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Background Worldwide, respiratory syncytial virus (RSV) infections are among the most common causes of infant hospitalization. Host genetic factors influencing the risk and severity of RSV infection are not well known. Methods We conducted a genome-wide association study (GWAS) to investigate single-nucleotide polymorphisms (SNPs) associated with severe RSV infections using a nested case-control design based on 2 Danish cohorts. We compared SNPs from 1786 children hospitalized with RSV to 45 060 controls without an RSV-coded hospitalization. We performed gene-based testing, tissue enrichment, gene-set enrichment, and a meta-analysis of the 2 cohorts. Finally, an analysis of potential associations between the severity of RSV infection and genetic markers was performed. Results We did not detect any significant genome-wide associations between SNPs and RSV infection or the severity of RSV. We did find potential loci associated with RSV infections on chromosome 5 in 1 cohort but failed to replicate any signals in both cohorts. Conclusions Despite being the largest GWAS of severe RSV infection, we did not detect any genome-wide significant loci. This may be an indication of a lack of power or an absence of signal. Future studies might include mild illness and need to be larger to detect any significant associations. [ABSTRACT FROM AUTHOR]

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene–Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene–environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB. [ABSTRACT FROM AUTHOR]

Background The clinical heterogeneity in major depressive disorder (MDD), variable treatment response, and conflicting findings limit the ability of genomics toward the discovery of evidence-based diagnosis and treatment regimen. This study attempts to curate all genetic association findings to evaluate potential variants for clinical translation. Methods We systematically reviewed all candidates and genome-wide association studies for both MDD susceptibility and antidepressant response, independently, using MEDLINE, particularly to identify replicated findings. These variants were evaluated for functional consequences using different in silico tools and further estimated their diagnostic predictability by calculating positive predictive values. Results A total of 217 significantly associated studies comprising 1200 variants across 545 genes and 128 studies including 921 variants across 412 genes were included with MDD susceptibility and antidepressant response, respectively. Although the majority of associations were confirmed by a single study, we identified 31 and 18 replicated variants (in at least 2 studies) for MDD and antidepressant response. Functional annotation of these 31 variants predicted 20% coding variants as deleterious/damaging and 80.6% variants with regulatory effect. Similarly, the response-related 18 variants revealed 25% coding variant as damaging and 88.2% with substantial regulatory potential. Finally, we could calculate the diagnostic predictability of 19 and 5 variants whose positive predictive values ranges from 0.49 to 0.66 for MDD and 0.36 to 0.66 for response. Conclusions The replicated variants presented in our data are promising for disease diagnosis and improved response outcomes. Although these quantitative assessment measures are solely directive of available observational evidence, robust homogenous validation studies are required to strengthen these variants for molecular diagnostic application. [ABSTRACT FROM AUTHOR]

Common genetic association models for structured populations, including principal component analysis (PCA) and linear mixed-effects models (LMMs), model the correlation structure between individuals using population kinship matrices, also known as genetic relatedness matrices. However, the most common kinship estimators can have severe biases that were only recently determined. Here we characterize the effect of these kinship biases on genetic association. We employ a large simulated admixed family and genotypes from the 1000 Genomes Project, both with simulated traits, to evaluate key kinship estimators. Remarkably, we find practically invariant association statistics for kinship matrices of different bias types (matching all other features). We then prove using statistical theory and linear algebra that LMM association tests are invariant to these kinship biases, and PCA approximately so. Our proof shows that the intercept and relatedness effect coefficients compensate for the kinship bias, an argument that extends to generalized linear models. As a corollary, association testing is also invariant to changing the reference ancestral population of the kinship matrix. Lastly, we observed that all kinship estimators, except for popkin ratio-of-means, can give improper non-positive semidefinite matrices, which can be problematic although some LMMs handle them surprisingly well, and condition numbers can be used to choose kinship estimators. Overall, we find that existing association studies are robust to kinship estimation bias, and our calculations may help improve association methods by taking advantage of this unexpected robustness, as well as help determine the effects of kinship bias in related problems. [ABSTRACT FROM AUTHOR]

