Background Cigarette smoking is a major risk factor for COPD and COPD severity. Previous genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) associated with the number of cigarettes smoked per day (CPD) and a Dopamine Beta-Hydroxylase (DBH) locus associated with smoking cessation in multiple populations. Objective To identify SNPs associated with lifetime average and current CPD, age at smoking initiation, and smoking cessation in COPD subjects. Methods GWAS were conducted in 4 independent cohorts encompassing 3,441 ever-smoking COPD subjects (GOLD stage II or higher). Untyped SNPs were imputed using HapMap (phase II) panel. Results from all cohorts were meta-analyzed. Results Several SNPs near the HLA region on chromosome 6p21 and in an intergenic region on chromosome 2q21 showed associations with age at smoking initiation, both with the lowest p=2×10−7. No SNPs were associated with lifetime average CPD, current CPD or smoking cessation with p<10−6. Nominally significant associations with candidate SNPs within alpha-nicotinic acetylcholine receptors 3/5 (CHRNA3/CHRNA5; e.g. p=0.00011 for SNP rs1051730) and Cytochrome P450 2A6 (CYP2A6; e.g. p=2.78×10−5 for a nonsynonymous SNP rs1801272) regions were observed for lifetime average CPD, however only CYP2A6 showed evidence of significant association with current CPD. A candidate SNP (rs3025343) in the DBH was significantly (p=0.015) associated with smoking cessation. Conclusion We identified two candidate regions associated with age at smoking initiation in COPD subjects. Associations of CHRNA3/CHRNA5 and CYP2A6 loci with CPD and DBH with smoking cessation are also likely of importance in the smoking behaviors of COPD patients.

Dyslipidemia and obesity are especially prevalent in populations with Amerindian backgrounds, such as Mexican–Americans, which predispose these populations to cardiovascular disease. Here we design an approach, known as the cross-population allele screen (CPAS), which we conduct prior to a genome-wide association study (GWAS) in 19,273 Europeans and Mexicans, in order to identify Amerindian risk genes in Mexicans. Utilizing CPAS to restrict the GWAS input variants to only those differing in frequency between the two populations, we identify novel Amerindian lipid genes, receptor-related orphan receptor alpha (RORA) and salt-inducible kinase 3 (SIK3), and three loci previously unassociated with dyslipidemia or obesity. We also detect lipoprotein lipase (LPL) and apolipoprotein A5 (APOA5) harbouring specific Amerindian signatures of risk variants and haplotypes. Notably, we observe that SIK3 and one novel lipid locus underwent positive selection in Mexicans. Furthermore, after a high-fat meal, the SIK3 risk variant carriers display high triglyceride levels. These findings suggest that Amerindian-specific genetic architecture leads to a higher incidence of dyslipidemia and obesity in modern Mexicans.

Tissue microarrays (TMAs) represent a powerful method for undertaking large-scale tissue-based biomarker studies. While TMAs offer several advantages, there are a number of issues specific to their use which need to be considered when employing this method. Given the investment in TMA-based research, guidance on design and execution of experiments will be of benefit and should help researchers new to TMA-based studies to avoid known pitfalls. Furthermore, a consensus on quality standards for TMA-based experiments should improve the robustness and reproducibility of studies, thereby increasing the likelihood of identifying clinically useful biomarkers. In order to address these issues, the National Cancer Research Institute Biomarker and Imaging Clinical Studies Group organized a 1-day TMA workshop held in Nottingham in May 2012. The document herein summarizes the conclusions from the workshop. It includes guidance and considerations on all aspects of TMA-based research, including the pre-analytical stages of experimental design, the analytical stages of data acquisition, and the postanalytical stages of data analysis. A checklist is presented which can be used both for planning a TMA experiment and interpreting the results of such an experiment. For studies of cancer biomarkers, this checklist could be used as a supplement to the REMARK guidelines.

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Current American Thoracic Society (ATS) standards promote the use of race and ethnicity-specific reference equations for pulmonary function test (PFT) interpretation. There is rising concern that the use of race and ethnicity in PFT interpretation contributes to a false view of fixed differences between races and may mask the effects of differential exposures. This use of race and ethnicity may contribute to health disparities by norming differences in pulmonary function. In the United States and globally, race serves as a social construct that is based on appearance and reflects social values, structures, and practices. Classification of people into racial and ethnic groups differs geographically and temporally. These considerations challenge the notion that racial and ethnic categories have biological meaning and question the use of race in PFT interpretation. The ATS convened a diverse group of clinicians and investigators for a workshop in 2021 to evaluate the use of race and ethnicity in PFT interpretation. Review of evidence published since then that challenges current practice and continued discussion concluded with a recommendation to replace race and ethnicity-specific equations with race-neutral average reference equations, which must be accompanied with a broader re-evaluation of how PFTs are used to make clinical, employment, and insurance decisions. There was also a call to engage key stakeholders not represented in this workshop and a statement of caution regarding the uncertain effects and potential harms of this change. Other recommendations include continued research and education to understand the impact of the change, to improve the evidence for the use of PFTs in general, and to identify modifiable risk factors for reduced pulmonary function.

Inferring the ancestry at each locus in the genome of recently admixed individuals (e.g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants (CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants (sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs.

BackgroundAlthough genome-wide association studies have successfully identified thousands of variants associated to complex traits, these variants only explain a small amount of the entire heritability of the trait. Gene-gene interactions have been proposed as a source to explain a significant percentage of the missing heritability. However, detecting gene-gene interactions has proven to be very difficult due to computational and statistical challenges. The vast number of possible interactions that can be tested induces very stringent multiple hypotheses corrections that limit the power of detection. These issues have been mostly highlighted for the identification of pairwise effects and are even more challenging when addressing higher order interaction effects. In this work we explore the use of local ancestry in recently admixed individuals to find signals of gene-gene interaction on human traits and diseases.ResultsWe introduce statistical methods that leverage the correlation between local ancestry and the hidden unknown causal variants to find distant gene-gene interactions. We show that the power of this test increases with the number of causal variants per locus and the degree of differentiation of these variants between the ancestral populations. Overall, our simulations confirm that local ancestry can be used to detect gene-gene interactions, solving the computational bottleneck. When compared to a single nucleotide polymorphism (SNP)-based interaction screening of the same sample size, the power of our test was lower on all settings we considered. However, accounting for the dramatic increase in sample size that can be achieve when genotyping only a set of ancestry informative markers instead of the whole genome, we observe substantial gain in power in several scenarios.ConclusionLocal ancestry-based interaction tests offer a new path to the detection of gene-gene interaction effects. It would be particularly useful in scenarios where multiple differentiated variants at the interacting loci act in a synergistic manner.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0283-z) contains supplementary material, which is available to authorized users.

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. Replication in GWAS studies has been well-studied in the context of winner's curse, which is the inflation of effect size estimates for significant variants in a study.Multiple methods have been proposed to correct for the effects of winner's curse. However, winner's curse is often not sufficient to explain lack of replication. Another reason why studies fail to replicate is that there are fundamental differences between the discovery and replication studies. A confounding factor can create the appearance of a significant finding while actually being an artifact that will not replicate in future studies. We propose a statistical framework that utilizes GWAS replication studies to model winner's curse and study-specific heterogeneity due to confounders and correct for these effects. We show through simulations and application to 100 human GWAS data sets that modeling both winner's curse and study-specific heterogeneity explains observed patterns of replication in GWAS studies better than modeling winner's curse alone.