We examined how markers enter the model as penalties and P-value thresholds are varied, and report the sensitivity and specificity of each of the methods. Results show that penalized methods outperform single marker analysis, with the main difference being that penalized methods allow the simultaneous inclusion of a number of markers, and generally do not allow correlated variables to enter the model, producing a sparse model in which most of the identified explanatory markers are accounted for. Genet. Epidemiol. 34:879–891, 2010. © 2010 Wiley-Liss, Inc.Penalized regression methods offer an attractive alternative to single marker testing in genetic association analysis. Penalized regression methods shrink down to zero the coefficient of markers that have little apparent effect on the trait of interest, resulting in a parsimonious subset of what we hope are true pertinent predictors. Here we explore the performance of penalization in selecting SNPs as predictors in genetic association studies. The strength of the penalty can be chosen either to select a good predictive model , through maximum likelihood-based model selection criterion (such as the BIC), or to select a model that controls for type I error, as done here. We have investigated the performance of several penalized logistic regression approaches, simulating data under a variety of disease locus effect size and linkage disequilibrium patterns. We compared several penalties, including the elastic net, ridge, Lasso, MCP and the normal-exponential-γ shrinkage prior implemented in the  Regression methods are commonly used in statistical analysis, and the recent move to single marker testing in genetic association studies WTCCC,  has beenP-values at many loci correlated with a causal locus, making it difficult to localize the causal locus. Penalized methods are more particular, selecting only one or several variables per causal locus.As an illustrative example, see In the genetics literature, use of these kinds of approach is just starting to emerge. Ridge regression has been used for distinguishing between causative and noncausative variants for quantitative traits [Malo et al., P-value for a coefficient. This must be done through a procedure such as bootstrapping, and still does not reflect a true P-value in the usual sense due to the complex selection procedure for the reduced model. Wu et al. [Unfortunately, all penalization methods require specification of a penalization parameter (often referred to as the tuning or regularization parameter), and the parameter value yielding the optimal model is data driven. The choice of penalty parameter controls the number of variables selected: the larger the penalty, the smaller the selected subset. The value of the penalization parameter must be chosen, e.g. through cross validation, to avoid selection of a sub-optimal value. An additional problem with penalization approaches is that there are no efficient ways of obtaining a confidence interval or u et al.  have sugdetection of a relevant SNP or region, and second the issue of differentiation/localization i.e. fine mapping within a region and distinguishing those variants that best explain the association and are thus most likely to be causal, or be in strongest LD with the causal variants. These two separate questions have not always been clearly distinguished in the literature—for example, Malo et al. [detection of effects, not for differentiation of effects, and so such an observation should actually have counted towards the power.)Given the current interest in these types of method, the objective of this study is to evaluate the performance of several software packages for performing penalized logistic regression, using computer simulations. We focus on two different problems: first the issue of o et al.  comparedP-values are varied, and report the sensitivity and specificity for each of the methods.We simulate data under a variety of disease locus effect size and linkage disequilibrium patterns. We compare several penalties, including the elastic net, the Lasso, a pseudo-ridge, the minimax concave penalty (MCP) and the NEG shrinkage prior to standard single locus analysis (the Armitage Trend Test) and simple forward stepwise regression. Software packages were selected for ease of use and to cover a wide range of penalty functions. We explore how markers enter a model as penalties or Y of n observations, and a matrix of SNP genotypes X, let p = P(Y = 1∥X = x). The likelihood is:Binary traits such as case/control status are generally analyzed using logistic regression. Given a dichotomous phenotype vector whereor equivalently,and β is our vector of coefficients.In genetics, the advent of large-scale genotyping has lead to underdetermined problems where the number of markers is much larger than the number of individuals. In this case, standard logistic regression cannot produce a unique interpretable model. Penalized likelihood methods maximize the loglikelihood subject to a penalty which is dependent on the magnitude of the estimated parameters. A penalty on the likelihood will penalize models which have a large number of large regression coefficients, and thus will be optimized with a sparser model. In genetics, we have typically have many variables, but suspect that there are only a few underlying causal variants. An ideal penalty would quickly weed out variables with little effect, with only the most relevant variables remaining in the model. Use of a selection criterion, such as the Bayesian Information Criterion (BIC), is a form of regularization with a penalty that relies only on the number of coefficients, but not on their magnitude.L1 penalty, was introduced in the form of the least absolute shrinkage and selection operator or Lasso constraint by Tibshirani in 1996. The selection process in the Lasso is based on constructing continuous trajectories of regression coefficients as functions of the penalty level, which results in a more stable solution than subset selection methods [Efron et al., penalized [Goeman, glmnet [Friedman et al., grplasso [Meier et al., The most well-known penalty,  Goeman, , glmnet L2 penalty) [Hoerl and Kennard, L1 and L2 penalties) [Zou and Hastie, Lq penalty for 1<q<2). Many recent methods strive to relieve some of this bias by introducing penalties with flatter tails, so that large coefficients are only minimally shrunk , the NEG prior implemented in hyperlasso [Hoggart et al., The Lasso will encourage sparsity, setting most small coefficients to zero, due to the penalty function's sharp peak at zero. However, given a sufficiently large penalty parameter, the Lasso will also impose heavy shrinkage on large coefficients due to the absence of tails , leading to biased coefficient estimates see . A similrunk see . These iIn penalized likelihood inference, our objective function may be written as:f is a function of the regression coefficients (and possibly a mixing parameter). The amount of shrinkage is directly controlled by the derivative of the penalty function, and many different penalty functions have been proposed. For example, the elastic net criterion is:where the penalty L1 and L2 norm, respectively. Here, λ controls the strength of the penalty while α is a mixing parameter that determines the strength of the L1 versus the L2 norm. The elastic net criterion above is reduced to the Lasso if we let α = 1. If α = 0, we have the L2 penalty used in ridge regression. The Lasso has been shown to be consistent for model selection under certain conditions given that the correlation between relevant (true) and irrelevant predictors is not too large [Zhao and Yu, n (the number of observations) nonzero parameters, and cannot have a unique solution if any two variables are completely collinear. The elastic net is a stabilized version of the Lasso; it encourages groups of correlated variables to enter the model together, and thus a small change in the data will not have a large effect on the model. In our investigation, we use the glmnet software by Zou and Hastie [where d Hastie , to impld Hastie , thus itgrpreg, and for our analyses we set a = 30, the default .The MCP [Breheny and Huang, hyperlasso [Hoggart et al., The method implemented in All penalized regression methods require input of one or more values for the penalization parameter(s). From here on we will refer to this parameter as λ. The parameter value must be selected  to avoid selection of a sub-optimal parameter value. In general, a very large value of λ will only allow a small number of variables to enter the model. If we choose the value too small, the number of variables in the model may be too large, and our coefficients become less reliable because of their high variances; we approach standard regression with most of the variables included the model and overfitting occurs. The best choice of λ is data dependent and may vary, for example, from chromosome to chromosome, or window to window, within the same data set.P-value threshold for declaring significance is relaxed. Penalized methods tend to select only one or few variables belonging to a group of correlated variables, resulting in a sparse model. The question remains: are they missing anything? In our investigation we vary the value of λ to allow approximately only one additional variable enter the model at each step. We record the number of true positives and false positives at each value of λ for each method, along with the maximum LD between a false signal and a causal locus and the maximum LD between a missed causal locus and a signal.Due to the difficulties in finding an optimal value for λ, we found it most insightful to look at how variables enter a model as the penalization parameter is relaxed, or in nonpenalized methods as the P-value threshold for declaring significance. In FSTEP, at each step the variable with the smallest P-value is added to the model, until no more variables reach the required threshold. We compare methods first with respect to detection of effects, in which detection of an allele in LD (r2>0.05) with a true causal variant counts as a success , and second with respect to localization/differentiation, in which we only count detection of the true causal locus itself as a success.We used computer simulation to compare five penalized methods , ridge, MCP and NEG) to the Armitage trend test (ATT) and simple forward stepwise regression (FSTEP). All methods were run under the assumption of an additive allelic disease model. For the ATT, we look at how variables become significant as we increase the Following the approach of Hoggart et al. , we usedglmnet and grpreg standardize the genotypes to have mean zero and variance 1 by default, and thus for comparability we also standardized the genotypes for the NEG and FSTEP. As the causal loci are not necessarily in the thinned SNP set, and our markers have low enough density that LD between them is fairly small, we report a true positive detection when a SNP of r2>0.05 with a causal variant is selected. A False-positive occurred when a SNP of r2≤0.05 was selected; however, a false positive was only recorded if it was more than 20 kb from a previously detected false positive, as was done by Hoggart et al. [Genotype variables were used as predictor variables in each of the methods. Both t et al. . This alCYP2D6, CFTR and CTLA4 containing 110, 190 and 228 SNPs, respectively. Although it would be possible to do standard logistic regression in this case , the fact that the genotype variables are highly correlated leads to estimates with large variances.We also performed simulations based on three known gene regions of varying LD patterns see : CYP2D6,With the software HAPGEN [Marchini et al., The simulated data were analyzed using the same five penalized methods as in Simulation Study 1. Since in this second experiment we were interested in the performance of the methods with respect to differentiation of SNPs, in common with Malo et al.  we countWith respect to detection, −5), one way to get a rough estimate of the appropriate λ is to use permutation. For Simulation Study I above, to calculate the appropriate value of λ in each replicate, we did 25 permutations of the case/control status and recorded the value of λ at which the first variable entered the model in each permutation. Since in each simulation replicate there are 4,000 markers and 25 permutations , we choose the largest value of the recorded λ from the 25 permutation replicates as an estimate of the λ that allows only one variable out of 100,000 markers to enter the model for that simulation replicate.  