Objective: Large amounts of health data are becoming available for biomedical research. Synthesizing information across databases may capture more comprehensive pictures of patient health and enable novel research studies. When no gold standard mappings between patient records are available, researchers may probabilistically link records from separate databases and analyze the linked data. However, previous linked data inference methods are constrained to certain linkage settings and exhibit low power. Here, we present ATLAS, an automated, flexible, and robust association testing algorithm for probabilistically linked data.Materials and Methods: Missing variables are imputed at various thresholds using a weighted average method that propagates uncertainty from probabilistic linkage. Next, estimated effect sizes are obtained using a generalized linear model. ATLAS then conducts the threshold combination test by optimally combining P values obtained from data imputed at varying thresholds using Fisher's method and perturbation resampling.Results: In simulations, ATLAS controls for type I error and exhibits high power compared to previous methods. In a real-world genetic association study, meta-analysis of ATLAS-enabled analyses on a linked cohort with analyses using an existing cohort yielded additional significant associations between rheumatoid arthritis genetic risk score and laboratory biomarkers.Discussion: Weighted average imputation weathers false matches and increases contribution of true matches to mitigate linkage error-induced bias. The threshold combination test avoids arbitrarily choosing a threshold to rule a match, thus automating linked data-enabled analyses and preserving power.Conclusion: ATLAS promises to enable novel and powerful research studies using linked data to capitalize on all available data sources. [ABSTRACT FROM AUTHOR]

Background Bloodstream infection and sepsis are major causes of health loss worldwide, and it is important to identify patients at risk of developing and dying from these conditions. The single-nucleotide polymorphism most strongly associated with sepsis mortality is FER rs4957796. However, it is not known how this variant is associated with bloodstream infection incidence and mortality. Methods We used prospective data from 1995–2017 from the population-based HUNT Study. Genotypes were ascertained from blood samples, and additional genotypes were imputed. Information on bloodstream infection and diagnosis codes at hospitalization were collected through record linkage with all hospitals in the area. Results A total of 69 294 patients were included. Patients with the rs4957796 CC genotype had an increased risk of developing a bloodstream infection compared with the TT genotype (hazard ratio [HR], 1.20; 95% confidence interval [CI], 1.00–1.43). However, there was a protective additive effect of the C allele in terms of mortality in the total study population (HR, 0.77; 95% CI,.64–.92 per copy of the C allele) and among bloodstream infection patients (odds ratio, 0.70; 95% CI,.58–.85 per copy of the C allele). The results did not appear to be affected by selection bias. Conclusions The rs4957796 CC genotype was associated with an increased risk of contracting a bloodstream infection but with a reduced risk of dying from one. The latter finding is in line with studies of sepsis case fatality, while the former expands our understanding of the immunoregulatory role of this polymorphism. [ABSTRACT FROM AUTHOR]

With the advent of next-generation sequencing, investigators have access to higher quality sequencing data. However, to sequence all samples in a study using next generation sequencing can still be prohibitively expensive. One potential remedy could be to combine next generation sequencing data from cases with publicly available sequencing data for controls, but there could be a systematic difference in quality of sequenced data, such as sequencing depths, between sequenced study cases and publicly available controls. We propose a regression calibration (RC)-based method and a maximum-likelihood method for conducting an association study with such a combined sample by accounting for differential sequencing errors between cases and controls. The methods allow for adjusting for covariates, such as population stratification as confounders. Both methods control type I error and have comparable power to analysis conducted using the true genotype with sufficiently high but different sequencing depths. We show that the RC method allows for analysis using naive variance estimate (closely approximates true variance in practice) and standard software under certain circumstances. We evaluate the performance of the proposed methods using simulation studies and apply our methods to a combined data set of exome sequenced acute lung injury cases and healthy controls from the 1000 Genomes project. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) is the leading cause of blindness in ageing societies, triggered by both environmental and genetic factors. The strongest genetic signal for AMD with odds ratios of up to 2.8 per adverse allele was found previously over a chromosomal region in 10q26 harboring two genes, ARMS2 and HTRA1, although with little knowledge as to which gene or genetic variation is functionally relevant to AMD pathology. In this study, we analyzed rare recombinant haplotypes in 16,144 AMD cases and 17,832 controls from the International AMD Genomics Consortium and identified variants in ARMS2 but not HTRA1 to exclusively carry the AMD risk with P-values between 1.0 × 10-773 and 6.7 × 10-5. This now allows prioritization of the gene of interest for subsequent functional studies. [ABSTRACT FROM AUTHOR]

Meta-analysis has become a popular tool for genetic association studies to combine different genetic studies. A key challenge in meta-analysis is heterogeneity, or the differences in effect sizes between studies. Heterogeneity complicates the interpretation of meta-analyses. In this paper, we describe ForestPMPlot, a flexible visualization tool for analyzing studies included in a meta-analysis. The main feature of the tool is visualizing the differences in the effect sizes of the studies to understand why the studies exhibit heterogeneity for a particular phenotype and locus pair under different conditions. We show the application of this tool to interpret a meta-analysis of 17 mouse studies, and to interpret a multi-tissue eQTL study. [ABSTRACT FROM AUTHOR]

