A common reference population from four European Holstein populations increases reliability of genomic predictions

Background

Size of the reference population and reliability of phenotypes are crucial factors influencing the reliability of genomic predictions. It is therefore useful to combine closely related populations. Increased accuracies of genomic predictions depend on the number of individuals added to the reference population, the reliability of their phenotypes, and the relatedness of the populations that are combined.

Methods

This paper assesses the increase in reliability achieved when combining four Holstein reference populations of 4000 bulls each, from European breeding organizations, i.e. UNCEIA (France), VikingGenetics (Denmark, Sweden, Finland), DHV-VIT (Germany) and CRV (The Netherlands, Flanders). Each partner validated its own bulls using their national reference data and the combined data, respectively.

Results

Combining the data significantly increased the reliability of genomic predictions for bulls in all four populations. Reliabilities increased by 10%, compared to reliabilities obtained with national reference populations alone, when they were averaged over countries and the traits evaluated. For different traits and countries, the increase in reliability ranged from 2% to 19%.

Conclusions

Genomic selection programs benefit greatly from combining data from several closely related populations into a single large reference population.     

Genetic architecture is more complex for resistance to Septoria tritici blotch than to Fusarium head blight in Central European winter wheat

Background

Fusarium head blight (FHB) and Septoria tritici blotch (STB) severely impair wheat production. With the aim to further elucidate the genetic architecture underlying FHB and STB resistance, we phenotyped 1604 European wheat hybrids and their 135 parental lines for FHB and STB disease severities and determined genotypes at 17,372 single-nucleotide polymorphic loci.

Results

Cross-validated association mapping revealed the absence of large effect QTL for both traits. Genomic selection showed a three times higher prediction accuracy for FHB than STB disease severity for test sets largely unrelated to the training sets.

Conclusions

Our findings suggest that the genetic architecture is less complex and, hence, can be more properly tackled to perform accurate prediction for FHB than STB disease severity. Consequently, FHB disease severity is an interesting model trait to fine-tune genomic selection models exploiting beyond relatedness also knowledge of the genetic architecture.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1628-8) contains supplementary material, which is available to authorized users.     

Effect of non-random mating on genomic and BLUP selection schemes

Background

The risk of long-term unequal contribution of mating pairs to the gene pool is that deleterious recessive genes can be expressed. Such consequences could be alleviated by appropriately designing and optimizing breeding schemes i.e. by improving selection and mating procedures.

Methods

We studied the effect of mating designs, random, minimum coancestry and minimum covariance of ancestral contributions on rate of inbreeding and genetic gain for schemes with different information sources, i.e. sib test or own performance records, different genetic evaluation methods, i.e. BLUP or genomic selection, and different family structures, i.e. factorial or pair-wise.

Results

Results showed that substantial differences in rates of inbreeding due to mating design were present under schemes with a pair-wise family structure, for which minimum coancestry turned out to be more effective to generate lower rates of inbreeding. Specifically, substantial reductions in rates of inbreeding were observed in schemes using sib test records and BLUP evaluation. However, with a factorial family structure, differences in rates of inbreeding due mating designs were minor. Moreover, non-random mating had only a small effect in breeding schemes that used genomic evaluation, regardless of the information source.

Conclusions

It was concluded that minimum coancestry remains an efficient mating design when BLUP is used for genetic evaluation or when the size of the population is small, whereas the effect of non-random mating is smaller in schemes using genomic evaluation.     

The accuracy of prediction of genomic selection in elite hybrid rye populations surpasses the accuracy of marker-assisted selection and is equally augmented by multiple field evaluation locations and test years

Background

Marker-assisted selection (MAS) and genomic selection (GS) based on genome-wide marker data provide powerful tools to predict the genotypic value of selection material in plant breeding. However, case-to-case optimization of these approaches is required to achieve maximum accuracy of prediction with reasonable input.

