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Alzheimer's disease (AD) is a genetically complex disease for which nearly 40 loci have now been identified via genome-wide association studies (GWAS). We attempted to identify groups of rare variants (alternate allele frequency <0.01) associated with AD in a region-based, whole-genome sequencing (WGS) association study (rvGWAS) of two independent AD family datasets (NIMH/NIA; 2247 individuals; 605 families). Employing a sliding window approach across the genome, we identified several regions that achieved association p values <10 -6 , using the burden test or the SKAT statistic. The genomic region around the dystobrevin beta (DTNB) gene was identified with the burden and SKAT test and replicated in case/control samples from the ADSP study reaching genome-wide significance after meta-analysis (p meta  = 4.74 × 10 -8 ). SKAT analysis also revealed region-based association around the Discs large homolog 2 (DLG2) gene and replicated in case/control samples from the ADSP study (p meta  = 1 × 10 -6 ). In conclusion, in a region-based rvGWAS of AD we identified two novel AD genes, DLG2 and DTNB, based on association with rare variants., (© 2022. The Author(s).)

Social isolation undermines health. Inequities in social networks exist due to historical and contemporary practices of socioeconomic and racial segregation. Thus, lower income and minority families are less likely to have the number, strength, and variety of social connections as higher income and white families. Therefore, social isolation may contribute to inequities in health and well-being across socioeconomic and racial groups. Disrupting social isolation by strengthening social networks may be a meaningful way to equitably improve population health. In this study we aimed to better understand the factors that influence the formation and sustainment of social connections in neighbourhoods experiencing a disproportionate burden of social needs and poor health outcomes. Participants were recruited through our community-academic partnership, Healthy Homes (HH). Healthy Homes serves families with pregnant women and/or children <6 years in two low-income, high-morbidity neighbourhoods, focusing on supporting families' needs and hopes. Between October 2016 and April 2017, we conducted in-depth qualitative interviews (n = 20) with English-speaking mothers and grandmothers of children under <6 years. Interviews were audio-recorded, transcribed verbatim and independently coded. After applying an a priori code list, we conducted emergent coding to identify additional themes. Themes focused on the social environment, including social connections and social isolation, among vulnerable populations in included neighbourhoods. Families want connection to one another and to resources but look to others to facilitate those connections. Families may want or need social connections but do not engage if it means sacrificing their values or sense of self-worth. These findings provide a deeper understanding of the factors that might allow us to disrupt social isolation by building relationships in communities that face social and health inequities., (© 2021 John Wiley & Sons Ltd.)

With the advent of whole genome-sequencing (WGS) studies, family-based designs enable sex-specific analysis approaches that can be applied to only affected individuals; tests using family-based designs are attractive because they are completely robust against the effects of population substructure. These advantages make family-based association tests (FBATs) that use siblings as well as parents especially suited for the analysis of late-onset diseases such as Alzheimer's Disease (AD). However, the application of FBATs to assess sex-specific effects can require additional filtering steps, as sensitivity to sequencing errors is amplified in this type of analysis. Here, we illustrate the implementation of robust analysis approaches and additional filtering steps that can minimize the chances of false positive-findings due to sex-specific sequencing errors. We apply this approach to two family-based AD datasets and identify four novel loci (GRID1, RIOK3, MCPH1, ZBTB7C) showing sex-specific association with AD risk. Following stringent quality control filtering, the strongest candidate is ZBTB7C (P inter  = 1.83 × 10 -7 ), in which the minor allele of rs1944572 confers increased risk for AD in females and protection in males. ZBTB7C encodes the Zinc Finger and BTB Domain Containing 7C, a transcriptional repressor of membrane metalloproteases (MMP). Members of this MMP family were implicated in AD neuropathology.

Protein-coding de novo mutations (DNMs) are significant risk factors in many neurodevelopmental disorders, whereas schizophrenia (SCZ) risk associated with DNMs has thus far been shown to be modest. We analyzed DNMs from 1,695 SCZ-affected trios and 1,077 published SCZ-affected trios to better understand the contribution to SCZ risk. Among 2,772 SCZ probands, exome-wide DNM burden remained modest. Gene set analyses revealed that SCZ DNMs were significantly concentrated in genes that were highly expressed in the brain, that were under strong evolutionary constraint and/or overlapped with genes identified in other neurodevelopmental disorders. No single gene surpassed exome-wide significance; however, 16 genes were recurrently hit by protein-truncating DNMs, corresponding to a 3.15-fold higher rate than the mutation model expectation (permuted 95% confidence interval: 1-10 genes; permuted P = 3 × 10 - 5 ). Overall, DNMs explain a small fraction of SCZ risk, and larger samples are needed to identify individual risk genes, as coding variation across many genes confers risk for SCZ in the population.