In recent years, the use of complex sampling (e.g., stratified multistage cluster sampling) has become more common in population-based case-control or cross-sectional studies for the selection of study subjects in genetic association studies. This has been prompted by the usual advantages of conducting complex sampling, such as time and cost efficiency. More important, proper complex sample designs can obtain representative samples from the target population and thus avoid the biased selection of controls and/or cases. However, two complications--differential population weights and intracluster correlation--are induced by the complex sampling. Although most single nucleotide polymorphism (SNP)-based association studies with complex sampling account for these two complications, many recent haplotype-based genetic association studies with complex sampling tend to ignore them in the estimation of haplotype frequencies, regression coefficients, or both, which can lead to invalid inferences. Therefore, there is a need to develop statistical methods for taking into account the complications induced by complex sampling in haplotype-based association studies with complex sample designs. In this paper, we propose methods that properly account for complex sample designs. Our methods are evaluated by simulation studies and illustrated using data from the U.S. Kidney Cancer Study and the Third National Health and Nutrition Examination Survey (NHANES III). [ABSTRACT FROM AUTHOR]

Host genetic variations may influence a changing profile of biochemical markers and outcome in patients with trauma/injury. The objective of this study was to assess clinical associations of single nucleotide polymorphisms (SNPs) in the genes of cytokines in critically ill patients. A total of 430 patients were genotyped for SNPs in the genes of pro- (IL1B, IL6, IL8) and anti-inflammatory (IL4, IL10, IL13) cytokines. The main end-points were sepsis, mortality and adult respiratory distress syndrome (ARDS). We evaluated the dynamic levels of bilirubin, blood urea nitrogen, creatine kinase, creatinine and lactate dehydrogenase in five points of measurements (between 1 and 14 days after admission) and correlated them with SNPs. High-producing alleles of proinflammatory cytokines protected patients against sepsis ( IL1B −511A and IL8 -251A) and mortality ( IL1B −511A). High-producing alleles of anti-inflammatory cytokines IL4 -589T and IL13 431A (144Gln) were less frequent in ARDS patients. The carriers of IL6 -174C/C genotypes were prone to the increased levels of biochemical markers and acute kidney and liver insufficiency. Genotype-dependent differences in the levels of biochemical indicators gradually increased to a maximal value on the 14th day after admission. These findings suggest that genetic variability in pro- and anti-inflammatory cytokines may contribute to different clinical phenotypes in patients at high risk of critical illness. [ABSTRACT FROM AUTHOR]

Congenital deficiency of the proprotein convertase subtilisine/kexin type 1 gene (PCSK1), which encodes proprotein convertase 1/3, causes a severe multihormonal disorder marked by early-onset obesity. The single nucleotide polymorphisms (SNPs) rs6232 and rs6234–rs6235 in PCSK1 have been associated with obesity. However, case-control studies carried out in populations of different ethnicities have only partly replicated this association. Moreover, these SNPs have only weakly been associated with body mass index (weight (kg)/height (m)2) at a genome-wide level of significance. To investigate this discrepancy, we conducted a systematic search for studies published before December 2013 and extracted relevant data. Pooled estimates were calculated for overall and subgroup analyses. This meta-analysis confirmed the association of PCSK1 SNPs with obesity and provides the first evidence that the association between PCSK1 rs6232 and obesity is stronger for childhood obesity than for adult obesity. Moreover, we identified weak associations with body mass index and significantly stronger associations with waist circumference for rs6234–rs6235. No difference was found in the association with different obesity grades, and no association of PCSK1 rs6234–rs6235 with obesity was identified in Asian populations. This systematic Human Genome Epidemiology (HuGE) review showed convincingly that the SNPs rs6232, rs6234, and rs6235 in PCSK1 are associated with obesity in Caucasians. [ABSTRACT FROM PUBLISHER]

The use of genetically isolated populations can empower next-generation association studies. In this review, we discuss the advantages of this approach and review study design and analytical considerations of genetic association studies focusing on isolates. We cite successful examples of using population isolates in association studies and outline potential ways forward. [ABSTRACT FROM AUTHOR]