Results

Based on extended field evaluation data for grain yield, plant height, starch content and total pentosan content of elite hybrid rye derived from testcrosses involving two bi-parental populations that were genotyped with 1048 molecular markers, we compared the accuracy of prediction of MAS and GS in a cross-validation approach. MAS delivered generally lower and in addition potentially over-estimated accuracies of prediction than GS by ridge regression best linear unbiased prediction (RR-BLUP). The grade of relatedness of the plant material included in the estimation and test sets clearly affected the accuracy of prediction of GS. Within each of the two bi-parental populations, accuracies differed depending on the relatedness of the respective parental lines. Across populations, accuracy increased when both populations contributed to estimation and test set. In contrast, accuracy of prediction based on an estimation set from one population to a test set from the other population was low despite that the two bi-parental segregating populations under scrutiny shared one parental line. Limiting the number of locations or years in field testing reduced the accuracy of prediction of GS equally, supporting the view that to establish robust GS calibration models a sufficient number of test locations is of similar importance as extended testing for more than one year.

Conclusions

In hybrid rye, genomic selection is superior to marker-assisted selection. However, it achieves high accuracies of prediction only for selection candidates closely related to the plant material evaluated in field trials, resulting in a rather pessimistic prognosis for distantly related material. Both, the numbers of evaluation locations and testing years in trials contribute equally to prediction accuracy.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-556) contains supplementary material, which is available to authorized users.     

Kinship rivalry does not trigger specific allocation strategies in Lupinus angustifolius

Background and Aims

Research on the ability of plants to recognize kin and modify plant development to ameliorate competition with coexisting relatives is an area of very active current exploration. Empirical evidence, however, is insufficient to provide a sound picture of this phenomenon.

Methods

An experiment was designed to assess multi-trait phenotypic expression in response to competition with conspecifics of varied degrees of genealogical relatedness. Groups of siblings, cousins and strangers of Lupinus angustifolius were set in competition in a pots assay. Several whole-plant and organ-level traits, directly related to competition for above- and below-ground resources, were measured. In addition, group-level root proliferation was measured as a key response trait to relatedness to neighbours, as identified in previous work.

Key Results

No major significant phenotypic differences were found between individuals and groups that could be assigned to the gradient of relatedness used here. This occurred in univariate models, and also when multi-trait interactions were evaluated through multi-group comparisons of Structural Equation Models. Root proliferation was higher in phenotypically more heterogeneous groups, but phenotypic heterogeneity was independent of the relatedness treatments of the experiment, and root proliferation was alike in the neighbourhoods of siblings, cousins and strangers.

Conclusions

In contrast to recent findings in other species, genealogical relatedness to competing neighbours has a negligible impact on the phenotypic expression of individuals and groups of L. angustifolius. This suggests that kin recognition needs further exploration to assess its generality, the ecological scenarios where it might have been favoured or penalized by natural selection, and its preponderance in different plant lineages.     

No difference between the sexes in fine-scale spatial genetic structure of roe deer

Background

Data on spatial genetic patterns may provide information about the ecological and behavioural mechanisms underlying population structure. Indeed, social organization and dispersal patterns of species may be reflected by the pattern of genetic structure within a population.

Methodology/Principal Findings

We investigated the fine-scale spatial genetic structure of a roe deer (Capreolus capreolus) population in Trois-Fontaines (France) using 12 microsatellite loci. The roe deer is weakly polygynous and highly sedentary, and can form matrilineal clans. We show that relatedness among individuals was negatively correlated with geographic distance, indicating that spatially proximate individuals are also genetically close. More unusually for a large mammalian herbivore, the link between relatedness and distance did not differ between the sexes, which is consistent with the lack of sex-biased dispersal and the weakly polygynous mating system of roe deer.

Conclusions/Significance

Our results contrast with previous reports on highly polygynous species with male-biased dispersal, such as red deer, where local genetic structure was detected in females only. This divergence between species highlights the importance of socio-spatial organization in determining local genetic structure of vertebrate populations.     

Using a Search Engine-Based Mutually Reinforcing Approach to Assess the Semantic Relatedness of Biomedical Terms

Background

Determining the semantic relatedness of two biomedical terms is an important task for many text-mining applications in the biomedical field. Previous studies, such as those using ontology-based and corpus-based approaches, measured semantic relatedness by using information from the structure of biomedical literature, but these methods are limited by the small size of training resources. To increase the size of training datasets, the outputs of search engines have been used extensively to analyze the lexical patterns of biomedical terms.

