Rare Variants Of Large Effect Research Articles

Abstract P494: Gene-Level Gene-Environment Interaction Testing Reveals Rare Variants With Large Impacts on the Omega-3 Fatty Acid-Inflammation Relationship

Dietary omega-3 fatty acid (dN3FA) intake affects plasma N3FA (pN3FA) concentrations, circulating biomarkers of chronic inflammation such as high-sensitivity C-reactive protein (hsCRP), and ultimately cardiometabolic disease risk. Genetic variation may modify the N3FA-inflammation relationship, facilitating applications in precision medicine and possibly explaining variable intervention trial responses. Here, we explored the dual hypothesis that pN3FA mediates the dN3FA-hsCRP relationship and that genetic variants across the frequency spectrum modify this association. Regression models in the UK Biobank (N = 342,120) tested main effects of oily fish intake (a primary dietary source of N3FA; measured by food frequency questionnaire) on hsCRP and its mediation by pN3FA (measured by nuclear magnetic resonance; N = 188,701). Three genome-wide interaction studies tested modification of the dN3FA-hsCRP (“full”), dN3FA-pN3FA (“upstream”), and pN3FA-hsCRP (“downstream”) associations. Regression covariates included demographics, lifestyle indicators, socioeconomic measures, and genetic principal components. Variant-specific GWIS were followed by gene-level enrichment testing using the MAGMA tool. Replication of gene-based signals was conducted in the Women’s Genome Health Study (WGHS; N = 23,294) by testing the interaction of the top variant in each gene with total dN3FA impacting hsCRP. Oily fish intake associated most strongly with hsCRP compared to other questionnaire-based estimates of fish or fish oil intake, with statistical evidence that plasma N3FA mediated approximately 100% of this diet-inflammation relationship. While the “full” genetic interaction analysis did not produce any FDR-significant gene-level interactions, the “upstream” analysis uncovered nine genes, including a cluster containing the biologically-relevant fatty acid desaturases FADS1 and FADS2 , and the “downstream” analysis uncovered four genes. Primary variant interaction effect sizes were small (e.g., -0.02 std. dev. bN3FA / std. dev. fish / allele for the top FADS1 variant rs174560), but in-depth follow-up at these genes revealed allelic spectra including rare variants with substantially larger effects (e.g., -0.11 for a FADS1 missense variant with allele frequency of 0.14%, after conditioning on rs174560). WGHS results supported the overall robustness of the findings (consistent interaction effect signs at 8/10 loci), replicated specific signals (p = 0.04 for FADS1 rs174560), and implicated additional inflammatory biomarkers (e.g., fibrinogen and ICAM-1 for the top GLTSCR1 variant). In conclusion, we report (1) the novel epidemiological finding that pN3FA fully mediates the dN3FA-hsCRP relationship, and (2) specific genes with large-effect rare variants that modify the upstream (absorption) and downstream (inflammatory pathways) components of this relationship.

An intermediate-effect size variant in UMOD confers risk for chronic kidney disease

The kidney-specific gene UMOD encodes for uromodulin, the most abundant protein excreted in normal urine. Rare large-effect variants in UMOD cause autosomal dominant tubulointerstitial kidney disease (ADTKD), while common low-impact variants strongly associate with kidney function and the risk of chronic kidney disease (CKD) in the general population. It is unknown whether intermediate-effect variants in UMOD contribute to CKD. Here, candidate intermediate-effect UMOD variants were identified using large-population and ADTKD cohorts. Biological and phenotypical effects were investigated using cell models, in silico simulations, patient samples, and international databases and biobanks. Eight UMOD missense variants reported in ADTKD are present in the Genome Aggregation Database (gnomAD), with minor allele frequency (MAF) ranging from 10-5 to 10-3. Among them, the missense variant p.Thr62Pro is detected in ∼1/1,000 individuals of European ancestry, shows incomplete penetrance but a high genetic load in familial clusters of CKD, and is associated with kidney failure in the 100,000 Genomes Project (odds ratio [OR] = 3.99 [1.84 to 8.98]) and the UK Biobank (OR = 4.12 [1.32 to 12.85). Compared with canonical ADTKD mutations, the p.Thr62Pro carriers displayed reduced disease severity, with slower progression of CKD and an intermediate reduction of urinary uromodulin levels, in line with an intermediate trafficking defect invitro and modest induction of endoplasmic reticulum (ER) stress. Identification of an intermediate-effect UMOD variant completes the spectrum of UMOD-associated kidney diseases and provides insights into the mechanisms of ADTKD and the genetic architecture of CKD.

Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative, teosinte.

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to large-effect mutations versus variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load in the maize population that we predicted from sequence data. Parental breeding values were highly consistent between outcross and selfed offspring, indicating that additive effects determine most of the genetic value even in the presence of strong inbreeding depression. We developed a novel linkage scan to identify quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, small-effect partially recessive effects in linkage disequilibrium underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against some large effect variants, but polygenic load is harder to purge and overall segregating mutational burden increased in maize compared to teosinte.

The Genetic Architecture of High Bone Mass.

The phenotypic trait of high bone mass (HBM) is an excellent example of the nexus between common and rare disease genetics. HBM may arise from carriage of many ‘high bone mineral density [BMD]’-associated alleles, and certainly the genetic architecture of individuals with HBM is enriched with high BMD variants identified through genome-wide association studies of BMD. HBM may also arise as a monogenic skeletal disorder, due to abnormalities in bone formation, bone resorption, and/or bone turnover. Individuals with monogenic disorders of HBM usually, though not invariably, have other skeletal abnormalities (such as mandible enlargement) and thus are best regarded as having a skeletal dysplasia rather than just isolated high BMD. A binary etiological division of HBM into polygenic vs. monogenic, however, would be excessively simplistic: the phenotype of individuals carrying rare variants of large effect can still be modified by their common variant polygenic background, and by the environment. HBM disorders—whether predominantly polygenic or monogenic in origin—are not only interesting clinically and genetically: they provide insights into bone processes that can be exploited therapeutically, with benefits both for individuals with these rare bone disorders and importantly for the many people affected by the commonest bone disease worldwide—i.e., osteoporosis. In this review we detail the genetic architecture of HBM; we provide a conceptual framework for considering HBM in the clinical context; and we discuss monogenic and polygenic causes of HBM with particular emphasis on anabolic causes of HBM.

Transcriptomic signatures across human tissues identify functional rare genetic variation.

Rare genetic variants are abundant across the human genome, and identifying their function and phenotypic impact is a major challenge. Measuring aberrant gene expression has aided in identifying functional, large-effect rare variants (RVs). Here, we expanded detection of genetically driven transcriptome abnormalities by analyzing gene expression, allele-specific expression, and alternative splicing from multitissue RNA-sequencing data, and demonstrate that each signal informs unique classes of RVs. We developed Watershed, a probabilistic model that integrates multiple genomic and transcriptomic signals to predict variant function, validated these predictions in additional cohorts and through experimental assays, and used them to assess RVs in the UK Biobank, the Million Veterans Program, and the Jackson Heart Study. Our results link thousands of RVs to diverse molecular effects and provide evidence to associate RVs affecting the transcriptome with human traits.

Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates

Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To identify roles of rare and common variants on BP, we conducted genetic analyses in 26 Colombia and Costa Rica pedigrees ascertained for bipolar disorder 1 (BP1), the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 838 individuals and high-coverage whole-genome sequencing of 449 individuals. We compared polygenic risk scores (PRS), estimated using the latest BP1 genome-wide association study (GWAS) summary statistics, between BP1 individuals and related controls. We also evaluated whether BP1 individuals had a higher burden of rare deleterious single-nucleotide variants (SNVs) and rare copy number variants (CNVs) in a set of genes related to BP1. We found that compared with unaffected relatives, BP1 individuals had higher PRS estimated from BP1 GWAS statistics (P = 0.001 ~ 0.007) and displayed modest increase in burdens of rare deleterious SNVs (P = 0.047) and rare CNVs (P = 0.002 ~ 0.033) in genes related to BP1. We did not observe rare variants segregating in the pedigrees. These results suggest that small-to-moderate effect rare and common variants are more likely to contribute to BP1 risk in these extended pedigrees than a few large-effect rare variants.

Corrigendum: Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer.