We performed a meta-analysis of the association of transforming growth factor α gene (TGFA) polymorphisms with the risk of cleft lip with or without cleft palate (CL/P) or cleft palate (CP). In total, data from 29 studies were pooled for the following 3 polymorphisms: TGFA/TaqI, TGFA/BamHI, and TGFA/RasI in the TGFA gene. A fixed-effects or random-effects model was used to calculate the pooled odds ratios based on the results from the heterogeneity tests. A significantly increased CL/P or CP risk was observed in persons carrying a C2 allele at the TaqI polymorphism (odds ratio (OR) = 1.70, 95% confidence interval (CI): 1.41, 2.05) compared with those with a C1 allele (OR = 1.57, 95% CI: 1.23, 2.01). For the TGFA/BamHI polymorphism, carriers of the minor A1 allele had an estimated relative decrease in CL/P risk (OR = 0.44, 95% CI: 0.30, 0.64). These associations remained significant when only high-quality studies were included. However, no significant association was observed between the TGFA/RasI variant and CL/P risk. In summary, this meta-analysis provided a robust estimate of the positive association of the TGFA/TaqI polymorphism with both CL/P and CP and suggests that persons with an A1 allele may have a markedly decreased risk of CL/P. [ABSTRACT FROM PUBLISHER]

Inflammation is implicated in preterm birth, but genetic studies of inflammatory genes have yielded inconsistent results. Maternal DNA from 1,646 participants in the Pregnancy, Infection, and Nutrition Cohort, enrolled in Orange and Wake counties, North Carolina (1995–2005), were genotyped for 432 tag single-nucleotide polymorphisms (SNPs) in 30 candidate genes. Gene-level and SNP associations were modeled within strata of genetic ancestry. Six genes were associated with preterm birth among European Americans: interleukin 12A (IL12A); colony-stimulating factor 2 (CSF2); interferon γ receptor 2 (IFNGR2); killer cell immunoglobulin–like receptor, three domain, long cytoplasmic tail, 2 (KIR3DL2); interleukin 4 (IL4); and interleukin 13 (IL13). Of these, relatively strong single-SNP associations were seen in IFNGR2 and KIR3DL2. Among the 4 genes related to natural killer cell function, 2 (IL12A and CSF2) were consistently associated with reduced risk of prematurity for both European and African Americans. SNPs tagging a locus control region for IL4 and IL13 were associated with an increased risk of spontaneous preterm birth for European Americans (rs3091307; risk ratio = 1.9; 95% confidence interval: 1.4, 2.5). Although gene-level associations were detected only in European Americans, single-SNP associations among European and African Americans were often similar in direction, though estimated with less precision among African Americans. In conclusion, we identified novel associations between variants in the natural killer cell immune pathway and prematurity in this biracial US population. [ABSTRACT FROM PUBLISHER]

Obesity is of global health concern. There are well-described inverse relationships between female pubertal timing and obesity. Recent genome-wide association studies of age at menarche identified several obesity-related variants. Using data from the ReproGen Consortium, we employed meta-analytical techniques to estimate the associations of 95 a priori and recently identified obesity-related (body mass index (weight (kg)/height (m)2), waist circumference, and waist:hip ratio) single-nucleotide polymorphisms (SNPs) with age at menarche in 92,116 women of European descent from 38 studies (1970–2010), in order to estimate associations between genetic variants associated with central or overall adiposity and pubertal timing in girls. Investigators in each study performed a separate analysis of associations between the selected SNPs and age at menarche (ages 9–17 years) using linear regression models and adjusting for birth year, site (as appropriate), and population stratification. Heterogeneity of effect-measure estimates was investigated using meta-regression. Six novel associations of body mass index loci with age at menarche were identified, and 11 adiposity loci previously reported to be associated with age at menarche were confirmed, but none of the central adiposity variants individually showed significant associations. These findings suggest complex genetic relationships between menarche and overall obesity, and to a lesser extent central obesity, in normal processes of growth and development. [ABSTRACT FROM PUBLISHER]

A genetic variant within the CHRNA5-CHRNA3-CHRNB4 region (rs1051730), previously associated with smoking quantity, was recently shown to interact with smoking on obesity predisposition. We attempted to replicate this finding in the Gene-Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk (GLACIER) Study, a prospective cohort study of adults from northern Sweden (n = 16,426). We also investigated whether a similar interaction is apparent between rs1051730 and snus, a type of moist oral tobacco, to determine whether this interaction is driven by factors that cigarettes and snus have in common, such as nicotine. Main effects of smoking, snus, and the rs1051730 variant and pairwise interaction terms (smoking × rs1051730 and snus × rs1051730) were tested in relation to body mass index (BMI; calculated as weight (kg)/height (m)2) through the use of multivariate linear models adjusted for age and sex. Smoking status and BMI were inversely related (β = −0.46 kg/m2, standard error (SE) = 0.08; P < 0.0001). Snus use and BMI were positively related (β = 0.35 kg/m2, SE = 0.12; P = 0.003). The rs1051730 variant was not significantly associated with smoking status or snus use (P > 0.05); the T allele was associated with lower BMI in the overall cohort (β = −0.10 kg/m2, SE = 0.05; P = 0.03) and with smoking quantity in those in whom this was measured (n = 5,304) (β = 0.08, SE = 0.01; P < 0.0001). Neither smoking status (Pinteraction = 0.29) nor snus use (Pinteraction = 0.89) modified the association between the rs1051730 variant and BMI. [ABSTRACT FROM PUBLISHER]

