Common Human Phenotypes Research Articles

Identification and analysis of individuals who deviate from their genetically-predicted phenotype.

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

Novel bicuspid aortic valve model with aortic regurgitation for hemodynamic status analysis using an ex vivo simulator

ObjectiveThe objective was to design and evaluate a clinically relevant, novel ex vivo bicuspid aortic valve model that mimics the most common human phenotype with associated aortic regurgitation. MethodsThree bovine aortic valves were mounted asymmetrically in a previously validated 3–dimensional-printed left heart simulator. The non-right commissure and the non-left commissure were both shifted slightly toward the left–right commissure, and the left and right coronary cusps were sewn together. The left–right commissure was then detached and reimplanted 10 mm lower than its native height. Free margin shortening was used for valve repair. Hemodynamic status, high-speed videography, and echocardiography data were collected before and after the repair. ResultsThe bicuspid aortic valve model was successfully produced and repaired. High-speed videography confirmed prolapse of the fused cusp of the baseline bicuspid aortic valve models in diastole. Hemodynamic and pressure data confirmed accurate simulation of diseased conditions with aortic regurgitation and the subsequent repair. Regurgitant fraction postrepair was significantly reduced compared with that at baseline (14.5 ± 4.4% vs 28.6% ± 3.4%; P = .037). There was no change in peak velocity, peak gradient, or mean gradient across the valve pre- versus postrepair: 293.3 ± 18.3 cm/sec versus 325.3 ± 58.2 cm/sec (P = .29), 34.3 ± 4.2 mm Hg versus 43.3 ± 15.4 mm Hg (P = .30), and 11 ± 1 mm Hg versus 9.3 ± 2.5 mm Hg (P = .34), respectively. ConclusionsAn ex vivo bicuspid aortic valve model was designed that recapitulated the most common human phenotype with aortic regurgitation. These valves were successfully repaired, validating its potential for evaluating valve hemodynamics and optimizing surgical repair for bicuspid aortic valves.

Leveraging mouse chromatin data for heritability enrichment informs common disease architecture and reveals cortical layer contributions to schizophrenia.

Genome-wide association studies have implicated thousands of noncoding variants across common human phenotypes. However, they cannot directly inform the cellular context in which disease-associated variants act. Here, we use open chromatin profiles from discrete mouse cell populations to address this challenge. We applied stratified linkage disequilibrium score regression and evaluated heritability enrichment in 64 genome-wide association studies, emphasizing schizophrenia. We provide evidence that mouse-derived human open chromatin profiles can serve as powerful proxies for difficult to obtain human cell populations, facilitating the illumination of common disease heritability enrichment across an array of human phenotypes. We demonstrate that signatures from discrete subpopulations of cortical excitatory and inhibitory neurons are significantly enriched for schizophrenia heritability with maximal enrichment in cortical layer V excitatory neurons. We also show that differences between schizophrenia and bipolar disorder are concentrated in excitatory neurons in cortical layers II-III, IV, and V, as well as the dentate gyrus. Finally, we leverage these data to fine-map variants in 177 schizophrenia loci nominating variants in 104/177. We integrate these data with transcription factor binding site, chromatin interaction, and validated enhancer data, placing variants in the cellular context where they may modulate risk.

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans.

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

Phenotype Ontologies and Cross-Species Analysis for Translational Research

The use of model organisms as tools for the investigation of human genetic variation has significantly and rapidly advanced our understanding of the aetiologies underlying hereditary traits. However, while equivalences in the DNA sequence of two species may be readily inferred through evolutionary models, the identification of equivalence in the phenotypic consequences resulting from comparable genetic variation is far from straightforward, limiting the value of the modelling paradigm. In this review, we provide an overview of the emerging statistical and computational approaches to objectively identify phenotypic equivalence between human and model organisms with examples from the vertebrate models, mouse and zebrafish. Firstly, we discuss enrichment approaches, which deem the most frequent phenotype among the orthologues of a set of genes associated with a common human phenotype as the orthologous phenotype, or phenolog, in the model species. Secondly, we introduce and discuss computational reasoning approaches to identify phenotypic equivalences made possible through the development of intra- and interspecies ontologies. Finally, we consider the particular challenges involved in modelling neuropsychiatric disorders, which illustrate many of the remaining difficulties in developing comprehensive and unequivocal interspecies phenotype mappings.