P-value cut off for the FSTEP and ATT  for this comparison was set at 10−5, which similarly produced a false-positive rate of the correct order of magnitude.The procedure described above estimates a  value of λ in each simulation replicate. A more accurate estimate of λ could be obtained by using all 500 simulation replicates, taking advantage of the fact that there should be a single value of λ that gives the same desired expected type I error in each replicate. This approach, however, could not be implemented in a real study that consists of essentially a single data replicate. Hoggart et al.  obtainedWith respect to differentiation/localization, in common with previous studies [Hoggart et al., We again compared the methods with respect to achieving a given error rate. Our desired false-positive rate was 0.05 divided by the number of markers, a Bonferroni correction. Within each replicate we therefore performed 20 permutations (1/20 = 0.05) and picked the maximal value of λ leading to just one marker entering the model, in order to obtain an estimate of the appropriate λ in the same way as before.CYP2D6, we would hope for 0.05/110 = 0.00045. This inflation in false-positive rate may be due to the fact that we have simulated causal loci in regions of very strong LD. The inflation in false-positive rates was even higher with common causal alleles where the degree of LD between other markers and these loci was larger. There are many markers in the model that are in very strong LD with our causal locus but are counted as a false positive. The fact that the penalized methods again give the same order of magnitude of false-positive rates as the ATT test  is confirmation that using permutation to pick the best penalization parameter seems a reasonable thing to do. Comparing the methods, forward stepwise regression seems to be the most conservative method, but is least powerful, while the ATT has high error rates and reasonable power, but becomes less powerful than the other methods as the P-value threshold is relaxed. Of the penalized methods, the NEG is the most conservative, with very little loss of power over the other methods, while the ridge penalty allows far too many variables in the model.For both rare and common causal alleles  and IV, For more common alleles, the results are less clear . Even thCYP2D6 region which had the highest level of LD. We did not count these perfectly correlated variables as false positives, and counted them as a detection if the causal locus was not originally detected. As expected, we found that the sensitivity improved, and the false-positive rate dropped, but that the effect was relatively small and fairly uniform across the different methods.On closer inspection of our results, we found a good proportion of the false positives in all methods to be in high LD with a true causal locus. In all methods but the ridge, and to a lesser extent the elastic net, if a true causal locus was missed, then there is often a marker in the model that is in high LD with the causal locus which gets selected instead of the true causal locus see  and 7. TCYP2D6 and CTLA4).The above results relate to scenario 6, in which five causal variants were simulated within each gene region. We also performed the same comparison for scenarios 1–5, in which only one causal allele was simulated in each gene region see . With reIn this study we have examined the performance of a variety of different penalized regression approaches and compared their performance with respect to (a) detection and (b) distinguishing of true from false causal variants in genetic association studies. Although the performance of some of the individual methods we considered has previously been examined, to our knowledge there has been no comprehensive comparison between methods and between such methods and the simpler approaches that are often used in genetic association studies (such as the Armitage trend test and forward stepwise regression). Moreover, some previous studies [Malo et al., Overall, we found broadly similar performance between the different penalization methods, with NEG giving the overall best and the ATT the overall worst performance. Although larger parameter estimates are always more heavily penalized, methods that apply larger relative penalties on small parameter estimates and relatively lower penalties to larger estimates seem to perform better and more accurately estimate the effect size of the selected SNPs. These penalties prevent variables that enter the model early from leaving the model later on, and exclude the entry of additional variables whose effects are solely due to correlation with already included variables. As we relax the error rate, penalized methods outperform the ATT in detection, and may be useful for further exploration of causal variants.P-values, for more relaxed P-values it generally performed slightly worse than the penalized methods with a higher false-positive rate and lower sensitivity, although it did perform well with respect to fine mapping in the simulations where only a single causal variant existed in a region. Forward stepwise regression has the disadvantage that once a variable enters the model it cannot correct itself by removing it if other variables are a better fit; it is known to be greedy and unstable [Breiman, Although forward stepwise regression is conservative for very stringent Breiman, . HoweverBreiman, . HoweverBreiman, , stepwisBreiman,  have freBreiman,  and appeBreiman, .As well as giving the overall best performance in our study, the NEG has the advantage of being genuinely applicable to genome-wide data comprising many thousands of predictor variables, unlike most of the other penalized approaches we considered, which suffer from limitations with respect to the number of markers that can be considered simultaneously [Croiseau and Cordell,


The sexes often have different phenotypic optima for important life-history traits, and because of a largely shared genome this can lead to a conflict over trait expression. In mammals, the obligate costs of reproduction are higher for females, making reproductive timing and rate especially liable to conflict between the sexes. While studies from wild vertebrates support such sexual conflict, it remains unexplored in humans. We used a pedigreed human population from preindustrial Finland to estimate sexual conflict over age at first and last reproduction, reproductive lifespan and reproductive rate. We found that the phenotypic selection gradients differed between the sexes. We next established significant heritabilities in both sexes for all traits. All traits, except reproductive rate, showed strongly positive intersexual genetic correlations and were strongly genetically correlated with fitness in both sexes. Moreover, the genetic correlations with fitness were almost identical in men and women. For reproductive rate, the intersexual correlation and the correlation with fitness were weaker but again similar between the sexes. Thus, in this population, an apparent sexual conflict at the phenotypic level did not reflect an underlying genetic conflict over the studied reproductive traits. These findings emphasize the need for incorporating genetic perspectives into studies of human life-history evolution. This leTo demonstrate sexually antagonistic selection, one needs to show: (i) that the phenotypic selection pressures on the trait differ between the sexes, (ii) that the trait has significant genetic variation (i.e. there must be additive genetic variance) and shows a strong positive intersexual genetic correlation (i.e. a shared genetic architecture), so that the ability of the sexes to evolve independently is constrained, and (iii) that the genetic covariance between the trait and relative fitness is positive in one sex but negative in the other. To study this, a general framework that has been developed to study selection and evolutionary response to selection can be applied (reviewed in ). RecentStudies of sexual conflict tend to focus on polygamous species, where there are large differences between the sexes in the variance of reproductive success. However, males and females might experience different phenotypic selection on a number of life-history traits even under monogamy . For exaet al. [et al. [In human studies, sexual conflict has recently been the subject of increasing interest . For exaet al. ). A numbet al. ) and othet al. –25. Thuset al. . Insteadet al. ,27), butet al. ,25,28,29 [et al.  for exceWe here apply quantitative genetic methods that are well tested in the field of evolutionary biology to a large longitudinal dataset from Finland on humans. This dataset contains accurate records of births, marriages and deaths in seven preindustrial populations and allows us to build pedigrees with up to 12 generations of data depth. It is thus ideally suited to establish whether sexually antagonistic selection occurred on the genetic level, in this case on traits related to reproductive timing and rate during a period of natural reproductive conditions . The detailed records allow us to statistically control for a range of environmental factors (such as aspects of culture) that are known to influence life-history traits in humans, for example socioeconomic status and patterns of wealth inheritance ,32.2.(a)We use demographic data  from seven farming and fishing human populations (‘parishes’) in Finland. Starting in the eighteenth century, the Lutheran church was obliged by law to accurately record all births, marriages and deaths in every parish . Our data priori decided to restrict our analyses to individuals with complete records of their life history until the age of 45 for women and 50 for men to focus on individuals whose potential reproductive period has been fully documented. Thus, we excluded individuals who either died before or were not tracked until adulthood at age 15. We also excluded individuals surviving to adulthood and with complete life histories, but who never reproduced because the timing and rate of reproduction can only be recorded in individuals who do reproduce. Post hoc inspection revealed that in the current full dataset, 99% of women but 93% of men who ever reproduced had stopped reproducing by the age of 45 and 50, respectively. The more stringent criterion of 99% for men would require men to have complete records until the age of 59 inclusive. All  who fulfilled all other subsetting criteria (see below) indeed had complete records until this age. Individuals who died before these ages were included if they fulfilled all other criteria because running all analyses on a subset of the data that included only individuals that survived to the age of 50 years did not significantly influence any of the results. To ensure complete records of the potential reproductive period of all offspring, we also required that all offspring of each included individual had complete records of their life history until the age of 45 for women and 50 for men. In addition, we required complete information for a number of parameters that were entered as fixed and random effects in the statistical models. Because social class was related to key life-history traits in these populations [We ulations , we usedulations . We alsoulations ), firstbulations , parish ulations . This reWe used four measures to capture the variation in timing and rate of reproduction; age at first reproduction, age at last reproduction, reproductive lifespan  and reproductive rate . Because the calculation of a rate requires two values that are separated in time, we included only individuals with more than one offspring, and excluded individuals with two offspring if those were twins, resulting in a subset of 903 females and 859 males for reproductive rate. To assess the degree of sexual dimorphism on the phenotypic level in these traits and in the number of grandchildren born, we used univariate ANOVAS with sex as the main fixed effect and the reproductive trait as the response variable . Note thThirteen per cent of men and 10% of women had more than one spouse over their lifetime . The number of offspring born over a lifetime ranged from 1 to 17 with an average (±s.d.) of 3.7 ± 2.7 for females that ever reproduced and 4.0 ± 2.8 for males that ever reproduced. Child mortality was high with only 65 ± 36% (average ± s.d.) of offspring surviving to adulthood (15 years). Fitness was quantified as the number of grandchildren born, to control for any quantity versus quality trade-off at the offspring stage , and to (b)MCMCglmm [MCMCglmm provides meaningful error estimates for derived variables by direct sampling from the posterior distribution. To test for sexually antagonistic selection on traits related to timing and rate of reproduction, we first investigated the relationship between each of the focal four traits and fitness (grandchildren) in models that included the interaction between sex and both the linear and the quadratic term for each trait. Models included socioeconomic status, twin status, firstborn son status and parish as fixed effects and birth cohort and maternal identity as random effects. Reproductive trait values were standardized within sexes prior to the analysis  and we used grandchildren as a Poisson response variable. The 95% Bayesian credibility intervals for the interaction term indicate whether the linear and quadratic slopes of the relationship between trait