Nat. Genet. 46, 736–741 (2014); published online 1 June 2014; corrected after print 23 January 2017 In the version of this article initially published, the name of author Florence Le Calvez-Kelm appeared incorrectly as Florence LeCalvez-Kelm. The error has been corrected in the HTML and PDF versionsof the article.

Discovery of rare variants for complex phenotypes

With the rise of sequencing technologies, it is now feasible to assess the role rare variants play in the genetic contribution to complex trait variation. While some of the earlier targeted sequencing studies successfully identified rare variants of large effect, unbiased gene discovery using exome sequencing has experienced limited success for complex traits. Nevertheless, rare variant association studies have demonstrated that rare variants do contribute to phenotypic variability, but sample sizes will likely have to be even larger than those of common variant association studies to be powered for the detection of genes and loci. Large-scale sequencing efforts of tens of thousands of individuals, such as the UK10K Project and aggregation efforts such as the Exome Aggregation Consortium, have made great strides in advancing our knowledge of the landscape of rare variation, but there remain many considerations when studying rare variation in the context of complex traits. We discuss these considerations in this review, presenting a broad range of topics at a high level as an introduction to rare variant analysis in complex traits including the issues of power, study design, sample ascertainment, de novo variation, and statistical testing approaches. Ultimately, as sequencing costs continue to decline, larger sequencing studies will yield clearer insights into the biological consequence of rare mutations and may reveal which genes play a role in the etiology of complex traits.

Investigation of Rare Single-Nucleotide PCDH15 Variants in Schizophrenia and Autism Spectrum Disorders.

Both schizophrenia (SCZ) and autism spectrum disorders (ASD) are neuropsychiatric disorders with overlapping genetic etiology. Protocadherin 15 (PCDH15), which encodes a member of the cadherin super family that contributes to neural development and function, has been cited as a risk gene for neuropsychiatric disorders. Recently, rare variants of large effect have been paid attention to understand the etiopathology of these complex disorders. Thus, we evaluated the impacts of rare, single-nucleotide variants (SNVs) in PCDH15 on SCZ or ASD. First, we conducted coding exon-targeted resequencing of PCDH15 with next-generation sequencing technology in 562 Japanese patients (370 SCZ and 192 ASD) and detected 16 heterozygous SNVs. We then performed association analyses on 2,096 cases (1,714 SCZ and 382 ASD) and 1,917 controls with six novel variants of these 16 SNVs. Of these six variants, four (p.R219K, p.T281A, p.D642N, c.3010-1G>C) were ultra-rare variants (minor allele frequency < 0.0005) that may increase disease susceptibility. Finally, no statistically significant association between any of these rare, heterozygous PCDH15 point variants and SCZ or ASD was found. Our results suggest that a larger sample size of resequencing subjects is necessary to detect associations between rare PCDH15 variants and neuropsychiatric disorders.

Efficient Software for Multi-marker, Region-Based Analysis of GWAS Data

Genome-wide association studies (GWAS) have associated many single variants with complex disease, yet the better part of heritable complex disease risk remains unexplained. Analytical tools designed to work under specific population genetic models are needed. Rare variants are increasingly shown to be important in human complex disease, but most existing GWAS data do not cover rare variants. Explicit population genetic models predict that genes contributing to complex traits and experiencing recurrent, unconditionally deleterious, mutation will harbor multiple rare, causative mutations of subtle effect. It is difficult to identify genes harboring rare variants of large effect that contribute to complex disease risk via the single marker association tests typically used in GWAS. Gene/region-based association tests may have the power detect associations by combining information from multiple markers, but have yielded limited success in practice. This is partially because many methods have not been widely applied. Here, we empirically demonstrate the utility of a procedure based on the rank truncated product (RTP) method, filtered to reduce the effects of linkage disequilibrium. We apply the procedure to the Wellcome Trust Case Control Consortium (WTCCC) data set, and uncover previously unidentified associations, some of which have been replicated in much larger studies. We show that, in the absence of significant rare variant coverage, RTP based methods still have the power to detect associated genes. We recommend that RTP-based methods be applied to all existing GWAS data to maximize the usefulness of those data. For this, we provide efficient software implementing our procedure.

Combined hyperlipidemia: familial but not (usually) monogenic.

