Shared Genetic Etiology of Obesity-Related Traits and Barrett's Esophagus/Adenocarcinoma: Insights from Genome-Wide Association Studies

BACKGROUND

Obesity is a major risk factor for esophageal adenocarcinoma (EA) and its precursor Barrett's esophagus (BE). Research suggests that individuals with high genetic risk to obesity have a higher BE/EA risk. To facilitate understanding of biological factors that lead to progression from BE to EA, the present study investigated the shared genetic background of BE/EA and obesity-related traits.

METHODS

Cross-trait linkage disequilibrium score regression was applied to summary statistics from genome-wide association meta-analyses on BE/EA and on obesity traits. Body mass index (BMI) was used as a proxy for general obesity, and waist-to-hip ratio (WHR) for abdominal obesity. For single marker analyses, all genome-wide significant risk alleles for BMI and WHR were compared with summary statistics of the BE/EA meta-analyses.

RESULTS

Sex-combined analyses revealed a significant genetic correlation between BMI and BE/EA (r_g = 0.13, P = 2 × 10^-04) and a r_g of 0.12 between WHR and BE/EA (P = 1 × 10^-02). Sex-specific analyses revealed a pronounced genetic correlation between BMI and EA in females (r_g = 0.17, P = 1.2 × 10^-03), and WHR and EA in males (r_g = 0.18, P = 1.51 × 10^-02). On the single marker level, significant enrichment of concordant effects was observed for BMI and BE/EA risk variants (P = 8.45 × 10^-03) and WHR and BE/EA risk variants (P = 2 × 10^-02).

CONCLUSIONS

Our study provides evidence for sex-specific genetic correlations that might reflect specific biological mecha-nisms. The data demonstrate that shared genetic factors are particularly relevant in progression from BE to EA.

IMPACT

Our study quantifies the genetic correlation between BE/EA and obesity. Further research is now warranted to elucidate these effects and to understand the shared pathophysiology.

Identification of genetic outliers due to sub-structure and cryptic relationships

Motivation

In order to minimize the effects of genetic confounding on the analysis of high-throughput genetic association studies, e.g. (whole-genome) sequencing (WGS) studies, genome-wide association studies (GWAS), etc., we propose a general framework to assess and to test formally for genetic heterogeneity among study subjects. As the approach fully utilizes the recent ancestor information captured by rare variants, it is especially powerful in WGS studies. Even for relatively moderate sample sizes, the proposed testing framework is able to identify study subjects that are genetically too similar, e.g. cryptic relationships, or that are genetically too different, e.g. population substructure. The approach is computationally fast, enabling the application to whole-genome sequencing data, and straightforward to implement.

Results

Simulation studies illustrate the overall performance of our approach. In an application to the 1000 Genomes Project, we outline an analysis/cleaning pipeline that utilizes our approach to formally assess whether study subjects are related and whether population substructure is present. In the analysis of the 1000 Genomes Project data, our approach revealed subjects that are most likely related, but had previously passed standard qc-filters.

Availability and Implementation

An implementation of our method, Similarity Test for Estimating Genetic Outliers (STEGO), is available in the R package stego from Github at https://github.com/dschlauch/stego .

Contact

dschlauch@fas.harvard.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification of shared risk loci and pathways for bipolar disorder and schizophrenia

Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.

