A test of linkage for complex discrete and continuous traits in nuclear families

This article presents a score test for genetic linkage in nuclear families which applies to any trait having a distribution belonging to the exponential family, which includes binary and normal distributions, and distributions which are skewed or have nonnormal kurtosis. The specific distribution need not be specified and the method applies to sibships of arbitrary size. Tests of complex genetic effects are given, including unspecified mode of inheritance or additive, dominant, overdominant, and recessive modes of inheritance, covariates, multiple-locus models, including gene-gene interactions, and gene-environment interactions. The relation of our method to the Haseman-Elston methods is studied theoretically and by simulation.     

Testing for Genetic Association in the Presence of Linkage and Gene–Covariate Interactions

In order to study family‐based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64 , 5–15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family‐based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene–covariate interaction, we propose a linear regression method where the family‐specific score statistic is regressed on family‐specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within‐family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene–covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti‐cyclic citrullinated peptide increased the significance of the association with the DR locus.     

Potential of mean force for protein-protein interaction studies.

Calculating protein-protein interaction energies is crucial for understanding protein-protein associations. On the basis of the methodology of mean-field potential, we have developed an empirical approach to estimate binding free energy for protein-protein interactions. This knowledge-based approach has been used to derive distance-dependent free energies of protein complexes from a nonredundant training set in the Protein Data Bank (PDB), with a careful treatment of homology. We calculate atom pair potentials for 16 pair interactions, which can reflect the importance of hydrophobic interactions and specific hydrogen-bonding interactions. The derived potentials for hydrogen-bonding interactions show a valley of favorable interactions at a distance of approximately 3 A, corresponding to that of an established hydrogen bond. For the test set of 28 protein complexes, the calculated energies have a correlation coefficient of 0.75 compared with experimental binding free energies. The performance of the method in ranking the binding energies of different protein-protein complexes shows that the energy estimation can be applied to value binding free energies for protein-protein associations.     

A method for reverse interactome analysis: High‐resolution mapping of interdomain interaction network in Dam1 complex and its specific disorganization based on the interaction domain expression

An experimental methodology that facilitates functional analysis of numerous protein–protein interactions, which have been found in genome‐wide interactome researches, has long been awaited. We propose herein an antagonistic inhibition‐based approach. The antagonizing polypeptide is generated in the course of interaction domain mapping based on yeast 2‐hybrid (Y2H) screening coupled with in vitro convergence of the Y2H‐selected fragments, which is performed in a formatted procedure. Using the coupled methodology, we first performed a high‐resolution mapping of an interdomain interaction network within budding yeast's Dam1 complex. Dam1 complex is a kinetochore protein complex composed of 10 essential subunits including Spc34p and Spc19p. The high‐resolution mapping revealed the overall network structure within the complex for the first time: Dam1 components form into two separated subnetworks on N‐terminal scaffolding domains of Spc34p and Spc19p, and the coiled‐coil interaction in their C‐terminal domains connects the subnetworks. Secondly, we show that the domain fragments converged in the high‐resolution mapping acted as potent inhibitors for the endogenous interactions when episomally overexpressed. The in vivo Dam1 interaction targeting with the fragments conferred a similar phenotype on the host cells; a critical and irreversible damage, which was accompanied with disturbed budding and chromosome mis‐segregation as a result of disorganized spindle. These phenotypes were strongly related to the cellular function of the Dam1 complex. The results and approach we demonstrated herein not only shed light on the Dam1 molecular architecture but also pave the road to reverse‐interactome analysis and discoveries of novel drugs that target disease‐related protein–protein interactions. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Membrane-SPINE: an improved method to identify protein-protein interaction partners of membrane proteins in vivo

Membrane proteins are crucial for many essential cellular processes. As membrane proteins function in complexes, methods to detect and to characterize membrane protein-protein interactions are undoubtedly required. Therefore, we developed the "Membrane-Strep-tagged protein interaction experiment" (Membrane-SPINE) that combines the specific purification of a Strep-tagged membrane protein with the reversible fixation of protein complexes by formaldehyde cross-linking. In combination with MS analysis, we suggest Membrane-SPINE as a powerful tool to identify unknown interaction partners of membrane proteins in vivo.     

Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley

The additive main effects and multiplicative interaction (AMMI) model has emerged as a powerful analytical tool for genotype x environment studies. The objective of the present study was to assess its value in quantitative trait locus (QTL) mapping. This was done through the analysis of a large two-way table of genotype-by-environment data of barley (Hordeum vulgare L.) grain yields, where the genotypes constituted a genetic population suitable for mapping studies. Grain yield data of 150 doubled haploid lines derived from the Steptoe x Morex cross, and the two parental lines, were taken by the North American Barley Genome Mapping Project (NABGMP) at 16 environments throughout the barley production areas of the USA and Canada. Four regions of the genome were responsible for most of the differential genotypic expression across environments. They accounted for approximately 50% of the genotypic main effect and 30% of the genotype x environment interaction (GE) sums of squares. The magnitude and sign of AMMI scores for genotypes and sites facilitate inferences about specific interactions. The parallel use of classification (cluster analysis of environments) and ordination (principal component analysis of GE matrix) techniques allowed most of the variation present in the genotype x environment matrix to be summarized in just a few dimensions, specifically four QTLs showing differential adaptation to four clusters of environments. Thus, AMMI genotypic scores, when the genotypes constituted a population suitable for QTL mapping, could provide an adequate way of resolving the magnitude and nature of QTL x environment interactions.Ignacio Romagosa was on sabbatical leave from the University of Lleida and the Institut de Recerca i Tecnologia Agroalimentàries, Lleida, Spain, when this study was conducted     

Susceptibility loci for metabolic syndrome and metabolic components identified in Han Chinese: a multi‐stage genome‐wide association study

Metabolic syndrome (MetS), a cluster of metabolic disturbances that increase the risk for cardiovascular disease and diabetes, was because of genetic susceptibility and environmental risk factors. To identify the genetic variants associated with MetS and metabolic components, we conducted a genome‐wide association study followed by replications in totally 12,720 participants from the north, north‐eastern and eastern China. In combined analyses, independent of the top known signal at rs651821 on APOA5, we newly identified a secondary triglyceride‐associated signal at rs180326 on BUD13 (P_combined = 2.4 × 10⁻⁸). Notably, by an integrated analysis of the genotypes and the serum levels of APOA5, BUD13 and triglyceride, we observed that BUD13 was another potential mediator, besides APOA5, of the association between rs651821 and serum triglyceride. rs671 (ALDH2), an east Asian‐specific common variant, was found to be associated with MetS (P_combined = 9.7 × 10⁻²²) in Han Chinese. The effects of rs671 on metabolic components were more prominent in drinkers than in non‐drinkers. The replicated loci provided information on the genetic basis and mechanisms of MetS and metabolic components in Han Chinese.     

Using aggregate data to estimate the standard error of a treatment–covariate interaction in an individual patient data meta‐analysis

Subgroup analyses are important to medical research because they shed light on the heterogeneity of treatment effectts. A treatment–covariate interaction in an individual patient data (IPD) meta‐analysis is the most reliable means to estimate how a subgroup factor modifies a treatment's effectiveness. However, owing to the challenges in collecting participant data, an approach based on aggregate data might be the only option. In these circumstances, it would be useful to assess the relative efficiency and power loss of a subgroup analysis without patient‐level data. We present methods that use aggregate data to estimate the standard error of an IPD meta‐analysis’ treatment–covariate interaction for regression models of a continuous or dichotomous patient outcome. Numerical studies indicate that the estimators have good accuracy. An application to a previously published meta‐regression illustrates the practical utility of the methodology.     