and fitness differed significantly between the two sexes. To further elucidate sex-specific phenotypic selection patterns and to facilitate comparison with previous studies, we then calculated standardized phenotypic selection gradients separately in the two sexes. Following Lande & Arnold [V = 1 and a degree of belief parameter (nu) of 0.002.We performed phenotypic selection analyses and quantitative genetic analyses in the R-package MCMCglmm , which u& Arnold , we used& Arnold , we ther& Arnold . This as& Arnold  and the & Arnold ) quadrat& Arnold . All phe(c)We employed a Bayesian version of a linear mixed-effects model approach  which has gained popularity in recent decades in evolutionary biology ,27,48. IVA/VP. We also calculate maternal effects and genetic correlations along with their 95% Bayesian credibility intervals.To allow comparison with the phenotypic analyses, we used relative grandchildren as the response and modelled all traits using Gaussian distributions. From the posterior distribution of the genetic variances and covariances, we calculate heritabilities as V of 1 and a nu of 2.002 (for a trivariate model). To account for the effects of the genetic correlations between traits , we attempted to run a multivariate analysis with all four reproductive traits (separately in the two sexes) and fitness in one single model. However, this model failed to reach convergence and we thus present results from the trait-specific models described above.By coding a trait as two separate traits in males and females (treating it as a sex-limited trait), it is possible to run a bivariate model and extract sex-specific heritabilities and the intersexual genetic correlation. To obtain sex-specific genetic correlations between traits and fitness, we used three-trait models with the trait in males, the trait in females and relative grandchildren  as the three response variables. Results remained qualitatively the same when relative grandchildren was treated as a sex-limited trait and coded as two separate traits in the two sexes, resulting in models with four responses (both reproductive trait and fitness sex-separate). The genetic correlations between reproductive traits and fitness were estimated in four separate models, one for each trait. All models included socioeconomic status, twin status, firstborn son status and parish as fixed effects and birth cohort and maternal identity as random effects. The reported heritabilities are therefore estimated after removing the variation due to the fixed effects. To minimize autocorrelation among samples, models were run for 1 000 000 iterations , and every 1000th iteration sampled for a total of 1000 samples from the posterior distribution. We specified a weakly informative prior by partitioning the phenotypic variance of the traits of interest evenly among each random effect (and covariances to zero) and specifying a degree of belief parameter (nu) equal to the size of the matrix (3 for a trivariate model). Estimates were robust to varying the degree of genetic control specified in the prior (0.95 versus 0.05) and to an alternative prior specifying a http://doi.org/10.5061/dryad.4h071.We used R v. 2.15.0  for all 3.(a)Three of the four reproductive traits showed significant sexual dimorphism . Women s(b)a,d). Further, a later age at last reproduction and an increased reproductive lifespan lead to higher fitness. In addition, a positive quadratic relationship for both traits indicated that intermediate values conferred lower fitness benefits than expected from the linear relationship. In men, selection on all traits was generally linear, with very weak quadratic selection gradients. Thus, men were selected for an earlier age at first and a later age at last reproduction, a longer reproductive lifespan and a slower reproductive rate. The linear selection gradients mean that a woman who advanced her age at first reproduction by one phenotypic standard deviation gained 3.1 grandchildren, while a man gained 2.3 grandchildren. Similarly, an increase in reproductive rate by 1 s.d. resulted in a decrease of 1.4 and 2.0 grandchildren in women and men, respectively. A delay in age at last reproduction by 1 s.d. resulted in a gain of 4.5 and 4.0 grandchildren, respectively. Finally, an extension of the reproductive lifespan with 1 s.d. resulted in a gain of 5.2 grandchildren in women and 6.1 grandchildren in men.figure 1(c)All reproductive traits were heritable with credibility intervals that did not overlap zero and heritabilities did not differ between men and women . All tra4.To our knowledge, no previous study on humans fulfils all three requirements needed to demonstrate sexually antagonistic selection on a given trait: a demonstration: (i) that the phenotypic selection pressures on the trait differ between the sexes, (ii) that the trait has significant genetic variation and shows a strong positive intersexual genetic correlation and (iii) that the genetic correlation between trait and fitness is positive in one sex but negative in the other. We found that the phenotypic selection pressures on four traits related to timing and rate of reproduction differed between men and women in a preindustrial Finnish human population. We also found significant heritabilities and generally strong intersexual genetic correlations for these traits, which could potentially lead to sexual conflict. The genetic correlations between trait and fitness were, however, remarkably similar between the sexes, showing that there is no genetic sexual conflict over traits related to the timing and rate of reproduction in this human population. Given persistent selection pressures, we would therefore not predict evolution of increased sexual dimorphism beyond current levels in these traits. Our multigenerational pedigree data allowed us to control for a range of environmental effects that may bias estimates of quantitative genetic parameters, such as inheritance of socioeconomic status and wealth, and therefore allowed us to estimate sex-specific heritabilities and genetic correlations between traits and fitness as well as intersexual genetic correlations with relatively high precision. We are therefore confident that there indeed is no sexual conflict over the traits of interest.(a)We found strong linear positive selection for a delayed age at last reproduction in both sexes. However, women ceased to reproduce earlier than men. In preindustrial Finland, previous studies have found that for females, the cessation of reproduction at menopause may be beneficial owing to increasing reproductive conflict over age with own offspring (the ‘Reproductive Conflict hypothesis’ ) and beclmer: βwomen = −0.18 ± 0.036, t = −5.0, N = 903, βmen = −0.15 ± 0.035, t = −4.2, N = 859). Alternatively, the causality may be reversed, such that high child mortality is a consequence of a high reproductive rate. Either process would result in couples with a slower birth rate and higher child survival having more grandchildren.The observed selection for a slower reproductive rate may at first glance seem counterintuitive. However, the reason behind this negative association between reproductive rate and fitness is likely to be that couples responded to high child mortality by increasing their rate of reproduction. In the analysed data, a higher reproductive rate was associated with a lower child survival until the age of 15 ((b)Consistent with previous findings in both humans and wild animals, we found that selection was acting to lengthen the reproductive period, both through selection for an earlier onset and later end of reproduction ,24,54. T(c)et al. [h2 of age at first reproduction and found an across-study average h2 of 0.11 in five studies performed before 2010. One more recent study estimated a heritability of age at first reproduction between 0.30 and 0.55 [h2 of LRS in women: less than 0.01 and 0.04 in two datasets, respectively, in [h2 of LRS in women: 0.47 in [Our estimates of the heritabilities and intersexual genetic correlations of traits related to timing and rate of reproduction fall within the range of those reported for other human populations as well as for other vertebrates ,6,54. Stet al.  recentlyand 0.55 . Similarand 0.55  and 0.42and 0.55 , in a Fiand 0.55 –25. Fitnand 0.55 ,57. Our  0.47 in , intrins 0.47 in ). Note t 0.47 in , this shA fundamental question in evolutionary biology concerns the maintenance of variation in fitness in wild populations . Bonduri(d)et al. [Brown et al.  recentlyet al. . Howeveret al. , the asyet al. , and theet al. , this shet al.  and theret al. . Populatet al. . Thus, h5.et al. [Stearns et al.  recently


FST statistics was used to assess the extent of PGx divergence among different strata of the Brazilian population. Major PGx implications of these findings are: first, extrapolation of data from relatively well-defined ethnic groups is clearly not applicable to the majority of Brazilians; second, the frequency distribution of polymorphisms in several pharmacogenes of clinical relevance  varies continuously among Brazilians and is not captured by race/Color self-identification; third, the intrinsic heterogeneity of the Brazilian population must be acknowledged in the design and interpretation of PGx studies in order to avoid spurious conclusions based on improper matching of study cohorts.By virtue of being the product of the genetic admixture of three ancestral roots: Europeans, Africans, and Amerindians, the present-day Brazilian population displays very high levels of genomic diversity, which have important pharmacogenetic/-genomic (PGx) implications. Recognition of this fact has prompted the creation of the Brazilian Pharmacogenomics Network (Refargen), a nationwide consortium of research groups, with the mission to provide leadership in PGx research and education in Brazil, with a population heath impact. Here, we present original data and review published results from a Refargen comprehensive study of the distribution of PGx polymorphisms in a representative cohort of the Brazilian people, comprising 1,034 healthy, unrelated adults, self-identified as white, brown, or black, according to the Color categories adopted by the Brazilian Census. Multinomial log-linear regression analysis was applied to infer the statistical association between allele, genotype, and haplotype distributions among Brazilians (response variables) and self-reported Color, geographical region, and biogeographical ancestry (explanatory variables), whereas Wright’s  The present-day Brazilian population, in excess of 190 million people, is highly heterogeneous and admixed, as result of five centuries of mating between native Amerindians, Europeans, and sub-Saharan Africans. This fact renders inappropriate extrapolation of pharmacogenetic/-genomic (PGx) data derived from well-defined ethnic groups to the majority of Brazilians. Recognition of this fact has prompted the creation of the Brazilian Pharmacogenomics Network or Refargen , pardo , and preto . The term Color is capitalized throughout the text, to call attention to its special meaning in the context of the Brazilian Census classification. This cohort is considered representative of the present-day Brazilian population since 99% of Brazilians self-identify in one of the three Color categories, and 93% live in one of the four regions, included in the studyCYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A5, COMT, and TPMT), transport  and effect (VKORC1). Pharmacogenomics Knowledge Base ” and two thirds of the 44 polymorphisms investigated as “Important Variants.”The study cohort consisted of 1,034 healthy, unrelated adults recruited in the North, Northeast, Southeast, and South regions of Brazil  was detected across the three Color groups, the median (interquartile range) number of polymorphisms being 17 (14–20), 16 (13–18), and 16 (13–19) in White, Brown, and Black individuals. This adds to 18.9% of the total number of alleles genotyped at the 44 loci in the overall cohort. However, the allele frequency at 11 of these loci differed significantly (chi square p < 0.05) across the Color groups. The pharmacogenes affected were ABCB1 (2 SNPs), CYP2C8 (1) CYP3A5 (3), NAT2 (3), SLCO1B1 (1), SCLO1B3  and VKORC1 (2). We applied the Wright’s FST statistics , 0.013 (0.017), and 0.004 (0.005) for pair-wise comparisons of White vs. Brown, White vs. Black, and Brown vs. Black, respectively  approached, the FST threshold for moderate genetic divergence in White vs. Black Brazilians in the entire cohort. Not surprisingly, these were the SNPs with the smallest p values for the Kruskal–Wallis analyses of frequency distribution in the overall cohort  in pair-wise comparisons of White vs. Black Brazilians.We will initially present data for the overall cohort and for each Color group within this cohort. Figure  Wright,  to estim2, Brazil is a country of continental size (the fifth largest in the world) and different regions have diverse population histories. For instance, the North had a large