We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but mathematically identical mixed model methods were developed for genomic best linear unbiased prediction (GBLUP) and genomic restricted maximum likelihood estimation (GREML) of additive and dominance effects using SNP markers. These two sets are referred to as the CE and QM sets, where the CE set was designed for large numbers of markers and the QM set was designed for large numbers of individuals. GBLUP and associated accuracy formulations for individuals in training and validation data sets were derived for breeding values, dominance deviations and genotypic values. Simulation study showed that GREML and GBLUP generally were able to capture small additive and dominance effects that each accounted for 0.00005–0.0003 of the phenotypic variance and GREML was able to differentiate true additive and dominance heritability levels. GBLUP of the total genetic value as the summation of additive and dominance effects had higher prediction accuracy than either additive or dominance GBLUP, causal variants had the highest accuracy of GREML and GBLUP, and predicted accuracies were in agreement with observed accuracies. Genomic additive and dominance relationship matrices using SNP markers were consistent with theoretical expectations. The GREML and GBLUP methods can be an effective tool for assessing the type and magnitude of genetic effects affecting a phenotype and for predicting the total genetic value at the whole genome level. Genomic prediction using genome-wide single nucleotide polymorphism (SNP) markers has been shown to be a powerful tool to capture small genetic effects dispersed over the genome for predicting an individual’s genetic potential of a phenotype From the point of view of missing heritability Genomic best linear unbiased prediction (GBLUP) and various Bayesian methods are available for genomic prediction, and GBLUP generally had good performance in real data Objectives of this study were to develop mixed model methods for the joint genomic prediction of and variance component estimation of additive and dominance effects based on the traditional quantitative genetics model that partitions a genotypic value into breeding value and dominance deviation. The methodology will have two complimentary computing strategies for large numbers of individuals and markers, and the genomic prediction methods for have GBLUP and associated reliability for both training and validation data sets. Accuracies of the new methods will be evaluated using simulation data based a true dairy cattle SNP structure.ija = ijd = The genetic model of SNP markers is an expansion of the additive model used in genomic evaluations c values : Part A.q = the number of individuals, m = the number of SNP markers, and c = the number of fixed effects. Based on Based on where  = ith diagonal element of ith diagonal element of As the number of SNP markers increases, the values of the diagonal elements of The additive relationship or correlation matrix (g’ is used to distinguish In With the m>q) and the MME set of Model 2, to be referred as the QM set (QM meaning q>m), is the best for large number of individuals (q>m). The MME set of Model 1  has no computing advantage because the matrix size is twice as large as that of CE and requires the inverses of the relationship matrices. The CE set of Model 2 (CE2) also has no computational advantage because CE2 requires more memory than QM if m>q. These two sets (MQ and CE2) are not considered further. In the following, we focus on the CE and QM sets of solutions, where each set consists of GBLUP, reliability of GBLUP and GREML formulations. We first present these three types of formulations in each set, CE for m>q or QM for q>m, and then summarize the main features of the CE and QM sets.b, The CE form of GBLUP from Model 1 can be calculated as:i, i, i of i of α) and dominance effects (δ) based on Model 2 are:The mixed model equations for predicting SNP additive effects  and the size of the MME coefficient matrix of 2m for QM so that V become easier to invert than the MME coefficient matrix of q<2m. Both sets do not require the inversions of the additive and dominance relationship matrices. The CE set uses relationship matrices explicitly whereas the QM set does so implicitly. Both sets are invariant to the invertibility of m>q where q>m where m>q and do not need to assess invertibility that is not guaranteed by m>q, e.g., the existence of identical twins results in non-invertible The CE and QM sets of formulations for GBLUP, reliability and GREML are mathematically identical, offer identical results, and offer complimentary computing efficiency. The CE set is designed for http://animalgene.umn.edu.Formulations for GBLUP-CE and GBLUP-QM for individuals without phenotypic observations  and reliability measures are given in Simulation study with known true values of genetic effects and parameters is an effective approach to evaluate the accuracy of a new methodology because the observed GBLUP and GREML estimates can be compared with the true values. We generated a large number of simulated data sets based on a true dairy cattle SNP structure of 1654 Holstein cows assuming true additive and dominance heritability levels of 0, 0.05, 0.15 and 0.30, and we applied seven SNP sets to the simulated data, 1K causal variants, 1K SNP, 2K SNP and causal variants, 3K, 7K 40K SNP markers, and 41K SNP markers and causal variants. Detailed information about these marker sets and the procedure to generate the simulation data are described in Causal SNP markers 1K_QTL,  had the Linked SNP markers were less accurate than causal SNP markers in nearly all cases but were still highly accurate for estimating additive variance. For additive effects, GREML using the 40K and 41K SNP sets had a tendency of slightly overestimating additive heritabilities and variance components. For dominance effects, the marker densities in this simulation study, 1K_SNP, 3K, 7K and 40K, were all insufficient to achieve accurate estimates of dominance heritabilities and variance components, although the 40K set was able to distinguish between high and low dominance heritabilites. Accuracy of dominance GREML increased as the density of linked SNP marker increased from 1K_SNP to 40K, indicating that further increase in marker density over 40K could improve the accuracy of dominance GREML .Estimating ‘0′ heritability generally is considerably more difficulty than estimating non-null heritability. Therefore, the accuracy in estimating ‘0′ heritability is a strong test for the accuracy of the GREML formulations. From the same simulation data set we generated above, we generated another set of simulation data requiring additive or dominance effects to be the only genetic effects such that tability . The 1K GBLUP of genotypic values  had lower observed accuracies than causal variants alone. The decrease in y levels . HoweverPredicted accuracy for breeding values (×(pedigree coancestry coefficient)’ was 0.282. Genomic dominance relationships were null for half-sibs and unrelated individuals, and genomic additive relationships for unrelated individuals were also null, as expected . For the 41K results with the removal of three full-sib outliers and nine half-sib outliers, additive and dominance relationships agreed well with theoretical expectations . For fulexpected . The obsq>m. In contrast, pedigree relationship matrices are positive definite in the absence of identical twins. Our cattle data showed that the invertibility of a genomic relationship matrix should not affect the use of genomic relationships as measures of genomic relatedness among individuals, because the genomic relationships calculated from genomic relationship matrices that were invertible or non-invertible had nearly identical values that were consistent with theoretical expectations (data not shown). The additive and dominance relationship matrices were non-invertible for the 1K SNP set, and were invertible for the 3K, 7K and 41K sets after removing a potentially identical twin or duplicated individual. The second difference is the range of relationship values. Genomic relationships by Definitions I-III could take negative values whereas pedigree relationships are non-negative. However, no negative values were observed for full-sib genomic relationships. Negative genomic additive relationships with small absolute values near ‘0′ were observed for unrelated individuals and some half-sibs, and negative dominance relationships with small absolute values near ‘0′ were observed among half-sib .(PDF)Click here for additional data file.Table S2GREML estimates and GBLUP accuracy for simulation data with additive or dominance effects only .(PDF)Click here for additional data file.Table S3GBLUP Accuracies for breeding values, dominance deviations and genotypic values .(PDF)Click here for additional data file.Table S4GREML estimates of variance components and GBLUP accuracies with and without genomic relationships for phenotypes with additive and dominance effects of 1006 QTL .(PDF)Click here for additional data file.Table S5GBLUP accuracies in simulation data assuming random additive effects and directional dominance effects values .(PDF)Click here for additional data file.Text S1Proofs, formulations and simulation study. Part A: Derivations for the traditional quantitative genetics model of SNP markers with unequal and equal allele frequencies. Part B: Genomic prediction of additive and dominance effects for individuals without phenotypic observations. Part C: AI-REML implementation. Part D: Simulation study to evaluate GREML and GBLUP accuracies.(PDF)Click here for additional data file.