A loss-of-function mutation (Q141K, rs2231142) in the ATP-binding cassette, subfamily G, member 2 gene (ABCG2) has been shown to be associated with serum uric acid levels and gout in Asians, Europeans, and European and African Americans; however, less is known about these associations in other populations. Rs2231142 was genotyped in 22,734 European Americans, 9,720 African Americans, 3,849 Mexican Americans, and 3,550 American Indians in the Population Architecture using Genomics and Epidemiology (PAGE) Study (2008–2012). Rs2231142 was significantly associated with serum uric acid levels (P = 2.37 × 10−67, P = 3.98 × 10−5, P = 6.97 × 10−9, and P = 5.33 × 10−4 in European Americans, African Americans, Mexican Americans, and American Indians, respectively) and gout (P = 2.83 × 10−10, P = 0.01, and P = 0.01 in European Americans, African Americans, and Mexican Americans, respectively). Overall, the T allele was associated with a 0.24-mg/dL increase in serum uric acid level (P = 1.37 × 10−80) and a 1.75-fold increase in the odds of gout (P = 1.09 × 10−12). The association between rs2231142 and serum uric acid was significantly stronger in men, postmenopausal women, and hormone therapy users compared with their counterparts. The association with gout was also significantly stronger in men than in women. These results highlight a possible role of sex hormones in the regulation of ABCG2 urate transporter and its potential implications for the prevention, diagnosis, and treatment of hyperuricemia and gout. [ABSTRACT FROM PUBLISHER]

Background. Genetically determined variation in the expression of innate defense molecules may explain differences in the propensity to be colonized with Staphylococcus aureus.Methods. We determined S. aureus nasal carriage in 603 volunteers; analyzed polymorphisms in the DEFB1 promoter at positions −52 G>A (rs1799946), −44 C>G (rs1800972), and −20 G>A (rs11362); and measured the content of human β-defensin 1 (hBD-1) and hBD-3 messenger RNA (mRNA) in 192 samples of healthy and experimentally wounded human skin.Results. Compared with GGG at the positions −52/−44/−20, the ACG haplotype was more common among persistent S. aureus nasal carriers (odds ratio, 1.93; 95% confidence interval [CI], 1.2–3.1; P = .006) and was associated with reduced expression of hBD-1 (GGG>ACG>GCA; P < .001) and hBD-3 (GGG>GCA>ACG; P = .04) in skin when measured 72 hours after wounding. Furthermore, a 50% decrease in hBD-1 and hBD-3 mRNA expression in wounded skin increased the odds of persistent carriage by 1.45 (95% CI, .93–2.26; P = .1) and 1.48 (95% CI, 1.01–2.17; P = .04), respectively. Adjustment for known risk factors of persistent S. aureus carriage did not substantially change the associations of both DEFB1 haplotypes and β-defensin expression with S. aureus colonization.Conclusions. DEFB1 polymorphisms may promote persistent S. aureus colonization by altering β-defensin expression in keratinocytes of human skin. [ABSTRACT FROM AUTHOR]

Association studies among admixed populations pose many challenges including confounding of genetic effects due to population substructure and heterogeneity due to different patterns of linkage disequilibrium (LD). We use simulations to investigate controlling for confounding by indicators of global ancestry and the impact of including a covariate for local ancestry. In addition, we investigate the use of an interaction term between a single-nucleotide polymorphism (SNP) and local ancestry to capture heterogeneity in SNP effects. Although adjustment for global ancestry can control for confounding, additional adjustment for local ancestry may increase power when the induced admixture LD is in the opposite direction as the LD in the ancestral population. However, if the induced LD is in the same direction, there is the potential for reduced power because of overadjustment. Furthermore, the inclusion of a SNP by local ancestry interaction term can increase power when there is substantial differential LD between ancestry populations. We examine these approaches in genome-wide data using the University of Southern California's Children's Health Study investigating asthma risk. The analysis highlights rs10519951 (P = 8.5 × 10−7), a SNP lacking any evidence of association from a conventional analysis (P = 0.5). [ABSTRACT FROM AUTHOR]

