Fetal hemoglobin level is a heritable complex trait that strongly correlates swith the clinical severity of sickle cell disease. Only few genetic loci have been identified as robustly associated with fetal hemoglobin in patients with sickle cell disease, primarily adults. The sole approved pharmacologic therapy for this disease is hydroxyurea, with effects largely attributable to induction of fetal hemoglobin.BCL11A and the beta and epsilon globin loci , with an additive attributable variance from these loci of 23%. Among a subset of children on hydroxyurea, baseline fetal hemoglobin levels explained 33% of the variance in induced levels. The variant in HBE accounted for an additional 13% of the variance in induced levels, while variants in the HBB and BCL11A loci did not contribute beyond baseline levels.In a multi-site observational analysis of children with sickle cell disease, candidate single nucleotide polymorphisms associated with baseline fetal hemoglobin levels in adult sickle cell disease were examined in children at baseline and induced by hydroxyurea therapy. For baseline levels, single marker analysis demonstrated significant association with These findings clarify the overlap between baseline and hydroxyurea-induced fetal hemoglobin levels in pediatric disease. Studies assessing influences of specific sequence variants in these and other genetic loci in larger populations and in unusual hydroxyurea responders are needed to further understand the maintenance and therapeutic induction of fetal hemoglobin in pediatric sickle cell disease. HBB)BCL11A repressor of HbF HS1L-MYB intergenic region In sickle cell disease, higher fetal hemoglobin (HbF) levels diminish de-oxygenated sickle globin polymerization in vitro Hydroxyurea is the only approved pharmacologic therapy for sickle cell disease. Its clinical and laboratory effect is understood to result largely from enhanced HbF expression HBB and epsilon globin (HBE) and BCL11A loci and baseline HbF in children; 2) Association between HBE and hydroxyurea-induced HbF; 3) Additive effects of these SNPs on baseline and induced HbF in children.Our multi-site observational study examined associations between baseline and hydroxyurea-induced HbF in children with sickle cell disease and candidate single nucleotide polymorphisms (SNP) in several genes associated with adult sickle cell HbF levels. Our results indicate: 1) A 33% contribution of baseline to induced levels; 2) Confirmation of single marker associations between the Studies were performed under the policies of and with specific approval from the Institutional Review Board (IRB) at Columbia University and the corresponding body at each of the other participating institutions: Yale University IRB, Albert Einstein College of Medicine IRB, Weill Cornell University Medical School IRB, University of Rochester Research Subjects Review Board, Children's Mercy Hospitals and Clinics IRB, Children’s Hospital & Research Center Oakland IRB. Written informed consent from parents of participating children and informed assent from children were obtained according to each institution’s IRB policies.0 thalassemia, ages 5–21 years. Exclusions criteria are: pregnancy, current or recent painful crisis, fever or other acute illness within three weeks prior to evaluation, transfusion within the prior 100 days or active transfusion therapy, abnormal elevated serum creatinine or liver transaminases. We excluded siblings to ensure genetic independence. Laboratory data at steady state represented the most recent values obtained during routine care or an average of three values from well visits over the preceding 6–12 months, if available. Percent HbF (%HbF) was determined by routine HPLC and was used as a quantitative trait at steady state for baseline and for drug-induced levels.Observational analysis of children attending sickle cell clinic was performed at five sites (see author affiliations), including prospective observation during 2010. The Oakland site provided archived data and samples. Clinical inclusion and exclusion criteria conformed to those previously described Duration of hydroxyurea treatment was for at least six months and was initiated for comparable clinical indications across sites . Stable hydroxyurea dosing was three months at or near maximal dose by ANC criteria, excluding data from subjects on less than 20 mg/kg/day, even if for dose-limiting toxicity. Across the six sites, drug dose averages ranged from 23.8–29 mg/kg/day and did not statistically differ between sites . Adherence to hydroxyurea was defined as parent report of at least 80% of prescribed doses. Laboratory data at steady state represented recent values during routine care or averaged three values from well visits over the preceding 6–12 months, if available. Percentage of HbF (HbF%) was used as a quantitative trait. Baseline HbF had not been recorded prior to treatment for nine of the 47 subjects on hydroxyurea, thus is not available. Hydroxyurea-associated increased mean red cell volume and decreased white blood cell count were used to confirm data quality. For children on hydroxyurea therapy, where available, clinical response to hydroxyurea was assessed by retrospective chart review of the number of sickle-related hospitalizations over the two-year periods preceding and while on hydroxyurea treatment.BCL11A locus; two in HBS1L-MYB intergenic region on chromosome 6q23; three in the globin locus on chromosome 11: two 5′ sites in HBB . PCR assays and mass extension reactions were designed using mass array assay design software (Sequenom). PCR assays used Applied Biosystems Geneamp PCR thermocyclers  and analyzed by mass array compact mass spectrometer  and Spectro TYPER software (Sequenom).  Genotyping was performed in duplicate with separate assays, with genotype frequency distribution at each SNP tested for deviations from Hardy-Weinberg equilibrium. Accurate genotyping of −3 for the 19 SNPs tested – see below). Given the fixed sample size, power was estimated based on the smallest detectable differences in the average levels of baseline and hydroxyurea-induced fetal hemoglobin HbF. Power was computed using QUANTO software (http://hydra.usc.edu/gxe/) assuming an additive allele effects model, an alpha threshold of 0.05, a range of different values for effect sizes (β) and SNP allele frequency , and the hydroxyurea-induced increment over baseline (“delta HbF”) . For eacrequency  [24].BCL11A, HBB and HBE was estimated using linear regression analysis comparing models consisting of age, sex, individual SNPs and multiple SNPs with a reduced model consisting of sex and age . To evaluate whether BCL11A haplotypes was in closer linkage disequilibrium with causal variants than single SNPs, haplotype association analysis was performed with PLINK software BCL11A SNPs in strong linkage disequilibrium (r2>0.70) Limited number of subjects of each genotype precluded analysis of potential dose effect of homozygotes. Trend analysis was performed by analysis of variance tested for individual and additive effects for each allele. Percentage of variance attributable to SNPs in 0 thalassemia , maximum HbF 18.2 , and delta HbF 11.6 . Among those subjects with complete data for baseline and hydroxyurea-induced HbF (N = 38), distributions of baseline and induced HbF were comparable to those previously reported for children on hydroxyurea  [18] the2 = 0.59, p<0.001), similar to previously reported 2 = 0.86, p<0.001); but not between baseline and delta HbF . Those in the lowest quartile for baseline levels were highly likely to remain in the lowest quartile (Q1) for induced HbF  (Pearson correlation for the 38 children with complete baseline and hydroxyurea-induced HbF demonstrated high levels of relatedness for: baseline and induced maximum HbF levels (rp<0.001) . Only twSAR1A gene SNP, rs428289, 10 transformation of baseline HbF had no effect on SNP associations; non-transformed results are shown for comparison with induced values  and HBE (rs7130110) loci demonstrated a statistically significant additive effect of each SNP and a two-fold difference for both (ptrend<0.004) (HBB marker (rs7482144) had effects identical to those of HBE (not shown). By linear regression analysis of individual markers accounting for age and sex, the percentage of baseline HbF variance attributable each to the BCL11A or HBE SNP was 13%, and 10% for HBB , with different prevalence of the minor allele: 18% for HBE and 12% for HBB.Trend analysis using the SNP allele associated with higher baseline HbF values  in one or both of the d<0.004) . The HBB for HBB . In the HBE SNP BCL11A SNPs associated with baseline HbF .The study had ≥80% power to detect SNPs with effect sizes larger than 2.5 with allele frequencies of 0.25 or higher; estimated power decreased with lower SNP allele frequencies . Among aline HbF . Howeverline HbF . HaplotyBCL11A or HBE loci had higher average values, 20.8 , compared to 15.5  for those without either allele (ptrend = 0.009) (HBB marker (rs7482144) had effects identical to those of HBE . By linear regression analysis of the three markers, alone or in combination, only the HBE SNP independently and significantly added to the phenotypic variance contributed by the baseline levels. In combination, baseline level (33%) and the HBE  contributed an estimated 46% of the variance  . As withvariance .GLP2R allele, not associated with baseline HbF, was nominally associated with maximum HbF; the SAR1A SNP, rs2310991, The Children with sickle cell disease generally have higher baseline HbF levels than adults and more pronounced HbF response to hydroxyurea BCL11A and HBB or HBE loci BCL11A and HBB in adult sickle cell disease BCL11A and either globin marker were additive, and were associated with two-fold higher levels for patients with favorable alleles in both loci. An apparent redundant influence exists between HBB and HBE markers, suggesting a statistical effect stemming from tight linkage and/or overlapping biologic effects.For baseline HbF, we confirm in children the associations between single candidate markers in the HBE marker was the most robustly associated with drug-induced HbF levels, an effect that was independent of and additive to baseline levels. Despite a sample size limiting statistical power for individual SNPs, trend analysis with BCL11A and HBE or HBB markers suggests substantially higher induced levels associated with one or both favorable alleles. In combination with baseline levels, the HBE accounted for almost half of the variance of induced levels. Taken together, these data support a model of overlapping genetic regulation of HbF in pediatric SCD for both steady states For hydroxyurea-induced HbF, the GLP2R for hydroxyurea-induced HbF, but not baseline, will need confirmation and biologic rationale. No associations were detected with SNPs in other candidate loci such as HBS1L-MYB, presumably from insufficient power and its lesser impact on baseline HbF in sickle cell disease Roles of other SNP markers are less clear. Nominal association with Limitations: Study limitations include sample size and observational assessment, where therapeutic approaches to hydroxyurea and drug adherence may vary among sites despite inclusion criteria. Nonetheless, hydroxyurea dose and distribution of drug-induced HbF were comparable to those from prospective pediatric hydroxyurea studies HBE marker data also suggest that induced levels may reflect effects that are additive to those of baseline. In summary, our findings suggest that baseline and hydroxyurea-induced HbF likely are influenced by these genetic loci, while distinct genetic, pharmacologic, and clinical determinants also exist Clinically, HbF level is arguably the strongest predictor of disease severity Figure S1Histograms for Baseline, Maximum and Delta HbF%.(JPG)Click here for additional data file.Figure S2Power analyses based on minor allele frequencies. S2A. Baseline HbF. S2B. Maximum HbF(DOC)Click here for additional data file.Table S1Clinical and Genetic Data by Study Site. S1A. Clinical data by study site. S1B. Minor allele frequencies by study site.(DOC)Click here for additional data file.Table S2HbF linear regression analysis to compare full model consisting of age, sex, individual and multiple SNPs. S2A. Baseline HbF%. S2B. Maximum HbF%(DOC)Click here for additional data file.