Combined hyperlipidemia (CHL) is a complex phenotype that is commonly encountered clinically and is often associated with the expression of early heart disease. The affixed adjective 'familial' gives the impression that the trait is monogenic, like familial hypercholesterolemia. But despite significant efforts, genetic studies have yielded little evidence of single gene determinants of CHL. Sophisticated linkage studies suggest that individual lipid components of the CHL phenotype - such as elevated LDL and triglyceride - each have several determinants that segregate independently in families. Furthermore, DNA sequencing shows that rare large-effect variants in genes such as LDL receptor (LDLR) and lipoprotein lipase are found in some CHL patients, explaining the elevated LDL cholesterol and triglyceride components, respectively. In addition, multiple common small-effect lipid-altering variants accumulate in an individual's genome, raising the LDL cholesterol and/or triglyceride components by multiple mechanisms. Finally, secondary factors, such as poor diet, obesity,fatty liver or diabetes further modulate the expression of the biochemically defined CHL phenotype. Given the current state of genetic understanding, CHL may be best conceptualized as a syndrome with common clinical presentation but multigenic causes, similar to other common conditions such as type 2 diabetes.

Genetic Architecture of Complex Human Traits: What Have We Learned from Genome-Wide Association Studies?

Genome-wide association studies have identified thousands of genetic variants associated with various complex human traits and diseases. These results opened a window to the genetic architecture of complex human traits. The fact that the identified variants usually confer small effects and explain only a small fraction of heritability has led the discussion of where to find the ‘missing heritability.’ Two competitive models have been proposed: the ‘infinitesimal model’ suggests large number of variants of small effects and the ‘rare variant model’ suggests multiple large-effect rare variants. Recent empirical studies and theoretical analyses suggest that the infinitesimal model might dominate the true genetic architecture of most complex traits. Large-effect rare variants may exist and might be functionally important but contribute little to heritability.

Abstract 942: Imputation from The 1000 Genomes Project identifies rare large effect variants of BRCA2-K3326X and CHEK2-I157T as risk factors for lung cancer; a study from the TRICL consortium

Abstract We conducted imputation to The 1000 Genomes Project of genome-wide association studies of lung cancer in populations of European ancestry, with 11,348 cases and 15,861 controls from four large studies, including subjects from 13 countries. As a follow-up, we conducted in-silico replication in two studies of 2,303 cases and 27,350 controls and directly genotyped an additional 7,943 cases and 10,945 controls from 14 countries. Data were imputed for all scans for over 10 million SNPs using data from The 1000 Genomes Project (Phase 1 integrated release 3, March 2012) as reference, using IMPUTE2, MaCH or minimac software. Tests of association between imputed SNPs and lung cancer were performed under a probabilistic dosage model in SNPTEST, ProbABEL, MaCH2dat or glm function in R. The fidelity of imputation as assessed by the correlation between imputed and directly typed SNPs was examined in a subset of samples from the four studies used for discovery and showed squared correlation coefficients ranging from 0.74 for the rare CHEK2 variant to 1.00 for the more common TP63 variant. The association between each SNP and lung cancer risk was assessed by the Cochran-Armitage trend test. Principle components generated using common SNPs were used to account for the possibility of inflation. Odds ratios (ORs) and associated 95% confidence intervals (CIs) were calculated by unconditional logistic regression. Meta-analysis was conducted using an inverse-variance approach. Cochran's Q-statistic to test for heterogeneity and the I2 statistic to quantify the proportion of the total variation due to heterogeneity were calculated. We identified large-effect genome-wide associations for squamous lung cancer with the rare variants of BRCA2-K3326X (rs11571833; odds ratio [OR]=2.47, P=4.74×10−20) and of CHEK2-I157T (rs17879961; OR=0.38 P=1.27×10−13). We also showed an association between common variation at 3q28 (TP63; rs13314271; OR=1.13, P=7.22×10−10) and lung adenocarcinoma previously only reported in Asians. There was no association between these loci and smoking quantity as measured by number of cigarettes smoked per day, using smoking information on 43,693 Icelandic subjects. These findings provide further evidence for inherited genetic susceptibility to lung cancer and its biological basis. Additionally, our analysis demonstrates that imputation can identify rare disease-causing variants having substantive effects on cancer risk from pre-existing GWAS data. Citation Format: Maria Teresa Landi, Yufei Wang, James D. Mckay, Thorunn Rafnar, Zhaoming Wang, Maria Timofeeva, Peter Broderick, Kari Stefansson, Angela Risch, Stephen J. Chanock, David C. Christiani, Rayjean J. Hung, Paul Brennan, Richard S. Houlston, Christopher I. Amos. Imputation from The 1000 Genomes Project identifies rare large effect variants of BRCA2-K3326X and CHEK2-I157T as risk factors for lung cancer; a study from the TRICL consortium. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 942. doi:10.1158/1538-7445.AM2014-942

Challenges of linkage analysis in the era of whole-genome sequencing.

