Direct power comparisons between simple LOD scores and NPL scores for linkage analysis in complex diseases.

Several methods have been proposed for linkage analysis of complex traits with unknown mode of inheritance. These methods include the LOD score maximized over disease models (MMLS) and the "nonparametric" linkage (NPL) statistic. In previous work, we evaluated the increase of type I error when maximizing over two or more genetic models, and we compared the power of MMLS to detect linkage, in a number of complex modes of inheritance, with analysis assuming the true model. In the present study, we compare MMLS and NPL directly. We simulated 100 data sets with 20 families each, using 26 generating models: (1) 4 intermediate models (penetrance of heterozygote between that of the two homozygotes); (2) 6 two-locus additive models; and (3) 16 two-locus heterogeneity models (admixture alpha = 1.0,.7,.5, and.3; alpha = 1.0 replicates simple Mendelian models). For LOD scores, we assumed dominant and recessive inheritance with 50% penetrance. We took the higher of the two maximum LOD scores and subtracted 0.3 to correct for multiple tests (MMLS-C). We compared expected maximum LOD scores and power, using MMLS-C and NPL as well as the true model. Since NPL uses only the affected family members, we also performed an affecteds-only analysis using MMLS-C. The MMLS-C was both uniformly more powerful than NPL for most cases we examined, except when linkage information was low, and close to the results for the true model under locus heterogeneity. We still found better power for the MMLS-C compared with NPL in affecteds-only analysis. The results show that use of two simple modes of inheritance at a fixed penetrance can have more power than NPL when the trait mode of inheritance is complex and when there is heterogeneity in the data set.     

A comparison of different linkage statistics in small to moderate sized pedigrees with complex diseases

ABSTRACT: BACKGROUND: In the last years GWA studies have successfully identified common SNPs associated with complex diseases. However, most of the variants found this way account for only a small portion of the trait variance. This fact leads researchers to focus on rare-variant mapping with large scale sequencing, which can be facilitated by using linkage information. The question arises why linkage analysis often fails to identify genes when analyzing complex diseases. Using simulations we have investigated the power of parametric and nonparametric linkage statistics (KC-LOD, NPL, LOD and MOD scores), to detect the effect of genes responsible for complex diseases using different pedigree structures. RESULTS: As expected, a small number of pedigrees with less than three affected individuals has low power to map disease genes with modest effect. Interestingly, the power decreases when unaffected individuals are included in the analysis, irrespective of the true mode of inheritance. Furthermore, we found that the best performing statistic depends not only on the type of pedigrees but also on the true mode of inheritance. CONCLUSIONS: When applied in a sensible way linkage is an appropriate and robust technique to map genes for complex disease. Unlike association analysis, linkage analysis is not hampered by allelic heterogeneity. So, why does linkage analysis often fail with complex diseases? Evidently, when using an insufficient number of small pedigrees, one might miss a true genetic linkage when actually a real effect exists. Furthermore, we show that the test statistic has an important effect on the power to detect linkage as well. Therefore, a linkage analysis might fail if an inadequate test statistic is employed. We provide recommendations regarding the most favorable test statistics, in terms of power, for a given mode of inheritance and type of pedigrees under study, in order to reduce the probability to miss a true linkage.     

Two-locus models of disease: comparison of likelihood and nonparametric linkage methods.

The power to detect linkage for likelihood and nonparametric (Haseman-Elston, affected-sib-pair, and affected-pedigree-member) methods is compared for the case of a common, dichotomous trait resulting from the segregation of two loci. Pedigree data for several two-locus epistatic and heterogeneity models have been simulated, with one of the loci linked to a marker locus. Replicate samples of 20 three-generation pedigrees (16 individuals/pedigree) were simulated and then ascertained for having at least 6 affected individuals. The power of linkage detection calculated under the correct two-locus model is only slightly higher than that under a single locus model with reduced penetrance. As expected, the nonparametric linkage methods have somewhat lower power than does the lod-score method, the difference depending on the mode of transmission of the linked locus. Thus, for many pedigree linkage studies, the lod-score method will have the best power. However, this conclusion depends on how many times the lod score will be calculated for a given marker. The Haseman-Elston method would likely be preferable to calculating lod scores under a large number of genetic models (i.e., varying both the mode of transmission and the penetrances), since such an analysis requires an increase in the critical value of the lod criterion. The power of the affected-pedigree-member method is lower than the other methods, which can be shown to be largely due to the fact that marker genotypes for unaffected individuals are not used.     