We conducted a genome-wide association study on the Genetic Analysis Workshop 17 simulated unrelated individuals data using a multilocus score test based on wavelet transformation that we proposed recently. Wavelet transformation is an advanced smoothing technique, whereas the currently popular collapsing methods are the simplest way to smooth multilocus genotypes. The wavelet-based test suppresses noise from the data more effectively, which results in lower type I error rates. We chose a level-dependent threshold for the wavelet-based test to suppress the optimal amount of noise according to the data. We propose several remedies to reduce the inflated type I error rate: using a window of fixed size rather than a gene; using the Bonferroni correction rather than comparing to the maxima of test values for multiple testing corrections; and removing the influence of other factors by using residuals for the association test. A wavelet-based test can detect multiple rare functional variants. Type I error rates can be controlled using the wavelet-based test combined with the mentioned remedies. Genome-wide association studies of common mutations in the genome have many important results, but most of the genetic components of the disease risk of common diseases cannot be explained by common mutations. Resequencing studies have identified numerous new rare mutations in the genome. The new hypothesis is that some common diseases may be caused by multiple rare mutations. If each rare mutation has only a small marginal effect, then using methods that are based on single-nucleotide polymorphisms (SNPs) cannot identify them. Therefore we consider a group of SNPs and study the association between the group and the disease. A group can be a gene or a window of consecutive SNPs. Because most common diseases have been studied before, all common mutations with strong marginal effects should have been discovered already. We focus on multiple rare mutations in a gene. Testing multiple SNPs simultaneously for association with diseases can potentially have higher power than testing single SNPs one at a time. The problem here is that a higher number of degrees of freedom is introduced by the increased number of SNPs. Collapsing is a popular idea for dealing with rare variants, whereas a common method is to take a weighted sum .Collapsing methods effectively reduce the number of degrees of freedom. However, useful genetic information may be buried in noise by collapsing all neighboring SNPs together, regardless of whether they are causal or not. Collapsing neighboring genotypes in a gene replaces each genotype at each SNP with the average genotype in a gene, which is the simplest way to smooth multilocus genotypes of an individual. More advanced smoothing techniques include kernel smoothing, Fourier transformation smoothing, and wavelet transformation smoothing. It is interesting to note that the sum of the neighboring genotypes is the first Fourier transformation coefficient of multilocus genotypes. Therefore collapsing methods are a special case of the test based on Fourier transformation , in whicWe earlier proposed a test based on wavelet transformation . The wavThe first step in the wavelet transformation is to transform a noisy, unsmooth weighted multilocus genotype of an individual into its wavelet coefficients. The second step is to threshold the wavelet coefficients by dropping coefficients of small magnitude. Last, the inverse wavelet transformation is used to transform the thresholded coefficients back into a clean and smooth modified weighted multilocus genotype of the individual. Our test is based on the modified weighted multilocus genotype. The choice of threshold procedure is crucial, and we use empirical Bayes thresholding . The thrIn the Genetic Analysis Workshop 17 (GAW17) simulated data there are 697 unrelated individuals consisting of 209 case subjects and 488 control subjects. These individuals were genotyped on 24,487 SNPs in 3,205 genes on 22 chromosomes. There are three quantitative traits  and a qualitative trait (Affected/Unaffected). Three environmental factors are also given: Sex, Age, and Smoking status. We have requested and obtained the answer document that describes the simulation model.k eigenvectors obtained by Eigenstrat to adjust genotypes and phenotypes . Note that in the second scenario the average of a(i) is 0 and the average of b(i) is 1. Under the alternative hypothesis, X(1) = 3 + e, and all other X(i) are the same as those under the null hypothesis. So X(1) is the functional SNP, and all other X(i) are nonfunctional SNPs.To compare these two methods of permutation, we run the following simulation. Consider 100 independent random variables X(i) under the null hypothesis to mimic results of 100,000 permutations. We then take a sample size of 1,000, T, …, T for each X(i) under the alternative hypothesis as the test values in 1,000 replicates to compute the power and type I error rates. We compute empirical p-values for each T using two methods: by comparing to the maxima and by applying the Bonferroni correction. We then use 1,000 replicates to find the power and type I error rates at a significance level of 0.05; the results are given in Table X(i) are not the same under the null hypothesis. On the other hand, the power of the first method drops to almost 0 when the distributions of X(i) are not the same under the null hypothesis, and the type I error rate is also inflated. In practice, if we use the first method, the type I error rate has to be inflated substantially in order to increase the power. Table For each scenario, we take a sample size of 100,000 for each AA, aA, and aa at each SNP are coded as 0, 1, and 2, respectively, where a is the minor allele at the SNP. Because most functional variants are rare alleles, we put a weight on a rare allele. A coded genotype is divided by the square root of nq(1 − q), where n is the total number of individuals and q is the estimated minor allele frequency (MAF) of the ith SNP [X be the matrix of weighted genotypes. The jth column X(j) of X is the weighted genotype of the jth individual. We apply wavelet transformation, thresholding, and inverse wavelet transformation consecutively on vector X(j), resulting in the modified genotype of the jth individual, denoted still as X(j).The genotypes Z are from a normal distribution with mean u. Suppose that u has a prior distribution of (1 − w)d(u) + we(u), where d is an atom probability density function at 0 and e is a symmetric density function. Let u′ be the median of u given Z = z. There exists a function t(w) such that u′ = 0 if and only if |z| is less than or equal to t(w). Let the threshold be t(w′), where w′ is the marginal maximum-likelihood estimator of w.Empirical Bayesian thresholding  is determ consecutive nonsynonymous SNPs , which is an n × m submatrix of X, still denoted by X. Subtract the mean of the ith row of X from each entry of X such that the mean of each row of X is 0. Let Y = (Y(1), …, Y(n)) be the traits of n individuals adjusted for population stratification. Assume the generalized linear model [Consider a window of ar model :(1)X and Y, where f is a link function. Let:between V(j) be an estimated variance of U(j) under the null hypothesis. The global score statistic of this window for trait Y is:be the score statistic for the markers. Let V is the square root of the sum of all V(j). We compute T for four different traits .where p-value after Bonferroni correction is less than 0.05. This process was done for 1,702 windows  using 200 replicates. For a functional window , the power is the number of times the null hypothesis is rejected divided by 200. For a nonfunctional window , the type I error rate is the number of times the null hypothesis is rejected divided by 200. The power and type I error rates are reported in Table We analyzed 200 replicates. The null hypothesis for a window (or a gene) is rejected if the FLT1 could be consistently identified. Note that the type I error rate for Q1 was 0.035, which means that among 1,691 nonfunctional windows with 200 replicates, a total of 338,200 tests, the null hypothesis was wrongly rejected only seven times.When using eight SNPs as a window and at a significance level of 0.05, the type I error rates for Q1, Q2, Q4, and Affected were 0.035, 0.075, 0.11, and 0.05, respectively. The type I error rates were under control for Q1 and Affected. For the other two traits, the type I error rates were still inflated, although smaller. The wavelet-based test is sensitive to the choice of window size. Using a window size of 4 or 16 led to inflated type I error rates. Instead of consecutive SNPs, if we used all nonsynonymous SNPs in a gene as a window, the type I error rates more than doubled with a small gain in power. This small gain is not worth it. The power on most functional variants was low, although p-values by permuting phenotypes on each window (or gene) followed by Bonferroni correction is a safer choice.Using Eigenstrat  can remoFLT1 and KDR have 11 and 10 functional variants, respectively, and all other functional genes have fewer than 6 functional variants. For the quantitative trait Q2, the genes BCHE, SIRT1, and SRERF1 have 13, 9, and 10 functional variants, respectively; all other genes have fewer functional variants. For the qualitative trait, all genes except one have less than five functional variants; the power of the wavelet-based test on the qualitative trait is lower.From Table For the GAW17 data set, the wavelet-based test works the best when the window size is 8. All genes except one contain fewer than 14 functional variants. Increasing the window size only increases noise and type I error rates but not the power of the test. When window size is reduced, the test can capture only a part of the functional variants in a gene, which reduces the power. One of the advantages of the wavelet-based test is to take into account the genetic information contained in the linkage disequilibrium structure of the neighboring SNPs. A small window size loses this advantage. With 24,487 SNPs on 22 chromosomes, the data set is sparse. For a denser data set, a larger window size may be needed to capture most of the functional variants in a gene.When the wavelet-based test is applied to less rare SNPs with MAFs greater than 1% with or without adjusting for population stratification and with or without removing the environmental factors, the power is decreased and the type I error rates are increased in all but one case: a window size of 4 without any adjustments. It seems that for less rare SNPs, a window size of 4 is better than a window size of 8, and not removing environmental factors is better than removing them. Each gene contains only a few functional variants with MAF greater than 1%. The wavelet-based test does not work well under this circumstance.As an advanced smoothing technique, wavelet-based tests are a generalization of collapsing methods. A level-dependent threshold can reduce noise and type I error rates. The inflated type I error rate is a serious problem faced by practitioners. This problem has several causes, and one remedy cannot solve it. We suggest the following: using a window of fixed size rather than using a gene as a window; using Bonferroni correction rather than the maxima of test values for the multiple testing correction; removing other influencing factors and using residuals for association tests; and using Eigenstrat  to removThe authors declare that there is no competing interest.RJ participated in the design of the study and performed the statistical analysis. JD conceived of the study and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.