Most efforts in understanding the cellular basis of schizophrenia have followed a “neuron-centric” approach, focusing on alterations in neurotransmitter systems and synapse cytoarchitecture. However, multiple lines of evidence coming from genetics and systems biology approaches suggest that apart from neurons, oligodendrocytes and potentially other glia are affected from schizophrenia risk loci. Neurobiological abnormalities linked with genetic association signal could identify abnormalities that are more likely to be primary, versus environmentally induced changes or downstream events. Here, we summarize genetic data that support the involvement of oligodendrocytes in schizophrenia, providing additional evidence for a causal role with the disease. Given the undeniable evidence of both neuronal and glial abnormalities in schizophrenia, we propose a neuro-glial model that invokes abnormalities at the node of Ranvier as a functional unit in the etiopathogenesis of the disease.Given that the genetic risk for schizophrenia is highly polygenic and the effect sizes, even for rare or  The core symptoms of schizophrenia include the presence of delusions and hallucinations (positive symptoms), apathy, and social withdrawal (negative symptoms), as well as, stable impairments in specific domains of cognitive function. Despite our best effort, we still lack a basic understanding of the etiopathogenesis of schizophrenia and therefore tools for curative treatment or prevention do not exist . Given tMost efforts in understanding the cellular basis of schizophrenia have followed a “neuron-centric” approach, focusing exclusively on the role(s) of neurons. This has led to findings of alterations in neuronal function, such as neurotransmission and synapse cytoarchitecture, often overlooking the fact that neuronal function and neurotransmission is inextricably dependent on interactions between neurons and glia. Increasing recognition of the importance of neuro-glial elements as basic functional units  that parHere, we first outline genetic evidence derived from candidate gene association studies or genome-wide approaches that support oligodendroglial and myelin related (OMR) abnormalities in schizophrenia as a primary contributor. In an attempt to provide a mechanistic and integrative interpretation of the neuronal and OMR changes in schizophrenia, we introduce a neuro-oligodendroglial model that involves abnormalities at the nodes of Ranvier (NOR) as a functional unit implicated in the etiopathogenesis of the disease.Myelination can increase action potential transmission speed and decrease refractory time, which increases the number of action potentials that can be transmitted per unit time. There is accumulating evidence supporting the notion of schizophrenia as a disorder of disconnectivity . While dGrowing evidence, accumulated over the last 15 years, provide strong support for OMR abnormalities in schizophrenia. The majority of those data is derived from neuroimaging and human postmortem studies . It is interesting to note, that the products of these same genes have been implicated in numerous direct studies of OMR gene and protein expression in the brains of persons with schizophrenia, as well as, neuroimaging studies, providing a stronger support for causality with the disease , this pathway may play a role in schizophrenia pathogenesis through other mechanisms aside from OMR effects. A direct way to assess the pathophysiologic effects of genetic variants would be to examine human subjects with risk variants for symptoms of schizophrenia. Along these lines, many groups have reported association of the NRG1 – ERBB4 risk variants with neurocognitive  is a strong candidate gene for schizophrenia and was initially identified in a large Scottish pedigree  is a myelin-associated protein that inhibits the outgrowth of neurites and nerve terminals  encodes a transcription factor central to oligodendrocyte development  and ERBB4 ; however, a handful of OMR genes  show suggestive association (P < 5 × 10-5). Future GWAS with increased sample sizes will determine whether OMR genetic variants are true associations at reduced thresholds, but without the needed power to reach genome-wide significance.Significant progress has been made over the last decade through large genome-wide association studies (GWAS). The GWAS design has provided evidence in an agnostic manner that specific common DNA genetic variants among people influence their genetic susceptibility to multiple different complex disorders, including schizophrenia . FurtherFigure 1A). Recent findings support the notion that polygenic risk for schizophrenia affects OMR gene networks and molecular pathways  to the NOR and the maintenance of adherens junctions between the axolemma and paranodal myelin loops . The voltage gated Na+ channel cytoplasmic loops are linked to the underlying spectrin-actin cytoskeleton through the anchoring protein Ankyrin G (ANK3). ANK3 plays an important role in the NOR complex by anchoring not only the NaV to the node but also because several cis acting nodal and paranodal adherens junction proteins, such as neurofacin (NFASC), neuronal cell adhesion molecule (NRCAM), contactin 1 and caspr, are also associated with it. Finally, contactin 2 (CNTN2), along with Caspr interact in trans with cell adhesion molecules of oligodendroglial paranodal loops that include an isoform of NFASC (Nf155), and CNTN2 to ensure the integrity of the myelin-axolemma tight junctions, which form a barrier that prevents the diffusion of Nav from the nodal region.Different and specific neuronal and oligodendroglial reciprocal interactions (for review, see ANK3, NFASC, NRCAM, CNTN2, and SCN8A are affected in schizophrenia (ANK3 to serious mental illness is further supported by recent GWAS implicating ANK3 in schizophrenia (ANK3 risk polymorphism is associated with decreased ANK3 gene expression, which is consistent with ANK3 gene expression downregulation in schizophrenia and neurocognitive and neuroimaging abnormalities (A recently study demonstrated that several NOR genes and proteins, including ophrenia . The impophrenia  and bipoophrenia . Furthermalities . OverallThere are multiple lines of evidence derived from neuroimaging, molecular and genetic data that support a role of OMR genes and molecular pathways in schizophrenia. Recent systems biology and pathway analyses that integrate gene expression and genetic data support the notion that oligodendroglial abnormalities are among the primary deficits in the disorder. Future studies need to integrate the larger GWAS and determine the polygenic effect of OMR genes in schizophrenia. While most efforts in understanding the cellular basis of schizophrenia have followed a “neuron-centric” approach, there is an emerging need to conduct more complex neurobiological studies and examine the neuro-glial unit as a whole. The data to date provide evidence for abnormalities in the NOR as a plausible biological substrate for the disconnectivity syndrome in schizophrenia. Recent evidence suggesting that antipsychotics may influence OMR function beneficially provides clues for improved treatment strategies . TherefoPanos Roussos and Vahram Haroutunian drafted and revised the manuscript.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.


Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) markers generate large quantities of tests results. Global and local graphical viewing of the test results is an effective approach to digest and interpret GWAS results.SNPEVG is a set of graphical tools for instant global and local viewing and graphing of GWAS results for all chromosomes and for each trait. The current version includes three programs, SNPEVG1, SNPEVG2 and SNPEVG3. SNPEVG1 is a graphical tool for SNP effect viewing of P-values allowing multiple traits. The total number of graphs that can be generated by one ‘Run’ is n(c + 2), where n is number of ‘traits’ with 0 < n ≤ 100, and c is the number of chromosomes. SNP effect viewing and graphing is accomplished through a user friendly graphical user interface (GUI) that provides a wide-range of options for the user to choose. The GUI can produce the Manhattan plot, the Q-Q plot of all SNP effects, and graphs for SNP effects by chromosome by clicking one command. Any or all the graphs can be saved with publication quality by clicking one command. SNPEVG2 is for the viewing and graphing of multiple traits on the same graph with options to graph any or all of the traits, customizable colors and user specified Y1 or Y2 axis for each traits. The SNPEVG3 program uses the output file of single-locus test results from the epiSNP computer package as the input file. Each chromosome figure can display three genetic effects , and the number of observations.The SNPEVG package is a versatile, flexible and efficient graphical tool for rapid digestion of large quantities of GWAS results with mouse clicks. GWAS analysis generally yields large quantities of test results. Global and local graphical viewing of the test results is an effective approach and often is a necessary step for interpreting GWAS results. A widely used graphical viewing of GWAS results is the Manhattan plot, which provides a global graphic view of GWAS results of all chromosomes for a trait on one graph to quickly identify genome locations with the most significant SNP effects ,2. FolloThe SNPEVG computer package is implemented in the C++ programming language. The object orientation feature of the C++ language enables the efficient software development cycle by easy reuse of modules for different applications with similar features. The SNPEVG computer package used the Qt library under the terms of the GNU Lesser General Public License (LGPL) version 2.1 as shown .http://animalgene.umn.edu. Full features of the SNPEVG package are described in the SNPEVG user manual [SNPEVG Version 3.2 includes three graphical programs: SNPEVG1, SNPEVG2 and SNPEVG3. SNPEVG1 is for graphing effects of one trait per graph for up to 100 traits, SNPEVG2 is for graphing multiple traits on the same graph, and SNPEVG3 processes directly uses an output file of EPISNP or EPISNPmpi [r manual  AdditionSNPEVG1 supports a maximum 100 traits. The GUI Figure A or sepaSNPEVG2 is designed to display P-values of multiple traits on the same graph. Each chromosome figure can display P-values in log scale or the original values of a variable on either Y1 or Y2 axis (up to 100 traits) Figure A. The Y2The SNPEVGconvert program is designed to convert an output file from any GWAS analysis software to the format of SNPEVG1 and SNPEVG2. With this format conversion program, virtually any GWAS software could SNPEVG1 and SNPEVG2. To use this program, the user only needs to specify the number of columns in the original files and identify the column numbers to be printed in the input file for SNPEVG1 and SNPEVG2.SNPEVG3 is developed for graphical analysis of GWAS using the output file of single-locus test results of EPISNP or EPISNPmpi  as the iCurrently, SNPEVG1, SNPEVG2 and SNPEVG3 have a Microsoft Windows 32-bit version and a 64-bit version for Mac OS X 10.6 or newer. A Windows 64-bit version is expected to become available at a later time. For practical purposes, either the 32-bit or the 64-bit version would be powerful enough for real GWAS data sets. For a single trait, the 32-bit version could process 10 million markers per trait in about 30 seconds but failed for 12 million markers, and the 64-bit version could process 30 million markers in 80.62 seconds : Microsoft Windows 7, Mac OS X 10.6 or newerOther requirements: none.License: none.Any restrictions to use by non-academics: none.GWAS: Genome-wide association study; SNP: Single nucleotide polymorphism.The authors declare that they have no competing interests.SW is the author of SNPEVG1, SNPEVG2, and SNPEVG3. DD is the author of the EPISNPPLOT program that is partially used in SNPEVG3. YD designed most functions of the computing tools, and is the lead writer of the manuscript. All authors read and approved this manuscript.SNPEVG_Win_3.2.zip: contains all executables and libraries for Microsoft Windows platforms and the user manual of SNPEVG package version 3.2.Click here for fileSNPEVG_Mac_3.2.zip: contains all executables for Mac OS X platforms and the user manual of SNPEVG package version 3.2.Click here for fileA Graphical Tool For SNP Effect Viewing and Graphing. (PDF 6838 kb)Click here for file


