Posterior Probability Of Linkage Research Articles

A genome-wide linkage study of autism spectrum disorder and the broad autism phenotype in extended pedigrees

BackgroundAlthough several genetic variants for autism spectrum disorder (ASD) have now been identified, these largely occur sporadically or are de novo. Much less progress has been made in identifying inherited variants, even though the disorder itself is familial in the majority of cases. The objective of this study was to identify chromosomal regions that harbor inherited variants increasing the risk for ASD using an approach that examined both ASD and the broad autism phenotype (BAP) among a unique sample of extended pedigrees.MethodsASD and BAP were assessed using standardized tools in 28 pedigrees from Canada and the USA, each with at least three ASD-diagnosed individuals from two nuclear families. Genome-wide linkage analysis was performed using the posterior probability of linkage (PPL) statistic, a quasi-Bayesian method that provides strength of evidence for or against linkage in an essentially model-free manner, with outcomes on the probability scale.ResultsThe results confirm appreciable interfamilial heterogeneity as well as a high level of intrafamilial heterogeneity. Both ASD and combined ASD/BAP specific loci are apparent.ConclusionsInclusion of subclinical phenotypes such as BAP should be more widely employed in genetic studies of ASD as a way of identifying inherited genetic variants for the disorder. Moreover, the results underscore the need for approaches to identifying genetic risk factors in extended pedigrees that are robust to high levels of inter/intrafamilial locus and allelic heterogeneity.

Validation of a microRNA target site polymorphism in H3F3B that is potentially associated with a broad schizophrenia phenotype.

Despite much progress, few genetic findings for schizophrenia have been assessed by functional validation experiments at the molecular level. We previously reported evidence for genetic linkage of broadly defined schizophrenia to chromosome 17q25 in a sample of 24 multiplex families. 2,002 SNPs under this linkage peak were analyzed for evidence of linkage disequilibrium using the posterior probability of linkage (PPL) framework. SNP rs1060120 produced the strongest evidence for association, with a PPLD|L score of 0.21. This SNP is located within the 3'UTR of the histone gene H3F3B and colocalizes with potential gene target miR-616. A custom miRNA target prediction program predicted that the binding of miR-616 to H3F3B transcripts would be altered by the allelic variants of rs1060120. We used dual luciferase assays to experimentally validate this interaction. The rs1060120 A allele significantly reduced luciferase expression, indicating a stronger interaction with miR-616 than the G allele (p = 0.000412). These results provide functional validation that this SNP could alter schizophrenia epigenetic mechanisms thereby contributing to schizophrenia-related disease risk.

Behavioral and Molecular Genetics of Reading-Related AM and FM Detection Thresholds.

Auditory detection thresholds for certain frequencies of both amplitude modulated (AM) and frequency modulated (FM) dynamic auditory stimuli are associated with reading in typically developing and dyslexic readers. We present the first behavioral and molecular genetic characterization of these two auditory traits. Two extant extended family datasets were given reading tasks and psychoacoustic tasks to determine FM 2Hz and AM 20Hz sensitivity thresholds. Univariate heritabilities were significant for both AM (h 2 =0.20) and FM (h 2 =0.29). Bayesian posterior probability of linkage (PPL) analysis found loci for AM (12q, PPL=81%) and FM (10p, PPL=32%; 20q, PPL=65%). Bivariate heritability analyses revealed that FM is genetically correlated with reading, while AM was not. Bivariate PPL analysis indicates that FM loci (10p, 20q) are not also associated with reading.

Revisiting schizophrenia linkage data in the NIMH Repository: reanalysis of regularized data across multiple studies.

The Combined Analysis of Psychiatric Studies (CAPS) project conducted extensive review and regularization across studies of all schizophrenia linkage data available as of 2011 from the National Institute of Mental Health-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI). The authors reanalyzed the data using statistical methods tailored to accumulation of evidence across multiple, potentially highly heterogeneous, sets of data. Data were subdivided based on contributing study, major population group, and presence or absence within families of schizophrenia with a substantial affective component. The posterior probability of linkage (PPL) statistical framework was used to sequentially update linkage evidence across these data subsets (omnibus results). While some loci previously implicated using the HGI data were also identified in the present omnibus analysis (2q36.1, 15q23), others were not. Several loci were found that had not previously been reported in the HGI samples but are supported by independent linkage or association studies (3q28, 12q23.1, 11p11.2, Xq26.1). Not surprisingly, differences were seen across population groups. Of particular interest are signals on 11p15.3, 11p11.2, and Xq26.1, for which data from families with a substantial affective component support linkage while data from the remaining families provide evidence against linkage. All three of these loci overlap with loci reported in independent studies of bipolar disorder or mixed bipolar-schizophrenia samples. Public data repositories provide the opportunity to leverage large multisite data sets for studying complex disorders. Analysis with a statistical method specifically designed for such data enables us to extract new information from an existing data resource.

