Genotype-phenotype Model Research Articles

Open-Pit Pushback Optimization by a Parallel Genetic Algorithm

Determining the design of pushbacks in an open-pit mine is a key part of optimizing the economic value of the mining project and the operational feasibility of the mine. This problem requires balancing pushbacks that have good geometric properties to ensure the smooth operation of the mining equipment and so that the scheduling of extraction maximizes the economic value by providing early access to the rich parts of the deposit. However, because of the challenging nature of the problem, practical approaches for finding the best pushbacks strongly depend on the expert criteria to ensure good operational properties. This paper introduces the Advanced Geometrically Constrained Production Scheduling Problem to account for operational space constraints, modeled as truncated cones of extraction. To find the best solution for this problem, we present a parallel genetic algorithm based on a genotype–phenotype model such that the genotype symbolizes the base block of a truncated cone, and the phenotype represents the cone itself. A central computer node evaluates these solutions, collaborating with various secondary nodes that evolve a population of feasible solutions. The PGA’s efficacy was validated using comprehensive test instances from established research. The PGA solution exhibited a consistent average copper grade across periods, with its incremental phases reflecting real-world mine geometry. Moreover, the benefits of the MeanShift clustering technique were evident, suggesting effective phase-based scheduling. The PGA’s approach ensures optimal resource utilization and offers insights into potential avenues for further model enhancements and fine-tuning.

A Genotype-Phenotype Model for Predicting Resistance Training Effects on Leg Press Performance.

This study develops a comprehensive genotype-phenotype model for predicting the effects of resistance training on leg press performance. A cohort of physically inactive adults (N=193) underwent 12 weeks of resistance training, and measurements of maximum isokinetic leg press peak force, muscle mass, and thickness were taken before and after the intervention. Whole-genome genotyping was performed, and genome-wide association analysis identified 85 novel SNPs significantly associated with changes in leg press strength after training. A prediction model was constructed using stepwise linear regression, incorporating seven lead SNPs that explained 40.4% of the training effect variance. The polygenic score showed a significant positive correlation with changes in leg press strength. By integrating genomic markers and phenotypic indicators, the comprehensive prediction model explained 75.4% of the variance in the training effect. Additionally, five SNPs were found to potentially impact muscle contraction, metabolism, growth, and development through their association with REACTOME pathways. Individual responses to resistance training varied, with changes in leg press strength ranging from -55.83% to 151.20%. The study highlights the importance of genetic factors in predicting training outcomes and provides insights into the potential biological functions underlying resistance training effects. The comprehensive model offers valuable guidance for personalized fitness programs based on individual genetic profiles and phenotypic characteristics.

Genotype-Phenotype Models Predicting V̇O 2max Response to High-Intensity Interval Training in Physically Inactive Chinese.

This study aimed to analyze the interindividual differences of the maximal oxygen uptake (V̇O 2max ) response to 12 wk of high-intensity interval training (HIIT), and the genotype-phenotype models were constructed to predict the effect of HIIT on V̇O 2max . A total of 228 physically inactive adults who completed a 12-wk HIIT were analyzed. A genome-wide association study (GWAS) was conducted to identify genetic variants associated with the V̇O 2max response. Nonresponders, responders, and the highest training responders were defined as the effect sizes (ES) <0.2, ≥0.2, and ≥0.8, respectively. We generated polygenic predictor score (PPS) using lead variants and constructed a predictive model for V̇O 2max response based on a linear stepwise regression analysis. The V̇O 2max increased significantly after HIIT (~14%, P < 0.001), but with interindividual differences (-7.8 to 17.9 mL·kg -1 ·min -1 ). In 27% of participants, the V̇O 2max showed no improvement. We identified one genetic locus near the γ-aminobutyric acid type A receptor subunit beta 3 gene ( GABRB3 , rs17116985) associated with V̇O 2max response at the genome-wide significance level ( P < 5 × 10 -8 ), and an additional nine single nucleotide polymorphisms (SNPs) at the suggestive significance level ( P < 1 × 10 -5 ). The SNPs rs474377, rs9365605, and rs17116985, respectively, explained 11%, 9%, and 6.2% of variance in V̇O 2max response. The 13 SNPs ( P < 1 × 10 -5 ) were found on chromosome 6 (position: 148209316-148223568). Individuals with a PPS greater than 1.757 had the highest response, and those with a PPS lower than -3.712 were nonresponders. The PPS, baseline V̇O 2max , sex, and body mass explained 56.4% of the variance in the V̇O 2max response; the major predictor was the PPS, which explained 39.4% of the variance. The PPS, baseline V̇O 2max , sex, and body mass could explain the variance in V̇O 2max response. Individuals who had a PPS greater than 1.757 had the highest training response after 12 wk of HIIT. Genetic variants in a region on chromosome 6, especially the sterile alpha motif domain containing 5 gene ( SAMD5 ), which had been explored influencing angiogenesis, might have a potential role in the V̇O 2max response.

Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy.

Human Immunodeficiency Virus (HIV) is one of the most common chronic infectious diseases in humans. Extending the expected lifetime of patients depends on the use of optimal antiretroviral therapies. Emergence of the drug-resistant strains can reduce the effectiveness of treatments and lead to Acquired Immunodeficiency Syndrome (AIDS), even with antiretroviral therapy. Investigating the genotype-phenotype relationship is a crucial process for optimizing the therapy protocols of the patients. Here, a mathematical modelling framework is proposed to address the impact of existing mutations, timing of initiation, and adherence levels of nucleotide reverse transcriptase inhibitors (NRTIs) on the evolutionary dynamics of the virus strains. For the first time, the existing Stanford HIV drug resistance data have been combined with a multi-strain within-host ordinary differential equation (ODE) model to track the dynamics of the most common NRTI-resistant strains. Overall, the D4T-3TC, D4T-AZT and TDF-D4T drug combinations have been shown to provide higher success rates in preventing treatment failure and further drug resistance. The results are in line with the genotype-phenotype data and pharmacokinetic parameters of the NRTI inhibitors. Moreover, we show that the undetectable mutant strains at the diagnosis have a significant effect on the success/failure rates of the NRTI treatments. Predictions on undetectable strains through our multi-strain within-host model yielded the possible role of viral evolution on the treatment outcomes. It has been recognized that the improvement of multi-scale models can contribute to the understanding of the evolutionary dynamics, and treatment options, and potentially increase the reliability of genotype-phenotype models.

Prediction of genome-wide imipenem resistance features in Klebsiella pneumoniae using machine learning.

Introduction. The resistance rate of Klebsiella pneumoniae (K. pneumoniae) to imipenem is increasing year by year, and the imipenem resistance mechanism of K. pneumoniae is complex. Therefore, it is urgent to develop new strategies to explore the resistance mechanism of imipenem for its effective and accurate use in clinical practice.Hypothesis/Gap sStatement. Machine learning could identify resistance features and biological process that influence microbial resistance from whole-genome sequencing (WGS) data.Aims. This work aimed to predict imipenem resistance genetic features in K. pneumoniae from whole-genome k-mer features, and analyse their function for understanding its resistance mechanism.Methods. This study analysed WGS data of K. pneumoniae combined with resistance phenotype for imipenem, and established K. pneumoniae to imipenem genotype-phenotype model to predict resistance features using chi-squared test and random forest. An external clinical dataset was used to verify prediction power of resistance features. The potential genes were identified through alignment the resistance features with the K. pneumoniae reference genome using blastn, the functions of potential genes were further analysed to explore its resistance-related signalling pathways with GO and KEGG analysis, the resistance sequence patterns were screened using streme software. Finally, the resistance features were combined and modelled through four machine-learning algorithms (logistic regression, SVM, GBDT and XGBoost) to evaluate their phenotype prediction ability.Results. A total of 16 670 imipenem resistance features were predicted from genotype-phenotype model. The 30 potential genes were identified by annotating the resistance features and corresponded to known antibiotic-related genes (mdtM, dedA, rne, etc.). GO and KEGG pathway analyses indicated the possible association of imipenem resistance with metabolism process and cell membrane. CRYCAGCDN and CGRDAAAN were found from the imipenem resistance features, which were widely presented in the reported β-lactam resistance genes (bla SHV, bla CTX-M, bla TEM, etc.), and YCYAGCMCAST with metabolic functions (organic substance metabolic process, nitrogen compound metabolic process and cellular metabolic process) was identified from the top 50 resistance features. The 25 resistance genes in the training dataset included 19 genes in the external dataset, which verified the accuracy of prediction. The area under curve values of logistics regression, SVM, GBDT and XGBoost were 0.965, 0.966, 0.969 and 0.969, respectively, indicating that the imipenem resistance features have a strong prediction power.Conclusion. Machine-learning methods could effectively predict the imipenem resistance feature in K. pneumoniae, and provide resistance sequence profiles for predicting resistance phenotype and exploring potential resistance mechanisms. It provides an important insight into the potential therapeutic strategies of K. pneumoniae resistance to imipenem, and speed up the application of machine learning in routine diagnosis.