Estimating the power of a proposed linkage study for a complex genetic trait.

Many genetic traits have complex modes of inheritance; they may exhibit incomplete or age-dependent penetrance or fail to show any clear Mendelian inheritance pattern. As primary linkage maps for the human genome near completion, it is becoming increasingly possible to map these traits. Prior to undertaking a linkage study, it is important to consider whether the pedigrees available for the proposed study are likely to provide sufficient information to demonstrate linkage, assuming a linked marker is tested. In the current paper, we describe a computer simulation method to estimate the power of a proposed study to detect linkage for a complex genetic trait, given a hypothesized genetic model for the trait. Our method simulates trait locus genotypes consistent with observed trait phenotypes, in such a way that the probability to detect linkage can be estimated by sample statistics of the maximum lod score distribution. The method uses terms available when calculating the likelihood of the trait phenotypes for the pedigree and is applicable to any trait determined by one or a few genetic loci; individual-specific environmental effects can also be dealt with. Our method provides an objective answer to the question, Will these pedigrees provide sufficient information to map this complex genetic trait?     

Osteopenia in 37 members of seven families: analysis based on a model of dominant inheritance.

BACKGROUND: The genetic factors involved in determining bone mineral density (BMD) have not been fully elucidated. We have begun genetic linkage analysis of seven families in which many members are osteopenic, in order to identify chromosomal loci that are potentially involved in determining BMD. MATERIALS AND METHODS: Spine BMD was measured in 143 members of seven kindred with familial osteopenia. The absolute BMD values for the spine (L2-L4) were converted to the age-, gender-, and weight-adjusted Z scores, and this corrected value was used as the quantitative trait on which to base subsequent genetic analyses. Simulations of linkage were performed in order to determine the information content of the pedigree set, and actual linkage analysis was conducted using polymorphic markers either within or near three candidate loci: COL1A1, COL1A2, and vitamin D receptor (VDR). RESULTS: The distribution of the corrected Z scores was bimodal (p = 0.001) suggesting a monogenic mode of inheritance of the low BMD trait. Simulation of linkage analysis suggested that the family data set was sufficient to detect linkage under a single major gene model. Actual linkage analysis did not support linkage to the three candidate loci. In addition, the VDR genotype was not statistically associated with low bone density at the spine. CONCLUSIONS: Loci other than COL1A1, COL1A2 and VDR are very likely responsible for the low BMD trait observed in these families. These families are suitable for a genome-wide screen using microsatellite repeats in order to identify the loci that are involved in osteopenia.     

Power comparison of parametric and nonparametric linkage tests in small pedigrees

When the mode of inheritance of a disease is unknown, the LOD-score method of linkage analysis must take into account uncertainties in model parameters. We have previously proposed a parametric linkage test called "MFLOD," which does not require specification of disease model parameters. In the present study, we introduce two new model-free parametric linkage tests, known as "MLOD" and "MALOD." These tests are defined, respectively, as the LOD score and the admixture LOD score, maximized (subject to the same constraints as MFLOD) over disease-model parameters. We compared the power of these three parametric linkage tests and that of two nonparametric linkage tests, NPLall and NPLpairs, which are implemented in GENEHUNTER. With the use of small pedigrees and a fully informative marker, we found the powers of MLOD, NPLall, and NPLpairs to be almost equivalent to each other and not far below that of a LOD-score analysis performed under the assumption the correct genetic parameters. Thus, linkage analysis is not much hindered by uncertain mode of inheritance. The results also suggest that both parametric and nonparametric methods are suitable for linkage analysis of complex disorders in small pedigrees. However, whether these results apply to large pedigrees remains to be answered.     