influence of the Amerindian root, the Northeast had a history of strong African presence due to slavery and the South was mostly settled by European immigrants , transport (ABCB1 and SLCO1B1) and response (VKORC1). Color per se associates significantly with the frequency distribution of CYP2C8 and CYP2C9 variant alleles, ABCB1 and SLCO1B1 haplotypes, and VKOC1 3673G > A alleles and genotypes; no association is observed with respect to the CYP2C19 polymorphisms. Color in combination with geographical region is significantly associated with distribution of CYP2C8 and CYP2C9 alleles, ABCB1 and SLCO1B1 haplotypes, whereas geographical region per se associates with CYP2C8 and CYP2C9 allele frequency.With an area of 8,511,960 KmFST statistics. First, we performed pair-wise comparisons between Color groups within each geographical region, and detected significant differences in the distribution of FST values for White vs. Brown  and White vs. Black (P < 0.0001), but not Brown vs. Black individuals, across regions  for moderate PGx divergence. Taken together, these FST results extend the conclusions of the multinomial analyses described above, that the distribution of PGx polymorphisms among Brazilians is influenced by self-reported Color, geographical region, and the interaction of these two variables. Collectively, these data reflect the notorious heterogeneity of the Brazilian population and highlight the inappropriateness of ascribing PGx polymorphisms’ frequencies for “Brazilians” based on data from one or more Color strata recruited at a given region (or city).We explored further the PGx heterogeneity among Brazilians by the n = 325), to Brown  and then to Black individuals , and the opposite trend is observed with respect to African ancestry, which averaged 0.10 (SD 0.14) in White, 0.25 (0.26) in Brown, and 0.42 (0.29) in Black persons. However, the individual proportions of European and African ancestry varies widely, and most importantly, as a continuum within each of these three Color categories, whereas the individual proportion of Amerindian ancestry remains relatively constant across the three groups, ranging from 0.10 to 0.13. To describe the association between PGx polymorphisms and the estimated individual biogeographical ancestry we fitted non-linear logistic regression modeling using maximum likelihood estimation. A consistent finding in these analyses  and SNPs in the ABCB1, SLCO1B3, and SLCO1B1 genes. These findings represent a caveat against extrapolation of PGx data from European-derived (“Caucasian”) cohorts to the ensemble of Brazilians.Drug assessment and regulatory processes in Brazil are carried out by the National Health Surveillance Agency, ANVISA, an independently administered, financially autonomous agency, managed by a Collegiate Board of DirectorsAdmixture is common in all developing nations in the American continent, although the relative contributions of the three major ancestral roots – native American, European, and sub-Saharan African – vary among these nations, as well as among ethnic groups and geographical regions within a given country. Hence, extrapolation of conclusions drawn from PGx studies in Brazilians to other admixed Latin American populations must take into account the specific patterns of population structure and diversity across the Americas. Therapeutic drugs are usually developed and investigated for their safety and efficacy in geographical and ethnical populations that do not encompass the diversity of Latin American peoples. Drivers and barriers to the adoption of PGx in developing countries, and specific ways in which these countries could benefit from PGx-based drug therapy deserve greater attention from academic and industrial scientists, prescribers, and legislators in developing nations across the Americas. This goal is not likely to be achieved simply by mandates to include subjects from ethnic minorities in clinical drug trials, especially when these groups are labeled by phenotypes which do not accurately reflect genetic ancestry (Suarez-Kurtz, 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.


FLT1 gene, based on knowledge of the generating model. Using all 200 replicate data sets, we similarly analyzed Q1 and Q4 (which is known to be free of association with FLT1) to evaluate relative efficiency, type I error, and power. Simulation study results suggest that the QT-dependent selection designs generally yield greater than 50% relative efficiency compared to using the entire cohort, implying cost-effectiveness of 50% sample selection and worthwhile reduction of sequencing costs.Use of trait-dependent sampling designs in whole-genome association studies of sequence data can reduce total sequencing costs with modest losses of statistical efficiency. In a quantitative trait (QT) analysis of data from the Genetic Analysis Workshop 17 mini-exome for unrelated individuals in the Asian subpopulation, we investigate alternative designs that sequence only 50% of the entire cohort. In addition to a simple random sampling design, we consider extreme-phenotype designs that are of increasing interest in genetic association analysis of QTs, especially in studies concerned with the detection of rare genetic variants. We also evaluate a novel sampling design in which all individuals have a nonzero probability of being selected into the sample but in which individuals with extreme phenotypes have a proportionately larger probability. We take differential sampling of individuals with informative trait values into account by inverse probability weighting using standard survey methods which thus generalizes to the source population. In replicate 1 data, we applied the designs in association analysis of Q1 with both rare and common variants in the  We assume an existing cohort of unrelated individuals, well phenotyped for a quantitative trait (QT) that is approximately normally distributed. At present, the cost of whole-genome sequencing of an entire cohort to test for association remains prohibitive, and selection of a subset of informative individuals for sequencing is a reasonable strategy to reduce costs. In the absence of relevant genetic information, such as family history or single-nucleotide polymorphism (SNP) association identified through genome-wide association studies, one might hypothesize that individuals at the extremes of the QT distribution would be more informative for the detection of association with sequence variants. For example, individuals with high QT values are more likely to carry high-risk variants, whereas those with low values do not. In contrast, taking a simple random sample from the cohort will tend to select individuals from the central part of the trait distribution and can fail to sample individuals with low-frequency variants. Because ascertainment by the phenotype needs to be taken into account, simple selection from the tails of the trait distribution complicates inference. The resulting trait distribution is bimodal, violating the usual linear regression assumptions and can produce distorted effect estimates and type I errors greater or less than nominal. In addition, the resulting sample is not representative of the original cohort, making it more difficult to design validation studies in nonselected populations. Finally, if both extremely high and extremely low QT values are “pathological” then excluding “normal” individuals would reduce the ability to detect protective variants associated with favorable QT values. As an alternative, we propose a sampling design in which all individuals have a nonzero probability of being selected into the sample but in which individuals with extreme phenotypes have a proportionately larger probability of being selected. We account for differential sampling in the association analysis by inverse probability weighting (IPW) with standard survey methods for variance estimation and hypothesis testing. The purpose of our analyses of the Genetic Analysis Workshop 17 (GAW17) mini-exome data is to compare alternative sampling strategies with respect to relative efficiency. We also examine type I error and power of hypothesis testing.iY. For individual i, we use the distance from the median of the trait distribution (Ymed) in the original cohort:We consider the analysis of sequencing data for the entire cohort as the ideal against which to compare sampling designs in which only 50% of the cohort is sequenced. Four alternative sampling designs are summarized in Table In particular, we specify the IPW weight as:where the sampling probabilities under design 5 are:or:which we refer to as 5.1 and 5.2, respectively. These probabilities are scaled by the maximum distance from the median, with:Ymax and Ymin are the maximum and minimum of the iY, respectively. In design 5.2 extreme observations are selected more frequently.where For common variants, it is feasible to analyze one SNP at a time. For rare variants, however, methods that accumulate multiple SNP genotypes across a region are necessary to improve the power to detect association. We calculate a rare variant score, defined as the total count of rare alleles within a gene ,2. AssocFLT1 gene to compare methods under a true alternative hypothesis of genetic association and use analysis of Q4 for comparisons under the null hypothesis. To reduce heterogeneity and potential for population stratification bias, we chose the Asian subpopulations for study: Chinese + Japanese (n = 321).We analyze quantitative traits Q1 and Q4 in replicates 1 to 200 of the GAW17 simulated unrelated mini-exome data [FLT1 gene that were monomorphic within the Asian subpopulation. Of the 20 remaining SNPs, 18 were classified as rare ; but according to the generating model, only 6 of these had functional variants. We calculated a rare variant score  from the 18 rare SNPs. The total count of rare alleles per person ranged from 0 to 3 . One SNP with a common functional variant, C13S523, was analyzed separately (MAF = 8.72% with no rare homozygotes).We excluded SNPs in the QT-dependent sampling and QT analysis for Q1 and for Q4 were based on residuals from a linear regression on Age, Sex and Smoking status. We conducted an association analysis under each of the five sampling designs listed in Table FLT1 was detected at near genome-wide significance levels for both the common SNP and the rare variant score . For the rare variant score , defined as 100 times the ratio of the empirical variance of design 1 to that of the alternative design Table . This meAs expected, use of a 50% SRS design is not a cost-effective approach for reducing sequencing costs: The empirical relative efficiency of estimation was less than 50% in the GAW17 simulations we examined. However, designs 3–5, in which RE generally exceeded 50% when conditioning on the QT and could be substantially higher, appeared to offer a net benefit. The more extreme IPW sampling design 5.2, which is similar to designs 3 and 4, behaves similarly, at least for the regression analyses that condition on the phenotype. However, the simple IPW linear regression analysis that we applied here may be less than optimal in finite samples under the alternative hypothesis. Although RE results for design 5 are encouraging, for hypothesis testing the source of parameter underestimation under the alternative hypothesis needs to be addressed and SE estimation improved. Evaluations reported here are limited, and it is quite possible that sampling and/or weighting schemes could be improved.In our extreme-phenotype implementation of designs 3 and 4, we used the observed QT values, in contrast to more common case-control approaches. Because the linear regression assumption of normal residuals is grossly violated, the observed exaggeration of regression coefficients is not surprising. The lack of elevated type I error may be ascribed to the use of symmetric tail selection, but for rare variant analysis there is a clear loss of efficiency. In the GAW17 simulation, in which rare and common variant effects were generated under an additive model, differences between designs 3 and 4 were trivial. In other settings, design 4 may be more robust.Although it has not been commonly applied, Poisson regression of allele counts in the rare variant score appears to be useful as a general approach for single-gene analysis in many designs, provided that the tendency toward elevated type I error is resolved. For design 5, Poisson regression avoids the complications of weighting, because the model conditions on the QT value.There is established literature on optimal design for continuous outcomes in experimental settings and survey samples, and sampling has been well exploited in the context of time-to-event outcomes in epidemiological designs, such as the case-cohort study . In compThe authors declare that there are no competing interests.YEY and SBB conceived of the study. YEY carried out the analyses and wrote the first draft of the manuscript. Both authors revised and approved the final manuscript.