The transmission disequilibrium test (TDT) is the gold standard for testing the association between a genetic variant and disease in samples consisting of affected individuals and their parents. In practice, more complex pedigree structures, that is siblings with no parents, or three-generational pedigrees with possibly missing genotypes, are common. There are several generalizations of the TDT that are suitable for use with arbitrary pedigree structures. We consider three such frequently used generalizations, family-based association test, pedigree disequilibrium test, and generalized disequilibrium test, that have accompanying software and compare them regarding validity and power in the single variant setting. We use simulations to study the effects of population admixture, populations whose genotypes are not in Hardy-Weinberg equilibrium (HWE), different pedigree structures, and the presence of linkage. Whereas our results show that some TDT generalizations can have a substantially increased Type 1 error, these tests are often used in substantive research without caveats about the validity of their Type 1 error. For the association analysis of rare variants in sequencing studies, region-based extensions of the TDT generalizations, that rely on the postulated robustness of the single variant tests, have been proposed. We discuss the implications of our results for these region-based extensions., (© 2019 Wiley Periodicals, Inc.)

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., (© 2017 WILEY PERIODICALS, INC.)

Both neurocognitive deficits and schizophrenia are highly heritable. Genetic overlap between neurocognitive deficits and schizophrenia has been observed in both the general population and in the clinical samples. This study aimed to examine if the polygenic architecture of susceptibility to schizophrenia modified neurocognitive performance in schizophrenia patients. Schizophrenia polygenic risk scores (PRSs) were first derived from the Psychiatric Genomics Consortium (PGC) on schizophrenia, and then the scores were calculated in our independent sample of 1130 schizophrenia trios, who had PsychChip data and were part of the Schizophrenia Families from Taiwan project. Pseudocontrols generated from the nontransmitted parental alleles of the parents in these trios were compared with alleles in schizophrenia patients in assessing the replicability of PGC-derived susceptibility variants. Schizophrenia PRS at the P-value threshold (PT) of 0.1 explained 0.2% in the variance of disease status in this Han-Taiwanese samples, and the score itself had a P-value 0.05 for the association test with the disorder. Each patient underwent neurocognitive evaluation on sustained attention using the continuous performance test and executive function using the Wisconsin Card Sorting Test. We applied a structural equation model to construct the neurocognitive latent variable estimated from multiple measured indices in these 2 tests, and then tested the association between the PRS and the neurocognitive latent variable. Higher schizophrenia PRS generated at the PT of 0.1 was significantly associated with poorer neurocognitive performance with explained variance 0.5%. Our findings indicated that schizophrenia susceptibility variants modify the neurocognitive performance in schizophrenia patients., (© 2017 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.)

Chronic obstructive pulmonary disease (COPD) is a complex disease with strong environmental and genetic influences and sexually dimorphic features. Although genetic risk factors for COPD have been identified, much of the heritability remains unexplained. Sex-based genetic association studies may uncover additional COPD genetic risk factors. We studied current and former smokers from COPD case-control cohorts (COPDGene non-Hispanic whites and African Americans, Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-Points, and Genetics of Chronic Obstructive Lung Disease). COPD was defined as post-bronchodilator forced expiratory volume in 1 second/forced vital capacity less than 0.70 and forced expiratory volume in 1 second percent predicted less than 80. Testing was performed across all cohorts and combined in a meta-analysis adjusted for age, pack-years, and genetic ancestry. We first performed genome-wide single-nucleotide polymorphism (SNP)-by-sex interaction testing on the outcome of COPD affection status. We performed sex-stratified association testing for SNPs with interaction P less than 10 -6 . We examined over 8 million SNPs in four populations, including 6,260 subjects with COPD (40.6% female) and 5,269 smoking control subjects (47.3% female). The SNP rs9615358 in the cadherin gene CELSR1 approached genome-wide significance for an interaction with sex (P = 1.24 × 10 -7 ). In the sex-stratified meta-analysis, this SNP was associated with COPD among females (odds ratio, 1.37 [95% confidence interval, 1.25-1.49]; P = 3.32 × 10 -7 ) but not males (odds ratio, 0.90 [95% confidence interval, 0.79-1.01]; P = 0.06). CELSR1 is involved in fetal lung development. In a human fetal lung tissue dataset, we observed greater CELSR1 expression in female compared with male samples. This SNP-by-sex genome-wide association analysis identified the fetal lung development gene, CELSR1, as a potential sex-specific risk factor for COPD. Identifying sex-specific genetic risk factors may reveal new insights into sexually dimorphic features of COPD.