Parametric and nonparametric multipoint linkage analysis with imprinting and two-locus-trait models: application to mite sensitization

We present two extensions to linkage analysis for genetically complex traits. The first extension allows investigators to perform parametric (LOD-score) analysis of traits caused by imprinted genes-that is, of traits showing a parent-of-origin effect. By specification of two heterozygote penetrance parameters, paternal and maternal origin of the mutation can be treated differently in terms of probability of expression of the trait. Therefore, a single-disease-locus-imprinting model includes four penetrances instead of only three. In the second extension, parametric and nonparametric linkage analysis with two trait loci is formulated for a multimarker setting, optionally taking imprinting into account. We have implemented both methods into the program GENEHUNTER. The new tools, GENEHUNTER-IMPRINTING and GENEHUNTER-TWOLOCUS, were applied to human family data for sensitization to mite allergens. The data set comprises pedigrees from England, Germany, Italy, and Portugal. With single-disease-locus-imprinting MOD-score analysis, we find several regions that show at least suggestive evidence for linkage. Most prominently, a maximum LOD score of 4.76 is obtained near D8S511, for the English population, when a model that implies complete maternal imprinting is used. Parametric two-trait-locus analysis yields a maximum LOD score of 6.09 for the German population, occurring exactly at D4S430 and D18S452. The heterogeneity model specified for analysis alludes to complete maternal imprinting at both disease loci. Altogether, our results suggest that the two novel formulations of linkage analysis provide valuable tools for genetic mapping of multifactorial traits.     

Genomewide linkage scan for split-hand/foot malformation with long-bone deficiency in a large Arab family identifies two novel susceptibility loci on chromosomes 1q42.2-q43 and 6q14.1

Split-hand/foot malformation with long-bone deficiency (SHFLD) is a rare, severe limb deformity characterized by tibia aplasia with or without split-hand/split-foot deformity. Identification of genetic susceptibility loci for SHFLD has been unsuccessful because of its rare incidence, variable phenotypic expression and associated anomalies, and uncertain inheritance pattern. SHFLD is usually inherited as an autosomal dominant trait with reduced penetrance, although recessive inheritance has also been postulated. We conducted a genomewide linkage analysis, using a 10K SNP array in a large consanguineous family (UR078) from the United Arab Emirates (UAE) who had disease transmission consistent with an autosomal dominant inheritance pattern. The study identified two novel SHFLD susceptibility loci at 1q42.2-q43 (nonparametric linkage [NPL] 9.8, P=.000065) and 6q14.1 (NPL 7.12, P=.000897). These results were also supported by multipoint parametric linkage analysis. Maximum multipoint LOD scores of 3.20 and 3.78 were detected for genomic locations 1q42.2-43 and 6q14.1, respectively, with the use of an autosomal dominant mode of inheritance with reduced penetrance. Haplotype analysis with informative crossovers enabled mapping of the SHFLD loci to a region of approximately 18.38 cM (8.4 Mb) between single-nucleotide polymorphisms rs1124110 and rs535043 on 1q42.2-q43 and to a region of approximately 1.96 cM (4.1 Mb) between rs623155 and rs1547251 on 6q14.1. The study identified two novel loci for the SHFLD phenotype in this UAE family.     

Further evidence for the increased power of LOD scores compared with nonparametric methods.

In genetic analysis of diseases in which the underlying model is unknown, "model free" methods-such as affected sib pair (ASP) tests-are often preferred over LOD-score methods, although LOD-score methods under the correct or even approximately correct model are more powerful than ASP tests. However, there might be circumstances in which nonparametric methods will outperform LOD-score methods. Recently, Dizier et al. reported that, in some complex two-locus (2L) models, LOD-score methods with segregation analysis-derived parameters had less power to detect linkage than ASP tests. We investigated whether these particular models, in fact, represent a situation that ASP tests are more powerful than LOD scores. We simulated data according to the parameters specified by Dizier et al. and analyzed the data by using a (a) single locus (SL) LOD-score analysis performed twice, under a simple dominant and a recessive mode of inheritance (MOI), (b) ASP methods, and (c) nonparametric linkage (NPL) analysis. We show that SL analysis performed twice and corrected for the type I-error increase due to multiple testing yields almost as much linkage information as does an analysis under the correct 2L model and is more powerful than either the ASP method or the NPL method. We demonstrate that, even for complex genetic models, the most important condition for linkage analysis is that the assumed MOI at the disease locus being tested is approximately correct, not that the inheritance of the disease per se is correctly specified. In the analysis by Dizier et al., segregation analysis led to estimates of dominance parameters that were grossly misspecified for the locus tested in those models in which ASP tests appeared to be more powerful than LOD-score analyses.     