A molecular genetic study of autism and related phenotypes in extended pedigrees

BackgroundEfforts to uncover the risk genotypes associated with the familial nature of autism spectrum disorder (ASD) have had limited success. The study of extended pedigrees, incorporating additional ASD-related phenotypes into linkage analysis, offers an alternative approach to the search for inherited ASD susceptibility variants that complements traditional methods used to study the genetics of ASD.MethodsWe examined evidence for linkage in 19 extended pedigrees ascertained through ASD cases spread across at least two (and in most cases three) nuclear families. Both compound phenotypes (i.e., ASD and, in non-ASD individuals, the broad autism phenotype) and more narrowly defined components of these phenotypes, e.g., social and repetitive behavior, pragmatic language, and anxiety, were examined. The overarching goal was to maximize the aggregate information available on the maximum number of individuals and to disaggregate syndromic phenotypes in order to examine the genetic underpinnings of more narrowly defined aspects of ASD behavior.ResultsResults reveal substantial between-family locus heterogeneity and support the importance of previously reported ASD loci in inherited, familial, forms of ASD. Additional loci, not seen in the ASD analyses, show evidence for linkage to the broad autism phenotype (BAP). BAP peaks are well supported by multiple subphenotypes (including anxiety, pragmatic language, and social behavior) showing linkage to regions overlapping with the compound BAP phenotype. Whereas 'repetitive behavior’, showing the strongest evidence for linkage (Posterior Probability of Linkage = 62% at 6p25.2-24.3, and 69% at 19p13.3), appears to be linked to novel regions not detected with other compound or narrow phenotypes examined in this study.ConclusionsThese results provide support for the presence of key features underlying the complexity of the genetic architecture of ASD: substantial between-family locus heterogeneity, that the BAP appears to correspond to sets of subclinical features segregating with ASD within pedigrees, and that different features of the ASD phenotype segregate independently of one another. These findings support the additional study of larger, even more individually informative pedigrees, together with measurement of multiple, behavioral- and biomarker-based phenotypes, in both affected and non-affected individuals, to elucidate the complex genetics of familial ASD.

KELVIN: A Software Package for Rigorous Measurement of Statistical Evidence in Human Genetics

This paper describes the software package KELVIN, which supports the PPL (posterior probability of linkage) framework for the measurement of statistical evidence in human (or more generally, diploid) genetic studies. In terms of scope, KELVIN supports two-point (trait-marker or marker-marker) and multipoint linkage analysis, based on either sex-averaged or sex-specific genetic maps, with an option to allow for imprinting; trait-marker linkage disequilibrium (LD), or association analysis, in case-control data, trio data, and/or multiplex family data, with options for joint linkage and trait-marker LD or conditional LD given linkage; dichotomous trait, quantitative trait and quantitative trait threshold models; and certain types of gene-gene interactions and covariate effects. Features and data (pedigree) structures can be freely mixed and matched within analyses. The statistical framework is specifically tailored to accumulate evidence in a mathematically rigorous way across multiple data sets or data subsets while allowing for multiple sources of heterogeneity, and KELVIN itself utilizes sophisticated software engineering to provide a powerful and robust platform for studying the genetics of complex disorders.

Increasing Genotype-Phenotype Model Determinism: Application to Bivariate Reading/Language Traits and Epistatic Interactions in Language-Impaired Families

