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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 (rg = 0.13, P = 2 × 10−04) and a rg 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 (rg = 0.17, P = 1.2 × 10−03), and WHR and EA in males (rg = 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.

For family-based association studies, Horvath et al. proposed an algorithm for the association analysis between haplotypes and arbitrary phenotypes when the phase of the haplotypes is unknown, that is, genotype data is given. Their approach to haplotype analysis maintains the original features of the TDT/FBAT-approach, that is, complete robustness against genetic confounding and misspecification of the phenotype. The algorithm has been implemented in the FBAT and PBAT software package and has been used in numerous substantive manuscripts. Here, we propose a simplification of the original algorithm that maintains the original approach but reduces the computational burden of the approach substantially and gives valuable insights regarding the conditional distribution. With the modified algorithm, the application to whole-genome sequencing (WGS) studies becomes feasible; for example, in sliding window approaches or spatial-clustering approaches. The reduction of the computational burden that our modification provides is especially dramatic when both parental genotypes are missing. For example, for eight variants and 441 nuclear families with mostly offspring-only families, in a WGS study at the APOE locus, the running time decreased from approximately 21 hr for the original algorithm to 0.11 sec after our modification.

SUMMARY To quantify polygenic effects, i.e. undetected genetic effects, in large-scale association studies, we propose a generalized estimating equation (GEE) based estimation framework. We develop a marginal model for single-variant association test statistics of complex diseases that generalizes existing approaches such as LD Score regression and that is applicable to population-based designs, to family-based designs or to arbitrary combinations of both. We extend the standard GEE approach so that the parameters of the proposed marginal model can be estimated based on working-correlation/linkage-disequilibrium (LD) matrices from external reference panels. Our method achieves substantial efficiency gains over standard approaches, while it is robust against misspecification of the LD structure, i.e. the LD structure of the reference panel can differ substantially from the true LD structure in the study population. In simulation studies and in applications to population-based and family-based studies, we illustrate the features of the proposed GEE framework. Our results suggest that our approach can be up to 100% more efficient than existing methodology.

For the association analysis of whole-genome sequencing (WGS) studies, we propose an efficient and fast spatial-clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed-window approaches, our approach is likely to group nearby variants together. Given existing biological evidence that disease-associated mutations tend to physically cluster in specific regions along the chromosome, the identification of meaningful groups of nearby located variants could thus lead to a potential power gain for association analysis. Our algorithm defines consecutive genomic regions based on the physical positions of the variants, assuming an inhomogeneous Poisson process and groups together nearby variants. As parameters are estimated locally, the algorithm takes the differing variant density along the chromosome into account and provides locally optimal partitioning of variants into consecutive regions. An R-implementation of the algorithm is provided. We discuss the theoretical advances of our algorithm compared to existing, window-based approaches and show the performance and advantage of our introduced algorithm in a simulation study and by an application to Alzheimer's disease WGS data. Our analysis identifies a region in the ITGB3 gene that potentially harbors disease susceptibility loci for Alzheimer's disease. The region-based association signal of ITGB3 replicates in an independent data set and achieves formally genome-wide significance. Software Implementation: An implementation of the algorithm in R is available at: https://github.com/heidefier/cluster_wgs_data.

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. Supplementary information Supplementary data are available at Bioinformatics online.

Genome-wide association studies have identified common variants associated with chronic obstructive pulmonary disease (COPD). Whole-genome sequencing (WGS) offers comprehensive coverage of the entire genome, as compared with genotyping arrays or exome sequencing. We hypothesized that WGS in subjects with severe COPD and smoking control subjects with normal pulmonary function would allow us to identify novel genetic determinants of COPD. We sequenced 821 patients with severe COPD and 973 control subjects from the COPDGene and Boston Early-Onset COPD studies, including both non-Hispanic white and African American individuals. We performed single-variant and grouped-variant analyses, and in addition, we assessed the overlap of variants between sequencing- and array-based imputation. Our most significantly associated variant was in a known region near HHIP (combined P = 1.6 × 10(−9)); additional variants approaching genome-wide significance included previously described regions in CHRNA5, TNS1, and SERPINA6/SERPINA1 (the latter in African American individuals). None of our associations were clearly driven by rare variants, and we found minimal evidence of replication of genes identified by previously reported smaller sequencing studies. With WGS, we identified more than 20 million new variants, not seen with imputation, including more than 10,000 of potential importance in previously identified COPD genome-wide association study regions. WGS in severe COPD identifies a large number of potentially important functional variants, with the strongest associations being in known COPD risk loci, including HHIP and SERPINA1. Larger sample sizes will be needed to identify associated variants in novel regions of the genome.

Male-pattern baldness (MPB) is a common and highly heritable trait characterized by androgen-dependent, progressive hair loss from the scalp. Here, we carry out the largest GWAS meta-analysis of MPB to date, comprising 10,846 early-onset cases and 11,672 controls from eight independent cohorts. We identify 63 MPB-associated loci (P, Male-pattern baldness is a common condition in which hair is progressively lost from the scalp. Here, the authors find 23 new genetic variants associated with this condition and suggest that it is not an isolated trait but may share an underlying biological basis with various diseases.

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. Contact: daq412@mail.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Population stratification is one of the major sources of confounding in genetic association studies, potentially causing false-positive and false-negative results. Here, we present a novel approach for the identification of population substructure in high-density genotyping data/next generation sequencing data. The approach exploits the co-appearances of rare genetic variants in individuals. The method can be applied to all available genetic loci and is computationally fast. Using sequencing data from the 1000 Genomes Project, the features of the approach are illustrated and compared to existing methodology (i.e. EIGENSTRAT). We examine the effects of different cutoffs for the minor allele frequency on the performance of the approach. We find that our approach works particularly well for genetic loci with very small minor allele frequencies. The results suggest that the inclusion of rare-variant data/sequencing data in our approach provides a much higher resolution picture of population substructure than it can be obtained with existing methodology. Furthermore, in simulation studies, we find scenarios where our method was able to control the type 1 error more precisely and showed higher power. Availability and implementation: Contact: dmitry.prokopenko@uni-bonn.de Supplementary information: Supplementary data are available at Bioinformatics online.

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. © 2012 Wiley Periodicals, Inc.