Genotype–Phenotype Correlations in a Spanish Cohort of 506 Families With Biallelic ABCA4 Pathogenic Variants

To define genotype-phenotype correlations in the largest cohort study worldwide of patients with biallelic ABCA4 variants, including 434 patients with Stargardt disease (STGD1) and 72 with cone-rod dystrophy (CRD). Cohort study. We characterized 506 patients with ABCA4 variants using conventional genetic tools and next-generation sequencing technologies. Medical history and ophthalmologic data were obtained from 372 patients. Genotype-phenotype correlation studies were carried out for the following variables: variant type, age at symptom onset (AO), and clinical phenotype. A total of 228 different pathogenic variants were identified in 506 ABCA4 patients, 50 of which were novel. Genotype-phenotype correlations showed that most of the patients with biallelic truncating variants presented with CRD and that these cases had a significantly earlier AO than patients with STGD1. Three missense variants are associated with CRD for the first time (c.1804C>T; p.[Arg602Trp], c.3056C>T; p.[Thr1019Met], and c.6320G>C; p.[Arg2107Pro]). Analysis of the most prevalent ABCA4 variant in Spain, c.3386G>T; p.(Arg1129Leu), revealed that is correlated to STGD1, later AO, and foveal sparing. Our study, conducted in the largest ABCA4-associated disease cohort reported to date, updates the genotype-phenotype model established for ABCA4 variants and broadens the mutational spectrum of the gene. According to our observations, patients with ABCA4 presenting with 2 truncating variants may first present features of STGD1 but eventually develop rod dysfunction, and specific missense variants may be associated with a different phenotype, underscoring the importance of an accurate genetic diagnosis. Also, it is a prerequisite for enrollment in clinical trials, and to date, no other treatment has been approved for STGD1.

P0056USE OF CLINICAL EXOME SEQUENCING IN THE DIAGNOSTIC FLOW OF MONOGENIC KIDNEY DISEASES: THE PIEDMONT EXPERIENCE

Abstract Background and Aims next-generation sequencing (NGS) technologies are becoming a powerful diagnostic tool in precision medicine. Specifically, exome sequencing can help in the diagnosis of selected diseases, in their medical management and therapeutic choices. Inherited kidney diseases (IKD) are among the major causes for kidney failure, both in children and adults, resulting in increased mortality, high health care costs and need for organ transplantation. In addition, it is worth mentioning that a significant proportion of patients in the kidney transplant lacks a clear diagnosis. This subset of diseases may thus benefit from the application of NGS technology, as the simultaneous investigation of hundreds of genes can lead to the identification of causative variants in a vast population of patients. The aim of this study is to validate the use of a clinical exome sequencing approach in the diagnostic flow for kidney diseases leading to organ failure to i) confirm the clinical diagnosis, ii) find the genetic cause of previously unrecognized diseases and iii) improve the outcome of organ transplantation by excluding live-donors carrying the same mutational burden. Method 160 patients were recruited, directly or following a genetic counseling, exploiting a network of 21 nephrology centers spread across the Piedmont region, coordinated by the “Centro Regionale Trapianti (CRT)” of Torino. Patients were then evaluated for NGS eligibility. DNA extracted from blood samples was checked for integrity, quantified and used for library preparation. A clinical exome sequencing (CES) kit by Illumina was used, allowing for targeted capture, enrichment and sequencing of 6700 clinically relevant genes. Reads were aligned to hg37 reference genome using the Isaac enrichment tool and variants filtered using an ad-hoc set up pipeline of analysis. Results clinical exome sequencing was performed on a diagnostic cohort of 138 patients, both children (37.7%) and adults (62.3%), with a prevalence of male subjects (56.5%). The majority of the cohort (51.5%) presented a positive family history for kidney disease, while 22 patients were excluded from the study as organ failure was most likely the result of secondary events. The cohort was highly heterogeneous with 21% of patients presenting with ciliopathies, 18.1% with glomerular disease, 7.2% with tubular disease while the remaining cohort presented other diseases or was undiagnosed (44.3%). An ad hoc analytical pipeline was designed, based on selected genotype-phenotype correlation database, filter-in metrics, inheritance model and annotation of variants based on public databases and in-silico prediction tools. By adopting well defined criteria of recruitment and analysis, causative genes were identified in 61.6% of cases and in the 57.3% of cases results were in line with the original diagnostic hypothesis. Moreover, 50.8% of cases with organ failure for unknown reasons were solved with the identification of causative genes. Out of the 133 total variants found in the cohort, 63 were classified as pathogenic or likely pathogenic. The remaining 70 identified variants were annotated as variant of unknown significance and will be further investigated. Conclusion Taken together, these results show that CES is a powerful non-invasive tool for the genetic diagnosis of IKD. Identification of disease causative variants may represent a critical step for the diagnosis, clinical management of the patients, and potentially for optimal live-donor selection.