Complete pedigree information is a prerequisite for modern breeding and the ranking of parents and offspring for selection and deployment decisions. DNA fingerprinting and pedigree reconstruction can substitute for artificial matings, by allowing parentage delineation of naturally produced offspring. Here, we report on the efficacy of a breeding concept called “Breeding without Breeding” (BwB) that circumvents artificial matings, focusing instead on a subset of randomly sampled, maternally known but paternally unknown offspring to delineate their paternal parentage. We then generate the information needed to rank those offspring and their paternal parents, using a combination of complete (full-sib: FS) and incomplete  analyses of the constructed pedigrees. Using a random sample of wind-pollinated offspring from 15 females (seed donors), growing in a 41-parent western larch population, BwB is evaluated and compared to two commonly used testing methods that rely on either incomplete  or complete (FS) pedigree designs. BwB produced results superior to those from the incomplete design and virtually identical to those from the complete pedigree methods. The combined use of complete and incomplete pedigree information permitted evaluating all parents, both maternal and paternal, as well as all offspring, a result that could not have been accomplished with either the OP or FS methods alone. We also discuss the optimum experimental setting, in terms of the proportion of fingerprinted offspring, the size of the assembled maternal and paternal half-sib families, the role of external gene flow, and selfing, as well as the number of parents that could be realistically tested with BwB. Plant breeding, including tree improvement, typically follows the classical recurrent selection scheme, which is characterized by systematic and repetitive cycles of breeding, testing, and selection et al.et al.The availability of affordable, highly informative DNA markers, coupled with the development of sophisticated pedigree reconstruction methods, has enhanced their utility in converting incomplete pedigree trials into (effectively) complete trials, thus eliminating the pitfalls associated with the invocation of unfulfilled assumptions Larix occidentalis Nutt.) “breeding population.” We compared the performance of the combined incomplete  + complete (full-sib: FS) analysis to that of both the incomplete and complete pedigree designs. Finally, we illustrate the optimum experimental efforts needed for the successful implementation of BwB and discuss the role of factors such as external gene flow, expansion of the test population , and the size of half- or full-sib family needed for accurate genetic parameter determination.Here we experimentally demonstrate the utility, the increased precision of genetic parameters estimation, and increased accuracy of predicted breeding values, hence the effectiveness of the “Breeding without Breeding” concept, using open-pollinated offspring from 15 of 41 parents in a western larch .Pedigree reconstruction resulted in the formation of 349 full-sib families, nested within the 15 maternal and 38 paternal half-sib families, respectively, indicating that three out of the orchard's potential 41 male parents did not participate in pollination, at least of these 15 maternal parents, most likely due to their recent introduction to the seed orchard population (pers. observation). The 15 maternal half-sib families had an average size of 283.9 (range: 222–397) and the 38 paternal half-sib families had an average size of 37.3 , the latter evidently reflecting male fecundity variation within the orchard. There was an apparently high correlation between the difficult to assess male reproductive investment  and male reproductive success , and was used in concert with the non-fingerprinted individuals within each of the 15 HS families to form a combined pedigree model, consisting of half- and full-sib families (HS+FS) (see below). A minimum paternal half-sib family size threshold of six individuals was established for inclusion in quantitative genetic analyses. Seven male parents did not meet this threshold, but two were retained, because they were also represented as seed-donors, thus far exceeding the established minimum family size threshold.N = 5,796 individuals . The second is also for the same 15 HS families  but after the inclusion of the male parent for 1,419 individuals  and also represents an incomplete pedigree). While the third representing full pedigree  and was solely based on full-sib families formed by the pedigree reconstruction (FS) ; p = 0.105, p = 0.105) correlations. The corresponding comparison of HS with HS+FS breeding values for the offspring yielded significant product-moment ; p = 0.0001, p = 0.0001) correlations. Both results clearly demonstrate the reduced utility of the HS model's estimates for forward selection, relative to the results from the HS+FS treatment as indicated by both product-moment and rank correlations. Finally, the average accuracy of predicted breeding values, calculated from the combined HS+FS model was higher for parents (0.81) and offspring (0.55), than their corresponding values , calculated from HS model.Following the classical individual-tree additive model, three analyses were conducted. The first is for the 15 open-pollinated families (HS) with sample size of ion (FS) ; Table 1r = 0.96 ; p = 0.0001, p = 0.0001) correlations). The same was true for offspring breeding values ; p = 0.0001, p = 0.0001) correlations). The results from the combined HS+FS pedigree approach are robust and reliable. Moreover, the average accuracy of breeding values from parents and offspring calculated from the FS model  were very similar to those estimated from the combined HS+FS model . It is interesting to note that predicted parental breeding values were produced for the entire parental population , even when only 15 maternal parents were used and these estimates were based on the entire population  for the combined HS+FS model as opposed to N = 1, 419 for the FS model.The full (FS) and combined HS+FS pedigree models produced comparable additive and heritability estimates, with similar precision . PredictWe implemented three selection options; namely, forwards, backwards, and combined (combination of backwards and forwards), utilizing either the parental (backwards) and/or offspring (forwards) “Best Linear Unbiased Predictors” (BLUPs) generated from the HS or the combined HS+FS models. The backwards selection option was applied exclusively to the combined HS+FS model as parental breeding values were determined from both maternal and paternal information. The limited number of maternal parents (15 seed donors) precluded the application of the backwards selection option under the HS model; however, maternal breeding values along with offspring was used in the HS combined selection. Additionally, the limited number of maternal parents minimized the response to selection's differences between the forwards and combined selections resulting in somewhat identical results . WithoutDrastic difference in the additive genetic variance magnitude and its standard error was observed with increasing the number of trees with known paternal information . Increasin situ nature , and most of all its suitability to long-lived organisms such as trees or wildlife that require long-term experiments or extensive field observations. The distribution of relatedness among the studied individuals is assumption-free, thus it is applicable to natural populations where a vast array of genetic relationships can occur a priori assumption about the expected distribution of genetic relationship becomes inappropriate for a network of full-sibs, half-sibs, and selfs . It should be stated; however, that the regression approach does not permit the prediction of parents and/or offspring breeding values, thus its application to selection and breeding is somewhat limited.The concept of marker-assisted estimation of quantitative genetic parameters was introduced by Ritland Conventional tree breeding programs are structured around three main activities: breeding, testing and selection et al.et al.To avoid the inaccuracies associated with quantitative genetic parameters assessment from incomplete pedigrees, Lambeth et al.El-Kassaby Quantitative genetic parameters comparison between the two incomplete-pedigree models  indicated that the HS model over-estimated the additive genetic variance and its surrogate heritability and under-estimated the environmental effects . As expeThe observed differences between the two incomplete pedigree models (HS and the combined HS+FS) support the beneficial role of including the pedigree reconstruction information even though it is only from a subset of the studied population. The inclusion of additional genetic information allowed the creation of linkages among the 15 half-sib families (known seed donors) with all parents participated in mating (pollen donors), thus increasing the sample size  and maximizing the BLUP-method utilization for breeding values prediction  what is the proportion of the population needed for pedigree reconstruction? 2) What is the minimal HS and/or FS family size required for proper BLUP analysis? 3) What is the role of elevated gene flow or selfing in the breeding population? 4) How many parents can be realistically tested? 5) How are we to expand testing beyond those parents present in the breeding population? The observed changes in the additive genetic variance estimates and their associated precision that accompany changes in the number of genotyped individual with known male parents  suggest that the inclusion of approximately 10% of the tested population is adequate to reach stable parameter estimates . The maiLarix occidentalis Nutt.) seed orchard. The sampled orchard is one of two genetically distinct (41 and 62 parents) orchards established by British Columbia Ministry of Forests, Lands and Natural Resource Operations to provide genetically improved seed to the Nelson  and East Kootenay  seed production units. These orchards are located near Vernon, B.C., Canada , in an area devoid of western larch background pollen. The orchards are separated by an 8 m wide road and a row of black cottonwood (Populus trichocarpa Torr. & Gray) trees, acting as a partial pollen barrier. Seed samples and orchard's reproductive survey data were provided by British Columbia Ministry of Forests, Lands and Natural Resource Operations as the orchard is part of a co-operative arrangements among government-private industry-academia. Seed were sown  by individual maternal family in a commercial nursery in growing blocks (80 cavities/block), soil mixes, irrigation, heating, and fertilization regimes similar to those operationally applied for reforestation seedling production. Seed pre-treatment  prior to sowing followed International Seed testing Association procedures In 2005, wind-pollinated seed samples from 15 unrelated parents were collected from a 41-parent western larch , laid out as a randomized complete design with four replications. Each replication consisted of 10×10 square plots at a spacing of 0.3×0.3 m (100 seedlings/family). At the end of the third field growing season , total seedling heights (HT in cm) were measured on all surviving trees . The trial was watered and weeded when needed, and survival was 88% at the