Detection of a major gene for heterocellular hereditary persistence of fetal hemoglobin after accounting for genetic modifiers.

"Heterocellular hereditary persistence of fetal hemoglobin" (HPFH) is the term used to describe the genetically determined persistence of fetal hemoglobin (Hb F) production into adult life, in the absence of any related hematological disorder. Whereas some forms are caused by mutations in the beta-globin gene cluster on chromosome 11, others segregate independently. While the latter are of particular interest with respect to the regulation of globin gene switching, it has not been possible to determine their chromosomal location, mainly because their mode of inheritance is not clear, but also because several other factors are known to modify Hb F production. We have examined a large Asian Indian pedigree which includes individuals with heterocellular HPFH associated with beta-thalassemia and/or alpha-thalassemia. Segregation analysis was conducted on the HPFH trait FC, defined to be the percentage of Hb F-containing cells (F-cells), using the class D regressive model. Our results provide evidence for the presence of a major gene, dominant or codominant, which controls the FC values with residual familial correlations. The major gene was detected when the effects of genetic modifiers, notably beta-thalassemia and the XmnI-G gamma polymorphism, are accounted for in the analysis. Linkage with the beta-globin gene cluster is excluded. The transmission of the FC values in this pedigree is informative enough to allow detection of linkage with an appropriate marker(s). The analytical approach outlined in this study, using simple regression to allow for genetic modifiers and thus allowing the mode of inheritance of a trait to be dissected out, may be useful as a model for segregation and linkage analyses of other complex phenotypes.     

Combining the meiosis Gibbs sampler with the random walk approach for linkage and association studies with a general complex pedigree and multimarker loci

A linkage analysis for finding inheritance states and haplotype configurations is an essential process for linkage and association mapping. The linkage analysis is routinely based upon observed pedigree information and marker genotypes for individuals in the pedigree. It is not feasible for exact methods to use all such information for a large complex pedigree especially when there are many missing genotypic data. Proposed Markov chain Monte Carlo approaches such as a single-site Gibbs sampler or the meiosis Gibbs sampler are able to handle a complex pedigree with sparse genotypic data; however, they often have reducibility problems, causing biased estimates. We present a combined method, applying the random walk approach to the reducible sites in the meiosis sampler. Therefore, one can efficiently obtain reliable estimates such as identity-by-descent coefficients between individuals based on inheritance states or haplotype configurations, and a wider range of data can be used for mapping of quantitative trait loci within a reasonable time.     

Genomewide linkage scan identifies a novel susceptibility locus for restless legs syndrome on chromosome 9p

Restless legs syndrome (RLS) is a common neurological disorder that affects 5%-12% of all whites. To genetically dissect this complex disease, we characterized 15 large and extended multiplex pedigrees, consisting of 453 subjects (134 affected with RLS). A familial aggregation analysis was performed, and SAGE FCOR was used to quantify the total genetic contribution in these families. A weighted average correlation of 0.17 between first-degree relatives was obtained, and heritability was estimated to be 0.60 for all types of relative pairs, indicating that RLS is a highly heritable trait in this ascertained cohort. A genomewide linkage scan, which involved >400 10-cM-spaced markers and spanned the entire human genome, was then performed for 144 individuals in the cohort. Model-free linkage analysis identified one novel significant RLS-susceptibility locus on chromosome 9p24-22 with a multipoint nonparametric linkage (NPL) score of 3.22. Suggestive evidence of linkage was found on chromosome 3q26.31 (NPL score 2.03), chromosome 4q31.21 (NPL score 2.28), chromosome 5p13.3 (NPL score 2.68), and chromosome 6p22.3 (NPL score 2.06). Model-based linkage analysis, with the assumption of an autosomal-dominant mode of inheritance, validated the 9p24-22 linkage to RLS in two families (two-point LOD score of 3.77; multipoint LOD score of 3.91). Further fine mapping confirmed the linkage result and defined this novel RLS disease locus to a critical interval. This study establishes RLS as a highly heritable trait, identifies a novel genetic locus for RLS, and will facilitate further cloning and identification of the genes for RLS.     