Investigation of the role of TCF4 rare sequence variants in schizophrenia

Transcription factor 4 (TCF4) is one of the most robust of all reported schizophrenia risk loci and is supported by several genetic and functional lines of evidence. While numerous studies have implicated common genetic variation at TCF4 in schizophrenia risk, the role of rare, small-sized variants at this locus-such as single nucleotide variants and short indels which are below the resolution of chip-based arrays requires further exploration. The aim of the present study was to investigate the association between rare TCF4 sequence variants and schizophrenia. Exon-targeted resequencing was performed in 190 German schizophrenia patients. Six rare variants at the coding exons and flanking sequences of the TCF4 gene were identified, including two missense variants and one splice site variant. These six variants were then pooled with nine additional rare variants identified in 379 European participants of the 1000 Genomes Project, and all 15 variants were genotyped in an independent German sample (n = 1,808 patients; n = 2,261 controls). These data were then analyzed using six statistical methods developed for the association analysis of rare variants. No significant association (P < 0.05) was found. However, the results from our association and power analyses suggest that further research into the possible involvement of rare TCF4 sequence variants in schizophrenia risk is warranted by the assessment of larger cohorts with higher statistical power to identify rare variant associations.

A novel method for detecting association between DNA methylation and diseases using spatial information

DNA methylation may represent an important contributor to the missing heritability described in complex trait genetics. However, technology to measure DNA methylation has outpaced statistical methods for analysis. Taking advantage of the recent finding that methylated sites cluster together, we propose a Spatial Clustering Method (SCM) to detect differentially methylated regions (DMRs) in the genome in case and control studies using spatial location information. This new method compares the distribution of distances in cases and controls between DNA methylation marks in the genomic region of interest. A statistic is computed based on these distances. Proper type I error rate is maintained and statistical significance is evaluated using permutation test. The effectiveness of the SCM we propose is evaluated by a simulation study. By simulating a simple disease model, we demonstrate that SCM has good power to detect DMRs associated with the disease. Finally, we applied the SCM to an exploratory analysis of chromosome 14 from a colorectal cancer data set and identified statistically significant genomic regions. Identification of these regions should lead to a better understanding of methylated sites and their contribution to disease. The SCM can be used as a reliable statistical method for the identification of DMRs associated with disease states in exploratory epigenetic analyses.

Nonsyndromic cleft lip with or without cleft palate: Increased burden of rare variants within Gremlin-1, a component of the bone morphogenetic protein 4 pathway

BACKGROUND

The genes Gremlin-1 (GREM1) and Noggin (NOG) are components of the bone morphogenetic protein 4 pathway, which has been implicated in craniofacial development. Both genes map to recently identified susceptibility loci (chromosomal region 15q13, 17q22) for nonsyndromic cleft lip with or without cleft palate (nsCL/P). The aim of the present study was to determine whether rare variants in either gene are implicated in nsCL/P etiology.

METHODS

The complete coding regions, untranslated regions, and splice sites of GREM1 and NOG were sequenced in 96 nsCL/P patients and 96 controls of Central European ethnicity. Three burden and four nonburden tests were performed. Statistically significant results were followed up in a second case-control sample (n = 96, respectively). For rare variants observed in cases, segregation analyses were performed.

RESULTS

In NOG, four rare sequence variants (minor allele frequency < 1%) were identified. Here, burden and nonburden analyses generated nonsignificant results. In GREM1, 33 variants were identified, 15 of which were rare. Of these, five were novel. Significant p-values were generated in three nonburden analyses. Segregation analyses revealed incomplete penetrance for all variants investigated.

CONCLUSION

Our study did not provide support for NOG being the causal gene at 17q22. However, the observation of a significant excess of rare variants in GREM1 supports the hypothesis that this is the causal gene at chr. 15q13. Because no single causal variant was identified, future sequencing analyses of GREM1 should involve larger samples and the investigation of regulatory elements.

On the simultaneous association analysis of large genomic regions: a massive multi-locus association test

MOTIVATION

For samples of unrelated individuals, we propose a general analysis framework in which hundred thousands of genetic loci can be tested simultaneously for association with complex phenotypes. The approach is built on spatial-clustering methodology, assuming that genetic loci that are associated with the target phenotype cluster in certain genomic regions. In contrast to standard methodology for multilocus analysis, which has focused on the dimension reduction of the data, our multilocus association-clustering test profits from the availability of large numbers of genetic loci by detecting clusters of loci that are associated with the phenotype.