time of height measurement.Larix occidentalisL. lyalliL. occidentalis, and L. kaempferiThe two orchards  represent the possible paternal parents for a randomly selected 1,538 offspring that were genotyped with 16 microsatellite (SSR) markers. The SSR markers used were: 1) seven developed for β), a normally distributed random additive genetic effect , with covariance matrix A = {A is the additive relationship matrix (see below p) with mean zero and variance e) with mean zero and variance y be a vector containing the tree individual observations for height. Then, in matrix notation, the classical individual-tree additive model can be described as:A classical individual-tree additive model, assuming no dominance and epistatic effects, was used. The model included a fixed effect of overall mean ) were evaluated using different pedigree files. Assuming that parent trees were unrelated, the first model, half-sib (HS model), was used with the known female parent of each individual, where all individuals are assumed not inbred , and the additive genetic relationship are 0.25 or 0.0 for both trees with different fathers (with unrelated pollen), thus being maternal half-sibs and unrelated trees, respectively. This model is commonly used by forest geneticists and is called the open-pollinated test, where individuals within an open-pollinated family are assumed to be half-sibs iF = 0.5) and additive relationship between trees ranging from selfs to half-sibs  were considered in the A matrix.Let et al.h2) was calculated as i = a, p, and e are the values of the additive, plot, and error variance of the individual-tree model (1). Additionally, the inclusion of male information in the pedigree matrix allowed expanding model (1) to estimate the additive genetic variance after considering the additional genetic relationships generated by pedigree reconstruction. This was done to allow comparing the classical individual-tree additive models used. An important limitation of the REML (co)variance estimates is that their distribution is unknown. Only an approximate measure of precision of the estimates based on asymptotic or large sample theory can be calculated. Approximate standard errors (s.e.) of the h2 were computed with the “delta method” based on the Taylor expansion Restricted Maximum Likelihood REML  was usediF is the inbreeding coefficient for the ith individual. The PEV is calculated as the diagonal elements of the inverse of the coefficient matrix from the mixed model equations The analysis of a progeny test normally involves two steps: first the estimation of variance components and second the prediction of breeding values for the individuals, using the variance components estimated in the first step. In the three models, the “Best Linear Unbiased Predictors” (BLUPs) of parent and offspring breeding values were computed with ASReml from the estimated variance components. The accuracy of the predicted breeding values was calculated using the following expression: To determinate the optimum number of individuals with known fathers needed for obtaining reliable genetic parameters and thus reducing the DNA fingerprinting efforts, the classical individual-tree additive model (1) was fitted with several pedigree files, where the male information was randomly and progressively deleted, thus increasing percentage of omitted male data from 7 to 92% . These pedigrees with randomly deleted males provided us with a range of values and standard errors associated with them that the different parameters may take and permitted us to investigate the robustness of results under reduced fingerprinting efforts . For this data set, we set the minimum paternal HS family to n = 6 for inclusion in the analyses and hence the generation of precise genetic parameters and their respective predicted breeding values.Table S1Ho) and expected (He) heterozygosities, and estimated frequencies of null alleles and genotyping error of the seed orchard population used in the present study (41-Parents).Annealing temperature in °C, number of alleles, observed ((DOCX)Click here for additional data file.


Hitchhiking mapping and association studies are two popular approaches to map genotypes to phenotypes. In this study we combine both approaches to complement their specific strengths and weaknesses, resulting in a method with higher statistical power and fewer false positive signals. We applied our approach to dairy cattle as they underwent extremely successful selection for milk production traits and since an excellent phenotypic record is available. We performed whole genome association tests with a new mixed model approach to account for stratification, which we validated via Monte Carlo simulations. Selection signatures were inferred with the integrated haplotype score and a locus specific permutation based integrated haplotype score that works with a folded frequency spectrum and provides a formal test of signifance to identify selection signatures.About 1,600 out of 34,851 SNPs showed signatures of selection and the locus specific permutation based integrated haplotype score showed overall good accordance with the whole genome association study. Each approach provides distinct information about the genomic regions that influence complex traits. Combining whole genome association with hitchhiking mapping yielded two significant loci for the trait protein yield. These regions agree well with previous results from other selection signature scans and whole genome association studies in cattle.We show that the combination of whole genome association and selection signature mapping based on the same SNPs increases the power to detect loci influencing complex traits. The locus specific permutation based integrated haplotype score provides a formal test of significance in selection signature mapping. Importantly it does not rely on knowledge of ancestral and derived allele states. Linking genotype to phenotype is one of the central questions in biological sciences. Current approaches to map intraspecific variation to causative sequence variation use either a quantitative genetics framework (association mapping) or rely on population genetic theory (hitchhiking mapping).Population genetic theory predicts that a favorably selected allele is either lost or increases in frequency until fixation . With thArabidopsis thaliana and Plasmodium falciparum Arabidopsosomes 6 . Barendsosomes 6 , Gibbs eosomes 6  and Hayeosomes 6  publishei et al.  between i et al.  among sei et al.  employedi et al.  to deteci et al.  recentlyStringent artificial selection resulted in an enormous improvement of production traits over the last couple of decades, especially for traits with moderate to high heritability. In combination with the availability of high density SNP arrays and high quality phenotypes, this intense selection renders the genome of dairy cattle an optimal model to look for signatures of recent positive selection.While for genetic model organisms very powerful genomic tools are available, these species frequently lack phenotypic records to link signatures of selection in the genome to actual variation in phenotype unless a huge additional phenotyping effort is undertaken. This is the great advantage of using livestock species, as numerous production- and fitness traits are routinely recorded and used in breeding value estimation.The estimated breeding value (EBV) expresses the genetic merit of a breeding animal estimated based on their own performance and performances of all available relatives. In the case of dairy bulls this typically includes hundreds to thousands of daughters. Furthermore EBVs are corrected for systematic environmental effects. Therefore the breeding value of an animal is the sum of its genes' additive effects based on Fisher's infinitesimal model , which aSince Sax's experiments with beans in 1923 -26 for aRapid improvements in high throughput SNP genotyping technologies and commercially available high density SNP arrays for livestock species allowed livestock geneticists to turn towards whole genome association (WGA) mapping approaches in the recent past e.g. -29 or sePopulation genetics provides information that is independent of phenotypic information on putative loci under strong directional artificial or natural selection. We show in this paper that combining a population genetics signal with association tests based on quantitative genetics in a composite statistic, increases power and reduces the number of false positive signals for localizing the source of selection.ST values the combination with association results is not straightforward. Akey at al. )i βis the effect of the i-th SNP (i from 1 to N),Var(iβ) the variance of the estimate and 0.456 the median of the with ribution .Recently, linear mixed models were proposed to effectively account for different levels of relatedness by incorporating pairwise genetic relatedness into the model . This apWe therefore employed the following single locus mixed model which we term "MIX" that explicitly models the polygenic relationships among inviduals, asGσa2) being the vector of polygenic effects, σa2 the additive genetic variance and G the genetic covariance matrix and e ~ N , a vector of residual effects. G was obtained from pairwise identical by descent (IBD) estimates using genome wide SNP data as implemented in PLINK 45] show P - Value < 0.001, < 0.005 and < 0.01 are relatively smaller for SNPs with low minor allele frequency. This tendency cannot be seen as clearly for the iHSVoight test statistic. For SNPs with MAF ≥ 0.20 we see that our iHS test yields a higher proportion of significant loci when compared to traditional iHSVoight.Figure Voight and iHS, respectively. Figure Voight.Figures Voight.Figures Our permutation based standardization allows a formal test against the null hypothesis of neutrality at a core SNP (expectation zero). Our standardization is against 1000 permuted test statistics at the same locus in the same LD background. We therefore do not need to define the state of ancestral and derived allele.P - Values for iHSVoight and iHS, respectively.Additional file Voight and iHS for each autosome except BTA 6 are shown in Additional file Manhattan plots for iHSVoight and iHS, respectively.Among the 34,851 SNPs tested genome wide 1,710 and 1,621 SNPs had a test statistics > |1.96| with method iHSDistribution among chromosomes is remarkably uneven: BTA 5, 6, 12, 19 harbor 148, 124, 98, 89 sites, respectively which corresponds to 8 - 11% of all investigated SNP on the corresponding chromosomes that show significance applying iHS. On other chromosomes, namely BTA 28 and 17 ~ 1% of investigated SNPs exhibit significant selection signatures.Voight BTA 5, 6, 12, 16 and 19 have 171, 131, 148, 136 and 112 SNPs that show an iHSVoight test statistic > |1.96| which corresponds to 8 - 14% of all SNPs on these chromosomes. BTA 7, 25 and 27 have only around ~ 1% sites with extreme iHSVoight test statistics.The same is true for iHSDGAT1 K232A polymorphism, located at 0.444-0.447 Mb, with strong effects on milk production traits  provides considerably weaker support. Hence, this might illustrate the increased power of our modified iHS as compared to the iHSVoight.One particularly illustrative example is given by SNP Hapmap52798-ss46526455 located in the proximal region of BTA 14 at 0.565311 Mb . In thets  albeit the near fixation of allele A in this breed. So it is likely that the selection signal that is picked up by iHS is not purely for the DGAT1 p.K232A polymorphism but for the proximal region of BTA14 as a whole including the VNTR polymorphism in the promoter region of the DGAT1 reported by  in windows of 500 kb in Brown Swiss, x in any of the other breeds investigated; Plot A: iHSVoight test statistics, blue line: threshold identifying the top 5%; B: iHS test statistics, blue line: threshold identifying the top 5%; C: combined iHSVoight and WGA results with model MIXstrat, D: combined iHS and WGA results with model MIXstrat; blue line is a at 10% false discovery rate threshold.Click here for fileSupplementary Figures S34-S36. The PDF shows Figure S34: Histogram of allele substitution effects from whole genome association study employing model MIXstrat in kilogram protein yield; Figure S35: Histogram of Stouffer's P - Values of combined model MIXstrat and iHSVoight test statistics; Figure S36: Histogram of Stouffer's P - Values of combined model MIXstrat and iHS test statistics.Click here for file