Epistasis and genotype-environment interaction for quantitative trait loci affecting flowering time in Arabidopsis thaliana

A major goal of evolutionary biology is to understand the genetic architecture of the complex quantitative traits that may lead to adaptations in natural populations. Of particular relevance is the evaluation of the frequency and magnitude of epistasis (gene–gene and gene–environment interaction) as it plays a controversial role in models of adaptation within and among populations. Here, we explore the genetic basis of flowering time in Arabidopsis thaliana using a series of quantitative trait loci (QTL) mapping experiments with two recombinant inbred line (RIL) mapping populations [Columbia (Col) x Landsberg erecta (Ler), Ler x Cape Verde Islands (Cvi)]. We focus on the response of RILs to a series of environmental conditions including drought stress, leaf damage, and apical damage. These data were explicitly evaluated for the presence of epistasis using Bayesian based multiple-QTL genome scans. Overall, we mapped fourteen QTL affecting flowering time. We detected two significant QTL–QTL interactions and several QTL–environment interactions for flowering time in the Ler x Cvi population. QTL–environment interactions were due to environmentally induced changes in the magnitude of QTL effects and their interactions across environments – we did not detect antagonistic pleiotropy. We found no evidence for QTL interactions in the Ler x Col population. We evaluate these results in the context of several other studies of flowering time in Arabidopsis thaliana and adaptive evolution in natural populations.     

Crowding and disease: effects of host density on response to infection in a butterfly–parasite interaction

Abstract. 1. Hosts experiencing frequent variation in density are thought to benefit from allocating more resources to parasite defence when density is high (‘density‐dependent prophylaxis’). However, high density conditions can increase intra‐specific competition and induce physiological stress, hence increasing host susceptibility to infection (‘crowding‐stress hypothesis’). 2. We studied monarch butterflies (Danaus plexippus) and quantified the effects of larval rearing density on susceptibility to the protozoan parasite Ophryocystis elektroscirrha. Larvae were inoculated with parasite spores and reared at three density treatments: low, moderate, and high. We examined the effects of larval density on parasite loads, host survival, development rates, body size, and wing melanism. 3. Results showed an increase in infection probability with greater larval density. Monarchs in the moderate and high density treatments also suffered the greatest negative effects of parasite infection on body size, development rate, and adult longevity. 4. We observed greater body sizes and shorter development times for monarchs reared at moderate densities, and this was true for both unparasitised and parasite‐treated monarchs. We hypothesise that this effect could result from greater larval feeding rates at moderate densities, combined with greater physiological stress at the highest densities. 5. Although monarch larvae are assumed to occur at very low densities in the wild, an analysis of continent‐wide monarch larval abundance data showed that larval densities can reach high levels in year‐round resident populations and during the late phase of the breeding season. Treatment levels used in our experiment captured ecologically‐relevant variation in larval density observed in the wild.     

An empirical test for a zone of canalization in thermal reaction norms

Theoretical models on the evolution of phenotypic plasticity predict a zone of canalization where reaction norms cross, and genetic variation is minimized in the environment a population most frequently encounter. Empirical tests of this prediction are largely missing, in particular for life‐history traits. We addressed this prediction by quantifying thermal reaction norms of three life‐history traits (somatic growth rate, age and size at maturation) of a Norwegian population of Daphnia magna and testing for the occurrence of an intermediate temperature (T_m) at which genetic variance in the traits is minimized. Size at maturation changed relatively little with temperature compared to the other traits, and there was no genetic variance in the shape of the reaction norm. Consequently, age at maturation and somatic growth rate were strongly negatively correlated. Both traits showed a strong genotype–environment interaction, and the estimated T_m was 14 °C for both age at maturation and growth rate. This value of T_m corresponds well with mean summer temperatures experienced by the population and suggests that the population has evolved under stabilizing selection in temperatures that fluctuate around this mean temperature. These results suggest local adaptation to temperature in the studied population and allow predicting evolutionary trajectories of thermal reaction norms under changing thermal regimes.     