While advances in network and pathway analysis have flourished in the era of genome-wide association analysis, understanding the genetic mechanism of individual loci on phenotypes is still readily accomplished using genetic modeling approaches. Here, we demonstrate two novel genotype-phenotype models implemented in a flexible genetic modeling platform. The examples come from analysis of families with specific language impairment (SLI), a failure to develop normal language without explanatory factors such as low IQ or inadequate environment. In previous genome-wide studies, we observed strong evidence for linkage to 13q21 with a reading phenotype in language-impaired families. First, we elucidate the genetic architecture of reading impairment and quantitative language variation in our samples using a bivariate analysis of reading impairment in affected individuals jointly with language quantitative phenotypes in unaffected individuals. This analysis largely recapitulates the baseline analysis using the categorical trait data (posterior probability of linkage (PPL) = 80%), indicating that our reading impairment phenotype captured poor readers who also have low language ability. Second, we performed epistasis analysis using a functional coding variant in the brain-derived neurotrophic factor (BDNF) gene previously associated with reduced performance on working memory tasks. Modeling epistasis doubled the evidence on 13q21 and raised the PPL to 99.9%, indicating that BDNF and 13q21 susceptibility alleles are jointly part of the genetic architecture of SLI. These analyses provide possible mechanistic insights for further cognitive neuroscience studies based on the models developed herein.

Expected Monotonicity – A Desirable Property for Evidence Measures?

We consider here the principle of ‘evidential consistency’ – that as one gathers more data, any well-behaved evidence measure should, in some sense, approach the true answer. Evidential consistency is essential for the genome-scan design (GWAS or linkage), where one selects the most promising locus(i) for follow-up, expecting that new data will increase evidence for the correct hypothesis. Earlier work [Vieland, Hum Hered 2006;61:144–156] showed that many popular statistics do not satisfy this principle; Vieland concluded that the problem stems from fundamental difficulties in how we measure evidence and argued for determining criteria to evaluate evidence measures. Here, we investigate in detail one proposed consistency criterion – expected monotonicity (ExpM) – for a simple statistical model (binomial) and four likelihood ratio (LR)-based evidence measures. We show that, with one limited exception, none of these measures displays ExpM; what they do display is sometimes counterintuitive. We conclude that ExpM is not a reasonable requirement for evidence measures; moreover, no requirement based on expected values seems feasible. We demonstrate certain desirable properties of the simple LR and demonstrate a connection between the simple and integrated LRs. We also consider an alternative version of consistency, which is satisfied by certain forms of the integrated LR and posterior probability of linkage.

Novel susceptibility locus at chromosome 6q16.3-22.31 in a family with GEFS+

Genetic epilepsy with febrile seizures plus (GEFS+) is a familial epilepsy syndrome with extremely variable expressivity. Mutations in 5 genes that raise susceptibility to GEFS+ have been discovered, but they account for only a small proportion of families. We identified a 4-generation family containing 15 affected individuals with a range of phenotypes in the GEFS+ spectrum, including febrile seizures, febrile seizures plus, epilepsy, and severe epilepsy with developmental delay. We performed a genome-wide linkage analysis using microsatellite markers and then saturated the potential linkage region identified by this screen with more markers. We evaluated the evidence for linkage using both model-based and model-free (posterior probability of linkage [PPL]) analyses. We sequenced 16 candidate genes and screened for copy number abnormalities in the minimal genetic region. All 15 affected subjects and 1 obligate carrier shared a haplotype of markers at chromosome 6q16.3-22.31, an 18.1-megabase region flanked by markers D6S962 and D6S287. The maximum multipoint lod score in this region was 4.68. PPL analysis indicated an 89% probability of linkage. Sequencing of 16 candidate genes did not reveal a causative mutation. No deletions or duplications were identified. We report a novel susceptibility locus for genetic epilepsy with febrile seizures plus at 6q16.3-22.31, in which there are no known genes associated with ion channels or neurotransmitter receptors. The identification of the responsible gene in this region is likely to lead to the discovery of novel mechanisms of febrile seizures and epilepsy.

MLIP: using multiple processors to compute the posterior probability of linkage

BackgroundLocalization of complex traits by genetic linkage analysis may involve exploration of a vast multidimensional parameter space. The posterior probability of linkage (PPL), a class of statistics for complex trait genetic mapping in humans, is designed to model the trait model complexity represented by the multidimensional parameter space in a mathematically rigorous fashion. However, the method requires the evaluation of integrals with no functional form, making it difficult to compute, and thus further test, develop and apply. This paper describes MLIP, a multiprocessor two-point genetic linkage analysis system that supports statistical calculations, such as the PPL, based on the full parameter space implicit in the linkage likelihood.ResultsThe fundamental question we address here is whether the use of additional processors effectively reduces total computation time for a PPL calculation. We use a variety of data – both simulated and real – to explore the question "how close can we get?" to linear speedup. Empirical results of our study show that MLIP does significantly speed up two-point log-likelihood ratio calculations over a grid space of model parameters.ConclusionObserved performance of the program is dependent on characteristics of the data including granularity of the parameter grid space being explored and pedigree size and structure. While work continues to further optimize performance, the current version of the program can already be used to efficiently compute the PPL. Thanks to MLIP, full multidimensional genome scans are now routinely being completed at our centers with runtimes on the order of days, not months or years.