A Likelihood-Based Trait-Model-Free Approach for Linkage Detection of Binary Trait

Summary .  Trait-model-free (or "allele-sharing") approach to linkage analysis is a popular tool in genetic mapping of complex traits, because of the absence of explicit assumptions about the underlying mode of inheritance of the trait. The likelihood framework introduced by Kong and Cox (1997,  American Journal of Human Genetics   61, 1179–1188) allows calculation of accurate p-values and LOD scores to test for linkage between a genomic region and a trait. Their method relies on the specification of a model for the trait-dependent segregation of marker alleles at a genomic region linked to the trait. Here we propose a new such model that is motivated by the desire to extract as much information as possible from extended pedigrees containing data from individuals related over several generations. However, our model is also applicable to smaller pedigrees, and has some attractive features compared with existing models ( Kong and Cox, 1997 ), including the fact that it incorporates information on both affected and unaffected individuals. We illustrate the proposed model on simulated and real data, and compare its performance with the existing approach ( Kong and Cox, 1997 ). The proposed approach is implemented in the program lm_ibdtests within the framework of MORGAN 2.8 ( http://www.stat.washington.edu/thompson/Genepi/MORGAN/Morgan.shtml ).     

Y-linkage and pseudoautosomal linkage.

Presently existing computer program allow for an autosomal or an X-linked mode of inheritance of loci to be analyzed for genetic linkage. They do not, however, specifically allow for more general sex-linked modes of inheritance. This study proposes methods that permit the carrying out of linkage analyses of loci following a Y-linked or a pseudoautosomal mode of inheritance.     

HLODs, trait models, and ascertainment: implications of admixture for parameter estimation and linkage detection

Maximizing the homogeneity lod is known to be an appropriate procedure for estimating parameters of the trait model in an approximately 'ascertainment assumption free' (AAF) manner. We have investigated whether this same property also holds for the heterogeneity lod (HLOD). We show that, when the genetic models at linked and unlinked loci differ, HLODs are not AAF, and maximizing the HLOD yields parameter estimates that are for all practical purposes meaningless; indeed, the admixture parameter alpha does not even measure the proportion of linked families within the sample, as is commonly supposed. In spite of this, our results confirm a large body of evidence supporting the use of HLODs as robust tools for linkage detection, and suggest further that maximizing the HLOD over both alpha and parameters of the trait model can improve accuracy in estimation of the recombination fraction theta;. These findings have important implications for the optimal handling of nuisance parameters in linkage analysis, particularly when evaluating the evidence for or against linkage based on multiple independent heterogeneous sets of data.     

Complete multipoint sib-pair analysis of qualitative and quantitative traits.

Sib-pair analysis is an increasingly important tool for genetic dissection of complex traits. Current methods for sib-pair analysis are primarily based on studying individual genetic markers one at a time and thus fail to use the full inheritance information provided by multipoint linkage analysis. In this paper, we describe how to extract the complete multipoint inheritance information for each sib pair. We then describe methods that use this information to map loci affecting traits, thereby providing a unified approach to both qualitative and quantitative traits. Specifically, complete multipoint approaches are presented for (1) exclusion mapping of qualitative traits; (2) maximum-likelihood mapping of qualitative traits; (3) information-content mapping, showing the extent to which all inheritance information has been extracted at each location in the genome; and (4) quantitative-trait mapping, by two parametric methods and one nonparametric method. In addition, we explore the effects of marker density, marker polymorphism, and availability of parents on the information content of a study. We have implemented the analysis methods in a new computer package, MAPMAKER/SIBS. With this computer package, complete multipoint analysis with dozens of markers in hundreds of sib pairs can be carried out in minutes.     