Increased reaction time variability (RTV) on cognitive tasks requiring a speeded response is characteristic of several psychiatric disorders. In attention deficit hyperactivity disorder (ADHD), the association with RTV is strong phenotypically and genetically, yet high RTV is not a stable impairment but shows ADHD-sensitive improvement under certain conditions, such as those with rewards. The state regulation theory proposed that the RTV difference score, which captures change from baseline to a rewarded or fast condition, specifically measures ‘state regulation’. By contrast, the interpretation of RTV baseline  scores is debated. We aimed to investigate directly the degree of phenotypic and etiological overlap between RTV baseline and RTV difference scores.n=1314) and an ADHD and control sibling-pair sample (n=1265).We conducted genetic model fitting analyses on go/no-go and fast task RTV data, across task conditions manipulating rewards and event rate, from a population-based twin sample (r = 0.75–0.83).Phenotypic and genetic/familial correlations were consistently high (0.72–0.98) between RTV baseline and difference scores, across tasks, manipulations and samples. By contrast, correlations were low between RTV in the manipulated condition and difference scores. A comparison across two different go/no-go task RTV difference scores (slow-fast/slow-incentive) showed high phenotypic and genetic/familial overlap (Our finding that RTV difference scores measure largely the same etiological process as RTV under baseline condition supports theories emphasizing the malleability of the observed high RTV. Given the statistical shortcomings of difference scores, we recommend the use of RTV baseline scores for most analyses, including genetic analyses. They were recruited from the Twins' Early Development Study ; recent participation in TEDS, as indicated by return of questionnaires at either a 4- or 7-year data collection point; no extreme pregnancy, perinatal difficulties, specific medical syndromes, chromosomal anomalies or epilepsy; not participating in other current TEDS substudies; and not on stimulant or other neuropsychiatric medications.et al.et al.s.d. = 0.67) and half of the sample were girls (51%). The mean IQ was 109.34 (s.d. = 14.72). Parents of all participants gave informed consent following procedures approved by the Institute of Psychiatry Ethical Committee.Of the 1230 suitable families contacted, 672 families (55%) agreed to participate. Thirty individual children were subsequently excluded due to: IQ < 70, epilepsy, obsessive–compulsive disorder, autism or other neurodevelopmental disorder, illness during testing or placement on stimulant medication for ADHD. The final sample consisted of 1314 individuals: 255 monozygotic (MZ) twin pairs, 184 same-sex dizygotic (DZ) and 207 opposite-sex DZ twin pairs, and also 22 singletons coming from pairs with one of the twins excluded. Data for the 22 singleton twins were also used in the structural equation modeling  project  and secondary (ages 12–18 years) schools in the UK, Germany and Spain, aiming for an age and sex match with the clinical sample. The same exclusion criteria were applied as for the clinical sample. One child subsequently withdrew after testing and three were excluded for having an IQ < 70. A further 10 controls were excluded for having both parent and teacher Conners' DSM-IV ADHD subscale s.d. = 2.73) for probands with ADHD, 11.38 years (s.d. = 2.96) for siblings of probands and 12.07 years (s.d. = 2.47) for controls. The percentage of males was 89.01, 49.78 and 70.43% respectively. The mean IQ was 102.02 (s.d. = 15.44) for probands with ADHD, 103.43 (s.d. = 13.59) for siblings of probands and 108.91 (s.d. = 13.71) for controls. Of the 1265 individuals, 524 with ADHD-CT were classified as affected, 16 who met criteria for the hyperactive-impulsive or inattentive subtypes were classified as a ‘subthreshold group’, and a further 664 individuals were unaffected siblings and controls. ADHD status was therefore included in the analyses in an ordinalized manner. A further 61 participants had cognitive data, but no clinical data, and their affection status was coded as missing. Some cognitive data are missing because two of the teams did not administer the go/no-go task, two did not administer the fast task, and there were occasional technical problems with equipment. Go/no-go data were available from 922 participants and fast task data from 687 participants. Of the 524 individuals with ADHD-CT, 151 had conduct disorder, 355 had oppositional defiant disorder and 63 had possible mood disorder (excluding bipolar disorder), derived as part of the PACS interview (see Measures). Ethical approval was obtained from local ethical review boards.The ADHD proband and sibling sample consisted of 920 individuals (464 ADHD probands and 456 siblings of ADHD probands) and the control sample of 345 individuals. The final total sample therefore consisted of 1265 individuals, which comprised 580 complete sibling pairs and 105 singletons. The mean age was 11.45 years  interview  and for each failure to respond within 2 s. Each commission error (incorrect response to O) led to the loss of five points. The points were shown in a box, immediately right of the screen center, and were updated continuously throughout. The child started with 40 points, to avoid the possibility of a negative tally. The child was asked to try to win as many points as possible, and was told that the points would be exchanged for a real prize when the game ended. This condition consisted of 72 trials and had an ISI of 8 s.The baseline condition, with a fore period of 8 s and consisting of 72 trials, followed a standard warned four-choice RT task. A warning signal  first appeared on the screen. At the end of the fore period , the circle designated as the target signal for that trial was filled (colored) in. The participant was asked to make a compatible choice by pressing the response key that directly corresponded in position to the location of the target stimulus. Following a response, the stimuli disappeared from the screen and a fixed inter-trial interval of 2.5 s followed. Speed and accuracy were emphasized equally. If the child did not respond within 10 s, the trial terminated.et al.s.d. of RTs.A comparison condition with a fast event rate (1 s) and incentives, consisting of 80 trials, followed the baseline condition. The participants were told to respond really quickly one after another to win smiley faces and earn real prizes in the end. The participants won a smiley face for responding faster than their own mean reaction time (MRT) during the baseline condition consecutively for three trials. The baseline MRT was calculated here based on the middle 94% of responses, therefore excluding extremely fast and extremely slow responses. The smiley faces appeared below the circles in the middle of the screen and were updated continuously. For analyses that compare performance across the baseline and fast-incentive conditions, the full fast-incentive condition data are compared to baseline condition data matched for length of time on task . All models were fitted to age- and sex-regressed residual scores. Genetic and familial structural equation models were conducted in Mx  that phenotypic correlations and variances are the same whether an individual is a member of an MZ or a DZ pair, and whether the individual is arbitrarily assigned to be twin 1 or twin 2 in the model; (ii) in addition, within MZ and DZ pairs, cross-twin cross-trait correlations are constrained to be independent of order (e.g. the correlation between ADHDtwin1 and RTVtwin2 = the correlation between ADHDtwin2 and RTVtwin1).Biometrical genetic modeling is based on three assumptions: (1) MZ twins share 100% of their segregating alleles and DZ twins share 50% of additive genetic (A) influences, but only 25% of non-additive genetic influences (D); (2) for twin pairs reared together, both members of MZ and DZ twin pairs are 100% concordant for their shared environmental (C) influences; and (3) child- or individual-specific environmental factors  do not contribute to the similarity between twin pairs. From this we can derive the following within-pair twin correlation expectations: (1) additive genetic influences (A) will double the MZ twin pair correlation in relation to the DZ twin pair correlation; (2) non-additive genetic influences (D) will more than double the MZ twin pair correlation in relation to the DZ twin pair correlation; (3) shared environmental effects (C) will increase within-pair MZ and DZ correlations to the same extent, reflected by DZ correlations that are more than half the MZ correlations; and (4) non-shared environment (E) will decrease both MZ and DZ correlations, most commonly identified in MZ correlations that are less than 1.rA), shared environmental (rC) and child-specific environmental (rE) correlations.Using the same logic outlined above, multivariate genetic models use the MZ:DZ ratio of cross-twin cross-trait correlations to additionally estimate the extent to which the correlations between traits are caused by A, C and E influences. A Cholesky (triangular) decomposition is fit to the data, but to avoid giving precedence to the first measured variable in the model (which is arbitrary), a correlated factors solution of the Cholesky model is interpreted Loehlin, . This giFor ADHD, an ADE model was the best fit, but for cognitive variables there was some, though very little, non-significant influence of C. Given power issues with distinguishing between A and D, and the non-significant C components, AE model results are presented for all multivariate analyses, which did not show a decrease in fit compared to the full model. The multivariate models included a scaling factor to account for male and female variance differences. No other quantitative and qualitative sex differences in genetic parameters were indicated.Both phenotypic and familial structural equation models were run in Mx. Constrained phenotypic