. To measure the prediction error rates from RF, the R package varSelRF is applied. The default argument values  are accepted for analyzing the gene sets and SNP sets. In addition to top T2D-associated gene sets, various SNP sets are analyzed with RF to measure the significance of T2D-associated gene sets.The detection of T2D causal SNP combinations considering common SNPs with low statistical power in single SNP association analysis may perform better in disease risk predictions because common SNPs can explain critical effects if they interact with other SNPs as a SNP combination. An optimal SNP combination can be selected by comparing the error rates from RF analysis with variable selection. To avoid overfitting and to reduce the computational complexity, the proposed method detects an optimal SNP dataset by comparing the error rates of SNP combinations from Bonferroni thresholds and p-value thresholds.First, Bonferroni thresholds are applied to 42,798 SNPs selected from the WTCCC T2D GWAS dataset. In Table Figure As shown in Table Figure The selected SNP combination from RF analysis contains 101 SNPs that can be mapped to 107 nearby genes  less than 0.05 with Fisher's exact test. Table Table From the 42,798 SNPs filtered from the WTCCC T2D GWAS dataset, 451 T2D-associated gene sets with a p-value threshold of <0.05 are detected from the expanded GSEA to match with the collected T2D-associated genes. In all, 2,112 gene sets contains T2D-associated genes from 3,663 expanded gene sets. Surprisingly, 441 gene sets contains T2D-associated genes from the detected 451 T2D-associated gene sets from the expanded GSEA. Moreover, the average number of T2D-associated genes from the selected 441 gene sets with expanded GSEA is 11.188, whereas the average number of T2D-associated genes from selected 2,121 gene sets from total expanded gene sets is 6.554.The proposed expanded GSEA successfully selected T2D-related gene sets with superior performance than previous analyses regarding significance and coverage. Table To measure the susceptibility of functional modules, we measured prediction error rates of functional modules from RF analysis. Table Various p-value based SNP sets were also applied as references. The prediction error rate of top 1 SNPs of p-value rank was 33.93%, and the prediction error rate of top 10 SNPs was 27.19%, which are relatively lower than the functional module based prediction error rates when considering 1500-2000 SNPs. On the basis of the Bonferroni threshold criteria, a SNP set with top 82 SNPs within the Bonferroni correction with p-values was selected, and the prediction error rate was 11.76%. The prediction error rate of a SNP set which was measured with a p-value cutoff of 0.1 was 11.70% and the prediction error rate of a SNP set with whole 42798 SNPs was 11.47%. Compared with various p-value-based SNP sets, no specific functional module  could significantly explain the T2D association alone.To enhance the predictive power, the combination of top 5 functional modules and top 66 functional modules was applied. However, the prediction error rates were slightly reduced, although the number of considered SNPs was dramatically increased.Various thresholds, including Bonferroni correction thresholds and p-value-based thresholds, are tested to find the optimal threshold with considering SNPs with low statistical power. SNP combinations that contain SNPs with low statistical power had lower error rates than SNP combinations with only significant SNPs. With the consideration of common SNPs with low statistical power, the disease risk prediction rate can be improved, especially for complex diseases.Notable disease genes could be found from SNP combinations. From the selected SNP combination, there are still many genes that are not yet identified as a T2D-related disease gene. SNP combinations have high statistical power. In addition, the activation or inhibition of a T2D-related pathway could prevent and cure T2D and T2D complications. For example, the Rho GTPase pathway inhibitor can prevent T2D development .No specific functional modules from the T2D-associated gene sets shows significance in T2D development from the RF-based prediction error rates. From the results of the measurement of prediction error rates from RF analysis, we can infer that significant T2D SNPs and genes with high importance are widespread in the genome and are not concentrated in a specific functional module. The expansion of functional modules with protein-protein interaction network may increase the susceptibility of T2D.To overcome the low statistical power of single SNPs, considering multiple SNPs together becomes a solution for analyzing complex diseases. A T2D causal SNP combination is detected using RF with variable selection from an optimal SNP dataset filtered with a p-value threshold and LD pruning. From the WTCCC T2D GWAS dataset, 101 SNPs are selected with a SNP combination. Not only significant SNPs but also common SNPs with low statistical power are combined as a SNP combination. Mapping the SNP combination at the SNP, gene, and functional module levels gives clues to the relationship with T2D. Functional module-based filtration is also tested using T2D associated functional modules from genome-wide SNPs and the results showed no significance compared to randomly selected SNP sets. The proposed method can detect a SNP combination with considering SNPs with low statistical power. Additionally this method can reveal the biological meaning of the detected SNP combination by mapping functional modules and mapping the T2D-related information at multiple levels including disease genes.The authors declare that they have no competing interests.CK designed and implemented the proposed method and wrote the manuscript. HY participated in the implementation of the proposed method. GSY designed and directed this study, and reviewed the manuscript. All authors worked on and approved the final manuscript.Selected SNPs from the type 2 diabetes causal SNP combinationClick here for file