Recent technological advances have allowed us to study individual genomes at a base-pair resolution and have demonstrated that the average exome harbors more than 15,000 genetic variants. However, our ability to understand the biological significance of the identified variants and to connect these observed variants with phenotypes is limited. The first step in this process is to identify genetic variation that is likely to result in changes to protein structure and function, because detailed studies, either population based or functional, for each of the identified variants are not practicable. Therefore algorithms that yield valid predictions of a variant’s functional significance are needed. Over the past decade, several programs have been developed to predict the probability that an observed sequence variant will have a deleterious effect on protein function. These algorithms range from empirical programs that classify using known biochemical properties to statistical algorithms trained using a variety of data sources, including sequence conservation data, biochemical properties, and functional data. Using data from the pilot3 study of the 1000 Genomes Project available through Genetic Analysis Workshop 17, we compared the results of four programs  used to predict the functional relevance of variants in 101 genes. Analysis was conducted without knowledge of the simulation model. Agreement between programs was modest ranging from 59.4% to 71.4% and only 3.5% of variants were classified as deleterious and 10.9% as tolerated across all four programs. Identification of genetic variation that affects human health has resulted in improvements in public health through the development of better treatments, diagnostics, and preventive strategies. One major source of this genetic variation is single-base changes in the DNA sequence, some of which lead to alterations in protein structure and function. These single-nucleotide polymorphisms (SNPs) occur in the genome approximately once every 1,200–1,500 base pairs and are nonrandomly distributed [Although it may be difficult to know with certainty the effect of a single amino acid change on protein function, some understanding can be gained through our knowledge of protein biochemistry or gene sequence itself. One sequence-based feature that can be examined is whether or not the sequence variant results in a codon change. If a nonsynonymous SNP results in a stop codon, then the variant is likely to be deleterious, unless this premature stop occurs near the end of a gene. The effect of an amino acid substitution can range from negligible to severe depending on the biochemical properties of the substituted amino acid. In addition to examining the amino acids found at the mutation site, one can use phylogeny to determine conserved sequences within a gene. Conserved sites are likely to have arisen because genetic variation in these regions is not tolerated across species as a result of strong negative selective pressure against variation in these regions. Thus variants that occur in nonconserved regions are more likely to be tolerated.In the past decade several programs have been developed with the goal of predicting whether or not an observed sequence variant is likely to have a deleterious effect on the protein product. These programs combine data from a variety of sources and use differing computational algorithms to estimate the probability that an observed genetic variant is deleterious. In this paper, we compare the findings of four nucleotide- and amino acid–based algorithms aimed at predicting the effect of an observed nonsynonymous sequence variation. We predict functional results for nonsynonymous SNPs using the nucleotide version of SIFT, PolyPhen-2, VarioWatch, and MAPP. Based on the predictions made by the various programs, we classify variants as deleterious or tolerated and calculate the agreement between programs.The data set consists of variants observed in the individual sequences as part of phase 3 of the 1000 Genomes Project, as released for Genetic Analysis Workshop 17 (GAW17). We selected 101 autosomal genes and analyzed only those nonsynonymous SNPs that caused a missense mutation in the subsequent amino acid. Overall, 3,781 nonsynonymous SNPs were identified within these genes. We identified amino acid changes using the ANNOVAR program [The functional analysis programs we examined  fall into two categories: nucleotide-based and amino acid–based. Some programs provide probabilities, whereas others classify their predictions into levels of functional threats. To carry out this analysis, we dichotomize predictions as either tolerated or deleterious. We also give an overview of each program.BRCA2 and MFTRR) by searching the literature.To compare our results to a gold standard, we examined the entire GAW17 data set to identify variants that had been previously analyzed in experimental models providing true functional results. Because the simulated GAW17 data do not represent true biological function, the simulated data could not be used as a gold standard. We identified functional variants by searching for information by gene name for each amino acid change in the Online Mendelian Inheritance in Man (OMIM) database  along wiBy querying data from five databases , VarioWatch provides functional annotation for SNPs. We used the following criteria, as implemented in the program: (1) whether or not the mutation affects the protein structure, (2) whether the SNP is in an exon-splicing enhancer or silencer, and (3) whether or not the SNPs abolish the protein domain . Once VaXenopus tropicalis). For this analysis, the severe and moderate levels were classified as deleterious, and the minor level was classified as tolerated.Multivariate Analysis of Protein Polymorphism (MAPP) is a phylogeny-based Java-script program that estimates the average deviation from six physiochemical properties  at an amino acid position across species to assess the effect of a substitution at a particular amino acid site . ProvideThe Sorting Intolerant from Tolerant (SIFT) program is a tool based on sequence homology and physiochemical properties that detects variation from the alignment and gives a probability score of the mutation being deleterious as a measure of a substitution affecting the protein function ,8. A semThe PolyPhen-2 program predicts the effect of a variant using eight sequence-based and three structure-based features previously selected by an iterative greedy algorithm ,10. SequWe obtained moderate levels of agreement for the four programs, with agreement ranging from 59% to 71% , only 54.6% were also classified as deleterious by MAPP. Overall, 135 (3.57%) variants were called deleterious by all programs, whereas only 414 (10.9%) were classified as tolerant by all four programs. Of the 3,781 variants examined, 876 were classified as deleterious by three or more programs, and 1,274 were classified as tolerated by three or four programs. This suggests that these programs are more likely to agree on benign variants than on potentially damaging variants.We also saw trends in the leniency of the predictions across the 101 genes, with some programs categorizing fewer variants as deleterious compared to others. To further examine the correlation between programs, we calculated the proportion of variants classified as deleterious per program. In addition, we examined the conditional probability that a variant classified as deleterious by a given program would also be classified as deleterious in a second program. For example, in Table We next compared the predictions obtained from these four programs to the results of the functional studies conducted on the 15 genetic variants for which functional data were available. Of these 15 variants, 13 had been demonstrated to result in a loss of function. However, only 6 of these 13 loss-of-function mutations were predicted to have a deleterious effect on protein function by all four programs  and its association with an increased risk for spina bifida. Fibroblast lines were isolated from patients with homocystinuria to isolate and identify mutations in the MTRR gene using reverse transcription polymerase chain reaction (PCR). Wilson and colleagues found that rs1801394 has the substitution I22M, or I49M, depending on the isomer. A population of individuals with spina bifida and their mothers was analyzed for the presence of this mutation. Two copies of the mutation in mothers resulted in 5 times the risk of their child having a neural tube defect compared to mothers lacking two copies of this mutation. This mutation was predicted to have a deleterious effect in all the programs  to determine the location of each variant. However, this assumption resulted in several instances in which the variant provided was identical to the reference allele according to the dbSNP database, showing inconsistencies between the reference sequence for this data set and the substitute reference sequence used from the UCSC Genome Browser. To determine the amino acid changes, we used coordinates of the nucleotide variant that were taken from the GAW17 data, and the nucleotide and amino acid sequences were taken from the CCDS sequence. We also likely missed functional variants, given the search terms in OMIM used to identify functional variants.The four programs compared in this analysis  differ greatly in their predictions of the probability that a SNP variant will be functionally deleterious. When selecting variants for experimental or functional follow-up study, a conservative approach, such as SIFT, should be used to minimize false positives. However, if the program is being used to select variants for further statistical analysis, a less conservative approach, such as PolyPhen-2, may be preferred to capture all possible deleterious variants. It is important to note that these programs have different methods and discordant results, so we do not recommend using any program as a true substitute for functional assays.The authors declare that there are no competing interests.AJ, GW, AC, AG, AM conducted primary analysis of the data. PD and APK conceived of the study and participated in its design and coordination. AJ, GW, PD and APK drafted the manuscript. All authors participated in edited and final approval of the manuscript.