A pathogenesis‐related protein GmPR08‐Bet VI promotes a molecular interaction between the GmSHMT08 and GmSNAP18 in resistance to Heterodera glycines

Soybean cyst nematode (SCN, Heterodera glycines) is the most devastating pest affecting soybean production worldwide. SCN resistance requires both the GmSHMT08 and the GmSNAP18 in ‘Peking’‐type resistance. Here, we describe the molecular interaction between GmSHMT08 and GmSNAP18, which is potentiated by a pathogenesis‐related protein GmPR08‐Bet VI. Like GmSNAP18 and GmSHMT08, GmPR08‐Bet VI expression was induced in response to SCN and its overexpression decreased SCN cysts by 65% in infected transgenic soybean roots. Overexpression of GmPR08‐Bet VI did not have an effect on SCN resistance when the two cytokinin‐binding sites in GmPR08‐Bet VI were mutated, indicating a new role of GmPR08‐Bet VI in SCN resistance. GmPR08‐Bet VI was mapped to a QTL for resistance to SCN using different mapping populations. GmSHMT08, GmSNAP18 and GmPR08‐Bet VI localize to the cytosol and plasma membrane. GmSNAP18 expression and localization hyper‐accumulated at the plasma membrane and was specific to the root cells surrounding the nematode in SCN‐resistant soybeans. Genes encoding key components of the salicylic acid signalling pathway were induced under SCN infection. GmSNAP18 and GmPR08‐Bet VI were also induced under salicylic acid and cytokinin exogenous treatments, while GmSHMT08 was induced only when the resistant GmSNAP18 was present, pointing to the presence of a molecular crosstalk between SCN‐resistant genes and defence genes. Expression analysis of GmSHMT08 and GmSNAP18 identified the need of a minimum expression requirement to trigger the SCN resistance reaction. These results provide insight into a new response mechanism towards plant nematode resistance involving haplotype compatibility, gene dosage and hormone signalling.     

Mutations in amyloid precursor protein and presenilin-1 genes increase the basal oxidative stress in murine neuronal cells and lead to increased sensitivity to oxidative stress mediated by amyloid beta-peptide (1-42), HO and kainic acid: implications for Alzheimer's disease

Oxidative stress is observed in Alzheimer's disease (AD) brain, including protein oxidation and lipid peroxidation. One of the major pathological hallmarks of AD is the brain deposition of amyloid beta-peptide (Abeta). This 42-mer peptide is derived from the beta-amyloid precursor protein (APP) and is associated with oxidative stress in vitro and in vivo. Mutations in the PS-1 and APP genes, which increase production of the highly amyloidogenic amyloid beta-peptide (Abeta42), are the major causes of early onset familial AD. Several lines of evidence suggest that enhanced oxidative stress, inflammation, and apoptosis play important roles in the pathogenesis of AD. In the present study, primary neuronal cultures from knock-in mice expressing mutant human PS-1 and APP were compared with those from wild-type mice, in the presence or absence of various oxidizing agents, viz, Abeta(1-42), H2O2 and kainic acid (KA). APP/PS-1 double mutant neurons displayed a significant basal increase in oxidative stress as measured by protein oxidation, lipid peroxidation, and 3-nitrotyrosine when compared with the wild-type neurons (p < 0.0005). Elevated levels of human APP, PS-1 and Abeta(1-42) were found in APP/PS-1 cultures compared with wild-type neurons. APP/PS-1 double mutant neuron cultures exhibited increased vulnerability to oxidative stress, mitochondrial dysfunction and apoptosis induced by Abeta(1-42), H2O2 and KA compared with wild-type neuronal cultures. The results are consonant with the hypothesis that Abeta(1-42)-associated oxidative stress and increased vulnerability to oxidative stress may contribute significantly to neuronal apoptosis and death in familial early onset AD.