Network-based model weighting to detect multiple loci influencing complex diseases

Lack of Association of Polymorphism Located Upstream of ABCA1 (rs2472493), in FNDC3B (rs7636836), and Near ANKRD55–MAP3K1 Genes (rs61275591) in Primary Open-Angle Glaucoma Patients of Saudi Origin

Polymorphisms rs2472493 near ABCA1, rs7636836 in FNDC3B, and rs61275591 near the ANKRD55–MAP3K1 genes were previously reported to exhibit genome-wide significance in primary open-angle glaucoma (POAG). Since these polymorphisms have not been investigated in the Arab population of Saudi Arabia, we examined their association with POAG in a Saudi cohort. Genotyping was performed in 152 POAG cases and 246 controls using Taqman real-time assays and their associations with POAG and clinical markers, such as intraocular pressure, cup/disc ratio, and the number of antiglaucoma medications, were tested by statistical methods. There was no association observed between POAG and the minor allele frequencies of rs2472493[G], rs7636836[T], or rs61275591[A]. None of the genetic models such as co-dominant, dominant, recessive, over-dominant, and log-additive demonstrated any genotype link. The Rs2472493 genotype showed a modest association (p = 0.044) with the number of antiglaucoma medications in the POAG group, but no significant genotype effect on post hoc analysis. In addition, a G-T allelic haplotype of rs2472493 (ABCA1) and rs7636836 (FNDC3B) did show an over two-fold increased risk of POAG (odds ratio = 2.18), albeit non-significantly (p = 0.092). Similarly, no other allelic haplotype of the three variants showed any significant association with POAG. Our study did not replicate the genetic association of rs2472493 (ABCA1), rs763683 (FNDC3B), and rs61275591 (ANKRD55–MAP3K1) in POAG and related clinical phenotypes, suggesting that these polymorphisms are not associated with POAG in a Saudi cohort of Arab ethnicity. However, large population-based multicenter studies are needed to validate these results.

Evaluation of ABCA1 and FNDC3B Gene Polymorphisms Associated With Pseudoexfoliation Glaucoma and Primary Angle-Closure Glaucoma in a Saudi Cohort

Objective: It is plausible that common disease mechanisms exist in glaucoma pathophysiology. Accordingly, we investigated the genetic association of two previously reported primary open-angle glaucoma (POAG)-related gene polymorphisms, rs2472493 (A > G) in ABCA1 and rs7636836 (C > T) in FNDC3B, in primary angle-closure glaucoma (PACG) and pseudoexfoliation glaucoma (PXG).

Methods: TaqMan genotyping was performed in a total of 442 subjects consisting of 246 healthy controls, 102 PACG patients, and 94 PXG patients. Statistical evaluations were performed to detect allelic and genotype association of the variants with the disease and clinical variables such as intraocular pressure (IOP) and cup/disc ratio.

Results: Overall, there was no allelic or genotype association of these variants in PACG and PXG. However, rs7636836[T] allele significantly increased the risk of PXG among men (p = 0.029, odds ratio [OR] = 2.69, 95% confidence interval = 1.11–6.51). Similarly, rs2472493 and rs7636836 genotypes also showed significant association with PXG among men in over-dominant model (p = 0.031, OR = 1.98, 95% CI = 1.06–3.71) and co-dominant model (p = 0.029, OR = 2.69, 95% CI = 1.11–6.51), respectively. However, none survived Bonferroni’s correction. Besides, the synergic presence of rs2472493[G] and rs7636836[T] alleles (G-T) was found to significantly increase the risk of PACG (p = 0.026, OR = 2.85, 95% CI = 1.09–7.46). No significant genotype influence was observed on IOP and cup/disc ratio.

Conclusion: Our results suggest that the polymorphisms rs2472493 in ABCA1 and rs7636836 in FNDC3B genes may be associated with PXG among men, and a G-T allelic combination may confer an increased risk of PACG in the middle-eastern Saudi cohort. Further research in a larger population-based sample is needed to validate these findings.

Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

Partial microduplication in the histone acetyltransferase complex member KANSL1 is associated with congenital heart defects in 22q11.2 microdeletion syndrome patients

22q11.2 microdeletion syndrome (22q11.2DS) is the most common microdeletion disorder in humans, with an incidence of 1/4000 live births. It is caused by a heterozygous deletion of 1.5–3 Mb on chromosome region 22q11.2. Patients with the deletion present features that include neuropsychiatric problems, craniofacial abnormalities and cardiovascular malformations. However, the phenotype is highly variable and the factors related to the clinical heterogeneity are not fully understood. About 65% of patients with 22q11.2DS have congenital heart defects (CHD). The main goal of this study was to identify common CNVs in 22q11.2DS patients that could be associated with the incomplete penetrance of CHD. Analysis of genomic DNA from 253 patients with 22q11.2DS using array technology showed an association between a microduplication located in region 17q21.31 and CHD (p-value = 0.023, OR = 2.75, 95% CI = 1.17–7.03). This region includes the first three exons of KANSL1 gene. Bioinformatic analysis showed that KANSL1 and CRKL, a gene in the commonly deleted region of 22q11.2DS, are part of the same regulatory module in a miRNA-mRNA network. These results show that a KANSL1 microduplication, in combination with the 22q11.2 deletion, is associated with increased risk of CHD in these patients, suggesting that KANSL1 plays a role as a modifier gene in 22q11.2DS patients.

Novel Somatic Copy Number Alteration Identified for Cervical Cancer in the Mexican American Population

Cervical cancer affects millions of Americans, but the rate for cervical cancer in the Mexican American is approximately twice that for non-Mexican Americans. The etiologies of cervical cancer are still not fully understood. A number of somatic mutations, including several copy number alterations (CNAs), have been identified in the pathogenesis of cervical carcinomas in non-Mexican Americans. Thus, the purpose of this study was to investigate CNAs in association with cervical cancer in the Mexican American population. We conducted a pilot study of genome-wide CNA analysis using 2.5 million markers in four diagnostic groups: reference (n = 125), low grade dysplasia (cervical intraepithelial neoplasia (CIN)-I, n = 4), high grade dysplasia (CIN-II and -III, n = 5) and invasive carcinoma (squamous cell carcinoma (SCC), n = 5) followed by data analyses using Partek. We observed a statistically-significant difference of CNA burden between case and reference groups of different sizes (>100 kb, 10-100 kb and 1-10 kb) of CNAs that included deletions and amplifications, e.g., a statistically-significant difference of >100 kb deletions was observed between the reference (6.6%) and pre-cancer and cancer (91.3%) groups. Recurrent aberrations of 98 CNA regions were also identified in cases only. However, none of the CNAs have an impact on cancer progression. A total of 32 CNA regions identified contained tumor suppressor genes and oncogenes. Moreover, the pathway analysis revealed endometrial cancer and estrogen signaling pathways associated with this cancer (p < 0.05) using Kyoto Encyclopedia of Genes and Genomes (KEGG). This is the first report of CNAs identified for cervical cancer in the U.S. Latino population using high density markers. We are aware of the small sample size in the study. Thus, additional studies with a larger sample are needed to confirm the current findings.

Knowns and unknowns for psychophysiological endophenotypes: integration and response to commentaries

We review and summarize seven molecular genetic studies of 17 psychophysiological endophenotypes that comprise this special issue of Psychophysiology, address criticisms raised in accompanying Perspective and Commentary pieces, and offer suggestions for future research. Endophenotypes are polygenic, and possibly influenced by rare genetic variants. Because they are not simpler genetically than clinical phenotypes, they are unlikely to assist gene discovery for psychiatric disorder. Once genetic variants for clinical phenotypes are identified, associated endophenotypes are likely to provide valuable insights into the psychological and neural mechanisms important to disorder pathology. This special issue provides a foundation for informed future steps in endophenotype genetics, including the formation of large sample consortia capable of fleshing out the many genetic variants contributing to individual differences in psychophysiological measures.