Whole-genome sequencing (WGS) is becoming an affordable technology for the study of the genetics of complex traits. With any new technology, experimental designs and statistical methods, both old and new, must be evaluated. One design seeing a resurgence of interest is the use of families. Genetic Analysis Workshop 18 provided the opportunity to evaluate statistical methods applied to WGS data for family-based studies. We summarize the results of five contributions that used linkage in the context of WGS. The investigators took differing approaches, including assessment of false-positive rates in classic two-point linkage, the effects of heterogeneity on linkage and association tests, and the use of linkage to focus association tests. We describe the primary findings of each contribution and note challenges that are not new to those working in family designs or specific to WGS data; for example, choice of phenotype definition, covariate adjustment, and use of longitudinal data may produce different results, making comparisons challenging. We detail new issues brought about by WGS, such as the elevated genome-wide false-positive rate for classic two-point parametric linkage analysis, computational demands in multipoint calculations, and lack of clarity in how to best use linkage to focus association testing. Finally, we comment on when linkage may be helpful for WGS, highlighting where additional research is needed; for example, although linkage analysis has been successful in the study of rare variants of large effect, how to best use family information in the context of rare variants of moderate effect remains an open research question.

Rare variants of large effect in BRCA2 and CHEK2 affect risk of lung cancer.

We conducted imputation to the 1000 Genomes Project of four genome-wide association studies of lung cancer in populations of European ancestry (11,348 cases and 15,861 controls) and genotyped an additional 10,246 cases and 38,295 controls for follow-up. We identified large-effect genome-wide associations for squamous lung cancer with the rare variants of BRCA2-K3326X (rs11571833; odds ratio [OR]=2.47, P=4.74×10−20) and of CHEK2-I157T (rs17879961; OR=0.38 P=1.27×10−13). We also showed an association between common variation at 3q28 (TP63; rs13314271; OR=1.13, P=7.22×10−10) and lung adenocarcinoma previously only reported in Asians. These findings provide further evidence for inherited genetic susceptibility to lung cancer and its biological basis. Additionally, our analysis demonstrates that imputation can identify rare disease-causing variants having substantive effects on cancer risk from pre-existing GWAS data.

Identification of a rare coding variant in complement 3 associated with age-related macular degeneration

Macular degeneration is a common cause of blindness in the elderly. To identify rare coding variants associated with a large increase in risk of age-related macular degeneration (AMD), we sequenced 2,335 cases and 789 controls in 10 candidate loci (57 genes). To increase power, we augmented our control set with ancestry-matched exome sequenced controls. An analysis of coding variation in 2,268 AMD cases and 2,268 ancestry matched controls revealed two large-effect rare variants; previously described R1210C in the CFH gene (fcase = 0.51%, fcontrol = 0.02%, OR = 23.11), and newly identified K155Q in the C3 gene (fcase = 1.06%, fcontrol = 0.39%, OR = 2.68). The variants suggest decreased inhibition of C3 by Factor H, resulting in increased activation of the alternative complement pathway, as a key component of disease biology.

Complex Genetic Architecture of Cardiac Disease in a Wild Type Inbred Strain of Drosophila melanogaster