correlation models were run to estimate sibling correlations that were corrected for sample ascertainment and the familial clustering within the dataset. Constraints (inherent to the genetic modeling) are applied to give: (i) one set of correlations between traits within siblings, regardless of affection status; (ii) sibling correlations for each trait apart from ADHD, which is fixed to 0.40; and (iii) one set of cross-sibling cross-trait correlations.z values of 1.64 to give a population prevalence of 5%, and to 1.87 to reflect the ‘subthreshold’ position. Similar threshold fixes are required in the constrained phenotypic correlation models described above (with the sibling correlation for ADHD = 0.40). This method for correcting for sample ascertainment has been previously validated  siblings, like DZ twins, share 50% of their segregating alleles and 100% of the shared environmental influences. Without a comparison group (such as MZ twins) it is impossible to exactly estimate A. Therefore, A and C are modeled together as one ‘familial’ factor (F). This parameter reflects all the contribution of C to the phenotypic similarity between siblings and 50–100% of A, such that if the variance in a trait was all due to C, F would be an exact estimate, but if C only partially underlies a trait, F becomes more conservative as the amount of C decreases. Thus, F estimates on this sample may be comparable to A estimates from the twin sample, but are likely to be conservative. (2) The selected nature of the sample was accounted for by including ADHD (the selection variable) in all models and fixing its parameters to known values. This necessitated ordinal analysis as ADHD status was coded categorically: affected/subthreshold/unaffected. Ordinal analysis assumes that the ordered categories reflect measurement of an underlying normal distribution of the trait. This liability distribution has one or more thresholds that distinguish between the categories. The familiality of ADHD was fixed to 40%, representing 80% genetic variance, as expected by population norms  in each condition and RTV difference scores between conditions. This meant that several parameters were estimated in more than one model. These differed only slightly , so we present the most conservative estimates in these cases.rA, rF and rE estimates  and genetic/familial (0.85 and 0.82) correlations with RTV-difslow-incentive in both samples , with low-to-moderate genetic/familial correlations (most of which were non-significant). A direct comparison across go/no-go task RTV-difslow-fast and RTV-difslow-incentive indicated high phenotypic (0.75 and 0.79) and genetic/familial (0.81 and 0.83) correlations  correlations were not significantly different between the general population and selected clinical samples , suggesting that comparisons across the two populations are appropriate. RTV samples . The samelations .baseline showed high phenotypic (0.72 and 0.85) and genetic/familial (0.98 and 0.93) correlations with RTV-difbaseline-FI (incentive and RTV-difbaseline-FI were low (−0.07 and 0.25); the genetic/familial correlations were moderately high (0.76 and 0.64) but need to be interpreted in light of the low phenotypic correlations, as there was no or limited phenotypic association to account for.The fast task showed a similar pattern of results. RTVeline-FI . By contThe RTV across-condition difference scores can be seen as an index of an individual's potential for reducing RTV under certain task conditions . Our results show that these difference scores measure largely the same underlying etiological process as RTV under baseline  conditions on the go/no-go and fast tasks. The findings were replicated across a clinical combined subtype ADHD and control sibling-pair sample and population-based twin sample, across clinical diagnostic and quantitative trait approaches, and across tasks and different task manipulations. By contrast, RTV under rewarded and/or fast conditions measures a partly distinct process, indexing ‘the best performance one is capable of’.On the go/no-go task, we observed high phenotypic correlations (0.78 and 0.83) between RTV under the slow condition and a difference score in RTV from the slow to the incentive condition, in both samples. RTV under the slow condition correlated, at the phenotypic level, equally highly (0.82 and 0.90) with RTV difference scores from the slow to the fast condition. Furthermore, the genetic model fitting analyses indicated shared etiology between RTV under the slow condition with either RTV difference score, with 78–85% of the familial/genetic influences shared between the two variables. By contrast, RTV under the fast condition showed no or low negative phenotypic correlations with the RTV slow-to-fast condition difference score, and low familial/genetic and individual-specific correlations. We obtained nearly identical results with RTV under the incentive condition, with low, negative phenotypic correlations with RTV difference scores from the slow to the incentive condition, and low to moderate genetic/familial correlations.et al.These findings indicate that there is little in the RTV difference scores that is not already captured by RTV baseline scores. Given possible psychometric disadvantages of difference scores (Peter Findings on the fast task further confirmed this pattern. The phenotypic correlations between RTV in a baseline condition and RTV difference from baseline to the fast-incentive condition were high (0.72 and 0.85) in both samples. Nearly all (93–98%) genetic/familial influences between these variables were shared. Low phenotypic correlations were again observed between RTV in the fast-incentive condition and the difference in RTV from the baseline to the fast-incentive condition (−0.07 and 0.25). The genetic/familial correlations were higher, but need to be interpreted cautiously, given the limited  extent of an observable association that they account for.et al.et al.et al.et al.et al.The theoretical implication of the finding of RTV baseline performance measuring the same process as the RTV difference scores is the support for theories that emphasize the malleability of the observed high RTV, such as the models incorporating an arousal regulation process in ADHD , the current conclusions would not apply. The investigation of the sensitivity of RTV to reward and event rate manipulations in disorders such as schizophrenia or dementia is an important direction for future research. With regard to the samples studied in the present analyses, we note that the replication of the findings from the clinical sample to the population-based twin sample emphasizes how the phenomenon is not restricted to clinically defined samples. The extent of replication across the two samples is indicative of the robustness of the findings, given the differences in sample selection, age and gender composition.


Advances in next-generation sequencing technology are enabling researchers to capture a comprehensive picture of genomic variation across large numbers of individuals with unprecedented levels of efficiency. The main analytic challenge in disease mapping is how to mine the data for rare causal variants among a sea of neutral variation. To achieve this goal, investigators have proposed a number of methods that exploit biological knowledge. In this paper, I propose applying a Bayesian stochastic search variable selection algorithm in this context. My multivariate method is inspired by the combined multivariate and collapsing method. In this proposed method, however, I allow an arbitrary number of different sources of biological knowledge to inform the model as prior distributions in a two-level hierarchical model. This allows rare variants with similar prior distributions to share evidence of association. Using the 1000 Genomes Project single-nucleotide polymorphism data provided by Genetic Analysis Workshop 17, I show that through biologically informative prior distributions, some power can be gained over noninformative prior distributions. T-test, logistic regression, or linear regression.Genome-wide association studies (GWAS) have been a powerful method for revealing common variants that confer a modest increase in disease risk in carriers. In general, the single-nucleotide polymorphisms (SNPs) that show the strongest evidence for association in GWAS do not perfectly tag the putative causal variant(s) nearby because of ancestral recombination events; therefore resequencing in these regions is necessary to resolve the precise location of the causal variant(s). Dickson et al.  postulatIn this paper, I describe how I adapted the concept of the CMC method into a Bayesian variable selection algorithm with the notion that common SNPs may also contribute valuable information to nearby causal rare variants, assuming that the shared haplotype model  is true.variable to define either a single common SNP or a SNP bin. The final marker panel included 7,385 variables: 1,029 bins containing collapsed rare variants and 6,356 bins containing common SNPs. I experimented with higher threshold values for bin definition , but this strategy did not recover an appreciable number of bins from the filtering step because most genes in the data set were small and harbored private mutations. True log relative risks (denoted β) for each SNP are provided in the simulation answer key, which quantifies each SNP’s effect on the quantitative traits Q1 and Q2. Thus, to assess how accurately my method can recover the true values of β at each SNP, I constrained the analyses only to models where the outcome phenotype was either trait Q1 or Q2.Details of the simulated GAW17 data set can be found in this same issue . I definm model variables. A design matrix X stores the variable values, and the vector Y stores values of Q1 or Q1 across all individuals:The statistical model I used was a two-level hierarchical model, described in detail by Chen and Thomas . One proβ in Eq. (1) using the annotation information provided by GAW17. For variable k, βk is distributed as a mixed-effects model, originally defined by Besag et al. [I define a prior distribution on π and the random effects components are:where the latent fixed effect is Z matrix stores external knowledge about each of the m variables currently in the model. To encode my belief that deleterious mutations would have higher or lower values of β relative to other types of mutations, I assigned a value of 1 to the nonsynonymous mutation in the second column (after reserving the first column as the intercept) of the m × 2 design matrix Z and a value of 0 for any other SNP category. The term π, estimated using ordinary linear regression, relates the magnitude of β in Eq. (1) to values in Z. Furthermore, to encode my belief that mutations within the same gene should have similar effects on disease, I specified an indicator encoding whether predictor k and any other model variables are in the same gene by means of a k × k adjacency matrix A. Specifically, the parameter k  defined by means of A. The variance term τ2 is inversely scaled by kv, the number of neighbors of k to weight the uncertainty about τ2. Finally, kθ soaks up any remaining variation in the second level of the model through the