Protein interaction networks reveal novel autism risk genes within GWAS statistical noise

Genome-wide association studies (GWAS) for Autism Spectrum Disorder (ASD) thus far met limited success in the identification of common risk variants, consistent with the notion that variants with small individual effects cannot be detected individually in single SNP analysis. To further capture disease risk gene information from ASD association studies, we applied a network-based strategy to the Autism Genome Project (AGP) and the Autism Genetics Resource Exchange GWAS datasets, combining family-based association data with Human Protein-Protein interaction (PPI) data. Our analysis showed that autism-associated proteins at higher than conventional levels of significance (P<0.1) directly interact more than random expectation and are involved in a limited number of interconnected biological processes, indicating that they are functionally related. The functionally coherent networks generated by this approach contain ASD-relevant disease biology, as demonstrated by an improved positive predictive value and sensitivity in retrieving known ASD candidate genes relative to the top associated genes from either GWAS, as well as a higher gene overlap between the two ASD datasets. Analysis of the intersection between the networks obtained from the two ASD GWAS and six unrelated disease datasets identified fourteen genes exclusively present in the ASD networks. These are mostly novel genes involved in abnormal nervous system phenotypes in animal models, and in fundamental biological processes previously implicated in ASD, such as axon guidance, cell adhesion or cytoskeleton organization. Overall, our results highlighted novel susceptibility genes previously hidden within GWAS statistical "noise" that warrant further analysis for causal variants.

A network-based kernel machine test for the identification of risk pathways in genome-wide association studies

Biological pathways provide rich information and biological context on the genetic causes of complex diseases. The logistic kernel machine test integrates prior knowledge on pathways in order to analyze data from genome-wide association studies (GWAS). In this study, the kernel converts the genomic information of 2 individuals into a quantitative value reflecting their genetic similarity. With the selection of the kernel, one implicitly chooses a genetic effect model. Like many other pathway methods, none of the available kernels accounts for the topological structure of the pathway or gene-gene interaction types. However, evidence indicates that connectivity and neighborhood of genes are crucial in the context of GWAS, because genes associated with a disease often interact. Thus, we propose a novel kernel that incorporates the topology of pathways and information on interactions. Using simulation studies, we demonstrate that the proposed method maintains the type I error correctly and can be more effective in the identification of pathways associated with a disease than non-network-based methods. We apply our approach to genome-wide association case-control data on lung cancer and rheumatoid arthritis. We identify some promising new pathways associated with these diseases, which may improve our current understanding of the genetic mechanisms.

RNA-Seq reveals novel transcriptional reorganization in human alcoholic brain

DNA microarrays have been used for over a decade to profile gene expression on a genomic scale. While this technology has advanced our understanding of complex cellular function, the reliance of microarrays on hybridization kinetics results in several technical limitations. For example, knowledge of the sequences being probed is required, distinguishing similar sequences is difficult because of cross-hybridization, and the relatively narrow dynamic range of the signal limits sensitivity. Recently, new technologies have been introduced that are based on novel sequencing methodologies. These next-generation sequencing methods do not have the limitations inherent to microarrays. Next-generation sequencing is unique since it allows the detection of all known and novel RNAs present in biological samples without bias toward known transcripts. In addition, the expression of coding and noncoding RNAs, alternative splicing events, and expressed single nucleotide polymorphisms (SNPs) can be identified in a single experiment. Furthermore, this technology allows for remarkably higher throughput while lowering sequencing costs. This significant shift in throughput and pricing makes low-cost access to whole genomes possible and more importantly expands sequencing applications far beyond traditional uses (Morozova & Marra, 2008) to include sequencing the transcriptome (RNA-Seq), providing detail on gene structure, alternative splicing events, expressed SNPs, and transcript size (Mane et al., 2009; Tang et al., 2009; Walter et al., 2009), in a single experiment, while also quantifying the absolute abundance of genes, all with greater sensitivity and dynamic range than the competing cDNA microarray technology (Mortazavi, Williams, McCue, Schaeffer, & Wold, 2008).

Pathway analyses and understanding disease associations

High throughput technologies have been applied to investigate the underlying mechanisms of complex diseases, identify disease-associations and help to improve treatment. However it is challenging to derive biological insight from conventional single gene based analysis of "omics" data from high throughput experiments due to sample and patient heterogeneity. To address these challenges, many novel pathway and network based approaches were developed to integrate various "omics" data, such as gene expression, copy number alteration, Genome Wide Association Studies, and interaction data. This review will cover recent methodological developments in pathway analysis for the detection of dysregulated interactions and disease-associated subnetworks, prioritization of candidate disease genes, and disease classifications. For each application, we will also discuss the associated challenges and potential future directions.