Although longevity mutants discovered in the lab are instructive of cellular physiology, lab studies might miss important genes that influence health and longevity in the wild. C. elegans has relatively low natural genetic variation and high levels of linkage disequilibrium, and thus is not optimal for studying natural variation in longevity. In contrast, its close relative C. remanei possesses very high levels of molecular genetic variation and low levels of linkage disequilibrium. To determine whether C. remanei may be a good model system for the study of natural genetic variation in aging, we evaluated levels of quantitative genetic variation for longevity and resistance to oxidative, heat and UV stress. Heritability (and the coefficient of additive genetic variation) was high for oxidative and heat stress resistance, low (but significant) for longevity, and essentially zero for UV stress response. Our results suggest that C. remanei may be a powerful system for studying natural genetic variation for longevity and oxidative and heat stress response, as well as an informative model for the study of functional relationships between longevity and stress response.Genetic approaches ( Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (soil-dwelling roundworm), and Drosophila melanogaster (common fruit fly) 2e.g. h = 0.03 2h = 0.73 Current understanding of the genetic basis of the aging process has been formed mainly by mutagenesis and RNA interference studies in the model systems C. elegans has been a workhorse in the genetics of aging: mutagenesis and gene expression studies in C. elegans have facilitated a detailed understanding of many longevity genes and their roles within key metabolic pathways C. elegans over D. melanogaster is that it can be frozen, allowing parental and descendant generations to be tested at the same time and allowing large numbers of lines to be maintained at minimal effort. Despite these advantages, the C. elegans model is not optimal for studies of natural genetic variation. Recently several studies have shown that C. elegans populations possess low levels of molecular genetic variation on a worldwide scale C. elegans, but published estimates are not significantly different from zero C. elegans will serve as a poor model for studying variation in aging within natural populations C. elegans, C. remanei, may be an ideal system in which to study natural genetic variation. Unlike the low genetic variation found within the selfing C. elegans, populations of C. remanei harbor significant amounts of molecular genetic variation C. elegans, C. remanei is composed of males and females, making it a better model for human aging. It is a non-parasitic, soil-dwelling roundworm that is easily collected in the wild. Significantly, it can be frozen within just a few generations of collection, thereby maintaining a majority of the natural genetic variation present in the population when in its natural habitat. Like C. elegans, its genome has been sequenced, it has short generation times (∼4 days), can be cultured at large population sizes, can be genetically transformed, and can be frozen, giving us the ability to compare evolved or manipulated genetic lines to the original ancestral population.In contrast, a close relative of C. remanei for a variety of molecular pathways C. remanei might possess the genetic variation for longevity and stress resistance necessary to make it a good model system for the study of natural variation in aging. Specifically, we looked for evidence of significant natural genetic variation for longevity and acute heat, oxidative and UV stress resistance in a genetically heterogeneous population of C. remanei. We found high heritability for oxidative and heat stress resistance, low but significant heritability for lifespan, and no heritability for UV stress resistance. These results suggest that C. remanei may be a good model in which to look for natural genetic variants increasing health and lifespan. In particular, it appears promising for investigations into the complex relationship between stress response and longevity.Given the large amount of molecular genetic variation previously found in C. remanei were isolated from terrestrial isopods (woodlice) from a single population near Dayton, Ohio and generously supplied to us by Scott Baird (Wright State University) with the permission of the State of Ohio Department of Natural Resources  was frozen at −80°C for future use . To measure heritable genetic variation for longevity and traits that may be correlated with longevity, we employed half-sib, strain-based, and parent-offspring regression approaches to measure genetic variation and estimate trait- and sex-specific heritability for various traits. All strains were thawed and given two generations to recover from freeze before collecting experimental individuals. Strains were maintained on NGM-Lite agar plates seeded with Escherichia coli (OP50) as a food source. All populations were housed in an incubator at 20°C after thaw and throughout the experiment.Wild To obtain stage-synchronized individuals for inbred strain-based measures of genetic variation, we randomly collected one female and one male fourth stage larvae  from each of ten isofemale strains . These mating pairs were given 24 hours to sexually mature and mate. Females were then moved to individual plates and allowed to lay eggs for 6–24 hours, after which all adults were discarded. Offspring were collected as L4s within a single six-hour window, and placed on seeded plates in groups of 10–15 overnight. 20 to 24 hours later the offspring had become young adults and were then used in either a heat shock or a longevity assay as described below.For each assay, ten males (sires) were collected as L4s from the genetically heterogeneous PX323 stock. We housed each sire on his own small (35×10 mm) plate with three to five randomly selected L4 females (dams) from the same stock. Each sire was given 24 hours to sexually mature and mate with dams. We then placed each dam on her own small plate and allowed her to lay eggs for 24 hours, after which she was discarded. Because each dam mated with only one sire, all offspring from a single dam were full sibs; offspring from different dams mated to the same male were half-sibs. From each dam, we collected eight to 16 full-sib L4 male and female offspring and placed them on seeded NGM plates in same-sex groups overnight. 20 to 24 hours later those offspring had become young adults and were then used in either an oxidative or UV shock assay as described below. For each sire, the offspring from three separate dams were tested .We randomly collected 120 L4 females and 360 male L4s  from the genetically heterogeneous PX323 stock. Each female was placed with three males. These mating groups were given 24 hours to sexually mature and mate, after which all adults were discarded. Female offspring (dams) were collected as L4s within a single six-hour window. Each dam was given three L4 sires (randomly selected from the PX323 stock) with which to mate. 24 hours later sires were discarded and dams were moved to new plates daily until reproduction had ceased, then once per week until death by natural causes. Three daughters were collected per female. Each L4 daughter was given three L4 males (randomly selected from the PX323 stock) with which to mate. 24 hours later, males were discarded and daughters were moved to new plates daily until reproduction had ceased, and then once per week until death by natural causes. Under this design, daughter lifespans  were regressed against dam lifespan.We estimated genetic variation for longevity using both strain-based and parent-offspring regression methods. For both methods we maintained worms individually on small NGM-Lite plates that were seeded with OP50. All worms were transferred to freshly seeded plates at weekly intervals. Longevity was determined by checking each individual daily at the same time of day. Worms were declared dead if they did not respond to being prodded with a platinum wire.N per strain: females = 35.9, males = 28.9. To confirm our strain-based estimate of heritability we performed a parent-offspring regression on the data collected as described above. We performed a general linear regression of daughter lifespan against parent (dam) lifespan. All lifespan data were log10 transformed to normalize residuals, and outliers were removed, N = 73 families. Standard errors on the estimates and significance tests were conducted via bootstrap analysis We performed statistical analyses using JMP 8.0 and SAS 9.2. We performed extensive preliminary testing to determine a temperature and length of exposure such that the genetically heterogeneous strain  experienced approximately 50% survival. We observed that thermal maximum for males is at least one full degree Celsius lower than that for females. Within sex, a change in temperature of half a degree made the difference between 80% and 0% survival (unpublished data). Therefore, we chose to test only females at this time.N) per strain = 8. Three of ten strains had fewer than six replicates and were therefore excluded from the data set during analysis.L4 females were collected from ten of the nineteen isofemale strains  as described above, and housed in same-sex groups of 10–12 on small, seeded NGM plates for 24–30 hours. We then placed them in strain-specific groups of 60 one-day-old virgin adults onto large, seeded NGM plates and exposed them to 35.5°C for 16 hours. We then moved the worms to 20°C and allowed them to recover for three hours before scoring plates for live and dead worms. Genetic variance for heat stress resistance ability was determined using an analysis of variance with random effects of strain and replicate. Heritability was calculated as for lifespan. Data were square root transformed to meet distribution assumptions for the analysis of variance. Average replication  or 40 µL of S-basal (controls). Worms were checked every 30 minutes until all experimental individuals failed to respond to prodding with a platinum wire for three continuous samplings (scored dead for 90 minutes). Estimates of genetic variance for oxidative stress resistance ability was determined using analysis of variance model with random effects of sire, dam within sire, and replicate. Heritability was calculated as We placed one-day-old virgin adults into either 40 µL of 3.5 mM HPreliminary tests suggested that moderate differences in resistance among PX323 families would be detectable if UV exposure-induced mortality caused 100% death within one to two weeks. As for oxidative stress resistance, males were less resistant to UV stress than were females, but the difference in resistance between sexes was not extreme, therefore we tested both sexes using a single duration of exposure to UV irradiation.2 UV irradiation (FisherBiotech Microprocessor-Controlled UV Crosslinker FB-UVXL-1000 using 254 nm UV tubes). Worms were immediately picked to large seeded plates and checked daily until all worms failed to respond to prodding with a platinum wire. Estimates of genetic variance for UV stress resistance ability was determined using analysis of variance with random effect of sire, dam within sire, and replicate. Heritability was calculated as for oxidative stress response.We placed one-day-old adults with their same-sex full sibs onto large unseeded plates and exposed to 2000 J/mACV) was calculated as μ is the mean of the phenotypic response 2h values was accomplished by bootstrapping the data and evaluating the extent of the overlap of the resulting distributions was via a Mann-Whitney U test.For all traits, the coefficient of additive genetic variance  was not significantly different among offspring of different sires . Despite some differences in age-specific mortality  as was age-specific mortality , and high heritability for two stressors hypothesized to be linked to longevity: heat and oxidative stress resistance. Heat, oxidative and UV stress resistance are all traits previously correlated with longevity in Age mutants ACV for longevity were low in this population, they are non-zero, and similar to published estimates for other species C. remanei was more than 11-fold that for longevity  use a limited number of common cellular pathways  and that those cellular responses are controlled through a likewise limited number of transcription factors . Indeed, the cellular response to any single stressor appears to be achieved through pathways at least partially overlapping cellular responses to other stressors Although UV stress resistance is typically correlated with both extended lifespan and oxidative stress resistance in Age mutants C. remanei, lifespan under relatively benign conditions (perhaps most relevant to current human lifespan) is not determined by the same suite of genes as is resistance to oxidative stress in the environmental context of this study.One possible reason for the difference in heritability estimates for longevity and oxidative stress resistance might be that longevity was measured in an artificially benign environment with regard to oxidative challenge. In a more naturalistic spatial and nutritional environment, oxidative stress resistance may indeed determine lifespan. Empirical studies have measured the heritabilities of traits in both stressful and non-stressful environments and found that heritabilities of some traits are higher under stressful conditions C. remanei, but that the relationship between stress resistance and longevity must be complex and environmentally contingent, if it exists at all. This illustrates that stress and longevity effects observed via mutagenesis may not always be reflected in natural variation; understanding the molecular basis of that variation should be highly informative of molecular mechanisms of longevity in its own right. C. remanei should provide a powerful system for such analyses.Overall, this study demonstrates that there is significant genetic variation for stress-related phenotypes within Figure S1Mean survival time (in days) of C. remanei daughters regressed upon lifespan of mothers. All worms were mated to three male C. remanei for 24 hours, and kept under typical laboratory conditions .(PDF)Click here for additional data file.Table S1C. remanei under typical laboratory conditions.Lifespan data (in days) for virgin  .(PDF)Click here for additional data file.Table S2C. remanei exposed to 35.5°C for 16 hrs.Percent survival of female  We used 60 one-day-old virgin adults per trial.(PDF)Click here for additional data file.Table S3C. remanei exposed to 3.5 mM H2O2 until death. N is number of offspring tested.Mean survival time (in minutes) of half-sib families of (PDF)Click here for additional data file.Table S4C. remanei exposed to 2000 J/m2 ultraviolet irradiation.Mean survival time (in days) of half-sib families of (PDF)Click here for additional data file.