Posterior probability of linkage analysis of autism dataset identifies linkage to chromosome 16.

To apply phenotypic and statistical methods designed to account for heterogeneity to linkage analyses of the autism Collaborative Linkage Study of Autism (CLSA) affected sibling pair families. The CLSA contains two sets of 57 families each; Set 1 has been analyzed previously, whereas this study presents the first analyses of Set 2. The two sets were analyzed independently, and were further split based on the degree of phrase speech delay in the siblings. Linkage analysis was carried out using the posterior probability of linkage (PPL), a Bayesian statistic that provides a mathematically rigorous mechanism for combining linkage evidence across multiple samples. Two-point PPLs from Set 1 led to the follow-up genotyping of 18 markers around linkage peaks on 1q, 13p, 13q, 16q, and 17q in both sets of families. Multipoint PPLs were then calculated for the entire CLSA sample. These analyses identified four regions with at least modest evidence in support of linkage: 1q at 173 cM, PPL=0.12; 13p at 21 cM, PPL=0.16; 16q at 63 cM, PPL=0.36; Xq at 40 cM, PPL=0.11. We find strengthened evidence for linkage of autism to chromosomes 1q, 13p, 16q, and Xq, and diminished evidence for linkage to 7q and 13q. The verity of these findings will be tested by continuing to update our PPL analyses with data from additional autism datasets.

Exploiting gene × gene interaction in linkage analysis

When two genes interact to cause a clinically important phenotype, it would seem reasonable to expect that we could leverage genotypic information at one of the loci in order to improve our ability to detect the other. We were therefore interested in extending the posterior probability of linkage (PPL), a class of linkage statistics we have been developing over the past decade, in order to explicitly allow for gene x gene interaction. In this report we utilize a new implementation of the PPL incorporating liability classes (LCs), which provide a direct parameterization of gene x gene interaction by allowing the penetrances at the locus being evaluated to depend upon measured genotypes at a known locus. With knowledge of the generating model for the simulated rheumatoid arthritis (RA) data, we selected two loci for examination: Locus A, which in interaction with the HLA-DR antigen locus affects risk of the dichotomous RA phenotype; and Locus E, which in interaction with DR affects quantitative levels of the anti-CCP phenotype. The data comprised nuclear families of two parents and an affected sib pair (ASP). Our results confirm theoretical work suggesting that gene x gene interactions CANNOT be leveraged to improve linkage detection for dichotomous traits based on affecteds-only data structures. However, incorporation of DR-based LCs did lead to appreciably higher quantitative trait PPLs. This suggests that gene x gene interactions could be effectively used in quantitative trait analyses even when families have been ascertained as ASPs for a related dichotomous trait.

A Bayesian Approach for Incorporating Variable Rates of Heterogeneity in Linkage Analysis

A widely used approach for dealing with locus heterogeneity in linkage analysis is based on mixture likelihood, in which a single mixing (heterogeneity) parameter represents the probability that each family is of linked type. However, in general, different types of families exhibit different heterogeneity levels. To incorporate this variability, we propose a new approach wherein each family has its own heterogeneity parameter representing the probability that it is of linked type. These parameters are nuisance parameters, whereas the main parameter of interest is the location of the disease gene, if there is any. We model the problem in the Bayesian framework and implement it using the Markov chain Monte Carlo (MCMC) methodology. In particular, we use the reversible-jump MCMC sampler to move between the two models: linkage and no linkage. We first estimate the posterior probability of linkage on a chromosome and the corresponding Bayes factor. If linkage is inferred, then the location of the disease gene along with its credible set is estimated. The asymptotic joint distribution of the estimators is derived. We show that this approach is more powerful than the currently used approach in detecting linkage, whereas the two approaches have comparable false-positive rates. The proposed method was applied to a lung cancer dataset of Genetic Epidemiology of Lung Cancer Consortium and an asthma dataset consisting of three samples from Genetic Analysis Workshop 12. Since both lung cancer and asthma are complex traits with heterogeneous genetic predisposition, they provide suitable applications for the proposed method.

