Examination of the Involvement of Cholinergic-Associated Genes in Nicotine Behaviors in European and African Americans.

INTRODUCTION: Cigarette smoking is a physiologically harmful habit. Nicotinic acetylcholine receptors (nAChRs) are bound by nicotine and upregulated in response to chronic exposure to nicotine. It is known that upregulation of these receptors is not due to a change in mRNA of these genes, however, more precise details on the process are still uncertain, with several plausible hypotheses describing how nAChRs are upregulated. We have manually curated a set of genes believed to play a role in nicotine-induced nAChR upregulation. Here, we test the hypothesis that these genes are associated with and contribute risk for nicotine dependence (ND) and the number of cigarettes smoked per day (CPD). METHODS: Studies with genotypic data on European and African Americans (EAs and AAs, respectively) were collected and a gene-based test was run to test for an association between each gene and ND and CPD. RESULTS: Although several novel genes were associated with CPD and ND at P < 0.05 in EAs and AAs, these associations did not survive correction for multiple testing. Previous associations between CHRNA3, CHRNA5, CHRNB4 and CPD in EAs were replicated. CONCLUSIONS: Our hypothesis-driven approach avoided many of the limitations inherent in pathway analyses and provided nominal evidence for association between cholinergic-related genes and nicotine behaviors. IMPLICATIONS: We evaluated the evidence for association between a manually curated set of genes and nicotine behaviors in European and African Americans. Although no genes were associated after multiple testing correction, this study has several strengths: by manually curating a set of genes we circumvented the limitations inherent in many pathway analyses and tested several genes that had not yet been examined in a human genetic study; gene-based tests are a useful way to test for association with a set of genes; and these genes were collected based on literature review and conversations with experts, highlighting the importance of scientific collaboration.

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study.

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (ß = 16.1, CI(ß) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (ß = 4.86,CI(ß) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest.

Whole-genome pathway analysis on 132,497 individuals identifies novel gene-sets associated with body mass index.

Whole genome pathway analysis is a powerful tool for the exploration of the combined effects of gene-sets within biological pathways. This study applied Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI). We used a discovery set composed of summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and an independent sample of 8,632 subjects to assess replication of significant pathways. We examined SNPs within nominally significant pathways using linear mixed models to estimate their contribution to overall BMI heritability. Six pathways replicated as having significant enrichment for association after correcting for multiple testing, including the previously unknown relationships between BMI and the Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway. Two non-overlapping sets of genes emerged from the six significant pathways. The clustering of shared genes based on previously identified protein-protein interactions listed in PubMed and OMIM supported the relatively independent biological effects of these two gene-sets. We estimate that the SNPs located in examined pathways explain ∼20% of the heritability for BMI that is tagged by common SNPs (3.35% of the 16.93% total).

Runs of homozygosity implicate autozygosity as a schizophrenia risk factor.

Autozygosity occurs when two chromosomal segments that are identical from a common ancestor are inherited from each parent. This occurs at high rates in the offspring of mates who are closely related (inbreeding), but also occurs at lower levels among the offspring of distantly related mates. Here, we use runs of homozygosity in genome-wide SNP data to estimate the proportion of the autosome that exists in autozygous tracts in 9,388 cases with schizophrenia and 12,456 controls. We estimate that the odds of schizophrenia increase by ~17% for every 1% increase in genome-wide autozygosity. This association is not due to one or a few regions, but results from many autozygous segments spread throughout the genome, and is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia. Such a bias towards recessivity suggests that alleles that increase the risk of schizophrenia have been selected against over evolutionary time.

Multiple testing in high-throughput sequence data: experiences from Group 8 of Genetic Analysis Workshop 17.

The use of high-throughput sequence data in genetic epidemiology allows the investigation of common and rare variants in the entire genome, thus increasing the amount of information and the potential number of statistical tests performed within one study. As a consequence, the problem of multiple testing may become even more pressing than in previous studies. As an important challenge, the exact number of statistical tests depends on the actual statistical method used. Furthermore, many statistical approaches for the analysis of sequence data require permutation. Thus it may be difficult to also use permutation to estimate correct type I error levels as in genome-wide association studies. In view of this, a separate group at Genetic Analysis Workshop 17 was formed with a focus on multiple testing. Here, we present the approaches used for the workshop. Apart from tackling the multiple testing problem, the new group focused on different issues. Some contributors developed and investigated modifications of existing collapsing methods. Others aimed at improving the identification of functional variants through a reduction and analysis of the underlying data dimensions. Two research groups investigated the overall accumulation of rare variation across the genome and its value in predicting phenotypes. Finally, other investigators left the path of traditional statistical analyses by reversing null and alternative hypotheses and by proposing a novel resampling method. We describe and discuss all these approaches.

Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk.

BACKGROUND: Traditional genome-wide association studies are generally limited in their ability explain a large portion of genetic risk for most common diseases. We sought to use both traditional GWAS methods, as well as more recently developed polygenic genome-wide analysis techniques to identify subsets of single-nucleotide polymorphisms (SNPs) that may be involved in risk of cardiovascular disease, as well as estimate the heritability explained by common SNPs. METHODS: Using data from the Framingham SNP Health Association Resource (SHARe), three complimentary methods were applied to examine the genetic factors associated with the Framingham Risk Score, a widely accepted indicator of underlying cardiovascular disease risk. The first method adopted a traditional GWAS approach - independently testing each SNP for association with the Framingham Risk Score. The second two approaches involved polygenic methods with the intention of providing estimates of aggregate genetic risk and heritability. RESULTS: While no SNPs were independently associated with the Framingham Risk Score based on the results of the traditional GWAS analysis, we were able to identify cardiovascular disease-related SNPs as reported by previous studies. A predictive polygenic analysis was only able to explain approximately 1% of the genetic variance when predicting the 10-year risk of general cardiovascular disease. However, 20% to 30% of the variation in the Framingham Risk Score was explained using a recently developed method that considers the joint effect of all SNPs simultaneously. CONCLUSION: The results of this study imply that common SNPs explain a large amount of the variation in the Framingham Risk Score and suggest that future, better-powered genome-wide association studies, possibly informed by knowledge of gene-pathways, will uncover more risk variants that will help to elucidate the genetic architecture of cardiovascular disease.

Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms.

BACKGROUND: A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers. METHOD: We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity. RESULTS: Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD). CONCLUSION: Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.

Mutational load analysis of unrelated individuals.

Evolutionary genetic models predict that the cumulative effect of rare deleterious mutations across the genome-known as mutational load burden-increases the susceptibility to complex disease. To test the mutational load burden hypothesis, we adopted a two-tiered approach: assessing the impact of whole-exome minor allele load burden and then conducting individual-gene screening. For our primary analysis, we examined various minor allele frequency (MAF) thresholds and weighting schemes to examine the overall effect of minor allele load on affection status. We found a consistent association between minor allele load and affection status, but this effect did not markedly increase within rare and/or functional single-nucleotide polymorphisms (SNPs). Our follow-up analysis considered minor allele load in individual genes to see whether only one or a few genes were driving the overall effect. Examining our most significant result-minor allele load of nonsynonymous SNPs with MAF < 2.4%-we detected no significantly associated genes after Bonferroni correction for multiple testing. After moderately significant genes (p < 0.05) were removed, the overall effect of rare nonsynonymous allele load remained significant. Overall, we did not find clear support for mutational load burden on affection status; however, these results are ultimately dependent on and limited by the nature of the Genetic Analysis Workshop 17 simulation.