Glut1 deficiency (G1D): Epilepsy and metabolic dysfunction in a mouse model of the most common human phenotype

Brain glucose supplies most of the carbon required for acetyl-coenzyme A (acetyl-CoA) generation (an important step for myelin synthesis) and for neurotransmitter production via further metabolism of acetyl-CoA in the tricarboxylic acid (TCA) cycle. However, it is not known whether reduced brain glucose transporter type I (GLUT-1) activity, the hallmark of the GLUT-1 deficiency (G1D) syndrome, leads to acetyl-CoA, TCA or neurotransmitter depletion. This question is relevant because, in its most common form in man, G1D is associated with cerebral hypomyelination (manifested as microcephaly) and epilepsy, suggestive of acetyl-CoA depletion and neurotransmitter dysfunction, respectively. Yet, brain metabolism in G1D remains underexplored both theoretically and experimentally, partly because computational models of limited brain glucose transport are subordinate to metabolic assumptions and partly because current hemizygous G1D mouse models manifest a mild phenotype not easily amenable to investigation. In contrast, adult antisense G1D mice replicate the human phenotype of spontaneous epilepsy associated with robust thalamocortical electrical oscillations. Additionally, and in consonance with human metabolic imaging observations, thalamus and cerebral cortex display the lowest GLUT-1 expression and glucose uptake in the mutant mouse. This depletion of brain glucose is associated with diminished plasma fatty acids and elevated ketone body levels, and with decreased brain acetyl-CoA and fatty acid contents, consistent with brain ketone body consumption and with stimulation of brain beta-oxidation and/or diminished cerebral lipid synthesis. In contrast with other epilepsies, astrocyte glutamine synthetase expression, cerebral TCA cycle intermediates, amino acid and amine neurotransmitter contents are also intact in G1D. The data suggest that the TCA cycle is preserved in G1D because reduced glycolysis and acetyl-CoA formation can be balanced by enhanced ketone body utilization. These results are incompatible with global cerebral energy failure or with neurotransmitter depletion as responsible for epilepsy in G1D and point to an unknown mechanism by which glycolysis critically regulates cortical excitability.

Rare De Novo Variants Associated with Autism Implicate a Large Functional Network of Genes Involved in Formation and Function of Synapses

Identification of complex molecular networks underlying common human phenotypes is a major challenge of modern genetics. In this study, we develop a method for network-based analysis of genetic associations (NETBAG). We use NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism. The genes forming the network are primarily related to synapse development, axon targeting, and neuron motility. The identified network is strongly related to genes previously implicated in autism and intellectual disability phenotypes. Our results are also consistent with the hypothesis that significantly stronger functional perturbations are required to trigger the autistic phenotype in females compared to males. Overall, the presented analysis of de novo variants supports the hypothesis that perturbed synaptogenesis is at the heart of autism. More generally, our study provides proof of the principle that networks underlying complex human phenotypes can be identified by a network-based functional analysis of rare genetic variants.

Common regulatory variation impacts gene expression in a cell type-dependent manner.

Studies correlating genetic variation to gene expression facilitate the interpretation of common human phenotypes and disease. As functional variants may be operating in a tissue-dependent manner, we performed gene expression profiling and association with genetic variants (single-nucleotide polymorphisms) on three cell types of 75 individuals. We detected cell type-specific genetic effects, with 69 to 80% of regulatory variants operating in a cell type-specific manner, and identified multiple expressive quantitative trait loci (eQTLs) per gene, unique or shared among cell types and positively correlated with the number of transcripts per gene. Cell type-specific eQTLs were found at larger distances from genes and at lower effect size, similar to known enhancers. These data suggest that the complete regulatory variant repertoire can only be uncovered in the context of cell-type specificity.