Reinforcement and sex linkage

We present a general model for the effect of sex linkage on the evolution of reinforcement of mating preferences on an island. We find that the level of reinforcement can vary up to 80% depending on the mode of inheritance of the female preference and male trait. When reinforcement is driven mainly by selection in the male trait and intrinsic hybrid incompatibilities are weak, sex-linked preferences and autosomal male traits are the most conducive to reinforcement, whereas autosomal preferences and X-linked traits are the least. Surprisingly, the effect of mode of inheritance on reinforcement is poorly predicted by its effect on the genetic correlation between the male trait and female preference. Sex-linkage of genetic incompatibility loci increases reinforcement, though this is not due solely to the occurrence of Haldane's rule. We find that reinforcement can lead to complete reproductive isolation in some cases but not others and that the mode of inheritance can determine which outcome occurs.     

Parametric and nonparametric linkage analysis: a unified multipoint approach.

In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.     

A genome‐wide quantitative trait loci scan of neurocognitive performances in families with schizophrenia

Patients with schizophrenia frequently display neurocognitive dysfunction, and genetic studies suggest it to be an endophenotype for schizophrenia. Genetic studies of such traits may thus help elucidate the biological pathways underlying genetic susceptibility to schizophrenia. This study aimed to identify loci influencing neurocognitive performance in schizophrenia. The sample comprised of 1207 affected individuals and 1035 unaffected individuals of Han Chinese ethnicity from 557 sib‐pair families co‐affected with DSM‐IV (Diagnostic and Statistical Manual, Fourth Edition) schizophrenia. Subjects completed a face‐to‐face semi‐structured interview, the continuous performance test (CPT) and the Wisconsin card sorting test (WCST), and were genotyped with 386 microsatellite markers across the genome. A series of autosomal genome‐wide multipoint nonparametric quantitative trait loci (QTL) linkage analysis were performed in affected individuals only. Determination of genome‐wide empirical significance was performed using 1000 simulated genome scans. One linkage peak attaining genome‐wide significance was identified: 12q24.32 for undegraded CPT hit rate [nonparametric linkage z (NPL‐Z) scores = 3.32, genome‐wide empirical P = 0.03]. This result was higher than the peak linkage signal obtained in the previous genome‐wide scan using a dichotomous diagnosis of schizophrenia. The identification of 12q24.32 as a QTL has not been consistently implicated in previous linkage studies on schizophrenia, which suggests that the analysis of endophenotypes provides additional information from what is seen in analyses that rely on diagnoses. This region with linkage to a particular neurocognitive feature may inform functional hypotheses for further genetic studies for schizophrenia.     

The genetics of phenotypic plasticity. XI. Joint evolution of plasticity and dispersal rate

In a spatially heterogeneous environment, the rate at which individuals move among habitats affects whether selection favors phenotypic plasticity or genetic differentiation, with high dispersal rates favoring trait plasticity. Until now, in theoretical explorations of plasticity evolution, dispersal rate has been treated as a fixed, albeit probabilistic, characteristic of a population, raising the question of what happens when the propensity to disperse and trait plasticity are allowed to evolve jointly. We examined the effects of their joint evolution on selection for plasticity using an individual-based computer simulation model. In the model, the environment consisted of a linear gradient of 50 demes with dispersal occurring either before or after selection. Individuals consisted of loci whose phenotypic expression either are affected by the environment (plastic) or are not affected (nonplastic), plus a locus determining the propensity to disperse. When dispersal rate and trait plasticity evolve jointly, the system tends to dichotomous outcomes of either high trait plasticity and high dispersal, or low trait plasticity and low dispersal. The outcome strongly depended on starting conditions, with high trait plasticity and dispersal favored when the system started at high values for either trait plasticity or dispersal rate (or both). Adding a cost of plasticity tended to drive the system to genetic differentiation, although this effect also depended on initial conditions. Genetic linkage between trait plasticity loci and dispersal loci further enhanced this strong dichotomy in evolutionary outcomes. All of these effects depended on organismal life history pattern, and in particular whether selection occurred before or after dispersal. These results can explain why adaptive trait plasticity is less common than might be expected.