variance term σ2.The π), so that regions of the search space that have strong empirical support and prior evidence are prioritized. Further details on how the variable selection algorithm works can be found in Chen and Thomas [A posterior density is defined on the basis of the likelihood and normal density function corresponding to the first (Eq. (1)) and second (Eq. (2)) levels of the hierarchical model. I use the product of this density function and a model transition function as the objective function of a reversible jump Monte Carlo Markov chain (MCMC) algorithm to stochastically explore the search space, fitting all possible sets of model variables to the data. The model transition kernel itself is informed through empirical Bayes estimates of the hyperparameters  stores both gene membership and SNP mutation type information in the A and Z matrices, respectively. In the second variant of the prior distribution (denoted Z only), I removed gene membership information so that matrix A was simply the identity matrix. Conversely, in the third variant of the prior distribution (denoted A only), I removed mutation class information so that the Z matrix included only the intercept. The last variant of the prior distribution (denoted UNINF) includes both the uninformative Z and A matrices and is equivalent to a the ridge style prior distribution ).In the next section I present results between a more conventional method and my proposed method. The first method is an ordinary least-squares regression between the quantitative trait  and each vector of variable scores, which I denote as the MLE method. This approach is equivalent to a conventional genome-wide association scan, testing for marginal effects. I compared this to four variations of the multivariate MCMC method. Specifically, I varied the degree of informativeness in the prior distribution by modifying the definition of the matrices p-value extracted from the Kolmogorov-Smirnov test.For each of the MCMC analyses, I sampled 2 million realizations from the posterior distribution, retaining statistics on only the last million realizations to minimize any correlation to the initial parameter values. Run time on a 2-GHz Xeon processor was approximately 8 h. I verified that the retained statistics reached convergence by comparing their distributions across multiple chains using a nonsignificant To quantify evidence for any specific variable (either common SNP or SNP bin), I empirically estimated Bayes factors (BFs) for each variable by dividing the posterior odds by the prior odds, as described by Chen and Thomas . BFs quaτ2) in the random effects component was smaller than the residual variance from the fixed effects component (σ2), indicating a good fit between the gene-membership prior distribution and the observed data. The posterior estimates for the prior mean (π) indicate a slightly positive correlation (0.03) between disease risk and presence of a nonsynonymous mutation, although the evidence is weak considering the large standard errors (0.06).Table β falls within the 95% confidence interval of the estimator. Thus a perfect estimator would have a value of 1. Hierarchical modeling achieved an MCR of 0.91 under the Q1 disease model, in contrast to an MCR of 0.56 when applying maximum likelihood. The second metric I considered, RMSE, is calculated by taking the square root of the average squared difference  between the estimated and true values of β. A smaller value of the RMSE indicates a more precise estimation of the true effect size. Under the Q1 disease model, the RMSE for the hierarchical model was 0.17, whereas for the maximum-likelihood model it was 0.38. When Q2 was the disease model, the RMSE and MSE were similar (within ±0.01), approximately 0.17 and 0.94, respectively, regardless of which method was used. Table FLT1 gene were poorly estimated using maximum likelihood.As alluded to earlier, hierarchical modeling shrinks unstable MLEs toward means informed through either informative or noninformative prior distributions. I considered two metrics that measure the accuracy of a method’s estimation of the true effect size: the mean coverage rate (MCR) and the root mean-square error (RMSE). I defined the MCR as the proportion across all causal SNPs and simulation replicates where the true value of A proved to be the most critical component for power overall. When Q2 was used as the outcome phenotype, the method showed greater sensitivity than the MLE method across all false discovery range (FDR) values, regardless of the prior distribution specification. Q1 performed slightly worse than the MLE at low FDRs when gene membership information was omitted from the prior specification. Table I next evaluated the ability of the MCMC sampler to perform variable selection by comparing sensitivity and specificity across the four variants of the prior distribution. The receiver operating characteristic (ROC) curves in Figures FLT1 gene, which had more “common” MAFs of 0.02, 0.03, and 0.07, respectively. These same SNPs also had fairly large simulated odds ratios , which most likely explain the improved overall performance of all the methods under the Q1 model, as shown in Figures A matrix information, which helps distribute evidence of association across a gene, was advantageous for SNPs within FLT1. In contrast, for SNPs in other genes, the Z matrix, which enables variables of the same mutation type to share a common mean, improved the method’s power to detect causal variants, as seen in the higher BFs in column 2 versus column 3 in Tables I noted a wide range of evidence across the variables considered. Tables A matrix in the hierarchical model). With any type of collapsing strategy, including mine, the choice of how bins are defined is arbitrary and some type of permutation procedure is necessary to alleviate an increase in type I error from overfitting the data. My Bayesian method, while also computationally expensive, does not involve permutation. Through Bayes model averaging and reporting of BFs, the problem of model overfitting is handled naturally. I previously demonstrated through simulations that the model is robust in light of multiple comparisons within the context of discovering interactions [In response to the missing heritability mystery plaguing the field of complex trait genetics, there is understandably massive interest in developing methods that can effectively investigate the relationship between rare variants and disease. In the methods described by Madsen and Browning  and a moractions .Z matrix, as demonstrated in the sensitivity analyses presented in Figures β based on biology could indeed improve power to detect variants. On closer inspection, I learned that mutation type (synonymous vs. nonsynonymous) information was more beneficial than gene-membership information for most of the SNP bins, but the opposite was true for the FLT1 gene. Thus I recommend providing as much external knowledge as possible in the model . Because my method is based on empirical Bayes estimates, it is robust to poor specification of the prior distribution, because this only leads to increased uncertainty (modeled in the prior variances τ2 and σ2), asymptotically reducing the prior distribution on β to a ridge prior distribution.The results from the analyses show that in certain cases, such as when Q1 is modeled as the outcome, rare variants can make accurate estimates of effect size difficult when operating under a conventional MLE framework. Hierarchical modeling can be particularly helpful here, even if the prior distributions are not particularly informative. However, I must provide an important caveat that the method, which still operates under a standard multivariate regression framework at the first level of the model, does not appear to work particularly well when rare variants , such as singleton mutations, are directly tested; convergence problems usually emerge when the design matrix becomes numerically singular. Thus I was unable to directly evaluate the method’s performance on any one specific SNP among the extremely rare causal variants. The LASSO (least absolute shrinkage and selection operator) method , anotherA and Z matrices accordingly). My method currently permits one to perform a global test of association  by testing fixed bins. An important property of enabling flexibility in bin assignment is that one can additionally perform local tests of association .Clearly, there is a need to develop methods to effectively mine the data for rare variants that confer disease risk. I am optimistic that my approach is more effective than other methods in many cases, but it does have the same limitations as those shared by collapsing-style methods, particularly the strong assumption that effect sizes will point in the same direction among SNPs inside a bin. I am considering other variations of the hierarchical model that might more flexibly accommodate this type of heterogeneity. One appealing idea is to include a new stochastic layer into the algorithm that randomly groups SNPs into bins (and consequently compresses the http://www-hsc.usc.edu/~garykche/).I have presented a computationally efficient Bayesian method that simultaneously provides additional power to discover rare disease variants and enhances estimation of true effect sizes. Users interested in the algorithm can download C++ source code and binaries from my website (I have no competing interests to declare.GKC conceived of the study, carried out the statistical analyses, and drafted the manuscript.


Aedes aegypti vectors in nature can be mapped to discrete loci in the mosquito genome and to document their genetic architecture. We developed an innovative genetic mapping strategy to survey G×G interactions using outbred mosquito families that were experimentally exposed to genetically distinct isolates of two dengue virus serotypes derived from human patients. Genetic loci associated with vector competence indices were detected in multiple regions of the mosquito genome. Importantly, correlation between genotype and phenotype was virus isolate-specific at several of these loci, indicating G×G interactions. The relatively high percentage of phenotypic variation explained by the markers associated with G×G interactions (ranging from 7.8% to 16.5%) is consistent with large-effect host genetic factors. Our data demonstrate that G×G interactions between dengue viruses and mosquito vectors can be assigned to physical regions of the mosquito genome, some of which have a large effect on the phenotype. This finding establishes the existence of tangible host genetic factors underlying specific interactions between invertebrates and their pathogens in a natural system. Fine mapping of the uncovered genetic loci will elucidate the molecular mechanisms of mosquito-virus specificity.Specific interactions between host genotypes and pathogen genotypes (G×G interactions) are commonly observed in invertebrate systems. Such specificity challenges our current understanding of invertebrate defenses against pathogens because it contrasts the limited discriminatory power of known invertebrate immune responses. Lack of a mechanistic explanation, however, has questioned the nature of host factors underlying