Methodology/Principal Findings

In this work, we propose the Mutually Reinforcing Lexical Pattern Ranking (ReLPR) algorithm for learning and exploring the lexical patterns of synonym pairs in biomedical text. ReLPR employs lexical patterns and their pattern containers to assess the semantic relatedness of biomedical terms. By combining sentence structures and the linking activities between containers and lexical patterns, our algorithm can explore the correlation between two biomedical terms.

Conclusions/Significance

The average correlation coefficient of the ReLPR algorithm was 0.82 for various datasets. The results of the ReLPR algorithm were significantly superior to those of previous methods.     

Reducing the risk of false discovery enabling identification of biologically significant genome-wide methylation status using the HumanMethylation450 array

Background

The Illumina HumanMethylation450 BeadChip (HM450K) measures the DNA methylation of 485,512 CpGs in the human genome. The technology relies on hybridization of genomic fragments to probes on the chip. However, certain genomic factors may compromise the ability to measure methylation using the array such as single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), repetitive DNA, and regions with reduced genomic complexity. Currently, there is no clear method or pipeline for determining which of the probes on the HM450K bead array should be retained for subsequent analysis in light of these issues.

Results

We comprehensively assessed the effects of SNPs, INDELs, repeats and bisulfite induced reduced genomic complexity by comparing HM450K bead array results with whole genome bisulfite sequencing. We determined which CpG probes provided accurate or noisy signals. From this, we derived a set of high-quality probes that provide unadulterated measurements of DNA methylation.

Conclusions

Our method significantly reduces the risk of false discoveries when using the HM450K bead array, while maximising the power of the array to detect methylation status genome-wide. Additionally, we demonstrate the utility of our method through extraction of biologically relevant epigenetic changes in prostate cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-51) contains supplementary material, which is available to authorized users.     

The effect of genomic information on optimal contribution selection in livestock breeding programs

Background

Long-term benefits in animal breeding programs require that increases in genetic merit be balanced with the need to maintain diversity (lost due to inbreeding). This can be achieved by using optimal contribution selection. The availability of high-density DNA marker information enables the incorporation of genomic data into optimal contribution selection but this raises the question about how this information affects the balance between genetic merit and diversity.

Methods

The effect of using genomic information in optimal contribution selection was examined based on simulated and real data on dairy bulls. We compared the genetic merit of selected animals at various levels of co-ancestry restrictions when using estimated breeding values based on parent average, genomic or progeny test information. Furthermore, we estimated the proportion of variation in estimated breeding values that is due to within-family differences.

Results

Optimal selection on genomic estimated breeding values increased genetic gain. Genetic merit was further increased using genomic rather than pedigree-based measures of co-ancestry under an inbreeding restriction policy. Using genomic instead of pedigree relationships to restrict inbreeding had a significant effect only when the population consisted of many large full-sib families; with a half-sib family structure, no difference was observed. In real data from dairy bulls, optimal contribution selection based on genomic estimated breeding values allowed for additional improvements in genetic merit at low to moderate inbreeding levels. Genomic estimated breeding values were more accurate and showed more within-family variation than parent average breeding values; for genomic estimated breeding values, 30 to 40% of the variation was due to within-family differences. Finally, there was no difference between constraining inbreeding via pedigree or genomic relationships in the real data.

Conclusions

The use of genomic estimated breeding values increased genetic gain in optimal contribution selection. Genomic estimated breeding values were more accurate and showed more within-family variation, which led to higher genetic gains for the same restriction on inbreeding. Using genomic relationships to restrict inbreeding provided no additional gain, except in the case of very large full-sib families.     

Detecting subtle changes in visuospatial executive function and learning in the amnestic variant of mild cognitive impairment

Background and Purpose

Amnestic mild cognitive impairment (aMCI) is a putative prodromal stage of Alzheimer''s disease (AD) characterized by deficits in episodic verbal memory. Our goal in the present study was to determine whether executive dysfunction may also be detectable in individuals diagnosed with aMCI.

Methods

This study used a hidden maze learning test to characterize component processes of visuospatial executive function and learning in a sample of 62 individuals with aMCI compared with 94 healthy controls.