Advances in genome sequencing are providing unprecedented resolution of rare and private variants. However, methods which assess the effect of these variants have relied predominantly on information within coding sequences. Assessing their impact in non-coding sequences remains a significant contemporary challenge. In this review, we highlight the role of regulatory variation as causative agents and modifiers of monogenic disorders. We further discuss how advances in functional genomics are now providing new opportunity to assess the impact of rare non-coding variants and their role in disease. The extent of which was highlighted by two recent exome-sequencing that uncovered as much as 95% of protein-coding variation is rare (MAF ≤ 5%) and among these rare variants are the majority of sites predicted to be deleterious  mapping requires three important steps:Data normalization:Normalization is critical to mitigating the influence of technical artifacts or “batch effects” . Generally, the expression trait is assumed to be additive where each allele contributes equivalently. For population data, the most commonly used approach is a single-marker to single-trait linear or non-parametric regression; this analysis can be performed by statistical packages like R or statistical genetics tools such as PLINK . However, the size of the selected interval and the number of genes tested can be as influential as differences in sample size and number of variants tested. Complementary approaches have aimed to increase statistical power by integrating information from genes sharing regulatory pathways  analyses, many of the same limitations as present in GWA studies are equally applicable – common covariates such as ethnicity, relatedness, age and sex are also confounding factors in eQTL studies and should be appropriately accounted.Despite the availability of tools and simple statistical methods that support eQTL discovery, there remain important caveats in their utilization. For instance, considerable statistical power advantages can be achieved by restricting tests to variants nearby a gene  or by generating synthetic genotypes and phenotypes . Using a permutation approach, a false-discovery rate can be estimated.Given the large numbers of tests and a typically small sample size, multiple testing correction is critical for eQTL mapping. Commonly used methods are Bonferroni correction, FDR/q-value   can provide a comprehensive survey of transcription factor binding sites across the genome. ChIP-seq studies comparing related and unrelated individuals . However, robust calls of ASE typically require heterozygous sites to be identified through genotyping or DNA sequencing. Genotypes may be directly observed from RNA data, however due to systematic variation of read depth of RNA (since transcripts are expressed at different levels and have different sizes) and reference genome mapping biases (due to unobserved variants or homologous sequences) such approaches are error prone (Degner et al., cis-eQTL (Sun, Allele-specific expression has also been shown to be a heritable trait under genetic control through family studies (Yan et al., QTL Sun,  and causQTL Sun, . For proMost early attempts to connect expression to phenotypic traits in families have involved using standard Pearson correlation between the expression and trait measurements. However, despite the statistical simplicity of this methodology, it can inflate significance when there are few families and the trait and expression variance has a large between family component, and can reduce significance if there is a large within family component and no correlation between families. To address this, expression trait concordance tests which account for family structure have been reported (Kraft et al., When combining genetic data to search for concordance of expression and trait association, an advantage is that the component of expression due to technical or environmental perturbation can be better controlled. We have previously reported methods that connect genetic effects on gene expression in unrelated samples to trait values by assessing if the genetic perturbation on expression is similar to the distribution of association scores for a trait (Nica et al., de novo mutations on the correct haplotype background and aid in investigations of genetic interaction. Furthermore, advances in ChIP-Seq and RNA-Seq will better aid in characterization of genetic effects on transcription factor binding and isoform expression and will ultimately unlock more complex functional interactions that underlie the etiology of diverse traits.As genome studies have been identifying large numbers of rare variants, it is expected that new methods and data will be required to uncover the impact of these variants and their involvement in diseases and traits. One of the most promising future technologies which will aid in interpreting the effect of rare regulatory variants in different tissues and developmental stages will be through the generation of induced pluripotent cells where gene expression can be assayed in multiple stages and differentiated cell types. Complementing this will be further advances in sequencing methods which provide phasing information – these advances will position rare and The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Drosophila melanogaster. Temperature during ontogenesis is an important environmental factor affecting development and phenotypic variation in holometabolous insects. In spite of the importance of phenotypic plasticity and genotype by environment interaction (GEI) for fitness related traits, its genetic basis has remained elusive. In this context, we analyzed five different adult morphological traits  in 42 co-isogenic single P-element insertional lines of Drosophila melanogaster raised at 17°C and 25°C. Our analyses showed that all lines differed from the control for at least one trait in males or females at either temperature. However, no line showed those differences for all traits in both sexes and temperatures simultaneously. In this sense, the most pleiotropic candidate genes were CG34460, Lsd-2 and Spn. Our analyses also revealed extensive genetic variation for all the characters mostly indicated by strong GEIs. Further, our results indicate that GEIs were predominantly explained by changes in ranking order in all cases suggesting that a moderate number of genes are involved in the expression of each character at both temperatures. Most lines displayed a plastic response for at least one trait in either sex. In this regard, P-element insertions affecting plasticity of a large number of traits were associated to the candidate genes Btk29A, CG43340, Drak and jim. Further studies will help to elucidate the relevance of these genes on the morphogenesis of different body structures in natural populations of D. melanogaster.Understanding the genetic architecture of any quantitative trait requires identifying the genes involved in its expression in different environmental conditions. This goal can be achieved by mutagenesis screens in genetically tractable model organisms such as  Drosophila melanogaster. In fact, quantitative genetic analysis of the effects of P-element mutations induced in an isogenic background P-element insertion lines raised at 25°C Understanding the genetic architecture of a quantitative trait requires identifying the genes implicated in its expression in different environmental conditions DrosophilaDrosophila’s wing as a model and, while some of them showed that thermal changes affected wing shape predominantly in the posterior compartment, most of them showed that wings elongated disproportionately as temperature decreased One of the most important environmental factors affecting body size in ectothermic animals is temperature Drosophila’s wing Most of the mentioned work has been done using isofemale lines raised at different temperatures or flies derived from natural populations distributed along geographic gradients . Regarding morphological traits, only a recent work has addressed the effect of genetic and environmental manipulations in The phenotypic response to a change in an environmental variable  may vary among genotypes which may be manifested as a genotype by environment interaction (GEI) et al.In this article, we studied different morphological traits in 42 mutants that have been previously analyzed under a different thermal treatment p[GT1]-element insertion lines, constructed in a coisogenic Canton-S background Drosophila Stock Center (Berkeley Drosophila Genome Project website. Available: http://www.fruitfly.org. Accessed 2013 July 3), represent a random sample of the 191 lines that have been used to study the same traits at 25°C P-element insertion free line with the same genetic background) was run in parallel with each batch.We used 42 independent homozygous viable single For each temperature, 300 pairs of sexually mature flies corresponding to each line were placed for 8 hours in separate oviposition chambers. Eggs were allowed to hatch and batches of 30 first-instar larvae were transferred to culture vials containing a standard cornmeal-agar-molasses medium (4 replicates per mutant line and 4–8 replicates for the control for each temperature). Larvae were raised at controlled temperature (4–8 replicates per line at 17±1°C and 4–8 replicates per line at 25±1°C) and 60–70% of humidity with a 12∶12 light:dark photoperiod until adult emergence. All adults emerged from each vial were preserved in a freezer at −20°C until quantification of morphological traits was performed.Five flies of each sex were randomly chosen from each vial  and the head, the thorax and the wings of each individual were removed and placed on a slide conserving their relative position. Separate images for 3-D structures  and flat structures  were captured using a binocular microscope (10×) and an attached digital camera connected to a computer. Different morphological traits were estimated using tpsDig p[GT1] insertions) between the mutants and the control in body size related traits. For WSh, one MANOVA was conducted with the RWs scores corresponding to each line and the respective control in males and females separately. Those lines that exhibited significant differences relative to the control were considered as lines bearing an insertion in a candidate gene. In order to identify these genes, we conducted homology searches of sequences flanking the P-element insertion against release 5 of the published