The authors performed a systematic review of the association of complement component 2(C2)/complement factor B (CFB) gene polymorphisms with age-related macular degeneration (AMD). In total, data from 19 studies published between 2006 and 2011 were pooled for 4 polymorphisms: rs9332739 and rs547154 in the C2 gene and rs4151667 and rs641153 in the CFB gene. Data extraction and assessments for risk of bias were independently performed by 2 reviewers. Allele frequencies and allele and genotypic effects were pooled. Heterogeneity and publication bias were explored. Pooled minor allele frequencies for all 4 SNPs were between 4.7% and 9.6% for all polymorphisms, except for an Indian population in which the C allele at rs9332739 was the major allele. For the C2 polymorphisms, the minor C allele at rs9332739 and the minor T allele at rs547154 carried estimated relative risks (odds ratios) of 0.55 (95% confidence interval (CI): 0.46, 0.65) and 0.47 (95% CI: 0.39, 0.57), respectively. For the CFB polymorphisms, the minor A alleles at rs4151667 and rs614153 carried estimated risks of 0.54 (95% CI: 0.45, 0.64) and 0.41 (95% CI: 0.34, 0.51), respectively. These allele effects contributed to an absolute lowering of the risk of all AMD in Caucasian populations by 2.0%–6.0%. This meta-analysis provides a robust estimate of the protective association of C2/CFB with AMD. [ABSTRACT FROM PUBLISHER]

In genetic association studies, analyses integrating data or estimates from unrelated case-control individuals and case trios (case offspring and their parents) can increase statistical power to identify disease susceptibility loci. Data on control trios may also be available, but how and when their use is advantageous is less familiar and is described here. In addition, the authors examine assumptions and properties of hybrid analyses combining association estimates from unrelated case-control individuals together with case and control family trios, focusing on low-prevalence disease. One such assumption is absence of population stratification bias (PSB), a potential source of confounding in case-control analyses. For detection of PSB, the authors discuss 4 possible tests that assess equality between individual-level and family-based estimates. Furthermore, a weighted framework is presented, in which estimates from analyses combining unrelated individuals and families (most powerful but subject to PSB) and family-based analyses (robust to PSB) are weighted according to the observed PSB test P value. In contrast to existing hybrid designs that combine individuals and families only if no significant PSB is detected, the weighted framework does not require specification of an arbitrary PSB testing level to establish significance. The statistical methods are evaluated using simulations and applied to a candidate gene study of childhood leukemia (Quebec Childhood Leukemia Study, 1980–2000). [ABSTRACT FROM PUBLISHER]

Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field. [ABSTRACT FROM PUBLISHER]

To facilitate an increase in the amount of data on minority subjects collected for genetic databases, the authors attempted to clarify barriers to African-American participation in genetic studies. They randomly sampled 78,072 subjects from the community (Missouri Family Registry, 2002–2007). Of these, 28,658 participated in a telephone screening interview, 3,179 were eligible to participate in the genetic study, and 1,919 participated in the genetic study. Response rates were examined in relation to the proportion of subjects in the area who were African-American according to US Census 2000 zip code demographic data. Compared with zip codes with fewer than 5% African Americans (average = 2% African-American), zip codes with at least 60% African Americans (average = 87% African-American) had higher proportions of subjects with an incorrect address or telephone number but lower proportions of subjects who did not answer the telephone and subjects who refused the telephone interview (P < 0.0001). Based on reported race from the telephone screening, 71% of eligible African Americans and 57% of eligible European Americans participated in the genetic study (P < 0.0001). The results of this study suggest that increasing the number of African Americans in genetic databases may be achieved by increasing efforts to locate and contact them. [ABSTRACT FROM AUTHOR]

The resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very computationally intensive. Here, we propose an efficient p-value evaluation procedure by adapting the stochastic approximation Markov chain Monte Carlo algorithm. The new procedure can be used easily for estimating the p-value for any resampling-based test. We show through numeric simulations that the proposed procedure can be 100–500 000 times as efficient (in term of computing time) as the standard resampling-based procedure when evaluating a test statistic with a small p-value (e.g. less than 10 − 6). With its computational burden reduced by this proposed procedure, the versatile resampling-based test would become computationally feasible for a much wider range of applications. We demonstrate the application of the new method by applying it to a large-scale genetic association study of prostate cancer. [ABSTRACT FROM PUBLISHER]