Accumulating quantitative trait linkage evidence across multiple datasets using the posterior probability of linkage

Genome scans for complex disorders are frequently inconclusive, prompting researchers to increase sample size in an effort to obtain stronger evidence. However, increasing sample size in the presence of locus heterogeneity may actually, on average, decrease the linkage signal at a true susceptibility gene. The posterior probability of linkage, or PPL, was specifically designed to address this issue in the context of categorical trait analysis, by appropriately accumulating evidence either for or against linkage as new data are added. We now formulate a quantitative trait (QT) analog, the QT-PPL, which directly measures the evidence that a QT is linked to a genetic marker or location. The new QT-PPL is based on a classical single-locus QT likelihood with the trait parameters (allele frequency, genotypic means and variances) integrated out. We show using simulations that the QT-PPL is robust to two key modeling violations (multiple trait loci and non-normality in the form of excess kurtosis), as well as being inherently ascertainment corrected, and illustrate the advantages of the QT-PPL for accumulating linkage evidence across multiple sets of data compared to other QT linkage methods.

A Posterior Probability of Linkage-Based Re-Analysis of Schizophrenia Data Yields Evidence of Linkage to Chromosomes 1 and 17

Objective: Linkage analysis using 22 Canadian pedigrees identified a promising schizophrenia candidate region on 1q23 with a maximum 2-point HLOD under a recessive model of 5.8 [Brzustowicz et al. 2000]. In the current study, we revisited this data set using a Bayesian linkage analysis technique, namely the posterior probability of linkage (PPL). Methods: The PPL has been developed as an alternative to traditional linkage analysis. It differs from both LOD scores and ‘non-parametric’ methods in that it directly measures the probability of linkage given the data, and incorporates prior genomic information. Results: As expected, PPL results for 1q23 supported the previously observed linkage, with an estimated multipoint PPL of 99.7%. However, the PPL supported two further results: a second peak on chromosome 1 at 1p13 with a multipoint with PPL of 70% and a chromosome 17 marker (D17S784 at 17q25) with a multipoint PPL of 44%. Conclusions: The PPL-based analysis presented has the advantage over other likelihood-based linkage methods in that it avoids maximization and produces a less complex view of the strength of evidence for linkage.

Two novel quantitative trait linkage analysis statistics based on the posterior probability of linkage: application to the COGA families

BackgroundIn this paper we apply two novel quantitative trait linkage statistics based on the posterior probability of linkage (PPL) to chromosome 4 from the GAW 14 COGA dataset. Our approaches are advantageous since they use the full likelihood, use full phenotypic information, do not assume normality at the population level or require population/sample parameter estimates; and like other forms of the PPL, they are specifically tailored to accumulate linkage evidence, either for or against linkage, across multiple sets of heterogeneous data.ResultsThe first statistic uses all quantitative trait (QT) information from the pedigree (QT-posterior probability of linkage, PPL); we applied the QT-PPL to the trait ecb21 (resting electroencephalogram). The second statistic allows simultaneous incorporation of dichotomous trait data into the QT analysis via a threshold model (QTT-PPL); we applied the QTT-PPL to combined data on ecb21 and ALDX1. We obtained a QT-PPL of 96% at GABRB1 and a QT-PPL of 18% at FABP2 while the QTT-PPL was 4% and 2% at the same two loci, respectively. By comparison, the variance-components (VC) method, as implemented in SOLAR, yielded multipoint VC LOD scores of 2.05 and 2.21 at GABRB1 and FABP2, respectively; no other VC LODs were greater than 2.ConclusionThe QTT-PPL was only 4% at GABARB1, which might suggest that the underlying ecb21 gene does not also cause ALDX1, although features of the data complicate interpretation of this result.