G×G interactions. In this study, we aimed to determine whether G×G interactions observed between dengue viruses and their  Aedes aegypti that govern specific interactions with dengue viruses. We used large mosquito families derived from a natural population in Thailand that we experimentally challenged with virus isolates obtained from human patients living in the same area. We identified several regions of the mosquito genome that control specific interactions with dengue viruses and contribute significantly to the observed variation in vector competence. Our study establishes the existence of tangible host genetic factors underlying specific interactions between invertebrates and their pathogens in a natural system that is relevant to human health. This represents a critical step towards identification of mechanisms underlying the genetic specificity of insect-virus interactions.The outcome of invertebrate host-pathogen interactions often depends on the specific pairing of host and pathogen genotypes. This genetic specificity challenges our current understanding of invertebrate resistance to pathogens because it contrasts the limited discriminatory power of known invertebrate defense mechanisms. However, genetic factors underlying this observed specificity have remained elusive, questioning their very existence. In this study, we developed an innovative strategy to localize factors in the genome of the mosquito  Most organisms engage in ecological interactions with organisms of different species that have profound effects on their fitness. These interactions, which can be antagonistic  or mutualistic , are major drivers of adaptive evolution and diversification. Understanding the evolution of traits mediating ecological interactions can be complicated by their genetic specificity, whereby fitness of a genotype depends on the genotype of the interacting species Among the most intriguing examples of G×G interactions are those involved in invertebrate host susceptibility to pathogens Aedes aegypti mosquitoes are the main vectors of dengue viruses, which cause the most prevalent mosquito-borne viral disease of humans Ae. aegypti populations throughout their wide geographical range Ae. aegyptiAe. aegypti susceptibility to dengue has a genetic basis. Subsequent studies based on laboratory crosses of resistant and susceptible mosquito lines mapped several quantitative trait loci (QTL) controlling Ae. aegypti susceptibility to dengue virus infection and dissemination Ae. aegypti vector competence for dengue viruses are governed by G×G interactions Ae. aegypti depends on the specific pairing of mosquito and virus genotypes.Ae. aegypti genome that are associated with G×G interactions influencing vector competence for dengue viruses. We developed an innovative genetic mapping strategy . The use of outbred families for genetic mapping was inspired from a validated study design previously developed to investigate the genetic basis of natural mosquito resistance to human malaria parasites Ae. aegypti genome, which consists of three chromosomes . The second level measured the dependence of the phenotype on the genotype conditional on the virus isolate . The methodology of our genetic survey  at two different levels for each marker. The first level measured the dependence of the phenotype on the mosquito genotype c survey  differs i) the proportion of mosquitoes that developed a midgut infection, (ii) the proportion of infected mosquitoes in which infection disseminated from the midgut to head tissues, and (iii) the infectious viral titer in virus-infected head tissues. Midgut infection and viral dissemination are prerequisites for virus transmission by mosquito bite Vector competence was scored 14 days after an infectious blood meal according to three distinct phenotypes:  were individually phenotyped and genotyped  and family 5 (p = 2.9×10−2), at marker 335CGA1 (38.2 cM) in family C01 (p = 5.55×10−4), and at marker 88CA1 (44.9 cM) in family 7 (p = 4.94×10−3) and family 54 (p = 4.0×10−2). Linkage was also detected on chromosome 3 at marker 301ACG1 (0.0 cM) in family 51 (p = 7.47×10−5) and at marker B19 (13.6 cM) in the same family (p = 5.22×10−3). The proportion of phenotypic variation explained by each significant marker ranged from 3.5% to 12.0%. Importantly, we also detected significant virus isolate-specific linkage on chromosome 3 at marker 301CT1 (0.0 cM) in family 5 , which ruled out that uncontrolled variation in infectious dose among virus isolates  and family 42 (p = 3.1×10−2) and on chromosome 3 at marker 69TGA1 (32.1 cM) in family J07 (p = 4.4×10−2). The proportion of phenotypic variation explained by each significant marker ranged from 16.5% to 22.6%. Marker 335CGA1 on chromosome 1 was in linkage with the dissemination phenotype in two different families. In family J07 the marker effect was general across virus serotypes and isolates (p = 0.287). For illustration, Significant linkage at the genome-wide level was detected in two of the nine families for the viral dissemination phenotype . Linkageisolates , whereasisolates . To verip = 3.24×10−3). Linkage was also detected on chromosome 3 at marker 17ATA1 (22.4 cM) in family J07 (p = 1.70×10−5), at marker 69TGA1 (32.1 cM) in family J07 (p = 4.16×10−3), at marker 201AAT1 (57.1 cM) in family J06 (p = 5.18×10−4), and at marker 470CT2 (64.2 cM) in family J07 (p = 1.35×10−2). The proportion of phenotypic variation explained by each significant marker ranged from 8.9% to 75.6%. The genotype-phenotype association was isolate-specific at marker 201AAT1 in family J06 and at marker 470CT2 in family J07. Again, substituting the isolate by the corresponding blood meal titer ruled out a confounding effect of the infectious dose because the interaction was no longer statistically significant at marker 201AAT1 (p = 0.434) or at marker 470CT2 (p = 0.130). For illustration, Significant linkage at the genome-wide level was detected in three of the nine families for the head titer phenotype . LinkageSupporting information includes genetic mapping results for chromosome 2 , S7, S8 Our genetic survey demonstrates that G×G interactions between dengue viruses and mosquito vectors can be assigned to physical regions of the mosquito chromosomes. To the best of our knowledge, this is the first study to successfully locate G×G interactions in an invertebrate genome by marker-based genetic mapping. In agreement with the conclusions of a previous meta-analysis Ae. aegypti populations to serve as vectors of dengue viruses was previously observed Ae. aegypti susceptibility to dengue virus infection and dissemination. Here, we provide direct evidence that a significant portion of natural phenotypic variation is genetically determined. We identify multiple genetic factors that control dengue susceptibility in a natural Ae. aegypti population, but show that the effect of these factors also depends on the virus genome.This study also provides important new information on the biology of dengue virus transmission in a natural situation. Phenotypic variation in the ability of field Ae. aegypti population. In infected mosquitoes, viral dissemination from the midgut to secondary tissues is also controlled by two or more QTL. Infectious titer of disseminated virus, a proxy for transmission potential Irrespective of G×G interactions, the relatively large proportion of phenotypic variation explained by the individual mosquito markers (up to 75.6%) reveals the existence of QTL with major effects. Interestingly, QTL underlying the midgut infection phenotype explained a smaller proportion of the phenotypic variation than QTL underlying the viral dissemination and dissemination titer phenotypes, suggesting a different genetic architecture. This hypothesis is supported by a similar observation in an earlier QTL mapping study Our mapping strategy relies on marker-by-marker tests and does not generate a confidence interval of the QTL location on the chromosomes. In other words, conventional techniques of interval mapping cannot be applied. Therefore, we cannot ascertain at this stage whether QTL identified on chromosomes 1 and 3 match those previously mapped for a DENV-2 strain in laboratory systems. On chromosome 1, a midgut infection QTL was previously identified at 19 cM Ae. aegypti susceptibility to dengue viruses comprises a generalist component that is effective against diverse isolates, including isolates belonging to different serotypes. This result was previously unknown and gives hope to identify antiviral genes that confer a generalist protection against a diverse array of viruses. On the other hand, our genetic survey detected an isolate-specific component of the mosquito genetic basis for dengue susceptibility, which we interpret as G×G interactions between the vector and the virus. Markers associated with G×G interactions explained a significant proportion of phenotypic variation (from 7.8% to 16.5%). Identification of QTL associated with G×G interactions rules out the possibility that genetic specificity in this system is solely driven by environmentally inherited symbiotic microbiota that could have been confounded with the host genotype The main innovation of our study design was to explicitly account for viral genetic diversity in the genetic mapping of mosquito susceptibility loci. This allowed detection of both generalist and isolate-specific susceptibility loci. Several of the significant markers were in linkage with the phenotype independently of the virus isolate. Thus, the genetic basis of 2/F3 generations to increase mapping resolution and locate candidate genes. Although several resistance mechanisms have been characterized in laboratory systems, mosquito genes underlying phenotypic variation in susceptibility to dengue viruses in nature have remained elusive. Leading candidates are genes known to be functionally involved in Ae. aegypti antiviral defense, including genes of the RNA interference (RNAi), JAK-STAT and Toll pathways Ae. aegypti in nature It will be interesting to carry out fine-scale mapping experiments to identify the causal polymorphisms and their allelic profiles in the genomic regions where significant markers were found. An extension of the same protocol could be used to generate outbred isofemale lines beyond the FAe. aegypti vector competence for dengue viruses is controlled by multiple segregating QTL in a natural population suggests that such strategies may need to knock-down a larger number of genes than previously thought to confer complete resistance. Second, our discovery that the effect of several QTL is dengue virus serotype- and/or isolate-specific highlights the requirement for engineered resistance to be effective across all possible virus serotypes and strains encountered in nature.Our results have at least two practical implications for the current development of novel strategies to interrupt virus transmission by genetically engineering resistant mosquitoes In conclusion, our findings reinforce the idea that contributions from different genomes to the genetic architecture of ecological interactions cannot be fully disentangled because they depend on one another. By analogy with epistasis within the genome of a single organism, whereby the