Reverse-engineering human regulatory networks

The explosion of genomic, transcriptomic, proteomic, metabolomic, and other omics data is challenging the research community to develop rational models for their organization and interpretation to generate novel biological knowledge. The development and use of gene regulatory networks to mechanistically interpret this data is an important development in molecular biology, usually captured under the banner of systems biology. As a result, the repertoire of methods for the reconstruction of comprehensive and cell-context-specific maps of regulatory interactions, or interactomes, has also exploded in the past few years. In this review, we focus on Network Biology and more specifically on methods for reverse engineering transcriptional, post-transcriptional, and post-translational human interaction networks and show how their interrogation is starting to impact our understanding of cellular pathophysiology and one's ability to predict cellular phenotypes from genome-wide molecular observations.

Pathway-Wide Association Study Implicates Multiple Sterol Transport and Metabolism Genes in HDL Cholesterol Regulation

Pathway-based association methods have been proposed to be an effective approach in identifying disease genes, when single-marker association tests do not have sufficient power. The analysis of quantitative traits may be benefited from these approaches, by sampling from two extreme tails of the distribution. Here we tested a pathway association approach on a small genome-wide association study (GWAS) on 653 subjects with extremely high high-density lipoprotein cholesterol (HDL-C) levels and 784 subjects with low HDL-C levels. We identified 102 genes in the sterol transport and metabolism pathways that collectively associate with HDL-C levels, and replicated these association signals in an independent GWAS. Interestingly, the pathways include 18 genes implicated in previous GWAS on lipid traits, suggesting that genuine HDL-C genes are highly enriched in these pathways. Additionally, multiple biologically relevant loci in the pathways were not detected by previous GWAS, including genes implicated in previous candidate gene association studies (such as LEPR, APOA2, HDLBP, SOAT2), genes that cause Mendelian forms of lipid disorders (such as DHCR24), and genes expressing dyslipidemia phenotypes in knockout mice (such as SOAT1, PON1). Our study suggests that sampling from two extreme tails of a quantitative trait and examining genetic pathways may yield biological insights from smaller samples than are generally required using single-marker analysis in large-scale GWAS. Our results also implicate that functionally related genes work together to regulate complex quantitative traits, and that future large-scale studies may benefit from pathway-association approaches to identify novel pathways regulating HDL-C levels.

Gene set analysis of genome-wide association studies: methodological issues and perspectives

Recent studies have demonstrated that gene set analysis, which tests disease association with genetic variants in a group of functionally related genes, is a promising approach for analyzing and interpreting genome-wide association studies (GWAS) data. These approaches aim to increase power by combining association signals from multiple genes in the same gene set. In addition, gene set analysis can also shed more light on the biological processes underlying complex diseases. However, current approaches for gene set analysis are still in an early stage of development in that analysis results are often prone to sources of bias, including gene set size and gene length, linkage disequilibrium patterns and the presence of overlapping genes. In this paper, we provide an in-depth review of the gene set analysis procedures, along with parameter choices and the particular methodology challenges at each stage. In addition to providing a survey of recently developed tools, we also classify the analysis methods into larger categories and discuss their strengths and limitations. In the last section, we outline several important areas for improving the analytical strategies in gene set analysis.

Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.

Web tools for the prioritization of candidate disease genes

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

Analysing biological pathways in genome-wide association studies

Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.

The power of protein interaction networks for associating genes with diseases

Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques.

Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required.

Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet

Contact:carlk@cs.umd.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Bioinformatics challenges for genome-wide association studies

Motivation: The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis issues including multiple testing. This work has been successful and has enabled the discovery of new associations that have been replicated in multiple studies. However, it is now recognized that most SNPs discovered via GWAS have small effects on disease susceptibility and thus may not be suitable for improving health care through genetic testing. One likely explanation for the mixed results of GWAS is that the current biostatistical analysis paradigm is by design agnostic or unbiased in that it ignores all prior knowledge about disease pathobiology. Further, the linear modeling framework that is employed in GWAS often considers only one SNP at a time thus ignoring their genomic and environmental context. There is now a shift away from the biostatistical approach toward a more holistic approach that recognizes the complexity of the genotype–phenotype relationship that is characterized by significant heterogeneity and gene–gene and gene–environment interaction. We argue here that bioinformatics has an important role to play in addressing the complexity of the underlying genetic basis of common human diseases. The goal of this review is to identify and discuss those GWAS challenges that will require computational methods.