THE PATTERNS OF NATURAL VARIATION IN HUMAN GENES

Currently, more than 10 million DNA sequence variations have been uncovered in the human genome. The most detailed variation discovery efforts have focused on candidate genes involved in cardiovascular disease or in susceptibilities associated with exposure to environmental agents. Here we provide an overview of natural genetic variation from the literature and in 510 human candidate genes resequenced for variation discovery. The average human gene contains 126 biallelic polymorphisms, 46 of which are common (> or =5% minor allele frequency) and 5 of which are found in coding regions. Using this complete picture of genetic diversity, we explore conservation, signatures of selection, and historical recombination to mine information useful for candidate gene association studies. In general, we find that the patterns of human gene variation suggest that no one approach will be appropriate for genetic association studies across all genes. Therefore, many different approaches may be required to identify the elusive genotypes associated with common human phenotypes.

A syndrome of altered cardiovascular, craniofacial, neurocognitive and skeletal development caused by mutations in TGFBR1 or TGFBR2

We report heterozygous mutations in the genes encoding either type I or type II transforming growth factor beta receptor in ten families with a newly described human phenotype that includes widespread perturbations in cardiovascular, craniofacial, neurocognitive and skeletal development. Despite evidence that receptors derived from selected mutated alleles cannot support TGFbeta signal propagation, cells derived from individuals heterozygous with respect to these mutations did not show altered kinetics of the acute phase response to administered ligand. Furthermore, tissues derived from affected individuals showed increased expression of both collagen and connective tissue growth factor, as well as nuclear enrichment of phosphorylated Smad2, indicative of increased TGFbeta signaling. These data definitively implicate perturbation of TGFbeta signaling in many common human phenotypes, including craniosynostosis, cleft palate, arterial aneurysms, congenital heart disease and mental retardation, and suggest that comprehensive mechanistic insight will require consideration of both primary and compensatory events.

TGF-β–dependent pathogenesis of mitral valve prolapse in a mouse model of Marfan syndrome

Mitral valve prolapse (MVP) is a common human phenotype, yet little is known about the pathogenesis of this condition. MVP can occur in the context of genetic syndromes, including Marfan syndrome (MFS), an autosomal-dominant connective tissue disorder caused by mutations in fibrillin-1. Fibrillin-1 contributes to the regulated activation of the cytokine TGF-beta, and enhanced signaling is a consequence of fibrillin-1 deficiency. We thus hypothesized that increased TGF-beta signaling may contribute to the multisystem pathogenesis of MFS, including the development of myxomatous changes of the atrioventricular valves. Mitral valves from fibrillin-1-deficient mice exhibited postnatally acquired alterations in architecture that correlated both temporally and spatially with increased cell proliferation, decreased apoptosis, and excess TGF-beta activation and signaling. In addition, TGF-beta antagonism in vivo rescued the valve phenotype, suggesting a cause and effect relationship. Expression analyses identified increased expression of numerous TGF-beta-related genes that regulate cell proliferation and survival and plausibly contribute to myxomatous valve disease. These studies validate a novel, genetically engineered murine model of myxomatous changes of the mitral valve and provide critical insight into the pathogenetic mechanism of such changes in MFS and perhaps more common nonsyndromic variants of mitral valve disease.

Red cell enzymes and serum proteins of Cercopithecus aethiops (South African green monkey).

Nine enzyme and 5 serum protein systems were investigated in blood samples from 8 South African green monkeys. Two phenotypes were observed in 3 systems, ADA, diaphorase, Pi, and variation may exist in PHI and caeruloplasmin systems. The phenotype observed in the PGM and erythrocyte acid phosphatase systems differed from the common human phenotype and that observed in the 6 PGD, LDH, AK, peptidase B, haptoglobin, transferrin and albumin systems was similar to the common human phenotype.