Results

Relative to controls, individuals with aMCI made more exploratory/learning errors (Cohen''s d = .41). Comparison of learning curves revealed that the slope between the first two of five learning trials was four times as steep for controls than for individuals with aMCI (Cohen''s d = .64). Individuals with aMCI also made a significantly greater number of rule-break/error monitoring errors across learning trials (Cohen''s d = .21).

Conclusions

These results suggest that performance on a task of complex visuospatial executive function is compromised in individuals with aMCI, and likely explained by reductions in initial strategy formulation during early visual learning and “on-line” maintenance of task rules.     

Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle

Background

Recently, artificial neural networks (ANN) have been proposed as promising machines for marker-based genomic predictions of complex traits in animal and plant breeding. ANN are universal approximators of complex functions, that can capture cryptic relationships between SNPs (single nucleotide polymorphisms) and phenotypic values without the need of explicitly defining a genetic model. This concept is attractive for high-dimensional and noisy data, especially when the genetic architecture of the trait is unknown. However, the properties of ANN for the prediction of future outcomes of genomic selection using real data are not well characterized and, due to high computational costs, using whole-genome marker sets is difficult. We examined different non-linear network architectures, as well as several genomic covariate structures as network inputs in order to assess their ability to predict milk traits in three dairy cattle data sets using large-scale SNP data. For training, a regularized back propagation algorithm was used. The average correlation between the observed and predicted phenotypes in a 20 times 5-fold cross-validation was used to assess predictive ability. A linear network model served as benchmark.

Results

Predictive abilities of different ANN models varied markedly, whereas differences between data sets were small. Dimension reduction methods enhanced prediction performance in all data sets, while at the same time computational cost decreased. For the Holstein-Friesian bull data set, an ANN with 10 neurons in the hidden layer achieved a predictive correlation of r=0.47 for milk yield when the entire marker matrix was used. Predictive ability increased when the genomic relationship matrix (r=0.64) was used as input and was best (r=0.67) when principal component scores of the marker genotypes were used. Similar results were found for the other traits in all data sets.

Conclusion

Artificial neural networks are powerful machines for non-linear genome-enabled predictions in animal breeding. However, to produce stable and high-quality outputs, variable selection methods are highly recommended, when the number of markers vastly exceeds sample size.     

AdmixKJump: identifying population structure in recently diverged groups

Motivation

Correctly modeling population structure is important for understanding recent evolution and for association studies in humans. While pre-existing knowledge of population history can be used to specify expected levels of subdivision, objective metrics to detect population structure are important and may even be preferable for identifying groups in some situations. One such metric for genomic scale data is implemented in the cross-validation procedure of the program ADMIXTURE, but it has not been evaluated on recently diverged and potentially cryptic levels of population structure. Here, I develop a new method, AdmixKJump, and test both metrics under this scenario.

Findings

I show that AdmixKJump is more sensitive to recent population divisions compared to the cross-validation metric using both realistic simulations, as well as 1000 Genomes Project European genomic data. With two populations of 50 individuals each, AdmixKJump is able to detect two populations with 100% accuracy that split at least 10KYA, whereas cross-validation obtains this 100% level at 14KYA. I also show that AdmixKJump is more accurate with fewer samples per population. Furthermore, in contrast to the cross-validation approach, AdmixKJump is able to detect the population split between the Finnish and Tuscan populations of the 1000 Genomes Project.

Conclusion

AdmixKJump has more power to detect the number of populations in a cohort of samples with smaller sample sizes and shorter divergence times.

Availability

A java implementation can be found at https://sites.google.com/site/igsevolgenomicslab/home/downloads     

Genomic Responses during Acute Human Anaphylaxis Are Characterized by Upregulation of Innate Inflammatory Gene Networks

Background

Systemic spread of immune activation and mediator release is required for the development of anaphylaxis in humans. We hypothesized that peripheral blood leukocyte (PBL) activation plays a key role.

Objective

To characterize PBL genomic responses during acute anaphylaxis.

Methods

PBL samples were collected at three timepoints from six patients presenting to the Emergency Department (ED) with acute anaphylaxis and six healthy controls. Gene expression patterns were profiled on microarrays, differentially expressed genes were identified, and network analysis was employed to explore underlying mechanisms.