Delayed Onset of Sleep in Adolescents With PAX6 Haploinsufficiency.

PAX6 haploinsufficiency ( +/-) can occur due to mutations involving only PAX6 in patients with isolated aniridia or as contiguous gene deletions in patients with Wilms tumor, aniridia, genitourinary anomalies, and range of developmental and intellectual disabilities syndrome. Given the role of PAX6 in pineal development and circadian regulation, adolescents with PAX6+/- may experience sleep-wake disturbances. The purpose of this observational study was to explore sleep-related phenotypes in adolescents with PAX6+/-. This study compared sleep phenotypes of nine subjects with PAX6+/- (aged 10-19 years) with previously published data on healthy adolescents ( n = 25, aged 10-18 years). Subjects completed the Cleveland Adolescent Sleepiness Questionnaire (CASQ), Patient Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance (v. 1.0; 8a), and PROMIS Sleep-Related Impairment (v. 1.0; 8b) Questionnaires and wore actigraphs for seven nights to record sleep patterns. Total CASQ, PROMIS sleep-related impairment, and PROMIS sleep disturbance scores were not statistically different between the groups ( ps > .15). Actigraph data for lights off to sleep-onset time were found to be significantly higher in subjects with PAX6+/- versus the healthy comparison group (adjusted mean [95% confidence interval]: 20.1 min [8.1, 49.8] vs. 6.2 min [3.7, 10.4], respectively, p = .04). Both adolescents with PAX6+/- and the healthy comparison group on average slept less than 8 hr/night, and overall sleep deprivation in adolescents may have masked differences between groups. This study used rare genetic disorders with biological vulnerability to sleep problems as a genotype-phenotype model. Knowledge of sleep-related phenotypes will assist in designing studies to manage sleep-related symptoms in adolescents.

Genotype-phenotype modeling considering intermediate level of biological variation: a case study involving sensory traits, metabolites and QTLs in ripe tomatoes.

Modeling genotype-phenotype relationships is a central objective in plant genetics and breeding. Commonly, variations in phenotypic traits are modeled directly in relation to variations at the DNA level, regardless of intermediate levels of biological variation. Here we present an integrative method for the simultaneous modeling of a set of multilevel phenotypic responses to variations at the DNA level. More specifically, for ripe tomato fruits, we use Gaussian graphical models and causal inference techniques to learn the dependencies of 24 sensory traits on 29 metabolites and the dependencies of those sensory and metabolic traits on 21 QTLs. The inferred dependency network which, though not essentially representing biological pathways, suggests how the effects of allele substitutions propagate through multilevel phenotypes. Such simultaneous study of the underlying genetic architecture and multifactorial interactions is expected to enhance the prediction and manipulation of complex traits.

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

Motivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem.Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype–phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis.Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype–phenotype models.Availability and implementation: MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis.Contact: mxli@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online.

Regularized machine learning in the genetic prediction of complex traits.

Regularized machine learning in the genetic prediction of complex traits.

Mutations in MFSD8, Encoding a Lysosomal Membrane Protein, Are Associated with Nonsyndromic Autosomal Recessive Macular Dystrophy