The common genetic variants identified through genome-wide association studies explain only a small proportion of the genetic risk for complex diseases. The advancement of next-generation sequencing technologies has enabled the detection of rare variants that are expected to contribute significantly to the missing heritability. Some genetic association studies provide multiple correlated traits for analysis. Multiple trait analysis has the potential to improve the power to detect pleiotropic genetic variants that influence multiple traits. We propose a gene-level association test for multiple traits that accounts for correlation among the traits. Gene- or region-level testing for association involves both common and rare variants. Statistical tests for common variants may have limited power for individual rare variants because of their low frequency and multiple testing issues. To address these concerns, we use the weighted-sum pooling method to test the joint association of multiple rare and common variants within a gene. The proposed method is applied to the Genetic Association Workshop 17 (GAW17) simulated mini-exome data to analyze multiple traits. Because of the nature of the GAW17 simulation model, increased power was not observed for multiple-trait analysis compared to single-trait analysis. However, multiple-trait analysis did not result in a substantial loss of power because of the testing of multiple traits. We conclude that this method would be useful for identifying pleiotropic genes. The common disease/common variant hypothesis states that common variants contribute substantially to common diseases ,2. FolloSome genetic association studies examine a qualitative trait, such as the case-control status and some additional correlated quantitative traits. For example, a genetic study of diabetes may examine the diabetic status and other related phenotypes, such as body mass index and other lipid profiles. Similarly, a glaucoma study may explore the related endophenotypes, such as central corneal thickness, intraocular pressure, and maximum vertical cup-to-disc ratio. One way to analyze these data is to perform single-trait analyses separately. An alternative way is to perform a multiple-trait analysis, which potentially has improved power to identify the pleiotropic variants for these traits ,6.p-values of multiple traits may not be independent because of the environmental or genetic correlations among multiple traits. Hence some classical methods of combining independent p-values, such as Fisher’s method, are not directly applicable to the analysis of multiple correlated traits. Our purpose in this paper is to develop a test statistic for combining these correlated univariate statistics by considering the correlation structure among multiple traits. Motivated by a recently proposed approach [Univariate test statistics or approach , we deveapproach  to combiY = T denote the m available traits. Assume that the gene k has L genotyped single-nucleotide polymorphisms (SNPs), including both common and rare ones. In the first step, the genetic score jS of the gene k for an individual j is calculated using the weighted sum of all SNPs within the gene. Second, a univariate test is performed to establish the association of genetic scores with all the traits separately. Then, a gene-level association test using the linear or quadratic combination of single-trait univariate statistics is constructed for multiple traits. Finally, the optimal subset of traits is selected for multiple-trait analysis. The details of the various steps are described in what follows.Let k for individual j is given by:The weighted-sum gene score assigns different weights to each variant based on the estimated allele frequencies . The scoijI is the number of minor alleles for SNP i in individual j,where im is the total number of minor alleles for SNP i in all n individuals. In the original study [and al study , the allS = S1, S2, …, nS)T denote the scores for gene k for n individuals. The test statistic for testing the association of S with each trait lY  is denoted lZ, and it is assumed to asymptotically follow N under the null hypothesis. The choice for lZ is discussed in the Results section. The test statistic for the combined multiple traits (CMT) method is a quadratic or linear combination of m univariate test statistics Z1, …, mZ.Let Z1, …, mZ are assumed to asymptotically follow N, the joint distribution of the random vector Z = T is asymptotically multivariate normal mN, where Σ is the correlation matrix of Z. The quadratic combination statistic The CMT method is motivated by a recently proposed approach  to test m degrees of freedom if Σ is full rank [Σ is unknown, it is estimated by permutations. The phenotypes are permuted, and the m univariate test statistics are computed under each permutation. The estimation of Σ is given by:and it is distributed asymptotically as a chi-square distribution with ull rank . BecauseN such permutations. The p-value of the quadratic statistic  [Σ is replaced by its estimate p-value of the linear statistic can be approximated by the standard normal quantile. Note that the linear combination statistic which is distributed asymptotically as N . Here alp-values for all possible subsets with at least two traits. The subset with the minimum p-value is selected as the optimal one, denoted A*. For any subset with at least two traits A, the quadratic combination statistic It is likely that some of the relevant rare variants are associated with only a subset of traits. In this case, combining all the test statistics using the CMT method may result in a loss of power because of the high degree of freedom in the chi-square distribution. Therefore we propose an association test for optimally combined multiple traits (OCMT) using a preselected subset of these traits. To select the optimal subset, we use the CMT method to calculate the ZA and ΣA are the subvector and submatrix of Z and Σ, respectively. The p-value p-value of A* . The p-value of OCMT .To control type I error, the We applied the CMT and OCMT methods to the GAW17 simulated mini-exome data sets . The rest-test statistic of the β coefficient corresponding to the gene score in the logistic regression (for the disease status D) or the linear regression  adjusted for three covariates . The test statistic lZ  was obtained from the inverse normal distribution transformation of the t-test statistic [For each gene, we tested each trait separately using the tatistic  and was p-value was approximated using the theoretical quantiles of the chi-square distribution. The correlation matrices among the test statistics Z1, …, mZ were estimated by 1,000 permutations. In addition, the association test using the OCMT method was performed, and its p-value was obtained from another set of 1,000 permutations. Given a predefined significance level α, the power of any association test was defined as the proportion of 200 replicates that returned a p-value less than or equal to α.The association tests for multiple traits were performed by using the CMT method with the quadratic combining statistic. The The samples in the GAW17 data set were collected from six cohorts, but population stratification was not considered in the simulation model. We performed the association tests with and without corrections for stratification. To correct for stratification, we corrected genotypes and phenotypes using the first 10 principal component scores derived using Eigenstrat .FLT1, KDR, VEGFA, and VEGFC) still have a power greater than or equal to 0.3. For Q2 and Q4, most of the genes have a power less than 0.1. Some of the genes also are associated with the disease status (power ≥ 0.3).Table We used the CMT method with the quadratic statistic to perform the multiple-trait analysis for Q1 and D (Q1+D). For all the Q1 causal genes, the power of the Q1+D analysis was less than or comparable to the power of the univariate test for Q1. The results show that all Q1 causal genes may have small or no pleiotropic effects that are insufficient to compensate for the increase of the critical value from ELAVL4 was assumed to have pleiotropic effects on Q1 and the latent liability. Moreover, only two rare variants in this gene with MAF = 0.000717 (C1S3181 and C1S3182) contributed to the latent liability. Therefore it could be difficult to detect the association of ELAVL4 with the latent liability. The other eight causal genes on Q1 were assumed to have no pleiotropic effects. Therefore multiple-trait analysis did not improve the power for Q1 causal genes. However, multiple-trait analysis did elucidate the genetic correlation between the disease status and related quantitative traits and aided in the identification of the pleiotropic genes.Among all the Q1 causal genes, under the simulation model, only p-value of the OCMT method is the minimum of the p-values of all subsets with at least two traits. Multiple-trait analysis was performed for all subsets of Q1, Q2, Q4, and D using the CMT method. The optimally combined multiple traits were selected on the basis of their p-values. The power of the OCMT method was comparable with that of the analysis for Q1+Q2+Q4+D. The last column of Table The ELAVL4 has a small pleiotropic effect on Q1; the other genes have no effects on Q1, Q2, or Q4.Table On the basis of the results adjusted for population stratification, we calculated the false-positive rates of single- and multiple-trait analyses. Genes with a power greater than or equal to 0.3 were considered the associated findings. Luedtke et al.  reportedIn some genetic association studies, multiple correlated traits are available that can be used to identify genes responsible for multiple traits. In single-trait analysis, some common associated genes may be found across different traits. These overlapping associations may be caused by pleiotropic genes and/or the correlation structure among traits. Multiple-trait analysis has the potential to improve the power to detect pleiotropic genes. The multiple-trait analysis proposed here did not suffer a significant loss of power, even though true models, such as the GAW17 simulated model, had small or no pleiotropic effects. The proposed method considers the correlation matrix, thereby ensuring that the false-positive rate is not inflated by the correlation among multiple traits.In next-generation sequencing data sets, huge numbers of rare variants are genotyped across the whole genome. Because of the small number of observations for each rare variant, statistical tests for common variants are inefficient at identifying the associations. Hence the proposed method performs a gene-level test using the weighted sum to test the joint effect of multiple variants within a gene. The weighted sum is a feasible choice for the gene-based score , but it From Tables 1 and 3, it can be seen that the correction for population stratification has a large effect on the power to detect some associations with Q1, D, and Q1+Q2+Q4+D. This phenomenon also was observed from the quantile-quantile plots in replicate 1 (data not shown). Although we did not consider the population structure in the simulation model, the principal component scores may influence the phenotypes, because the population structures would be similar to those of the true causal genes in this data set . We concWith the advent of next-generation sequencing technologies, the identification of rare variants has become realistic. Statistical methods for common variants are not applicable in rare variant analysis because of the small number of observations and the huge number of rare variants across the whole genome. Thus efficient statistical methods are needed. When multiple related traits are available, it is expected that multiple-trait analysis has the improved power to detect pleiotropic genes. We proposed the CMT and OCMT methods to examine the joint effect of multiple variants on multiple traits. The proposed method uses the quadratic or linear combination of univariate test statistics and thus considers the correlation structure among multiple correlated traits. The CMT and OCMT methods were applied to the GAW17 mini-exome data. The results show that the method is suitable for multiple trait analysis.The authors declare that they have no competing interests.JZ conceived of the study, participated in its design and wrote the paper. AT participated in the design of the study and made the major edits. Both authors read and approved the final manuscript.