Results

Patients presented with moderately severe anaphylaxis after oral aspirin (2), peanut (2), bee sting (1) and unknown cause (1). Two genes were differentially expressed in patients compared to controls at ED arrival, 67 genes at 1 hour post-arrival and 2,801 genes at 3 hours post-arrival. Network analysis demonstrated that three inflammatory modules were upregulated during anaphylaxis. Notably, these modules contained multiple hub genes, which are known to play a central role in the regulation of innate inflammatory responses. Bioinformatics analyses showed that the data were enriched for LPS-like and TNF activation signatures.

Conclusion

PBL genomic responses during human anaphylaxis are characterized by dynamic expression of innate inflammatory modules. Upregulation of these modules was observed in patients with different reaction triggers. Our findings indicate a role for innate immune pathways in the pathogenesis of human anaphylaxis, and the hub genes identified in this study represent logical candidates for follow-up studies.     

DupChecker: a bioconductor package for checking high-throughput genomic data redundancy in meta-analysis

Background

Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates could lead false positive finding, misleading clustering pattern or model over-fitting issue, etc in the subsequent data analysis.

Results

We developed a Bioconductor package Dupchecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. A real data example was demonstrated to show the usage and output of the package.

Conclusions

Researchers may not pay enough attention to checking and removing duplicated samples, and then data contamination could make the results or conclusions from meta-analysis questionable. We suggest applying DupChecker to examine all gene expression data sets before any data analysis step.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-323) contains supplementary material, which is available to authorized users.     

Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction

Background

The purpose of this work was to study the impact of both the size of genomic reference populations and the inclusion of a residual polygenic effect on dairy cattle genetic evaluations enhanced with genomic information.

Methods

Direct genomic values were estimated for German Holstein cattle with a genomic BLUP model including a residual polygenic effect. A total of 17,429 genotyped Holstein bulls were evaluated using the phenotypes of 44 traits. The Interbull genomic validation test was implemented to investigate how the inclusion of a residual polygenic effect impacted genomic estimated breeding values.

Results

As the number of reference bulls increased, both the variance of the estimates of single nucleotide polymorphism effects and the reliability of the direct genomic values of selection candidates increased. Fitting a residual polygenic effect in the model resulted in less biased genome-enhanced breeding values and decreased the correlation between direct genomic values and estimated breeding values of sires in the reference population.

Conclusions

Genetic evaluation of dairy cattle enhanced with genomic information is highly effective in increasing reliability, as well as using large genomic reference populations. We found that fitting a residual polygenic effect reduced the bias in genome-enhanced breeding values, decreased the correlation between direct genomic values and sire''s estimated breeding values and made genome-enhanced breeding values more consistent in mean and variance as is the case for pedigree-based estimated breeding values.     

Thinking about eating food activates visual cortex with reduced bilateral cerebellar activation in females with anorexia nervosa: an fMRI study

Background

Women with anorexia nervosa (AN) have aberrant cognitions about food and altered activity in prefrontal cortical and somatosensory regions to food images. However, differential effects on the brain when thinking about eating food between healthy women and those with AN is unknown.

Methods

Functional magnetic resonance imaging (fMRI) examined neural activation when 42 women thought about eating the food shown in images: 18 with AN (11 RAN, 7 BPAN) and 24 age-matched controls (HC).

Results

Group contrasts between HC and AN revealed reduced activation in AN in the bilateral cerebellar vermis, and increased activation in the right visual cortex. Preliminary comparisons between AN subtypes and healthy controls suggest differences in cortical and limbic regions.

Conclusions

These preliminary data suggest that thinking about eating food shown in images increases visual and prefrontal cortical neural responses in females with AN, which may underlie cognitive biases towards food stimuli and ruminations about controlling food intake. Future studies are needed to explicitly test how thinking about eating activates restraint cognitions, specifically in those with restricting vs. binge-purging AN subtypes.     

Adults with Down syndrome have reduced cardiac response after light exercise testing

Objective

Physical fitness is reduced in adults with Down syndrome (DS). The present study was conducted to elucidate the exercise response in adults with DS.

Design

Case controlled before-after trial.

Setting

Residential centre for people with intellectual disabilities.