The authors performed a meta-analysis to estimate the magnitude of polymorphism effects for the complement component C3 gene (C3) and their possible mode of action on age-related macular degeneration (AMD). The meta-analysis included 16 and 7 studies for rs2230199 and rs1047286, respectively. Data extraction and risk of bias assessments were performed in duplicate, and heterogeneity and publication bias were explored. There was moderate evidence for association between both polymorphisms and AMD in Caucasians. For rs2230199, patients with CG and GG genotypes were 1.44 (95% confidence interval (CI): 1.33, 1.56) and 1.88 (95% CI: 1.59, 2.23) times more likely to have AMD than patients with the CC genotype. For rs1047286, GA and AA genotypes had 1.27 (95% CI: 1.15, 1.41) and 1.70 (95% CI: 1.27, 2.11) times higher risk of AMD than did GG genotypes. These gene effects suggested an additive model. The population attributable risks for the GG/GC and AA/GA genotypes are approximately 5%–10%. Subgroup analysis by ethnicity indicates that these variants are very infrequent in Asians and that the observed gene effects are based largely on the high frequency within Caucasian populations. This meta-analysis supports the association between C3 and AMD and provides a robust estimate of the genetic risk. [ABSTRACT FROM PUBLISHER]

Although previous investigations have indicated a role for genetic factors in smoking initiation, the underlying genetic mechanisms are still unknown. In 2,339 adolescents from a Chinese Han population in the Wuhan Smoking Prevention Trial (Wuhan, China, 1998–1999), the authors explored the association of 57 genes in the dopamine pathway with smoking initiation. Using a conservative approach for declaring significance, positive findings were further examined in an independent sample of 603 Caucasian adolescents followed for up to 10 years as part of the Children's Health Study (Southern California, 1993–2009). The authors identified 1 single nucleotide polymorphism (rs2298122) in the calcyon neuron-specific vesicular protein gene (CALY) that was positively associated with smoking initiation in females (odds ratio = 2.21, 95% confidence interval: 1.49, 3.27; P = 8.4 × 10−5) in the Wuhan Smoking Prevention Trial cohort, and they replicated the association in females from the Children's Health Study cohort (hazard rate ratio = 2.05, 95% confidence interval: 1.27, 3.31; P = 0.003). These results suggest that the CALY gene may influence smoking initiation in adolescents, although the potential roles of underlying psychological characteristics that may be components of the smoking-initiation phenotype, such as impulsivity or novelty-seeking, remain to be explored. [ABSTRACT FROM PUBLISHER]

Confounding due to population stratification is a potential source of concern in population-based genetic association studies, particularly in recently admixed populations such as African Americans. Several methods have been developed to control for population stratification in the context of genome-wide association studies. Because these approaches require thousands of genotypes from genetic markers, they are not well suited to be used in genetic association analyses without genome-wide data. An alternative approach to control for population stratification is to estimate admixture proportions by using ancestral informative markers (AIMs). The authors evaluated whether a relatively small number of AIMs would be sufficient to estimate ancestral proportions in African Americans. They first estimated European admixture proportions in 1,757 subjects from the Black Women's Health Study (1995–2009) by genotyping an admixture panel of 1,373 AIMs; they then compared these results with those obtained using smaller sets of AIMs. The authors found that just 30 AIMs are needed to obtain very high correlation of estimates with the entire set (r = 0.89; P < 0.0001). A set of 200 AIMs gave an almost perfect correlation with the entire set (r = 0.98; P < 0.0001). These results show that a small number of AIMs are sufficiently precise to estimate European admixture in African Americans. [ABSTRACT FROM PUBLISHER]

Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm. [ABSTRACT FROM AUTHOR]

Complex trait variation is likely to be explained by the combined effects of genes, environmental factors, and gene × environment (G × E) interaction. The authors introduce a novel 2-step method for detecting a G × E interaction in a genome-wide association study (GWAS) of case-parent trios. The method utilizes 2 sources of G × E information in a trio sample to construct a screening step and a testing step. Across a wide range of models, this 2-step procedure provides substantially greater power to detect G × E interaction than a standard test of G × E interaction applied genome-wide. For example, for a disease susceptibility locus with minor allele frequency of 15%, a binary exposure variable with 50% prevalence, and a GWAS scan of 1 million markers in 1,000 case-parent trios, the 2-step method provides 87% power to detect a G × E interaction relative risk of 2.3, as compared with only 25% power using a standard G × E test. The method is easily implemented using standard software. This 2-step scan for G × E interaction is independent of any prior scan that may have been conducted for genetic main effects, and thus has the potential to uncover new genes in a GWAS that have not been previously identified. [ABSTRACT FROM PUBLISHER]