Fusarium ear rot is a common disease of maize that affects food and feed quality globally. Resistance to the disease is highly quantitative, and maize breeders have difficulty incorporating polygenic resistance alleles from unadapted donor sources into elite breeding populations without having a negative impact on agronomic performance. Identification of specific allele variants contributing to improved resistance may be useful to breeders by allowing selection of resistance alleles in coupling phase linkage with favorable agronomic characteristics. We report the results of a genome-wide association study to detect allele variants associated with increased resistance to Fusarium ear rot in a maize core diversity panel of 267 inbred lines evaluated in two sets of environments. We performed association tests with 47,445 single-nucleotide polymorphisms (SNPs) while controlling for background genomic relationships with a mixed model and identified three marker loci significantly associated with disease resistance in at least one subset of environments. Each associated SNP locus had relatively small additive effects on disease resistance , but nevertheless were associated with 3 to 12% of the genotypic variation within or across environment subsets. Two of three identified SNPs colocalized with genes that have been implicated with programmed cell death. An analysis of associated allele frequencies within the major maize subpopulations revealed enrichment for resistance alleles in the tropical/subtropical and popcorn subpopulations compared with other temperate breeding pools. Fusarium verticillioides (Sacc) Nirenberg is endemic in most maize fields in the United States and is present in many arable regions of the world  have relatively small effects and are not consistent between populations  were included once per set.In 2011 and 2012, the maize core diversity panel was part of a larger sample of inbreds evaluated. The larger population consisted of 771 diverse entries divided into eight sets on the basis of maturity and replicated across years. Although disease measurements were collected on all experimental entries in both years, genotypic data were not available on inbreds outside of the core diversity panel at the time of analysis. Sets were randomized within the field, and each set was blocked using an Fusarium verticillioides isolates via the toothpick method . In Galicia, between 7 and 14 d after flowering, five primary ears per plot were inoculated with 2 mL of a spore suspension of the local toxigenic isolate of F. verticillioides. The spore suspension contained 106 spores mL−1 and was prepared following the protocol established by Fusarium ear rot symptoms by the use of a seven-point scale  devised by File S1. Climate data from on-farm weather stations were obtained from http://www.nc-climate.ncsu.edu/ and http://www2.meteogalicia.es/galego/observacion/estacions/listaEstacions.asp.The three North Carolina environments were artificially inoculated with local toxigenic http://www.genetics.org/content/suppl/2012/12/05/genetics.112.147595.DC1/genetics.112.147595-3.txt).The genotypic data were 47,445 SNPs from the Illumina maize 50k genotyping array filtered by File S2) with the use of ASReml version 3 software , block within replication , set (North Carolina only), and block within set (North Carolina only) as random effects. The mixed linear model for the Galicia experiment across years included line as a fixed effect, silking date as a fixed linear covariate, and year, line × year interaction, replication within year, and block within replication as random effects. The North Carolina experiment was analyzed across years with a model including line as a fixed effect, silking date as a fixed linear covariate, and year, line × year interaction, set within year, and block within set as random effects. In the combined experiment analysis, each combination of location and year was considered an environment. The combined analysis model included a fixed line effect, silking date as a fixed covariate nested within environment, a random line × environment interaction effect, and nested random experimental design effects . All analyses were weighted by the number of ears scored within each plot and used a heterogeneous error variance structure. In both experiments, larger predicted ear rot values were associated with larger residuals, so a natural logarithmic transformation of raw ear rot scores  was used for all analyses. Least square means were estimated for 267 inbred lines within each experiment and across experiments (Fusarium ear rot resistance in the diversity panel. The same models as above were used except lines were treated as random effects to obtain estimates of genetic variance. Line mean-basis heritability was estimated asG) matrix as unstructured, thereby allowing estimation of unique genetic variance within each environment and a unique genetic correlation between each pair of environments. For the purpose of estimating heritability, the average of the 10 pair-wise covariance estimates between environments  was used in the denominator of the above equation.We conducted a second analysis treating inbred lines as random effects for the purposes of estimating heritability for Silking date heritabilities also were calculated for each environment and across environments. The same models used to compute ear rot heritabilities were used to estimate silking date heritabilites, but silking date was treated as the dependent variable instead of as a fixed linear covariate.K; File S3) based on observed allele frequencies (K. Markers were uniformly distributed across the genome (at least 60 kbp between adjacent markers) and had no missing data after we excluded heterozygous genotypes. K matrix that is scaled appropriately to represent realized genomic average identity by descent relationships among the lines (A genetic kinship matrix (quencies . A subsehe lines .File S2). The mixed linear model implemented by Tassel wasy is the vector of ear rot least square means , β is a vector of fixed effects, including SNP marker effects, u is a vector of random additive genetic effects from background QTL for lines, X and Z are design matrices, and e is a vector of random residuals. The variance of the u vector was modeled asK is the n × n matrix of pairwise kinship coefficients ranging 0−2 and Tassel version 4.1.24 was used for the GWAS based on a mixed linear model . Lines of mixed ancestry (the result of admixture among the subpopulations) were dropped from the analysis. On the basis of the results of the association analyses, the frequencies of alleles that reduced disease severity at significant SNPs were estimated within each subpopulation using the FREQ procedure using SAS software version 9.2  on the basis of the population structure analysis of the maize core diversity panel reported by sion 9.2 . At eachsion 9.2  was usedP < 0.001) genotypic variation for ear rot resistance was observed in both the North Carolina and Galicia experiments. The mean ear rot observed among 267 inbred lines of the association panel ranged from 4.4 to 100% with an overall mean of 41.1% in North Carolina and from 0 to 89.3% with an overall mean of 7.4% in Galicia (File S2 and Table S1). In the combined analysis, mean ear rot ranged from 1.6 to 79.6% with an overall mean of 22.1%. The silking date covariate was highly heritable (P < 0.001), but not in the Galicia analysis .Significant (P < 0.001) line × environment interaction was detected in the combined analysis. Results of the mixed model analysis that estimate unique genotypic covariances for each pair of environments indicated that the two Galicia environments had a much stronger genotypic correlation  than did any other pair of environments . Thus, there was little genotype-by-environment (G × E) interaction between the two Galicia environments, and the heritability of line means across the 2 yr in Galicia was 0.71. In contrast, pair-wise genotypic correlations were much lower among the North Carolina environments and between North Carolina and Galicia environments , generating much of the observed G × E interaction in the combined analysis. Despite the strong G × E interaction among North Carolina environments, heritability of genotype means across the 3 yr in North Carolina (0.73) was greater than within any single North Carolina environment (Table S1). In addition, heritability of line means across all five environments was 0.75, greater than within any single environment or group of environments (Table S1). Therefore, we conducted separate association analyses on three different sets of genotypic mean values for ear rot: (1) means from three North Carolina environments, (2) means from two Galicia environments, and (3) means from the combined analysis of all five environments.A significant , and one additional SNP was identified at q ≤ 0.10  over-represented in TS and PC lines relative to other temperate  lines. At the chromosome 9 SNP locus, the allele associated with reduced disease severity is significantly (P = 3.846 × 10−4) overrepresented in PC lines compared with the other four subpopulations . Mean ear rot in North Carolina 2012 was particularly high . The very strong genotypic correlation between Galician environments  justified their grouping as one environmental set in the analysis. Genotypic effects were significantly correlated between each pair of North Carolina environments, but at much lower magnitude . Genotypic values in North Carolina 2010 had slightly higher correlations with the genotypic values in Galicia than in other years of North Carolina  were greater in all three North Carolina environments compared with the two Galicia environments (Table S2).The relationship between the Heritabilities observed across environments in this study (K) was much smaller in North Carolina . The P-values for truly null tests follows a flat distribution, such that if the observed proportion of very low P-values is lower than expected based on the flat distribution, the false discovery rate will be high even for the lowest P-values, as we observed in the Galicia analysis. Whereas a few significant SNPs were identified in the North Carolina and combined analyses at q < 0.10, no SNP had a q-value of <0.9 in the Galicia analysis . The disparity between the two individual experiment analyses highlights the importance of conducting individual environment association analyses in the presence of significant G × E interaction. It should be noted, however, that the appropriate threshold proportion of variation caused by G × E interaction to warrant individual location analyses instead of an overall combined analysis is not clear.Analyzing the Galicia environments separately from the North Carolina environments revealed no significant SNPs, whereas the North Carolina analysis identified three SNPs significantly associated with sistance . ExaminaTable S1). The largest proportion of phenotypic variance explained by K was in Galicia  were more difficult to detect compared with polymorphisms that more frequently associated with greater LD temperate subpopulations (such as Vgt1). The results of Using the same association panel and marker set as this study, CslA) protein family. Given that the associated SNP localized to an intron segment within this gene, it is likely that this SNP is in LD with the causal variant and not the causal variant itself. The expression of this gene is highest in the endosperm of the developing seed kernel between 20 and 24 d after flowering during the growing season (http://www.plexdb.org). Peak expression of this gene coincides with the initial onset of Fusarium ear rot symptoms, which occurs approximately 28 d after flowering (CslA family encode for noncellulose polysaccharides (such as mannan polymers) that form part of the wall matrix in plant cells  that was identified in both the North Carolina and combined analyses belongs to the cellulose synthase-like family A (http://www.plexdb.org). HSP60s are chaperonins that are involved with protein folding under plant stress primarily in the mitochondria and cholorplasts (The SNP on chromosome 5 is located downstream of an HSP60 gene (GRMZM2G111477). Expression levels of this gene are highest in the developing endosperm 12 d after flowering (Fusarium ear rot in maize. The identified loci each explain a relatively small proportion of the overall phenotypic variance for ear rot, and each locus has a very small additive genetic effect on resistance, consistent with the highly quantitative nature of the F. verticillioides-maize pathosystem. The large amount of variation captured by the kinship matrix, in combination with high false discovery rates for the vast majority of SNPs, suggests that additive polygenic variation across many loci underlies resistance to Fusarium ear rot. Given the rapid decay of LD along the chromosomes in the maize core diversity panel (In conclusion, we used GWAS to identify three novel loci associated with improved resistance to