D. melanogaster genomic sequence (http://flybase.bio.indiana.edu/). Only the gene nearest to the insertion was selected as candidate gene, except in those cases in which two genes were closer than 1 Kb to the P-element insertion site and neither disruption occurred in the gene.Dunnett contrasts were performed in males and females separately, to detect significant differences  in the same analyses, the values corresponding to each variable were transformed by subtracting from each individual value the mean value of the respective control line and dividing it by the same value.r (correlation coefficient) values.A correlation analysis was performed between each pair of size traits within each sex and with each variable between sexes. The mean of the values corresponding to each line was used in all correlation analyses. In addition, we carried out Mantel tests to compare correlation matrices between temperatures for each sex separately using Infostat Differences in wing shape between each mutant line and the respective control were estimated performing a thin-plate spline analysis using the respective Procrustes coordinates Transformed values (see above) corresponding to each univariate variable  were analyzed using an ANOVA with line (random) and temperature (fixed) as factors in each sex separately. This procedure allowed us to estimate variance components corresponding to random sources of variation and, consequently, the percentage of total variance explained by the genetic factors (line and line by temperature interaction).GEIV = [(σE1 − σE2)2+2×σE1×σE2× (1 − GEIr)]/2; where GEIV is the GEI variance component, GEIr is the cross-environment genetic correlation and, σE1 and σE2 are the square roots of the among-lines variance components at 17°C and 25°C (which were obtained after performing ANOVAs for each temperature separately). The first term corresponds to differences in among-lines variance whereas the second to deviations from the perfect correlation across environments (GEIr <1). The cross-environment genetic correlation is the genetic correlation of measurements of the same trait in different environments and here reflects the degree in which the same genes control trait expression across temperatures. GEIr was estimated for each trait as: GEIr = COVE1 E2/σE1 σE2; where COVE1E2 is the covariance of lines means for each sex measured in different temperatures.A significant GEI can arise as a consequence of differences in among-lines variance in separate environments  and/or deviations from unity of the cross-environment genetic correlation (change in ranking order). The contribution of the two sources of variation to GEI was analyzed by means of the equation derived by Robertson Finally, we studied the phenotypic effect of thermal change in each mutant, sex and trait separately with a fixed ANOVA. Those lines that exhibited significant differences between temperatures were considered as lines bearing a mutation in a candidate gene involved in the plastic response of the respective character to temperature variation. A homogeneity test was conducted to compare the number of candidate lines associated to the plasticity of each trait between sexes.In general, statistical analyses were performed using the STATISTICA software package The analyses revealed that all 42 lines differed from the control for at least one trait in either sex at 17°C . FurtherP-element within 5 Kb from the transcription initiation site , BG02462 (CG34460) and BG02830 (Lsd-2), which showed at least 13 significant differences considering all traits in both sexes and temperatures (We identified 46 candidate genes (39 protein coding genes) based on insertion of the ion site . Nine oferatures .As the effects of mutations on size traits were apparently different between 17°C and 25°C  and S2, In general, transformed values in flies raised at 25°C were larger than in individuals grown at 17°C . ConsideGxEr) between measurements of each trait in different environments  suggesting that the genetic basis underlying plasticity for these characters is sexually dimorphic.We identified 39 lines displaying a plastic response for at least one morphological trait in either sex . AccordiP-element insertion ocurred in Drak (jim) as well as BG01354 (CG43340) displayed a plastic response for all five morphological traits whereas BG01290 (Btk29A) showed differences between temperatures in four of them .The analyses revealed that the control line tended to grow significantly more than the mutant lines at 17°C, while the control flies reared at 25°C showed sizes lying in the middle of the range of genotypic effects. This is interesting, especially because the control line was raised together with 20 lines at both temperatures at the same time, giving support to the idea that the insertions effect could be responsible for the plastic response. However, it should be noticed that the control line has been kept for 10 years as an isogenic stock and, during this period of time, it might have accumulated novel mutations affecting its thermal plastic response. In spite of this, our observations are remarkably similar to those made by Debat Drosophila for different traits, including the morphological ones Analyses of GEI showed that the genetic correlation between the values of each trait measured at different temperatures was relatively low indicating that a moderate number of genes are associated to variation of each character at both temperatures. The type of GEI observed indicate that, if natural populations present analogous genetic variants, selection might favor different genotypes in environments with distinct temperatures contributing to maintain intra-specific genetic variability D. melanogaster population under 20 different environments CG13333, which was assigned to Class II in males and, on the other side, Hsp 27, CG17574 and Lsd-2, which were nominated as Class I genes in both sexes Lsd-2 was the only one which was associated with developmental time and starvation resistance Spn and CG34460, the other most pleiotropic genes for morphological traits. These observations are somewhat in line with preliminary results that showed that Lsd-2 present low nucleotidic polymorphism as well as little genetic differentiation among natural populations of D. melanogaster . Furthermore, an exploratory analysis of the sequence of this gene in different Drosophila species indicates that its evolution has occurred according to the postulates of the neutral theory  which is in agreement with recent works that did not find evidence of positive selection for Lsd-2 in D. melanogasterResults of the line-specific analyses suggest that most genes involved in the genetic basis of morphological traits cause disparate phenotypic effects at different temperatures  although these effects are highly trait- and sex-specific. This seems to contradict results derived from a recent study of variation in genome-wide gene expression of an outbred Btk29A, CG43340 and jim in females; the unidentified gene affected by the P-element insertion in BG02239 in males and Drak in both sexes. It would be interesting to investigate if these genes also present important nucleotidic variability in natural populations and thus are less conserved than the others. However, some genes might present high levels of genetic variability for a trait and, concurrently, low levels of plasticity for it which might indicate that the character is subjected to environmental canalization. Studies on the molecular genetics of populations of these candidate genes might help to clarify this and other issues regarding evolution of morphological traits.On the contrary, the few genes that affected plasticity of a large number of traits did not affect many characters in each temperature. This is the case of Figure S1Head and thorax of a fly and related morphological traits. Picture showing the positioning of 3-D body structures on a slide and related measurements taken with tpsDig.(TIF)Click here for additional data file.Figure S2Ventral view of left wing and positioning of landmarks. LV: longitudinal vein, HCV: humeral cross vein, ACV: anterior cross-vein, PCV: posterior cross-vein.(TIF)Click here for additional data file.Figure S3Lines showing wing shape deformations at both temperatures in females. Lines showing significant wing shape deformations with respect to the control line in females raised at 17°C (in blue) and 25°C (in red). The gene affected by the P-element insertion is shown between parentheses for each line. Arrows indicate the magnitude and direction of landmarks displacement with respect to the corresponding control line. Arrows size has been magnified three times to show more clearly wing shape changes.(TIF)Click here for additional data file.Figure S4Lines showing wing shape deformations at both temperatures in males. Lines showing significant wing shape deformations with respect to the control line in males raised at 17°C (in blue) and 25°C (in red). The gene affected by the P-element insertion is shown between parentheses for each line. Arrows indicate the magnitude and direction of landmarks displacement with respect to the corresponding control line. Arrows size has been magnified three times to show more clearly wing shape changes.(TIF)Click here for additional data file.Table S1Lines in which the P-element insertion affected one or more morphological traits in either sex at 17°C. The candidate gene, the site of the mutation and its morphological effect are given for each line.(PDF)Click here for additional data file.Table S2Principal results of genetic correlation analyses between body size related traits at 17°C and 25°C. Correlation coefficients corresponding to the analyses within each sex and between sexes for each variable are shown.(PDF)Click here for additional data file.Table S3Principal results of ANOVAs for morphological traits in each temperature and sex separately and analyses of GEI. The F values and the genetic variance components derived from the ANOVAs are shown. Also, the correlation coefficients and the components explaining the interaction between temperatures are given.(PDF)Click here for additional data file.Table S4Principal results of the ANOVAs performed to study the change of the phenotypic effect of the P-element insertion with thermal change in each line, sex and trait separately. The unsigned difference between the means of the transformed values at 17°C and 25°C for each morphological trait are given.(PDF)Click here for additional data file.