Functional loss of semaphorin 3C and/or semaphorin 3D and their epistatic interaction with ret are critical to Hirschsprung disease liability.

Innervation of the gut is segmentally lost in Hirschsprung disease (HSCR), a consequence of cell-autonomous and non-autonomous defects in enteric neuronal cell differentiation, proliferation, migration, or survival. Rare, high-penetrance coding variants and common, low-penetrance non-coding variants in 13 genes are known to underlie HSCR risk, with the most frequent variants in the ret proto-oncogene (RET). We used a genome-wide association (220 trios) and replication (429 trios) study to reveal a second non-coding variant distal to RET and a non-coding allele on chromosome 7 within the class 3 Semaphorin gene cluster. Analysis in Ret wild-type and Ret-null mice demonstrates specific expression of Sema3a, Sema3c, and Sema3d in the enteric nervous system (ENS). In zebrafish embryos, sema3 knockdowns show reduction of migratory ENS precursors with complete ablation under conjoint ret loss of function. Seven candidate receptors of Sema3 proteins are also expressed within the mouse ENS and their expression is also lost in the ENS of Ret-null embryos. Sequencing of SEMA3A, SEMA3C, and SEMA3D in 254 HSCR-affected subjects followed by in silico protein structure modeling and functional analyses identified five disease-associated alleles with loss-of-function defects in semaphorin dimerization and binding to their cognate neuropilin and plexin receptors. Thus, semaphorin 3C/3D signaling is an evolutionarily conserved regulator of ENS development whose dys-regulation is a cause of enteric aganglionosis.

Pathways systematically associated to Hirschsprung's disease.

Despite it has been reported that several loci are involved in Hirschsprung's disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung's disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.

A multilevel model to address batch effects in copy number estimation using SNP arrays.

Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of base pairs in the genome. Genomewide association studies (GWAS) may simultaneously screen for copy number phenotype and single nucleotide polymorphism (SNP) phenotype associations as part of the analytic strategy. However, genomewide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post hoc quality control procedures to exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of biallelic genotype calls from experimental data to estimate batch-specific and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in the quantile-normalized intensities, while the latter illustrates the robustness of our approach to a data set in which approximately 27% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package crlmm at Bioconductor (http:www.bioconductor.org).

Genetic variations in nitric oxide synthase 1 adaptor protein are associated with sudden cardiac death in US white community-based populations.

BACKGROUND: The ECG QT interval is associated with risk of sudden cardiac death (SCD). A previous genome-wide association study demonstrated that allelic variants (rs10494366 and rs4657139) in the nitric oxide synthase 1 adaptor protein (NOS1AP), which encodes a carboxy-terminal PDZ ligand of neuronal nitric oxide synthase, are associated with the QT interval in white adults. The present analysis was conducted to validate the association between NOS1AP variants and the QT interval and to examine the association with SCD in a combined population of 19 295 black and white adults from the Atherosclerosis Risk In Communities Study and the Cardiovascular Health Study. METHODS AND RESULTS: We examined 19 tagging single-nucleotide polymorphisms in the genomic blocks containing rs10494366 and rs4657139 in NOS1AP. SCD was defined as a sudden pulseless condition of cardiac origin in a previously stable individual. General linear models and Cox proportional hazards regression models were used. Multiple single-nucleotide polymorphisms in NOS1AP, including rs10494366, rs4657139, and rs16847548, were significantly associated with adjusted QT interval in whites (P<0.0001). In whites, after adjustment for age, sex, and study, the relative hazard of SCD associated with each C allele at rs16847548 was 1.31 (95% confidence interval 1.10 to 1.56, P=0.002), assuming an additive model. In addition, a downstream neighboring single-nucleotide polymorphism, rs12567209, which was not correlated with rs16847548 or QT interval, was also independently associated with SCD in whites (relative hazard 0.57, 95% confidence interval 0.39 to 0.83, P=0.003). Adjustment for QT interval and coronary heart disease risk factors attenuated but did not eliminate the association between rs16847548 and SCD, and such adjustment had no effect on the association between rs12567209 and SCD. No significant associations between tagging single-nucleotide polymorphisms in NOS1AP and either QT interval or SCD were observed in blacks. CONCLUSIONS: In a combined analysis of 2 population-based prospective cohort studies, sequence variations in NOS1AP were associated with baseline QT interval and the risk of SCD in white US adults.