effect of a particular genotype on the phenotype depends on the genetic background, the direction and/or magnitude of the effect of host genes may depend on the pathogen genetic make-up. Like epistasis 2. F0 eggs were brought back to the AFRIMS laboratory in Bangkok and allowed to hatch in filtered tap water. F0 pupae were separated and allowed to emerge in individual vials. Aedes aegypti adults were identified by visual inspection.Wild mosquito eggs were collected using ovitraps in several households in the Nhong Pling, Kon Tee, Mae Na Ree, Nhong Ping Kai, and Thep Na Korn subdistricts, Muang district, Kamphaeng Phet Province, Thailand, during May 2010 and February 2011. Kamphaeng Phet Province is an agrarian, sparsely populated area located approximately 350 km northwest of Bangkok where dengue is endemic and the four dengue virus serotypes co-circulate 0 pairs consisting of one virgin male and one virgin female were allowed to mate for 2–3 days following emergence. To avoid that F0 parents were siblings from the same wild mother, the male and the female of each pair were chosen from different collection sites. Inseminated females were offered daily blood meals and allowed to lay eggs. Egg batches from a single female were merged to obtain a pool of F1 eggs. F0 males and females were saved for later DNA extraction and typing. F2 and F3 families were produced by mass sib-mating and collective oviposition from the F1 offspring. Although the mass-mating step reduces statistical power to detect genetic linkage because parental genetic information is partially lost, it is traded for a considerable increase in sample size Ae. aegypti pair can produce several thousands progeny per generation after as few as 2–3 generations in the laboratory.Single F1 adults were allowed to emerge in the laboratory, mate randomly, and feed on defibrinated sheep blood  through a membrane feeding system. The F2 and F3 eggs were collected and stored on dry pieces of paper towel and maintained under high humidity no longer than 6 months.FAe. aegypti females are inseminated by a single male in nature 0 parents prior to phenotyping. Families are not equal in the information they bring to QTL detection. Only families with the highest proportion of polymorphic markers were retained for genetic mapping. The aim of choosing families was to maximize the number of informative  meiosis at both marker and susceptibility loci. Out of a total of 184 initial mating pairs, nine families were selected that had >3,000 F2/F3 eggs and >80% polymorphic markers.Although most Aedes albopictus cells , which is the minimum required to obtain a viral titer sufficiently high to infect mosquitoes orally using an artificial blood meal. To prepare virus stock, 0.2 ml of human serum was inoculated onto 2-day-old confluent C6/36 cells in a 25-cm2 flask and incubated for 7 days at 28°C. The virus-infected cell culture supernatant was harvested and inoculated into a fresh flask of 2-day-old C6/36 cells for the second passage, of which supernatant was aliquoted and stored at −70°C.Four low-passage dengue virus isolates (two DENV-1 and two DENV-3) were used to orally challenge mosquitoes in vector competence assays . They dewww.applied-maths.com). Phylogenetic relationships were inferred using the maximum-likelihood method with the Tamura-Nei model implemented in MEGA v5 Viral genomic RNA was extracted from viral stock with the QIAamp viral RNA kit . RT-PCR was performed using the SuperScript One-Step RT-PCR kit with platinum Taq polymerase  according to the manufacturer's recommendations, with a set of primers covering the entire genome . RT-PCR Ae. aegypti females of the F2 or F3 generation were used in vector competence assays to score their relative susceptibility to the four low-passage dengue virus isolates. Experimental infections were run in three large experiments that involved different triplets of mosquito families  per tray. Pupae were transferred to plastic screened 30×30×30 cm cages  and adults were maintained on a diet of 10% sucrose. They were kept in an insectary at 28±1°C, under a relative humidity of 70–80% and a 12∶12 h light-dark cycle. The day before the oral challenge, females were transferred from the rearing cage to 1-pint feeding cups of ∼100 females.families . F2/F3 e2 flasks of 2-day-old C6/36 cells were inoculated with a 1-ml aliquot from the viral stock and incubated for 45 min to 1 hour. At the end of the adsorption, 4.0 ml of maintenance medium were added and the cells were incubated at 35±1°C under 5% CO2 for 5 days. At day 5, 1.0 ml of heat-inactivated fetal bovine serum containing 15% of sodium bicarbonate 7.5% solution (HIFBS-NaHCO3) was added to the virus-infected cell culture supernatant, which was then harvested to prepare the infectious blood meal. The virus suspension was diluted 1∶3 or 1∶2 with RPMI 1640 medium containing 5% HIFBS and then mixed 1∶1 with defibrinated sheep blood . The infectious blood meal was placed in water-jacketed glass feeders maintained at a constant temperature of 37°C and covered with a piece of desalted porcine intestine. Four- to 7-day-old Ae. aegypti females deprived of sucrose and water for 24 h prior to blood feeding were offered an infectious blood meal for 30 min. Samples of the blood meals were saved for subsequent titration. Blood meal titers ranged from 2.0×104 to 1.5×106 plaque-forming units per ml (PFU/ml); the majority (83.3%) ranged between 1.0×105 and 1.0×106 PFU/ml  midgut infection, (ii) viral dissemination from the midgut, and (iii) infectious titer in head tissues. Viral infection of midgut epithelial cells and subsequent dissemination to secondary tissues are two essential steps of dengue virus propagation in Ae. aegypti. Both events are prerequisites for virus transmission by mosquito bite and have been used to define a ‘midgut infection barrier’ and a ‘midgut escape barrier’ underlying Ae. aegypti susceptibility to dengue viruses in vitroVector competence was scored in the FUpon harvest, the head of each female was cut off on a chill table and placed individually in 500 µl of mosquito diluent . The remainder of the body (thorax and abdomen) was stored separately in 900 µl of MD with one 4.5 mm stainless steel bead in a 2-ml safe-lock tube. Samples were stored at −70°C until testing by plaque assay. They were quickly thawed in a water bath at 35±2°C and homogenized in a mixer mill (Qiagen) at 24 cycles/sec for 2 min. Four hundreds µl of each body homogenate were transferred into a new 1.5 ml safe-lock tube containing 400 µl of lysis buffer BQ1  and stored at −20°C for DNA genotyping.2, ATCC CCL-7) as previously described 2 cells in 24-well plates. The virus was adsorbed at room temperature (20–28°C) on a rocker platform for 90 min. The inoculum was removed and 0.5 ml/well of a first overlay of medium was added. The cells were incubated for 5 days at 35±1°C under 5±0.5% CO2. The cells were stained with a second overlay of medium containing 4% neutral red . Mosquito infection and dissemination status was determined based on the presence of plaques in their body and head homogenates, respectively. Mosquito whose bodies were negative by plaque assay were considered uninfected, and their heads were not processed further. Head titer of infected bodies was determined by plaque assay of 1∶10 and 1∶100 dilutions of head homogenates.Infectious virus was detected and quantified by plaque assay performed in rhesus monkey kidney epithelial cells . For each marker in the final map (Ae. aegypti genome Ae. aegypti genome. Twenty-two of these microsatellites (15 genetic positions) are located on chromosomes 1 or 3. Based on an estimated genome size of 1,376 Mbp and a genetic size of 205 centiMorgans (cM), the relationship between physical and recombination distance is 6.71 Mbp/cM QTL detection was performed in the outbred mosquito families using a set of 25 microsatellite markers broadly distributed across the genome . Geneticinal map , we veri2 or F3 generation. Each mosquito family descended from a single pair of F0 parents collected in the field, providing an independent sample of up to four different alleles per locus from the original natural mosquito population. Based on the number of alleles present at the F0 generation, we verified at each marker that the correct number of genotypes was observed in the progeny. Three, six and ten different genotypes are expected in the progeny when F0 parents harbor two, three and four different alleles, respectively.The genetic survey was based on the analysis of outbred mosquito families at the F1 generation because the number of reproducing adults is relatively small. Some genotypes could also be selected because they have a fitness advantage over other genotypes in insectary conditions. Departure from a neutral reproductive model may reduce the statistical power to detect marker-trait associations, but not the statistical significance of results. The same is true for null alleles or genotyping errors that would confound the observed genotypes. Our genetic model does not specify allelic codominance or recessivity. It simply compares genotypes  regardless of their frequency.The originality of the strategy is to use families with incomplete pedigree information due to the mass-mating step 0 parent is heterozygous both at a QTL and at a linked segregating marker, which is a prerequisite to generate a marker-trait association in the progeny. We maximized statistical power by genotyping F0 parents and choosing the most informative families  for phenotyping. In addition, the linkage phase between the marker and the QTL can vary in the progeny. This can reduce QTL detection power, if for example the same marker allele is associated with different QTL alleles in the F0 parents. Again, this would increase the probability to declare significant evidence against marker-trait association  but not the statistical significance of results.Statistical power is also limited by the extent of heterozygosity in the family. There is no guarantee that every Fhttp://aaegypti.vectorbase.org/) and submitted to the Tandem Repeats Finder program Ae. aegypti genome in VectorBase to verify their occurrence in single copy. PCR primers were designed in flanking sequences of selected microsatellites using Primer3 v0.4.0 5′-TCATTCCCAGTACCACACAAACG-3′ (forward) and 5′-ACTCGTTACTGGATGTGCTATCCC-3′ (reverse) for marker 14ATT1 and 5′-GAACGCGCTCGTAAGCGAGA-3′ (forward) and 5′-CACTGTGCGTTGGTTTCGGCT-3′ (reverse) for marker 210TTC1. Individual primer pairs were further subjected to BLASTn analysis to verify that they were predicted to amplify single copy sequences in the Ae. aegypti genome. PCR products were run by electrophoresis on 2% agarose gel to confirm that amplicons were unique.Microsatellite markers 210TTC1 and 14ATT1 on chromosome 2 were developed as previously described 2, 200 µM dNTPs (Invitrogen Life Technologies), 0.2 µM of each primer, 1 unit of Taq DNA polymerase (Invitrogen Life Technologies), and 2 µl of genomic DNA purified as described above. Thermocycling conditions were 5 min at 94°C, followed by 35 cycles of a 30-sec denaturation at 94°C, a 30-sec annealing at 50°C, and a 1-min extension at 72°C, followed by a 7-min final extension at 70°C. Multiplexed PCR products