This study aimed to identify the genetic defects in 2 families with autosomal recessive macular dystrophy with central cone involvement. Case series. Two families and a cohort of 244 individuals with various inherited maculopathies and cone disorders. Genome-wide linkage analysis and exome sequencing were performed in 1 large family with 5 affected individuals. In addition, exome sequencing was performed in the proband of a second family. Subsequent analysis of the identified mutations in 244 patients was performed by Sanger sequencing or restriction enzyme digestion. The medical history of individuals carrying the MFSD8 variants was reviewed and additional ophthalmic examinations were performed, including electroretinography (ERG), multifocal ERG (mfERG), perimetry, optical coherence tomography (OCT), fundus autofluorescence, and fundus photography. MFSD8 variants, age at diagnosis, visual acuity, fundus appearance, color vision defects, visual field, ERG, mfERG, fundus autofluorescence, and OCT findings. Compound heterozygous variants in MFSD8, a gene encoding a lysosomal transmembrane protein, were identified in 2 families with macular dystrophy with a normal or subnormal ERG, but reduced mfERG. In both families, a heterozygous missense variant p.Glu336Gln was identified, which was predicted to have a mild effect on the protein. In the first family, a protein-truncating variant (p.Glu381*) was identified on the other allele, and in the second family, a variant (c.1102G>C) was identified that results in a splicing defect leading to skipping of exon 11 (p.Lys333Lysfs*3). The p.Glu336Gln allele was found to be significantly enriched in patients with maculopathies and cone disorders (6/488) compared with ethnically matched controls (35/18 682; P < 0.0001), suggesting that it may act as a genetic modifier. In this study, we identified variants in MFSD8 as a novel cause of nonsyndromic autosomal recessive macular dystrophy with central cone involvement. Affected individuals showed no neurologic features typical for variant late-infantile neuronal ceroid lipofuscinosis (vLINCL), a severe and devastating multisystem lysosomal storage disease previously associated with mutations in MFSD8. We propose a genotype-phenotype model in which a combination of a severe and a mild variant cause nonsyndromic macular dystrophy with central cone involvement, and 2 severe mutations cause vLINCL.

Modeling the evolution of molecular systems from a mechanistic perspective

Systems biology-inspired genotype-phenotype mapping models are increasingly being used to study the evolutionary properties of molecular biological systems, in particular the general emergent properties of evolving systems, such as modularity, robustness, and evolvability. However, the level of abstraction at which many of these models operate might not be sufficient to capture all relevant intricacies of biological evolution in sufficient detail. Here, we argue that in particular gene and genome duplications, both evolutionary mechanisms of potentially major importance for the evolution of molecular systems and of special relevance to plant evolution, are not adequately accounted for in most GPM modeling frameworks, and that more fine-grained mechanistic models may significantly advance understanding of how gen(om)e duplication impacts molecular systems evolution.

Stargardt Disease: towards developing a model to predict phenotype

Stargardt disease is an ABCA4-associated retinopathy, which generally follows an autosomal recessive inheritance pattern and is a frequent cause of macular degeneration in childhood. ABCA4 displays significant allelic heterogeneity whereby different mutations can cause retinal diseases with varying severity and age of onset. A genotype-phenotype model has been proposed linking ABCA4 mutations, purported ABCA4 functional protein activity and severity of disease, as measured by degree of visual loss and the age of onset. It has, however, been difficult to verify this model statistically in observational studies, as the number of individuals sharing any particular mutation combination is typically low. Seven founder mutations have been identified in a large number of Caucasian Afrikaner patients in South Africa, making it possible to test the genotype-phenotype model. A generalised linear model was developed to predict and assess the relative pathogenic contribution of the seven mutations to the age of onset of Stargardt disease. It is shown that the pathogenicity of an individual mutation can differ significantly depending on the genetic context in which it occurs. The results reported here may be used to identify suitable candidates for inclusion in clinical trials, as well as guide the genetic counselling of affected individuals and families.

Bridging the genotype–phenotype gap: what does it take?

The genotype–phenotype map (GP map) concept applies to any time point in the ontogeny of a living system. It is the outcome of very complex dynamics that include environmental effects, and bridging the genotype–phenotype gap is synonymous with understanding these dynamics. The context for this understanding is physiology, and the disciplinary goals of physiology do indeed demand the physiological community to seek this understanding. We claim that this task is beyond reach without use of mathematical models that bind together genetic and phenotypic data in a causally cohesive way. We provide illustrations of such causally cohesive genotype–phenotype models where the phenotypes span from gene expression profiles to development of whole organs. Bridging the genotype–phenotype gap also demands that large-scale biological (‘omics’) data and associated bioinformatics resources be more effectively integrated with computational physiology than is currently the case. A third major element is the need for developing a phenomics technology way beyond current state of the art, and we advocate the establishment of a Human Phenome Programme solidly grounded on biophysically based mathematical descriptions of human physiology.

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.