Normalization of microarray expression data using within-pedigree pool and its effect on linkage analysis.

"Genetical genomics", the study of natural genetic variation combining data from genetic marker-based studies with gene expression analyses, has exploded with the recent development of advanced microarray technologies. To account for systematic variation known to exist in microarray data, it is critical to properly normalize gene expression traits before performing genetic linkage analyses. However, imposing equal means and variances across pedigrees can over-correct for the true biological variation by ignoring familial correlations in expression values. We applied the robust multiarray average (RMA) method to gene expression trait data from 14 Centre d'Etude du Polymorphisme Humain (CEPH) Utah pedigrees provided by GAW15 (Genetic Analysis Workshop 15). We compared the RMA normalization method using within-pedigree pools to RMA normalization using all individuals in a single pool, which ignores pedigree membership, and investigated the effects of these different methods on 18 gene expression traits previously found to be linked to regions containing the corresponding structural locus. Familial correlation coefficients of the expressed traits were stronger when traits were normalized within pedigrees. Surprisingly, the linkage plots for these traits were similar, suggesting that although heritability increases when traits are normalized within pedigrees, the strength of linkage evidence does not necessarily change substantially.

Covariate-based linkage analysis: application of a propensity score as the single covariate consistently improves power to detect linkage.

Successful identification of genetic risk loci for complex diseases has relied on the ability to minimize disease and genetic heterogeneity to increase the power to detect linkage. One means to account for disease heterogeneity is by incorporating covariate data. However, the inclusion of each covariate will add one degree of freedom to the allele sharing based linkage test, which may in fact decrease power. We explore the application of a propensity score, which is typically used in causal inference to combine multiple covariates into a single variable, as a means of allowing for multiple covariates with the addition of only one degree of freedom. In this study, binary trait data, simulated under various models involving genetic and environmental effects, were analyzed using a nonparametric linkage statistic implemented in LODPAL. Power and type I error rates were evaluated. Results suggest that the use of the propensity score to combine multiple covariates as a single covariate consistently improves the power compared to an analysis including no covariates, each covariate individually, or all covariates simultaneously. Type I error rates were inflated for analyses with covariates and increased with increasing number of covariates, but reduced to nominal rates with sample sizes of 1000 families. Therefore, we recommend using the propensity score as a single covariate in the linkage analysis of a trait suspected to be influenced by multiple covariates because of its potential to increase the power to detect linkage, while controlling for the increase in the type I error.

Linkage mapping methods applied to the COGA data set: presentation Group 4 of Genetic Analysis Workshop 14.

Presentation Group 4 participants analyzed the Collaborative Study on the Genetics of Alcoholism data provided for Genetic Analysis Workshop 14. This group examined various aspects of linkage analysis and related issues. Seven papers included linkage analyses, while the eighth calculated identity-by-descent (IBD) probabilities. Six papers analyzed linkage to an alcoholism phenotype: ALDX1 (four papers), ALDX2 (one paper), or a combination both (one paper). Methods used included Bayesian variable selection coupled with Haseman-Elston regression, recursive partitioning to identify phenotype and covariate groupings that interact with evidence for linkage, nonparametric linkage regression modeling, affected sib-pair linkage analysis with discordant sib-pair controls, simulation-based homozygosity mapping in a single pedigree, and application of a propensity score to collapse covariates in a general conditional logistic model. Alcoholism linkage was found with > or =2 of these approaches on chromosomes 2, 4, 6, 7, 9, 14, and 21. The remaining linkage paper compared the utility of several single-nucleotide polymorphism (SNP) and microsatellite marker maps for Monte Carlo Markov chain combined oligogenic segregation and linkage analysis, and analyzed one of the electrophysiological endophenotypes, ttth1, on chromosome 7. Linkage was found with all marker sets. The last paper compared the multipoint IBD information content of several SNP sets and the microsatellite set, and found that while all SNP sets examined contained more information than the microsatellite set, most of the information contained in the SNP sets was captured by a subset of the SNP markers with approximately 1-cM marker spacing. From these papers, we highlight three points: a 1-cM SNP map seems to capture most of the linkage information, so denser maps do not appear necessary; careful and appropriate use of covariates can aid linkage analysis; and sources of increased gene-sharing between relatives should be accounted for in analyses.

Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism.

BACKGROUND: Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated. RESULTS: Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12. CONCLUSION: These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets.

Identification of tag single-nucleotide polymorphisms in regions with varying linkage disequilibrium.