Natural populations of the fruit fly, Drosophila melanogaster, segregate genetic variation that leads to cardiac disease phenotypes. One nearly isogenic line from a North Carolina peach orchard, WE70, is shown to harbor two genetically distinct heart phenotypes: elevated incidence of arrhythmias, and a dramatically constricted heart diameter in both diastole and systole, with resemblance to restrictive cardiomyopathy in humans. Assuming the source to be rare variants of large effect, we performed Bulked Segregant Analysis using genomic DNA hybridization to Affymetrix chips to detect single feature polymorphisms, but found that the mutant phenotypes are more likely to have a polygenic basis. Further mapping efforts revealed a complex architecture wherein the constricted cardiomyopathy phenotype was observed in individual whole chromosome substitution lines, implying that variants on both major autosomes are sufficient to produce the phenotype. A panel of 170 Recombinant Inbred Lines (RIL) was generated, and a small subset of mutant lines selected, but these each complemented both whole chromosome substitutions, implying a non-additive (epistatic) contribution to the “disease” phenotype. Low coverage whole genome sequencing was also used to attempt to map chromosomal regions contributing to both the cardiomyopathy and arrhythmia, but a polygenic architecture had to be again inferred to be most likely. These results show that an apparently simple rare phenotype can have a complex genetic basis that would be refractory to mapping by deep sequencing in pedigrees. We present this as a cautionary tale regarding assumptions related to attempts to map new disease mutations on the assumption that probands carry a single causal mutation.

Rare Genomic Structural Variants in Complex Disease: Lessons from the Replication of Associations with Obesity

The limited ability of common variants to account for the genetic contribution to complex disease has prompted searches for rare variants of large effect, to partly explain the ‘missing heritability’. Analyses of genome-wide genotyping data have identified genomic structural variants (GSVs) as a source of such rare causal variants. Recent studies have reported multiple GSV loci associated with risk of obesity. We attempted to replicate these associations by similar analysis of two familial-obesity case-control cohorts and a population cohort, and detected GSVs at 11 out of 18 loci, at frequencies similar to those previously reported. Based on their reported frequencies and effect sizes (OR≥25), we had sufficient statistical power to detect the large majority (80%) of genuine associations at these loci. However, only one obesity association was replicated. Deletion of a 220 kb region on chromosome 16p11.2 has a carrier population frequency of 2×10−4 (95% confidence interval [9.6×10−5–3.1×10−4]); accounts overall for 0.5% [0.19%–0.82%] of severe childhood obesity cases (P = 3.8×10−10; odds ratio = 25.0 [9.9–60.6]); and results in a mean body mass index (BMI) increase of 5.8 kg.m−2 [1.8–10.3] in adults from the general population. We also attempted replication using BMI as a quantitative trait in our population cohort; associations with BMI at or near nominal significance were detected at two further loci near KIF2B and within FOXP2, but these did not survive correction for multiple testing. These findings emphasise several issues of importance when conducting rare GSV association, including the need for careful cohort selection and replication strategy, accurate GSV identification, and appropriate correction for multiple testing and/or control of false discovery rate. Moreover, they highlight the potential difficulty in replicating rare CNV associations across different populations. Nevertheless, we show that such studies are potentially valuable for the identification of variants making an appreciable contribution to complex disease.

Genetic Complexity of Human Myelomeningocele

Myelomeningocele (MM) is the most severe form of lumbosacral open neural tube defect (NTD) compatible with survival and it results in various degrees of disability. Approximately 4 in 10,000 live births are affected in the United States (US). Mexican Americans, the fastest growing ethnic population in the US, have the highest prevalence rate for NTDs follows by Caucasian Americans. Occurrence of MM represents a significant economic and public health burden in the US. Both genetic and environmental factors contribute to the development of MM. The genetic contribution to NTD is complex. Over 205 genetic loci are implicated in mouse mutants and strains. Majority of mouse NTDs affect the rostral CNS and only a handful present only spina bifida. In contrast, the genetic variability and molecular mechanism(s) in the development of MM in humans are largely unknown. The contribution of de novo mutations to MM in humans is unknown and can be determined by characterizing the variations present in the exomes of MM affected individuals. Whole exome sequencing is a cost-effective and efficient tool to discover rare variants of large effect as well as the entire site frequency spectrum of variations in the exomes to establish disease association. In addition, sequencing of the regulatory elements in the genomes of MM affected individuals can discover rare variants that may affect expression of genes critical to the normal development of the neural tube

Decanalization mediating gene-environment interactions in schizophrenia and other psychiatric disorders with neurodevelopmental etiology

OPINION article Front. Behav. Neurosci., 13 November 2013Sec. Pathological Conditions Volume 7 - 2013 | https://doi.org/10.3389/fnbeh.2013.00157