Pinus pinaster half-sibs to the Fusarium circinatum pathogen was studied using 10 mother genotypes clonally replicated in two contrasting environments. Necrosis length of infected seedlings was 16% shorter in seedlings grown from favourable maternal environment seeds than in seedlings grown from unfavourable maternal environment seeds. Damage caused by F. circinatum was mediated by maternal environment and maternal genotype, but not by seed mass. Mechanisms unrelated to seed provisioning, perhaps of epigenetic nature, were probably involved in the transgenerational plasticity of P. pinaster, mediating its resistance to biotic stress. Our findings suggest that the transgenerational resistance of pines due to an abiotic stress may interact with the defensive response of pines to a biotic stress.The resistance to abiotic stress is increasingly recognised as being impacted by maternal effects, given that environmental conditions experienced by parent (mother) trees affect stress tolerance in offspring. We hypothesised that abiotic environmental maternal effects may also mediate the resistance of trees to biotic stress. The influence of maternal environment and maternal genotype and the interaction of these two factors on early resistance of  A plant’s phenotype may depend not only on its genotype and the environmental conditions where it grows, as conventionally thought, but can also be determined by the environment experienced by the parents (mainly the mother) The maternal environment is known to influence many traits, e.g. seed traits Transgenerational responses to maternal environments are not restricted to abiotic cues. Biotic stresses are also known drivers of transgenerational phenotypic changes  issued the permission for sampling. For fungal isolation and plant cultivation, no specific permissions were required for these activities. The field study (sampling) did not involve endangered or protected species.P. pinaster seed orchards established within the Galician Maritime Pine Breeding Programme  to provide seeds of high genetic quality for reforestation in the area. In this programme, 116 unrelated genotypes selected for superior growth and stem form along the Atlantic coast of Galicia were included in each plantation , in a favourable maternal environment for P. pinaster in terms of growth and reproduction rate, with mild temperatures, adequate moisture throughout the year and well drained, deep soil. The Monfero seed orchard , however, is sited in an unfavourable maternal environment for P. pinaster, with low winter and spring temperatures, extreme wind exposure, and thin soil exposed to waterlogging  were sampled. Cones were oven-dried at 35°C and all seeds were removed. Twelve randomly selected filled seeds per cone were used. Seeds were individually weighed (±0.0001 g) and stored at 4°C until sowing. A total of 1440 seeds  were sown.F. circinatum strain used (MAT-2) was isolated in May 2011 from a stem canker on a P. radiata tree in Cantabria, northern Spain . Species identification was based on morphology and confirmed using molecular techniques after the virulence of the strain had been tested. As F. circinatum virulence in Spain is fairly homogeneous F. circinatum strains do not reveal significantly different rankings of susceptibility among the same host genotypes The In April 2010, pre-weighed seeds were sown in 2 l pots containing a commercial substrate of sandy soil and peat  and covered with a thin layer of sterilised white sand. Pots were set in a factorial design of 12 blocks, each including one plant from each of the 120 cones, with both the maternal environment and the maternal genotype randomly distributed within each block. Seedlings were grown in a greenhouse at Universidad de Extremadura , with temperatures fluctuating in the range of 23±5°C, under 70% full sunlight, and were watered every 2–3 days as necessary. Individual seedling height was measured monthly and individual stem diameter at ground level was measured when plant material was 18 months old.F. circinatum and the other half were mock inoculated with distilled sterile water to serve as control. On the day of inoculation, a spore suspension was prepared by firstly flooding F. circinatum cultures growing on PDA plates with a sterilised aqueous solution of 0.5 g KCl l–1 and then gently scraping the fungus from the surface with a sterile glass slide. The suspended fungal biomass was filtered through a double layer of sterile cheesecloth that retained mycelium and allowed most of the spores to pass through. The concentration of spores in the suspension was estimated using a hematocytometer and adjusted to a final concentration of 5·103 spores ml−1On 17 October 2011, when plant material was 18 months old and 47.0±0.3 cm tall, half the blocks were inoculated with Four weeks after inoculation, seedlings were examined. Bark was removed from the area surrounding the inoculation point and the length of the lesion on the sapwood was measured to the nearest mm . When neF. circinatum from a random subsample of 25% of the seedlings harvested was re-isolated. A 5-cm segment near the point of inoculation was cut from each stem. Needles were removed and the stem surface was disinfested firstly in an aqueous solution of 0.1% (v/v) Tween® 20 , then 70% (v/v) ethanol for 30 s and finally 20% (v/v) bleach for 1 minute. Each segment was plated onto FSM agar F. circinatum were identified morphologically To confirm the presence of the fungus in the inoculated seedlings, The effects of design factors on seedling growth (monthly seedling heights and stem diameters) were analysed with a general linear mixed model using the SAS PROC-MIXED procedure. For height growth analysis, we first attempted to fit a repeated measures mixed model, but this failed to converge. We then analysed each monthly height separately using a hierarchical model similar to those used to solve a split-split design, with three levels of nested experimental units  For necrosis length analysis we used a general mixed model similar to the one just described, adding the fixed effect of the inoculation treatment (I) and its interaction with all the fixed and random factors indicated above. The I×E interaction indicates whether the maternal environment influences necrosis length after inoculation; i.e., whether there are significant maternal effects on seedling resistance to the pathogen. The I×G interaction random term accounts for genetic variation in susceptibility to the pathogen, whereas the I×G×E interaction was interpreted as genetic variation in the transmission of environmental maternal effects. Germination time was included again as a fixed covariate, but was removed as it was not significant and did not improve the resolution of the model.As seed size differed between the maternal environments due to contrasting site quality , but no differences between genotypes were observed .Without adjusting for seed mass covariation, seedling height was significantly influenced by both maternal environment and maternal genotype -left. ThF ratios . At age F ratios  and seedF ratios . Stem diThe effect of maternal environment on seedling height and diameter became negligible after including the covariation with the seed mass of each individual seedling in the mixed model . This reNo seedlings died during the study and no control seedlings showed necrosis lengths greater than the 0.3 cm caused by the drill bit. Only 3% of inoculated seedlings had lesion lengths less than or equal to 0.3 cm and were considered non-susceptible .Mean necrosis length was 16% shorter in inoculated seedlings from the favourable maternal environment than in inoculated seedlings from the unfavourable environment , rangingP. pinaster progenies for at least 1.5 years after sowing. Seedlings grown from favourable maternal environment seeds were taller and had thicker stems than seedlings grown from unfavourable maternal environment seeds and this trend was consistent among all genotypes. Earlier studies also identified larger offspring phenotypes from seeds from favourable maternal environments P. pinaster seedling height became less pronounced with time, confirming that maternal environment effects mediated by seed provisioning diminish with age Abiotic conditions of the maternal environment influenced height and stem diameter of Seed mass is a key trait for many aspects of the ecology and evolution of plant species F. circinatum was largely influenced by maternal environment and maternal genotype. Interestingly, the influence of these factors was significant even after considering the differential seed provisioning in the model. This result suggests that transgenerational defensive plasticity to pitch canker disease in P. pinaster offspring was not a passive response of resource availability in seed provisioning. In the following, transgenerational responses will be referred to as the effects transmitted to a single generation.The results showed that the plant damage caused by Most studies examining transgenerational plastic responses of plants have focused on determining whether seedlings derived from mother plants growing under a particular biotic or abiotic stress are better able to tolerate that particular stress P. pinaster trees in breeding programmes would not necessarily imply increased susceptibility to pathogens.Growth and resistance are not independent features of an individual plant, and usually there is a trade-off between the two Seed mass had no statistical relation to seedling ability to resist the pathogen. Other non-exclusive transmission factors may be involved in this form of transgenerational plasticity. Seeds from the two contrasting maternal environments could, for example, differ qualitatively in their seed biochemistry. Differential allocation of defensive chemicals and/or defence-inducing hormones to seeds may influence seedling resistance P. radiata in response to F. circinatum infection has been demonstrated in greenhouse and field conditions P. pinaster resistance to pitch canker disease Systemic induced resistance of F. circinatum traits in the subsequent generation, up to 1.5 years after germination. Quantitative maternal investment in seed mass explained the transgenerational plasticity of seedling growth but not of seedling resistance to F. circinatum, in which other mechanisms must be involved. The P. pinaster progenies derived from mother trees growing in a favourable maternal environment in terms of growth and reproduction showed higher growth rates and improved resistance to the F. circinatum pathogen than progenies derived from mother trees growing in the unfavourable environment. From a practical point of view, we recommend examining the possibilities of transgenerational responses as drivers of seedling resistance and phenotypic performance in nurseries, breeding programmes, forests plantations and management practices.This study has demonstrated that abiotic differences in the maternal environment affected both plant growth and resistance to Figure S1Necrosis length of Pinus pinaster seedlings four weeks after inoculation with the Fusarium circinatum pathogen. Scale bar = 0.3 cm.(TIF)Click here for additional data file.Figure S2Mean seed mass of Pinus pinaster seedlings. Seedlings were derived from 10 maternal genotypes clonally replicated in two contrasting maternal environments, one favourable (white bars) and one unfavourable (black bars) for pine growth and reproduction. Means ± standard errors are shown (N = 72).(TIF)Click here for additional data file.Figure S3Mean seedling height of Pinus pinaster seedlings. Seedlings were derived from 10 maternal genotypes clonally replicated in two contrasting maternal environments, one favourable (white bars) and one unfavourable (black bars) for pine growth and reproduction. Means ± standard errors are shown (N = 72).(TIF)Click here for additional data file.Table S1Climatic, edaphic and dasometric characteristics of the two contrasting maternal environments.(DOC)Click here for additional data file.Table S2Results of the general linear mixed model for analysis of individual seed mass of Pinus pinaster.(DOC)Click here for additional data file.Table S3Results of the general linear mixed model for analysis of monthly heights (February–October 2011) of the Pinus pinaster seedlings.(DOC)Click here for additional data file.