Evaluation of the chromosome 2q37.3 geneCENTG2as an autism susceptibility gene

Autism is a highly heritable neurodevelopmental syndrome with a complex genetic etiology for which no disease genes have yet been definitively identified. We ascertained three subjects with autism spectrum disorders and chromosome 2q37.3 terminal deletions, and refined the deletion breakpoint regions using polymorphism mapping and fluorescence in situ hybridization (FISH) probes. We then genotyped polymorphic markers downstream from the breakpoint region in a sample of autism affected sibling pair families. Both the chromosomal breakpoints and linkage analyses focused our attention on the gene centaurin gamma-2 (CENTG2), an attractive candidate gene based also on its function and pattern of expression. We therefore assessed CENTG2 for its involvement in autism by (1) screening its exons for variants in 199 autistic and 160 non-autistic individuals, and (2) genotyping and assessing intra-genic polymorphisms for linkage and linkage disequilibrium (LD). The exon screen revealed a Ser --> Gly substitution in one proband, an Arg --> Gly substitution in another, and a number of additional variants unique to the autism families. No unique variants were found in the control subjects. The genotyping produced strong evidence for linkage from two intronic polymorphisms, with a maximum two-point HLOD value of 3.96 and a posterior probability of linkage (PPL) of 51%. These results were contradicted, however, by substantially weaker evidence for linkage from multi-point analyses and by no evidence of LD. We conclude, therefore, that 2q37.3 continues to be a region of interest for autism susceptibility, and that CENTG2 is an intriguing candidate gene that merits further scrutiny for its role in autism.

Effects of Updating Linkage Evidence across Subsets of Data: Reanalysis of the Autism Genetic Resource Exchange Data Set

Results of autism linkage studies have been difficult to interpret across research groups, prompting the use of ever-increasing sample sizes to increase power. However, increasing sample size by pooling disparate collections for a single analysis may, in fact, not increase power in the face of genetic heterogeneity. Here, we applied the posterior probability of linkage (PPL), a method designed specifically to analyze multiple heterogeneous data sets, to the Autism Genetic Resource Exchange collection of families by analyzing six clinically defined subsets of the data and updating the PPL sequentially over the subsets. Our results indicate a substantial probability of linkage to chromosome 1, which had been previously overlooked; our findings also provide a further characterization of the possible parent-of-origin effects at the 17q11 locus that were previously described in this sample. This analysis illustrates that the way in which heterogeneity is addressed in linkage analysis can dramatically affect the overall conclusions of a linkage study.

The Incorporation of Prior Genomic Information Does Not Necessarily Improve the Performance of Bayesian Linkage Methods: An Example Involving Sex-Specific Recombination and the Two-Point PPL

Objective: We continue statistical development of the posterior probability of linkage (PPL). We present a two-point PPL allowing for unequal male and female recombination fractions, Θ_M and Θ_F, and consider alternative priors on Θ_M, Θ_F. Methods: We compare the sex-averaged PPL (PPL_SA), assuming Θ_M = Θ_F, to the sex-specific PPL (PPL_SS) in (Θ_M, Θ_F), in a series of simulations; we also compute the PPL_SS using alternative priors on (Θ_M, Θ_F). Results: The PPL_SS based on a prior that ignores prior genomic information on sex specific recombination rates performs essentially identically to the PPL_SA, even in the presence of large Θ_M, Θ_F differences. Moreover, adaptively skewing the prior, to incorporate (correct) genomic information on Θ_M, Θ_F differences, actually worsens performance of the PPL_SS. We demonstrate that this has little to do with the PPL_SS per se, but is rather due to extremely high levels of variability in the location of the maximum likelihood estimates of (Θ_M, Θ_F) in realistic data sets. Conclusions: Incorporating (correct) prior genomic information is not always helpful. We recommend that the PPL_SA be used as the standard form of the PPL regardless of the sex-specific recombination rates in the region of the marker in question.

How useful is the fine-scale mapping of complex trait linkage peaks? Evaluating the impact of additional microsatellite genotyping on the posterior probability of linkage

The two-stage linkage mapping protocol for complex traits (a primary genome scan with low marker density followed by the high-density genotyping around linkage peaks) is a near-universal practice. The behavior (an increase or a decrease) of the peak upon such fine mapping frequently leads to inferences regarding the veracity of the primary scan finding, namely a true, or a false, positive. We examined by simulation, under the null hypothesis of no linkage and the alternative hypothesis of true linkage, the inferences that can be made regarding the posterior probability of linkage given either a peak increase, or alternatively, a peak decrease, following fine mapping. We considered different models of missing genotype data, fine-mapping LOD score thresholds, and prior probabilities of linkage. Our simulations show that evidence for linkage can increase frequently upon fine mapping under both null and alternative hypotheses, although large increases in LOD scores are more common under the alternative hypothesis. Increased LOD scores accompany an increased posterior probability of linkage, and large LOD score changes and the presence of dominance at the trait locus accentuate this effect. We demonstrate that the greatest changes in the posterior probability of linkage occur when the genotyping data are least complete (and especially when parental genotypes are missing), and the LOD score threshold for fine mapping is relaxed.