were examined by electrophoresis on 1% agarose gel and diluted 1∶10 in sterile water. Two µl of this dilution was added to 10 µl of Hi-Di Formamide (Applied Biosystems) containing 7.5% of 6-carboxy-X-rhodamine (ROX)-labeled Geneflo 625 size standards . Capillary electrophoresis of multiplexed PCR products was performed on a 3730xl DNA Analyser (Applied Biosystems). Sizes of microsatellite alleles were called and manually checked using the GeneMapper v4.0 software package (Applied Biosystems).Genomic DNA was extracted from mosquito homogenates using the NucleoSpin 96 Tissue Core Kit (Macherey-Nagel) and stored at −20°C until use. Genotyping of microsatellite repeats was performed by PCR amplification using fluorochrome-labeled forward primers   to generate fluorescent PCR products. Primer pairs producing different amplicon sizes were assembled into multiplex groups of 4–6 markers. Amplification was performed in 25 µl volumes in Thermo-Fast 96-wells PCR plates  in a Veriti thermal cycler (Applied Biosystems). Each reaction contained 1× Taq buffer  (Invitrogen Life Technologies), 1.5 mM MgCl2 or F3 generation. In contrast with association studies performed at the population level, high linkage disequilibrium in the families strongly reduces the marker density required for the genetic mapping.Our approach is a combination of linkage and association analyses. Linkage analysis generally uses pedigrees to infer the location of a susceptibility locus based on coinheritance of the disease phenotype with genetic markers whose chromosomal location is known. Association analysis does not rely on pedigree structure but assumes that strong associations between marker alleles and disease phenotype in a population will be due to linkage, rather than chance. In association studies, IBD due to coancestry is inferred from IBS in the form of observed allelic associations. In the present study, linkage was inferred from IBS as in association studies. Tests of genotype-phenotype associations, however, were performed in sibships (single-generation families) at the at the Fi) midgut infection status, (ii) viral dissemination status of midgut-infected mosquitoes, and (iii) head titer in mosquitoes with a disseminated infection. For binary phenotypes (infection and dissemination), the model was fitted with a binomial error structure and a logit link function . For the continuous phenotype (head titer), the variable was log-transformed and the model was fitted with a normal error distribution and an identity link function . The model was fitted separately for each informative microsatellite marker in each mosquito family. Depending on the number of alleles of the marker, the factor genotype had from three to ten different categories, whereas the factor isolate always had four categories . If, due to random sampling effects in the progeny, one category of the genotype was not encountered in one or more categories of the isolate, this genotype category was excluded from the analysis so that the genotype by isolate interaction could be tested in the model. Depending on the marker, this could result in a different number of mosquitoes included in the analysis for the same family.Genetic linkage was inferred from the significance of the genotype effect in a generalized linear model of the phenotype that included the factors mosquito genotype, virus isolate and their interaction as explanatory variables. Response variables were the three vector competence indices that we measured:  and continuous (head titer) variables. For binary phenotypes, statistical significance was tested with an analysis of deviance p-values to ensure a genome-wide type I error of at most α = 0.05 . The genome-wide significance level of the test at each marker was α/N, where N is the number of informative markers tested in each family. A genotype-phenotype association was declared significant at the genome-wide level if the nominal p-value was smaller than α/N. When a significant genotype by isolate interaction was found, we verified that uncontrolled differences in the infectious titer of the artificial blood meal  and DENV-3 (B). Bootstrap values  are indicated at the major nodes. Horizontal branch lengths are drawn to scale, with scale bars representing the number of substitutions per site. Sequences are labeled with their accession number followed by the sampling year and the country of origin. Previously described dengue virus ‘genotypes’ (large phylogenetic clades) are indicated. The four virus isolates of the study are in bold font. The background set of sequences was retrieved from GenBank.(TIFF)Click here for additional data file.Figure S2Marker map. Microsatellites used for the genetic survey are indicated with their genetic position in Kosambi centiMorgans (cM). Chromosomes are drawn to scale according to the reference genetic map (TIFF)Click here for additional data file.Figure S3Ae. aegypti females and their 95% confidence intervals for each genotype at the marker. The four panels correspond to the four dengue virus isolates tested in the study. Horizontal, dotted lines show the average percentage for each isolate.Isolate-specific association between marker 301CT1 genotype and midgut infection. Bars represent the percentage of infected (TIFF)Click here for additional data file.Figure S42 genotypes were used to reconstruct the segregation history. Numbers 439 and 480 refer to the size of PCR amplicons used to genotype the microsatellite alleles. Expected genotype frequencies are shown at the F1 and F2 generations for both sexes. The red asterisk indicates co-segregation with the male allele of the sex-determining locus (38.0 cM) closely linked with the marker (38.2 cM) on chromosome 1. In the F2 generation, only females were phenotyped so that the male genotypes (hatched) were not represented. Observed frequency of genotypes at the F2 generation were 45.5% of 439/439 and 54.5% of 480/439.Inferred segregation of marker 335CGA1 in isofemale family 42. Parental genotypes and the observed frequency of F(TIFF)Click here for additional data file.Figure S5Ae. aegypti females with a disseminated infection for each genotype at the marker. The four panels correspond to the four dengue virus isolates tested in the study. Horizontal, dotted lines show the average head titer for each isolate. Only four marker genotypes are shown instead of the expected six because two genotypes were not represented in all isolates and were therefore excluded from the analysis.Isolate-specific association between marker 470CT2 genotype and dissemination titer. Box plots represent the distribution of log-transformed head titers of (TIFF)Click here for additional data file.Figure S6Ae. aegypti chromosome 2 loci associated with midgut infection. Nominal p-values are shown as a function of genetic marker positions (excluding uninformative markers) along chromosome 2 (represented below the graphs with genetic distances in Kosambi cM) in outbred mosquito families shown in Genetic survey for (TIFF)Click here for additional data file.Figure S7Ae. aegypti chromosome 2 loci associated with viral dissemination. Nominal p-values are shown as a function of genetic marker positions (excluding uninformative markers) along chromosome 2 (represented below the graphs with genetic distances in Kosambi cM) in outbred mosquito families shown in Genetic survey for (TIFF)Click here for additional data file.Figure S8Ae. aegypti chromosome 2 loci associated with head titer. Nominal p-values are shown as a function of genetic marker positions (excluding uninformative markers) along chromosome 2 (represented below the graphs with genetic distances in Kosambi cM) in outbred mosquito families shown in Genetic survey for (TIFF)Click here for additional data file.Figure S9Ae. aegypti families with no significant locus associated with midgut infection. Nominal p-values are shown as a function of genetic marker positions (excluding uninformative markers) along the three chromosomes (represented below the graphs with genetic distances in Kosambi cM). Dashed, horizontal lines indicate the nominal (green) and Bonferroni-corrected (blue) α = 0.05 statistical significance thresholds, respectively. The black line represents generalist effects (across virus serotypes and isolates) and the red line shows isolate-specific effects (genotype by isolate interactions). Different graphs (A–D) correspond to different mosquito families.(TIFF)Click here for additional data file.Figure S10Ae. aegypti families with no significant locus associated with viral dissemination. Nominal p-values are shown as a function of genetic marker positions (excluding uninformative markers) along the three chromosomes (represented below the graphs with genetic distances in Kosambi cM). Dashed, horizontal lines indicate the nominal (green) and Bonferroni-corrected (blue) α = 0.05 statistical significance thresholds, respectively. The black line represents generalist effects (across virus serotypes and isolates) and the red line shows isolate-specific effects (genotype by isolate interactions). Different graphs (A–G) correspond to different mosquito families.(TIFF)Click here for additional data file.Figure S11Ae. aegypti families with no significant locus associated with head titer. Nominal p-values are shown as a function of genetic marker positions (excluding uninformative markers) along the three chromosomes (represented below the graphs with genetic distances in Kosambi cM). Dashed, horizontal lines indicate the nominal (green) and Bonferroni-corrected (blue) α = 0.05 statistical significance thresholds, respectively. The black line represents generalist effects (across virus serotypes and isolates) and the red line shows isolate-specific effects (genotype by isolate interactions). Different graphs (A–E) correspond to different mosquito families. Note that this analysis could not be performed for family 40 because the number of females with a disseminated infection was to small to support the statistical model.(TIFF)Click here for additional data file.Table S12 or F3 generation were simultaneously challenged with the four isolates.Summary of raw vector competence data. For each pair of mosquito family and virus isolate, the number of mosquitoes, the number of informative markers, the percentage of mosquitoes with a midgut infection, the percentage of infected mosquitoes with a disseminated infection and the log-transformed mean viral titer (± standard deviation) in infected head tissues are indicated. In each experiment a different triplet of mosquito families at the F(DOC)Click here for additional data file.Table S22 (Experiment 2) or F3 generation (Experiments 1 and 3) were simultaneously challenged with the four isolates.Description of virus isolates. The date of collection, serotype, number of passages in C6/36 cells, and measured infectious titers in the artificial infectious blood meals (in plaque-forming units per ml) are indicated. In each experiment a different triplet of mosquito families at the F(DOC)Click here for additional data file.Table S3Primers used for virus sequencing. Nucleotide positions and primer sequences are shown for each of the overlapping amplicons covering the viral genome.(DOC)Click here for additional data file.