We compared seven different tagging single-nucleotide polymorphism (SNP) programs in 10 regions with varied amounts of linkage disequilibrium (LD) and physical distance. We used the Collaborative Studies on the Genetics of Alcoholism dataset, part of the Genetic Analysis Workshop 14. We show that in regions with moderate to strong LD these programs are relatively consistent, despite different parameters and methods. In addition, we compared the selected SNPs in a multipoint linkage analysis for one region with strong LD. As the number of selected SNPs increased, the LOD score, mean information content, and type I error also increased.

Candidate high myopia loci on chromosomes 18p and 12q do not play a major role in susceptibility to common myopia.

BACKGROUND: To determine whether previously reported loci predisposing to nonsyndromic high myopia show linkage to common myopia in pedigrees from two ethnic groups: Ashkenazi Jewish and Amish. We hypothesized that these high myopia loci might exhibit allelic heterogeneity and be responsible for moderate /mild or common myopia. METHODS: Cycloplegic and manifest refraction were performed on 38 Jewish and 40 Amish families. Individuals with at least -1.00 D in each meridian of both eyes were classified as myopic. Genomic DNA was genotyped with 12 markers on chromosomes 12q21-23 and 18p11.3. Parametric and nonparametric linkage analyses were conducted to determine whether susceptibility alleles at these loci are important in families with less severe, clinical forms of myopia. RESULTS: There was no strong evidence of linkage of common myopia to these candidate regions: all two-point and multipoint heterogeneity LOD scores were < 1.0 and non-parametric linkage p-values were > 0.01. However, one Amish family showed slight evidence of linkage (LOD>1.0) on 12q; another 3 Amish families each gave LOD >1.0 on 18p; and 3 Jewish families each gave LOD >1.0 on 12q. CONCLUSIONS: Significant evidence of linkage (LOD> 3) of myopia was not found on chromosome 18p or 12q loci in these families. These results suggest that these loci do not play a major role in the causation of common myopia in our families studied.

Results of a genome-wide linkage scan for stuttering.

We performed a linkage study of stuttering using 392 markers distributed across the genome in a series of 68 families identified in the general outbred population of North America and Europe. Standardized diagnosis was performed using recorded samples of both conversation and reading, in which stuttering dysfluencies were scored as percentage of dysfluent words and syllables. Analysis was first performed using non-parametric methods implemented in GENEHUNTER, where we obtained maximum statistical support for markers of chromosome 18, with a maximum NPL (Sall) of 1.51 at D18S976. The single largest pedigree within our sample (pedigree 0006) alone gave an NPL of 4.72 at D18S976. For fine mapping, we analyzed 18 markers on chromosome 18 across all families using ALLEGRO. Overall NPL (Srobdom) scores >5 were obtained with markers on 18p, and Z(lr) scores >/=2.5 on 18p and proximal 18q. Furthermore, pedigree 0006 alone gave an NPL (Srobdom) of 5.35. Overall our results suggest chromosome 18 may harbor a predisposing locus for this disorder, and additional genes may exist.

Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis.

Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.

An autosomal recessive form of bilateral frontoparietal polymicrogyria maps to chromosome 16q12.2-21.

Polymicrogyria is a cerebral cortical malformation that is grossly characterized by excessive cortical folding and microscopically characterized by abnormal cortical layering. Although polymicrogyria appears to have one or more genetic causes, no polymicrogyria loci have been identified. Here we describe the clinical and radiographic features of a new genetic form of polymicrogyria and localize the responsible gene. We studied two consanguineous Palestinian pedigrees with an autosomal recessive form of bilateral frontoparietal polymicrogyria (BFPP), using linkage analysis. Five affected children had moderate-to-severe mental retardation, developmental delay, and esotropia, and four of the five affected children developed seizures. Brain magnetic-resonance imaging revealed polymicrogyria that was most prominent in the frontal and parietal lobes but involved other cortical areas as well. A genomewide linkage screen revealed a single locus that was identical by descent in affected children in both families and showed a single disease-associated haplotype, suggesting a common founder mutation. The locus for BFPP maps to chromosome 16q12.2-21, with a minimal interval of 17 cM. For D16S514, the maximal pooled two-point LOD score was 3.98, and the maximal multipoint LOD score was 4.57. This study provides the first genetic evidence that BFPP is an autosomal recessive disorder and serves as a starting point for the identification of the responsible gene.