Participants

96 Adults with DS, 25 non-DS adults with an intellectual disability, 33 controls.

Interventions

Echocardiography to exclude heart defects and to measure cardiac index (CI) in the supine position, supine position with raised legs, and following ten knee bends.

Main outcome measure

Exercise testing

Results

At rest, mean CI was not significantly different between persons with DS and controls (2.3 vs. 2.4 l/min/m², p = 0.3). However, mean CI after exercise was significantly lower in DS (2.9 vs. 3.7 l/min/m², p < 0.001) and mean CI increase from rest to exercise was more than 50% lower in DS. On the contrary, CI after exercise was similar among controls and non-DS adults with an intellectual disability. Significantly lower stroke volumes in DS were found with insufficient heart rate response.

Conclusions

CI at rest was similar in adults with DS and controls; however persons with DS have a diminished cardiac response to exercise. Stroke volumes were significantly lower in DS during exercise and a compensated heightened heart rate was absent.     

How do SNP ascertainment schemes and population demographics affect inferences about population history?

Background

The selection of variable sites for inclusion in genomic analyses can influence results, especially when exemplar populations are used to determine polymorphic sites. We tested the impact of ascertainment bias on the inference of population genetic parameters using empirical and simulated data representing the three major continental groups of cattle: European, African, and Indian. We simulated data under three demographic models. Each simulated data set was subjected to three ascertainment schemes: (I) random selection; (II) geographically biased selection; and (III) selection biased toward loci polymorphic in multiple groups. Empirical data comprised samples of 25 individuals representing each continental group. These cattle were genotyped for 47,506 loci from the bovine 50 K SNP panel. We compared the inference of population histories for the empirical and simulated data sets across different ascertainment conditions using F_ST and principal components analysis (PCA).

Results

Bias toward shared polymorphism across continental groups is apparent in the empirical SNP data. Bias toward uneven levels of within-group polymorphism decreases estimates of F_ST between groups. Subpopulation-biased selection of SNPs changes the weighting of principal component axes and can affect inferences about proportions of admixture and population histories using PCA. PCA-based inferences of population relationships are largely congruent across types of ascertainment bias, even when ascertainment bias is strong.

Conclusions

Analyses of ascertainment bias in genomic data have largely been conducted on human data. As genomic analyses are being applied to non-model organisms, and across taxa with deeper divergences, care must be taken to consider the potential for bias in ascertainment of variation to affect inferences. Estimates of F_ST, time of separation, and population divergence as estimated by principal components analysis can be misleading if this bias is not taken into account.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1469-5) contains supplementary material, which is available to authorized users.     

No Myocardial Vulnerability to Mental Stress in Takotsubo Stress Cardiomyopathy

Objectives

Due to the frequent use of coronary angiography the awareness of Takotsubo stress cardiomyopathy (TSC) has increased although the exact pathophysiology of TSC is still largely unknown. Our objective was to investigate the effects of mental stress on myocardial function, heart rate variability (HRV) and salivary cortisol (SC) in TSC patients.

Design

This study is a case-control study and a sub-study of the Stockholm Myocardial Infarction with Normal Coronaries (SMINC) study.

Setting

Mental stress test was performed more than 6 months after the acute event in TSC patients and age- and sex-matched controls. Standard echocardiography and tissue Doppler imaging (TDI) - derived time-phases of cardiac cycle were recorded to calculate myocardial performance index (MPI) to assess ventricular function before and during mental stress. Holter-ECG recording was made to estimate HRV before, during and after mental stress. SC was measured at baseline, before and 20 minutes after mental stress.

Subjects

Twenty-two TSC patients and 22 sex-and age-matched controls were recruited from the SMINC-study and investigated with a mental stress test. All TSC patients had a previous normal cardiovascular magnetic resonance investigation.

Results

There were no significant differences at rest or during mental stress for left and right ventricular MPI or other standard diastolic variables between TSC patients and controls. HRV did not differ between TSC patients and controls. There was a trend towards less increase in SC after mental stress in TSC patients compared to controls.

Conclusion

Mental stress did not induce a significant difference in myocardial function or HRV response between TSC and controls. Moreover, no significant difference could be seen in SC response at baseline, during or after mental stress. This study indicates that myocardial vulnerability to mental stress does not persist in TSC patients.