Background: Early social experiences are believed to shape neurodevelopment, with potentially lifelong consequences. Yet minimal evidence exists regarding the role of the social environment on children's neural functioning, a core domain of neurodevelopment., Methods: We analysed data from 36 443 participants in the United States Collaborative Perinatal Project, a socioeconomically diverse pregnancy cohort conducted between 1959 and 1974. Study outcomes included: physician (neurologist or paediatrician)-rated neurological abnormality neonatally and thereafter at 4 months and 1 and 7 years; indicators of neurological hard signs and soft signs; and indicators of autonomic nervous system function., Results: Children born to socioeconomically disadvantaged parents were more likely to exhibit neurological abnormalities at 4 months [odds ratio (OR) = 1.20; 95% confidence interval (CI) = 1.06, 1.37], 1 year (OR = 1.35; CI = 1.17, 1.56), and 7 years (OR = 1.67; CI = 1.48, 1.89), and more likely to exhibit neurological hard signs (OR = 1.39; CI = 1.10, 1.76), soft signs (OR = 1.26; CI = 1.09, 1.45) and autonomic nervous system dysfunctions at 7 years. Pregnancy and delivery complications, themselves associated with socioeconomic disadvantage, did not account for the higher risks of neurological abnormalities among disadvantaged children., Conclusions: Parental socioeconomic disadvantage was, independently from pregnancy and delivery complications, associated with abnormal child neural development during the first 7 years of life. These findings reinforce the importance of the early environment for neurodevelopment generally, and expand knowledge regarding the domains of neurodevelopment affected by environmental conditions. Further work is needed to determine the mechanisms linking socioeconomic disadvantage with children's neural functioning, the timing of such mechanisms and their potential reversibility., (Published by Oxford University Press on behalf of the International Epidemiological Association 2015. This work is written by US Government employees and is in the public domain in the US.)

In this paper, we revisit a 1986 article we published in this Journal, Meta-Analysis in Clinical Trials, where we introduced a random-effects model to summarize the evidence about treatment efficacy from a number of related clinical trials. Because of its simplicity and ease of implementation, our approach has been widely used (with more than 12,000 citations to date) and the "DerSimonian and Laird method" is now often referred to as the 'standard approach' or a 'popular' method for meta-analysis in medical and clinical research. The method is especially useful for providing an overall effect estimate and for characterizing the heterogeneity of effects across a series of studies. Here, we review the background that led to the original 1986 article, briefly describe the random-effects approach for meta-analysis, explore its use in various settings and trends over time and recommend a refinement to the method using a robust variance estimator for testing overall effect. We conclude with a discussion of repurposing the method for Big Data meta-analysis and Genome Wide Association Studies for studying the importance of genetic variants in complex diseases., (Published by Elsevier Inc.)

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., (© 2014 WILEY PERIODICALS, INC.)

Rationale: Emphysema is a heritable trait that occurs in smokers with and without chronic obstructive pulmonary disease. Emphysema occurs in distinct pathologic patterns, but the genetic determinants of these patterns are unknown., Objectives: To identify genetic loci associated with distinct patterns of emphysema in smokers and investigate the regulatory function of these loci., Methods: Quantitative measures of distinct emphysema patterns were generated from computed tomography scans from smokers in the COPDGene Study using the local histogram emphysema quantification method. Genome-wide association studies (GWAS) were performed in 9,614 subjects for five emphysema patterns, and the results were referenced against enhancer and DNase I hypersensitive regions from ENCODE and Roadmap Epigenomics cell lines., Measurements and Main Results: Genome-wide significant associations were identified for seven loci. Two are novel associations (top single-nucleotide polymorphism rs379123 in MYO1D and rs9590614 in VMA8) located within genes that function in cell-cell signaling and cell migration, and five are in loci previously associated with chronic obstructive pulmonary disease susceptibility (HHIP, IREB2/CHRNA3, CYP2A6/ADCK, TGFB2, and MMP12). Five of these seven loci lay within enhancer or DNase I hypersensitivity regions in lung fibroblasts or small airway epithelial cells, respectively. Enhancer enrichment analysis for top GWAS associations (single-nucleotide polymorphisms associated at P < 5 × 10(-6)) identified multiple cell lines with significant enhancer enrichment among top GWAS loci, including lung fibroblasts., Conclusions: This study demonstrates for the first time genetic associations with distinct patterns of pulmonary emphysema quantified by computed tomography scan. Enhancer regions are significantly enriched among these GWAS results, with pulmonary fibroblasts among the cell types showing the strongest enrichment.