HFE (6p22) was associated with MCH and MCHC in this family. Linkage studies like the one presented here will allow investigators to focus the search for rare variants amidst the noise encountered in the large amounts of data generated by whole-genome sequencing.We characterized a large Amish pedigree and, in 384 pedigree members, analyzed the genetic variance components with covariate screen as well as genome-wide quantitative trait locus (QTL) linkage analysis of red blood cell count (RBC), hemoglobin (HB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), platelet count (PLT), and white blood cell count (WBC) using SOLAR. Age and gender were found to be significant covariates in many CBC traits. We obtained significant heritability estimates for RBC, MCV, MCH, MCHC, RDW, PLT, and WBC. We report four candidate loci with Logarithm of the odds (LOD) scores above 2.0: 6q25 (MCH), 9q33 (WBC), 10p12 (RDW), and 20q13 (MCV). We also report eleven candidate loci with LOD scores between 1.5 and <2.0. Bivariate linkage analysis of MCV and MCH on chromosome 20 resulted in a higher maximum LOD score of 3.14. Linkage signals on chromosomes 4q28, 6p22, 6q25, and 20q13 are concomitant with previously reported QTL. All other linkage signals reported herein represent novel evidence of candidate QTL. Interestingly rs1800562, the most common causal variant of hereditary hemochromatosis in  Complete blood counts (CBC) are one of the most commonly ordered laboratory tests. Standard CBC analyses report several erythrocyte measures including red blood cell count (RBC), hemoglobin levels (HB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC) and red blood cell distribution width (RDW) as well as platelet count (PLT) and white blood cell count (WBC). In addition to providing a cursory overall evaluation of current status, several associations have been reported between CBC traits and thrombotic disease. RBC and HCT demonstrate association with increased risk of cardiovascular disease (CVD); HCT and HB are risk factors for ischemic heart disease; MCV is a risk factor for peripheral artery disease (PAD); and RDW is associated with increased risk of myocardial infarction, heart failure, and stroke  and 16p13.3 , 6q23.3 , 6q24.1 (CITED2), 10p11.21, 12p24, and 22q12.3 (TMPRSS6)  of CBC traits. Within Caucasian, African-American, and Japanese population cohorts, associations with different erythrocyte traits were reported on chromosomes 6p22  resulting in VWD type 2. The number of examined pedigree members included in the analysis presented herein is 384; eleven pedigree members were not included because of their uncertain position in the pedigree. All 384 samples were obtained over the course of a 3-day outreach clinic and CBC's were analyzed within 24 h of collection. Although there is limited outbreeding in the Amish community, no consanguineous marriages were identified. Sample processing, aliquoting, and freezing when indicated were performed immediately on site at each clinic. CBC analyses were performed within 24 h for all individuals and consisted of RBC, HB, HCT, MCV, MCH, MCHC, RDW, PLT, and WBC. RBC, HB, and MCV were directly measured, whereas HCT, MCH, and MCHC were mathematically derived from directly measured erythrocyte traits   in http://research.marshfieldclinic.org/genetics/home/index.asp), using Screening Set 16 with 400 short tandem repeat polymorphic (STRP) markers at a 10 centiMorgan (cM) average interval. This generated a total of approximately 160,000 genotypes for the 384 samples. Data were processed and linkage analysis data files were created. Mendelian inconsistencies were identified using the computer program PEDCHECK and resolved (O'Connell and Weeks DNA was extracted with the Gentra Puregene Kit (Qiagen) and genotyping of pedigree members was completed by the NHLBI Mammalian Genotyping Service at Marshfield, WI (HFE C282Y) and rs687289 (ABO) were genotyped in the Amish cohort using a commercially available 5′ allelic discrimination high throughput fluorescent genotyping assay  , and VWF C4120T genotype as covariates for RBC, HB, HCT, MCV, MCH, MCHC, RDW, PLT, and WBC was assessed  and the significance of age, gender, rs687289  for each marker. Table HFE, the most common causal variant of hereditary hemochromatosis (MIM #235200). Within the Amish cohort, rs1800562 was found to be in Hardy–Weinberg equilibrium with a minor allele frequency of 0.16. With adjustment for age and gender, there was a significant association of rs1800562 genotype with MCH (P = 0.0036) and MCHC (P = 0.0056), but not with RBC (P = 0.4966), HB (P = 0.0666), HCT (P = 0.5241), MCV (P = 0.3354), or RDW (P = 0.1025).rs1800562, which has been associated erythrocyte traits in multiple studies , correspCBC traits are multifactorial phenotypes determined by environmental and genetic components. Several genes have been implicated in red cell biology due to the identification of rare mutations in patients with Mendelian phenotypes. However, common variants in these same and other genes are likely involved in modulating more mild variation in hematologic phenotypes. In this study in a large Amish pedigree, which is expected to be environmentally more homogeneous, we found novel QTL that will allow for the discovery of genetic variants involved in erythropoiesis and red cell physiology.SEC23B is a COPII vesicle coat component involved in ER-to-Golgi trafficking. Mutations in SEC23B have been identified in several familial cases of congenital dyserythropoietic anemia (CDA) type II, a form of anemia due to ineffective erythropoiesis that is characterized by phenotypic variability, although interestingly, these patients do not have evident abnormalities in the MCV or MCH , and encodes a bone morphogenetic protein that is part of the transforming growth factor-beta superfamily. Although not yet implicated in erythropoiesis, a significant association was demonstrated between rs235756 in BMP2 and ferritin levels in a study of genetic modifiers of hemochromatosis. , a member of the Src tyrosine kinase family primarily expressed in myeloid and B-lymphoid cell lineages, are also contained within the 1-LOD interval but are less likely modulators of erythrocyte traits. EPB41, which maps to chromosome 1p35.3, encodes an erythrocyte membrane protein (erythrocyte membrane protein band 4.1) that complexes with actin and spectrin provide shape to the discoid erythrocyte  or other erythrocyte traits : MAD2L1, EXOSC9, and CCNA2. MAD2L1 encodes a component of the mitotic spindle assembly checkpoint and is homologous to MAD2, a checkpoint component that stabilizes kinetochore-microtubule attachments (Kabeche and Compton CCNA2) is a mammalian cell cycle regulator that promotes G1/S and G2/M transitions, and siRNA-mediated knockdown of CCNA2 in K562 cells resulted in a significant decrease in erythroid differentiation following doxorubicin administration  interacting protein, a component of the kinetochore complex. Overexpression of Hls7, the mouse ortholog of MLF1, results in suppression of erythropoietin-induced differentiation of the J2E erythroleukemic cell line, suggesting MLF1 and its binding partners (including MLF1IP) may play a role in suppression of erythrocyte maturation  and whole-genome sequencing, linkage studies in large pedigrees like the one presented here will likely allow investigators to focus the search for rare variants amidst the noise encountered in the large amounts of data generated by these new technologies. A recent report by Desch et al. underscores this concept. In two young healthy large cohorts, making use of some of the sibling structure within the cohorts, they found a strong linkage signal in chromosome 2 for von Willebrand Factor levels that had not been detected by association in the same group, suggesting that aggregates of rare variants may explain some of the heritability for that trait (Desch et al.