RESULTS

The approach is computationally fast and powerful, enabling the simultaneous association testing of large genomic regions. Even the entire genome or certain chromosomes can be tested simultaneously. Using simulation studies, the properties of the approach are evaluated. In an application to a genome-wide association study for chronic obstructive pulmonary disease, we illustrate the practical relevance of the proposed method by simultaneously testing all genotyped loci of the genome-wide association study and by testing each chromosome individually. Our findings suggest that statistical methodology that incorporates spatial-clustering information will be especially useful in whole-genome sequencing studies in which millions or billions of base pairs are recorded and grouped by genomic regions or genes, and are tested jointly for association.

AVAILABILITY AND IMPLEMENTATION

Implementation of the approach is available upon request.

Resequencing of VAX1 in patients with nonsyndromic cleft lip with or without cleft palate

BACKGROUND

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is one of the most common of all congenital anomalies, and has a multifactorial etiology involving both environmental and genetic factors. Recent genome-wide association studies (GWAS) identified strong association between a locus on chromosome 10q25.3 and NSCL/P in European samples. One gene at 10q25.3, the ventral anterior homeobox 1 (VAX1) gene, is considered a strong candidate gene for craniofacial malformations. The purpose of the present study was to provide further evidence that VAX1 is the causal gene at the 10q25.3 locus through identification of an excess of rare mutations in patients with NSCL/P.

METHODS

The 5'UTR, complete coding regions, and adjacent splice sites of the two known VAX1 isoforms were sequenced in 384 patients with NSCL/P and 384 controls of Central European descent. Observed variants were investigated with respect to familial cosegregation or de novo occurrence, and in silico analyses were performed to identify putative effects on the transcript or protein level.

RESULTS

Eighteen single-base variants were found, 15 of them rare and previously unreported. In the long VAX1 isoform, predicted functionally relevant variants were observed more often in NSCL/P cases, although this difference was not significant (p = 0.17). Analysis of family members demonstrated incomplete cosegregation in most pedigrees.

CONCLUSION

Our data do not support the hypothesis that highly penetrant rare variants in VAX1 are a cause of NSCL/P. To determine whether VAX1 is the causative gene at 10q25.3 further research, in particular into the biologic function of its long isoform, is warranted. Birth Defects Research (Part A), 2012.

'Location, Location, Location': a spatial approach for rare variant analysis and an application to a study on non-syndromic cleft lip with or without cleft palate

Motivation: For the analysis of rare variants in sequence data, numerous approaches have been suggested. Fixed and flexible threshold approaches collapse the rare variant information of a genomic region into a test statistic with reduced dimensionality. Alternatively, the rare variant information can be combined in statistical frameworks that are based on suitable regression models, machine learning, etc. Although the existing approaches provide powerful tests that can incorporate information on allele frequencies and prior biological knowledge, differences in the spatial clustering of rare variants between cases and controls cannot be incorporated. Based on the assumption that deleterious variants and protective variants cluster or occur in different parts of the genomic region of interest, we propose a testing strategy for rare variants that builds on spatial cluster methodology and that guides the identification of the biological relevant segments of the region. Our approach does not require any assumption about the directions of the genetic effects.

Results: In simulation studies, we assess the power of the clustering approach and compare it with existing methodology. Our simulation results suggest that the clustering approach for rare variants is well powered, even in situations that are ideal for standard methods. The efficiency of our spatial clustering approach is not affected by the presence of rare variants that have opposite effect size directions. An application to a sequencing study for non-syndromic cleft lip with or without cleft palate (NSCL/P) demonstrates its practical relevance. The proposed testing strategy is applied to a genomic region on chromosome 15q13.3 that was implicated in NSCL/P etiology in a previous genome-wide association study, and its results are compared with standard approaches.

Availability: Source code and documentation for the implementation in R will be provided online. Currently, the R-implementation only supports genotype data. We currently are working on an extension for VCF files.

Contact:heide.fier@googlemail.com