Background: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes., Results: We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches., Conclusion: Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.

Background: The consequences of subclinical coeliac disease (CD) in Type 1 diabetes mellitus (T1DM) remain unclear. We looked at growth, anthropometry and disease management in children with dual diagnosis (T1DM + CD) before and after CD diagnosis., Methods: Anthropometry, glycated haemoglobin (HbA1c) and IgA tissue transglutaminase (tTg) were collected prior to, and following CD diagnosis in 23 children with T1DM + CD. This group was matched for demographics, T1DM duration, age at CD diagnosis and at T1DM onset with 23 CD and 44 T1DM controls., Results: No differences in growth or anthropometry were found between children with T1DM + CD and controls at any time point. Children with T1DM + CD, had higher BMI z-score two years prior to, than at CD diagnosis (p < 0.001). BMI z-score change one year prior to CD diagnosis was lower in the T1DM + CD than the T1DM group (p = 0.009). At two years, height velocity and change in BMI z-scores were similar in all groups. No differences were observed in HbA1c between the T1DM + CD and T1DM groups before or after CD diagnosis. More children with T1DM + CD had raised tTg levels one year after CD diagnosis than CD controls (CDx to CDx + 1 yr; T1DM + CD: 100% to 71%, p = 0.180 and CD: 100% to 45%, p < 0.001); by two years there was no difference., Conclusions: No major nutrition or growth deficits were observed in children with T1DM + CD. CD diagnosis does not impact on T1DM glycaemic control. CD specific serology was comparable to children with single CD, but those with dual diagnosis may need more time to adjust to gluten free diet.

Rationale: Emphysema occurs in distinct pathologic patterns, but little is known about the epidemiologic associations of these patterns. Standard quantitative measures of emphysema from computed tomography (CT) do not distinguish between distinct patterns of parenchymal destruction., Objectives: To study the epidemiologic associations of distinct emphysema patterns with measures of lung-related physiology, function, and health care use in smokers., Methods: Using a local histogram-based assessment of lung density, we quantified distinct patterns of low attenuation in 9,313 smokers in the COPDGene Study. To determine if such patterns provide novel insights into chronic obstructive pulmonary disease epidemiology, we tested for their association with measures of physiology, function, and health care use., Measurements and Main Results: Compared with percentage of low-attenuation area less than -950 Hounsfield units (%LAA-950), local histogram-based measures of distinct CT low-attenuation patterns are more predictive of measures of lung function, dyspnea, quality of life, and health care use. These patterns are strongly associated with a wide array of measures of respiratory physiology and function, and most of these associations remain highly significant (P < 0.005) after adjusting for %LAA-950. In smokers without evidence of chronic obstructive pulmonary disease, the mild centrilobular disease pattern is associated with lower FEV1 and worse functional status (P < 0.005)., Conclusions: Measures of distinct CT emphysema patterns provide novel information about the relationship between emphysema and key measures of physiology, physical function, and health care use. Measures of mild emphysema in smokers with preserved lung function can be extracted from CT scans and are significantly associated with functional measures.