Homology blocks of Plasmodium falciparum var genes and clinically distinct forms of severe malaria in a local population

BackgroundThe primary target of the human immune response to the malaria parasite Plasmodium falciparum, P. falciparum erythrocyte membrane protein 1 (PfEMP1), is encoded by the members of the hyper-diverse var gene family. The parasite exhibits antigenic variation via mutually exclusive expression (switching) of the ~60 var genes within its genome. It is thought that different variants exhibit different host endothelial binding preferences that in turn result in different manifestations of disease.ResultsVar sequences comprise ancient sequence fragments, termed homology blocks (HBs), that recombine at exceedingly high rates. We use HBs to define distinct var types within a local population. We then reanalyze a dataset that contains clinical and var expression data to investigate whether the HBs allow for a description of sequence diversity corresponding to biological function, such that it improves our ability to predict disease phenotype from parasite genetics. We find that even a generic set of HBs, which are defined for a small number of non-local parasites: capture the majority of local sequence diversity; improve our ability to predict disease severity from parasite genetics; and reveal a previously hypothesized yet previously unobserved parasite genetic basis for two forms of severe disease. We find that the expression rates of some HBs correlate more strongly with severe disease phenotypes than the expression rates of classic var DBLα tag types, and principal components of HB expression rate profiles further improve genotype-phenotype models. More specifically, within the large Kenyan dataset that is the focus of this study, we observe that HB expression differs significantly for severe versus mild disease, and for rosetting versus impaired consciousness associated severe disease. The analysis of a second much smaller dataset from Mali suggests that these HB-phenotype associations are consistent across geographically distant populations, since we find evidence suggesting that the same HB-phenotype associations characterize this population as well.ConclusionsThe distinction between rosetting versus impaired consciousness associated var genes has not been described previously, and it could have important implications for monitoring, intervention and diagnosis. Moreover, our results have the potential to illuminate the molecular mechanisms underlying the complex spectrum of severe disease phenotypes associated with malaria—an important objective given that only about 1% of P. falciparum infections result in severe disease.

Analysis of Large Phenotypic Variability of EEC and SHFM4 Syndromes Caused by K193E Mutation of the TP63 Gene

EEC (ectrodactyly, ectodermal dysplasia, clefting; OMIM 604292) is an autosomal dominant developmental disorder resulting mainly from pathogenic mutations of the DNA-binding domain (DBD) of the TP63 gene. In this study, we showed that K193E mutation in nine affected individuals of a four-generation kindred with a large degree of phenotypic variability causes four different syndromes or TP63-related disorders: EEC, Ectrodactyly-ectodermal dysplasia (EE), isolated ectodermal dysplasia, and isolated Split Hand/Foot Malformation type 4 (SHFM4). Genotype-phenotype and DBD structural modeling analysis showed that the K193-located loop L2-A is associated with R280 through hydrogen bonding interactions, while R280 mutations also often cause large phenotypic variability of EEC and SHFM4. Thus, we speculate that K193 and several other DBD mutation-associated syndromes may share similar pathogenic mechanisms, particularly in the case of the same mutation with different phenotypes. Our study and others also suggest that the phenotypic variability of EEC is attributed, at least partially, to genetic and/or epigenetic modifiers.

A large scale multivariate parallel ICA method reveals novel imaging–genetic relationships for Alzheimer's disease in the ADNI cohort

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype-phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5 T scanners at over 50 sites in the USA/Canada. Four primary "genetic components" were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.

A functional-structural model of rice linking quantitative genetic information with morphological development and physiological processes.

Although quantitative trait loci (QTL) analysis of yield-related traits for rice has developed rapidly, crop models using genotype information have been proposed only relatively recently. As a first step towards a generic genotype-phenotype model, we present here a three-dimensional functional-structural plant model (FSPM) of rice, in which some model parameters are controlled by functions describing the effect of main-effect and epistatic QTLs. The model simulates the growth and development of rice based on selected ecophysiological processes, such as photosynthesis (source process) and organ formation, growth and extension (sink processes). It was devised using GroIMP, an interactive modelling platform based on the Relational Growth Grammar formalism (RGG). RGG rules describe the course of organ initiation and extension resulting in final morphology. The link between the phenotype (as represented by the simulated rice plant) and the QTL genotype was implemented via a data interface between the rice FSPM and the QTLNetwork software, which computes predictions of QTLs from map data and measured trait data. Using plant height and grain yield, it is shown how QTL information for a given trait can be used in an FSPM, computing and visualizing the phenotypes of different lines of a mapping population. Furthermore, we demonstrate how modification of a particular trait feeds back on the entire plant phenotype via the physiological processes considered. We linked a rice FSPM to a quantitative genetic model, thereby employing QTL information to refine model parameters and visualizing the dynamics of development of the entire phenotype as a result of ecophysiological processes, including the trait(s) for which genetic information is available. Possibilities for further extension of the model, for example for the purposes of ideotype breeding, are discussed.