Testing for deviations from Hardy-Weinberg equilibrium is a widely recommended practice for population-based genetic association studies. However, current methods for this test assume a simple random sample and may not be appropriate for sample surveys with complex survey designs. In this paper, the authors present a test for Hardy-Weinberg equilibrium that adjusts for the sample weights and correlation of data collected in complex surveys. The authors perform this test by using a simple adjustment to procedures developed to analyze data from complex survey designs available within the SAS statistical software package (SAS Institute, Inc., Cary, North Carolina). Using 90 genetic markers from the Third National Health and Nutrition Examination Survey, the authors found that survey-adjusted and -unadjusted estimates of the disequilibrium coefficient were generally similar within self-reported races/ethnicities. However, estimates of the variance of the disequilibrium coefficient were significantly different between the 2 methods. Because the results of the survey-adjusted tests account for correlation among participants sampled within the same cluster, and the possibility of having related individuals sampled from the same household, the authors recommend use of this test when analyzing genetic data originating from sample surveys with complex survey designs to assess deviations from Hardy-Weinberg equilibrium. [ABSTRACT FROM PUBLISHER]

BackgroundThe adrenergic receptor (adrenoceptor) family genes have been extensively studied as candidate genes in hypertension but the results of individual genetic association studies (GAS) are controversial and inconclusive. To clarify these data, a systematic assessment of GAS for adrenoceptor family genes in hypertension was conducted.MethodsData from 163 GAS involving 7 genes and 37 distinct genetic variants were analyzed and cataloged in CUMAGAS-HYPERT (Cumulative Meta-analysis of Genetic Association Studies–HYPERTension; a web-based information system, which allows the retrieval and synthesis of data from GAS in hypertension, available at http://biomath.med.uth.gr). Data from genome-wide association studies involving the adrenoceptor family genes were also systematically searched.ResultsIndividual GAS reported inconsistent associations and had limited power to detect modest genetic effects, with only 1.2% having power >80%. Thirteen variants were investigated by three or more studies and their results were subject to meta-analysis. In the main meta-analyses, significant results were shown for five variants (ADRB1 p.Arg389Gly, ADRB1 p.Ser49Gly, ADRB2 g.9368308A>G, ADRB3 p.Trp64Arg, and ADRA1A p.Cys347Arg) under the allelic contrast and/or the dominant model. Subgroup analyses by ethnicity and gender detected significant associations for three variants (ADRB1 p.Arg389Gly in east Asians, ADRB2 p.Gln27Glu in whites, and ADRB3 p.Trp64Arg in whites and in males). Heterogeneity ranged from none to high. No significant associations were recorded from genome-wide studies.ConclusionsThere is evidence to implicate adrenoceptor genes in hypertension, although future studies designed to investigate epistatic and gene–environment interactions would allow more solid conclusions to be drawn about the role of these genes in hypertension.American Journal of Hypertension 2010; doi:10.1038/ajh.2009.251 [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have become the method of choice for investigating the genetic basis of common diseases and complex traits. The immense scale of these experiments is unprecedented, involving thousands of samples and up to a million variables. The careful execution of exploratory data analysis (EDA) prior to the actual genotype–phenotype association analysis is crucial as this identifies problematic samples and poorly assayed genetic polymorphisms that, if undetected, can compromise the outcome of the experiment. EDA of such large-scale genetic data sets thus requires specialized numerical and graphical strategies, and this article provides a review of the current exploratory tools commonly used in GWAS. [ABSTRACT FROM PUBLISHER]

When synthesizing data from genetic association studies researchers frequently perform several related meta-analyses, perhaps on different polymorphisms of the same gene, or on different outcomes, or they might define subgroups of studies by factors such as ethnicity, gender or study design. Current practice is to perform a totally separate meta-analysis of each set of studies; however, when the meta-analyses investigate related questions, it is possible that the estimates in one meta-analysis could be improved by using information from another. The meta-analytic model for a genetic association study can be parameterized in terms of four meaningful parameters: the size of the genetic effect, the genetic model, the allele frequency in controls and the degree of departure from Hardy–Weinberg equilibrium in controls. Even when the size of the genetic effect differs across meta-analyses, it may be possible to assume that some of the other parameters are common. The models are applied to a meta-analysis of the same gene–disease relationship in three different ethnic groups. [ABSTRACT FROM AUTHOR]