Background: For genetic association studies in designs of unrelated individuals, current statistical methodology typically models the phenotype of interest as a function of the genotype and assumes a known statistical model for the phenotype. In the analysis of complex phenotypes, especially in the presence of ascertainment conditions, the specification of such model assumptions is not straight-forward and is error-prone, potentially causing misleading results., Results: In this paper, we propose an alternative approach that treats the genotype as the random variable and conditions upon the phenotype. Thereby, the validity of the approach does not depend on the correctness of assumptions about the phenotypic model. Misspecification of the phenotypic model may lead to reduced statistical power. Theoretical derivations and simulation studies demonstrate both the validity and the advantages of the approach over existing methodology. In the COPDGene study (a GWAS for Chronic Obstructive Pulmonary Disease (COPD)), we apply the approach to a secondary, quantitative phenotype, the Fagerstrom nicotine dependence score, that is correlated with COPD affection status. The software package that implements this method is available., Conclusions: The flexibility of this approach enables the straight-forward application to quantitative phenotypes and binary traits in ascertained and unascertained samples. In addition to its robustness features, our method provides the platform for the construction of complex statistical models for longitudinal data, multivariate data, multi-marker tests, rare-variant analysis, and others.

Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.

Many genome-wide association studies (GWAS) have signals with unknown etiology. This paper addresses the question-is such an association signal caused by rare or common variants that lead to increased disease risk? For a genomic region implicated by a GWAS, we use single nucleotide polymorphism (SNP) data in a case-control setting to predict how many common or rare variants there are, using a Bayesian analysis. Our objective is to compute posterior probabilities for configurations of rare and/or common variants. We use an extension of coalescent trees--the ancestral recombination graphs--to model the genealogical history of the samples based on marker data. As we expect SNPs to be in linkage disequilibrium with common disease variants, we can expect the trees to reflect the type of variants. To demonstrate the application, we apply our method to candidate gene sequencing data from a German case-control study on nonsyndromic cleft lip with or without cleft palate., (© 2012 Wiley Periodicals, Inc.)

Two recent metaanalyses of genome-wide association studies conducted by the CHARGE and SpiroMeta consortia identified novel loci yielding evidence of association at or near genome-wide significance (GWS) with FEV(1) and FEV(1)/FVC. We hypothesized that a subset of these markers would also be associated with chronic obstructive pulmonary disease (COPD) susceptibility. Thirty-two single-nucleotide polymorphisms (SNPs) in or near 17 genes in 11 previously identified GWS spirometric genomic regions were tested for association with COPD status in four COPD case-control study samples (NETT/NAS, the Norway case-control study, ECLIPSE, and the first 1,000 subjects in COPDGene; total sample size, 3,456 cases and 1,906 controls). In addition to testing the 32 spirometric GWS SNPs, we tested a dense panel of imputed HapMap2 SNP markers from the 17 genes located near the 32 GWS SNPs and in a set of 21 well studied COPD candidate genes. Of the previously identified GWS spirometric genomic regions, three loci harbored SNPs associated with COPD susceptibility at a 5% false discovery rate: the 4q24 locus including FLJ20184/INTS12/GSTCD/NPNT, the 6p21 locus including AGER and PPT2, and the 5q33 locus including ADAM19. In conclusion, markers previously associated at or near GWS with spirometric measures were tested for association with COPD status in data from four COPD case-control studies, and three loci showed evidence of association with COPD susceptibility at a 5% false discovery rate.

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.

Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.

Issues of multiple-testing and statistical significance in genomewide association studies (GWAS) have prompted statistical methods utilizing prior data to increase the power of association results. Using prior findings from genome-wide linkage studies on bipolar disorder (BPD), we employed a weighted false discovery approach (wFDR; [Roeder et al. 2006. Am J Hum Genet 78(2): 243–252]) to previously reported GWAS data drawn from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Using this method, association signals are up or down-weighted given the linkage score in that genomic region. Although no SNPs in our sample reached genome-wide significance through the wFDR approach, the strongest single SNP result from the original GWAS results (rs4939921 in myosin VB) is strongly up-weighted as it occurs on a linkage peak of chromosome 18. We also identify regions on chromosome 9, 17, and 18 where modestly associated SNP clusters coincide with strong linkage scores, implicating them as possible candidate regions for further analysis. Moving forward, we believe the application of prior linkage information will be increasingly useful to future GWAS studies that incorporate rarer variants into their analysis.