Contact:jason.h.moore@dartmouth.edu

The metabotropic glutamate receptor 1, GRM1: evaluation as a candidate gene for inherited forms of cerebellar ataxia

The metabotropic glutamate (mGlu) 1 receptor, coded by the GRM1 gene, is involved in synaptic activities, learning and neuroprotection. Eleven different mouse Grm1 mutations, either induced or spontaneously occurring, have been reported, including one from our group. All the mutations result in a complex phenotype with ataxia and intention tremor in mice. Moreover, autoantibodies against mGlu1 receptor have been associated with paraneoplastic cerebellar ataxia in humans. In spite of the large clinical and genetic heterogeneity displayed by the inherited forms of cerebellar ataxia, forms remain with a yet unknown molecular definition. With the evidence coming out from mouse models and from paraneoplastic ataxia, it seems that GRM1 represents a good candidate gene for early-onset ataxia forms, though no GRM1 mutations have thus far been looked for. The aim of this study was to investigate the possible involvement of GRM1 in early-onset or familial forms of ataxia. We searched for gene mutations in a panel of patients with early-onset ataxia as yet molecularly undefined. No causative mutations were found, though we detected synonymous variants in the exons and changes in flanking intronic sequences which are unlikely to alter correct splicing upon bioinformatics prediction. As for other known forms of inherited ataxias, absence of mutations in GRM1 seems to suggest a relatively low frequency in cerebellar ataxias.

Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network

An evidence-weighted functional-linkage network of human genes reveals associations among diseases that share no known disease genes and have dissimilar phenotypes

We integrate 16 genomic features to construct an evidence-weighted functional-linkage network comprising 21,657 human genes. The functional-linkage network is used to prioritize candidate genes for 110 diseases, and to reliably disclose hidden associations between disease pairs having dissimilar phenotypes, such as hypercholesterolemia and Alzheimer's disease. Many of these disease-disease associations are supported by epidemiology, but with no previous genetic basis. Such associations can drive novel hypotheses on molecular mechanisms of diseases and therapies.

Idiopathic-type scoliosis is not exclusive to bipedalism

Human familial/idiopathic-type scoliosis (IS) is a complex genetic disorder for which the cause is unknown. The curve phenotype characteristically demonstrates pronounced morphological and developmental variability that is likely a consequence of biomechanical, environmental, and genetic differences between individuals. In addition, risk factors that affect the propensity for curves to progress to severity are unknown. Progress in understanding the fundamental biology of idiopathic-type scoliosis has been limited by the lack of a genetic/developmental animal model. Prior to consideration of teleosts, developmental idiopathic-type scoliosis has been considered to be exclusive to humans. Consequently, there is the notion that the syndrome is a result of bipedalism, and many studies try to explain the deformity from this anthrocentric viewpoint. This perspective has been reinforced by the choice of animals used for study, in that chickens and bipedal rats and mice demonstrate idiopathic-type curvature when made melatonin-deficient, but quadrupedal animals do not. Overlooked is the fact that teleosts also demonstrate similar curvature when made melatonin-deficient. Our characterization of the guppy curveback has demonstrated that non-induced idiopathic-type curvature is not exclusive to humans, nor bipedalism. We hypothesize that unique morphological, developmental and genetic parallels between the human and guppy syndromes are due to common molecular pathways involved in the etiopathogenesis of both phenotypes. We explore established gene conservation between human and teleost genomes that are in pathways hypothesized to be involved in the IS syndrome. We present non-induced vertebral wedging as a unique shared feature in IS and curveback that suggests a similar interaction between a molecular phenotype on the level of the vertebral anatomy, and biomechanics. We propose that rather than bipedalism per se, expression of idiopathic-type scoliosis is dependent on normal spinal loading applied along the cranio-caudal axis that interacts with an unknown factor causing the primary curve. In this regard, a comparative biological approach using a simplified teleost model will promote discovery of basic processes integral to idiopathic-type scoliosis in teleosts and humans, and highlight human-specific aspects of the deformity.