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for members of families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). We describe an ACE model for binary family data; this structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. We then introduce our contribution, a likelihood-based approach to fitting the model to singly ascertained case-control family data. The approach, which involves conditioning on the proband's disease status and also setting prevalence equal to a prespecified value that can be estimated from the data, makes it possible to obtain valid estimates of the A, C, and E variance components from case-control (rather than only from population-based) family data. In fact, simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly made assumptions hold. Further, when our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Background: Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity., Methods: We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster., Results: We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1) post-bronchodilator FEV1 percent predicted, 2) percent bronchodilator responsiveness, and quantitative CT measurements of 3) apical emphysema and 4) airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1) emphysema predominant, 2) bronchodilator responsive, with higher FEV1; 3) discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4) airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant) was associated with TGFB1 SNP rs1800470., Conclusions: Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

Objective: Published studies suggest associations between circadian gene polymorphisms and bipolar I disorder (BPI), as well as schizoaffective disorder (SZA) and schizophrenia (SZ). The results are plausible, based on prior studies of circadian abnormalities. As replications have not been attempted uniformly, we evaluated representative, common polymorphisms in all three disorders., Methods: We assayed 276 publicly available 'tag' single nucleotide polymorphisms (SNPs) at 21 circadian genes among 523 patients with BPI, 527 patients with SZ/SZA, and 477 screened adult controls. Detected associations were evaluated in relation to two published genome-wide association studies (GWAS)., Results: Using gene-based tests, suggestive associations were noted between EGR3 and BPI (p = 0.017), and between NPAS2 and SZ/SZA (p = 0.034). Three SNPs were associated with both sets of disorders (NPAS2: rs13025524 and rs11123857; RORB: rs10491929; p < 0.05). None of the associations remained significant following corrections for multiple comparisons. Approximately 15% of the analyzed SNPs overlapped with an independent study that conducted GWAS for BPI; suggestive overlap between the GWAS analyses and ours was noted at ARNTL., Conclusions: Several suggestive, novel associations were detected with circadian genes and BPI and SZ/SZA, but the present analyses do not support associations with common polymorphisms that confer risk with odds ratios greater than 1.5. Additional analyses using adequately powered samples are warranted to further evaluate these results.

The INSIG2 rs7566605 polymorphism was identified for obesity (BMI> or =30 kg/m(2)) in one of the first genome-wide association studies, but replications were inconsistent. We collected statistics from 34 studies (n = 74,345), including general population (GP) studies, population-based studies with subjects selected for conditions related to a better health status ('healthy population', HP), and obesity studies (OB). We tested five hypotheses to explore potential sources of heterogeneity. The meta-analysis of 27 studies on Caucasian adults (n = 66,213) combining the different study designs did not support overall association of the CC-genotype with obesity, yielding an odds ratio (OR) of 1.05 (p-value = 0.27). The I(2) measure of 41% (p-value = 0.015) indicated between-study heterogeneity. Restricting to GP studies resulted in a declined I(2) measure of 11% (p-value = 0.33) and an OR of 1.10 (p-value = 0.015). Regarding the five hypotheses, our data showed (a) some difference between GP and HP studies (p-value = 0.012) and (b) an association in extreme comparisons (BMI> or =32.5, 35.0, 37.5, 40.0 kg/m(2) versus BMI<25 kg/m(2)) yielding ORs of 1.16, 1.18, 1.22, or 1.27 (p-values 0.001 to 0.003), which was also underscored by significantly increased CC-genotype frequencies across BMI categories (10.4% to 12.5%, p-value for trend = 0.0002). We did not find evidence for differential ORs (c) among studies with higher than average obesity prevalence compared to lower, (d) among studies with BMI assessment after the year 2000 compared to those before, or (e) among studies from older populations compared to younger. Analysis of non-Caucasian adults (n = 4889) or children (n = 3243) yielded ORs of 1.01 (p-value = 0.94) or 1.15 (p-value = 0.22), respectively. There was no evidence for overall association of the rs7566605 polymorphism with obesity. Our data suggested an association with extreme degrees of obesity, and consequently heterogeneous effects from different study designs may mask an underlying association when unaccounted for. The importance of study design might be under-recognized in gene discovery and association replication so far., Competing Interests: Dawn Waterworth and Vincent Moser are employers of GSK. Gudmar Thorleifsson is employer and shareholder of deCODE.

Biomedical researchers usually test the null hypothesis that there is no difference of the population mean of pharmacokinetics (PK) parameters between genotypes by the Kruskal-Wallis test. Although a monotone increasing pattern with a number of alleles is expected for PK-related genes, the Kruskal-Wallis test does not consider a monotonic response pattern. For detecting such patterns in clinical and toxicological trials, a maximum contrast method has been proposed. We show how that method can be used with pharmacogenomics data to a develop test of association. Further, using simulation studies, we compare the power of the modified maximum contrast method to those of the maximum contrast method and the Kruskal-Wallis test. On the basis of the results of those studies, we suggest rules of thumb for which statistics to use in a given situation. An application of all three methods to an actual genome-wide pharmacogenomics study illustrates the practical relevance of our discussion.

The different genetic variation discovery projects (The SNP Consortium, the International HapMap Project, the 1000 Genomes Project, etc.) aim to identify as much as possible of the underlying genetic variation in various human populations. The question we address in this article is how many new variants are yet to be found. This is an instance of the species problem in ecology, where the goal is to estimate the number of species in a closed population. We use a parametric beta-binomial model that allows us to calculate the expected number of new variants with a desired minimum frequency to be discovered in a new dataset of individuals of a specified size. The method can also be used to predict the number of individuals necessary to sequence in order to capture all (or a fraction of) the variation with a specified minimum frequency. We apply the method to three datasets: the ENCODE dataset, the SeattleSNPs dataset, and the National Institute of Environmental Health Sciences SNPs dataset. Consistent with previous descriptions, our results show that the African population is the most diverse in terms of the number of variants expected to exist, the Asian populations the least diverse, with the European population in-between. In addition, our results show a clear distinction between the Chinese and the Japanese populations, with the Japanese population being the less diverse. To find all common variants (frequency at least 1%) the number of individuals that need to be sequenced is small ( approximately 350) and does not differ much among the different populations; our data show that, subject to sequence accuracy, the 1000 Genomes Project is likely to find most of these common variants and a high proportion of the rarer ones (frequency between 0.1 and 1%). The data reveal a rule of diminishing returns: a small number of individuals ( approximately 150) is sufficient to identify 80% of variants with a frequency of at least 0.1%, while a much larger number (> 3,000 individuals) is necessary to find all of those variants. Finally, our results also show a much higher diversity in environmental response genes compared with the average genome, especially in African populations.

Objective: This study concerns the question of whether obese subjects in a community sample experience depression in a different way from the nonobese, especially whether they overeat to the point of gaining weight during periods of depression., Design: A representative sample of adults was interviewed regarding depression and obesity., Subjects: The sample consisted of 1396 subjects whose interviews were studied regarding relationships between obesity and depression and among whom 114 had experienced a major depressive episode at some point in their lives and provided information about the symptoms experienced during the worst or only episode of major depression., Measurements: The Diagnostic Interview Schedule (DIS) was used to identify major depressive episodes. Information was also derived from the section on Depression and Anxiety (DPAX) of the Stirling Study Schedule. Obesity was calculated as a body mass index >30. Logistic regressions were employed to assess relationships, controlling for age and gender, by means of odds ratios and 95% confidence intervals., Results: In the sample as a whole, obesity was not related to depression although it was associated with the symptom of hopelessness. Among those who had ever experienced a major depressive episode, obese persons were 5 times more likely than the nonobese to overeat leading to weight gain during a period of depression (P<0.002). These obese subjects, compared to the nonobese, also experienced longer episodes of depression, a larger number of episodes, and were more preoccupied with death during such episodes., Conclusions: Depression among obese subjects in a community sample tends to be more severe than among the nonobese. Gaining weight while depressed is an important marker of that severity. Further research is needed to understand and possibly prevent the associations, sequences and outcomes among depression, obesity, weight gain and other adversities.

A genome-wide association study (GWAS) is a standard strategy for detecting disease susceptibility genes, despite unsettled controversies on many aspects, including optimal study design and statistical analysis. As for study design, a two-stage design has been applied to maximize cost-effectiveness. However, there has been little consensus on appropriate statistical analysis for two-stage design. Thereby perplexing the researchers as to which statistical measures should be applied at the first stage, and how to determine the significance level of the differences at the second stage. Here, using simulation studies, we compared statistical operating characteristics of the screening in a two-stage GWAS by taking into consideration the proper balance of false-positive and false-negative error. As a result, the lower bound of confidence interval for odds ratios is recommended as the first stage measure, and then the second stage criteria should primarily depend on the purpose of the genome screen or its role in the overall gene-hunting scheme. Based on the simulation study, we suggest rules of thumb about which statistics to use in a given situation. An application of all operating characteristics of the screening method to an actual GWAS for gastric cancer illustrates the practical relevance of our discussion.

We discuss several new powerful family-based approaches for testing genetic association when the traits are obtained from longitudinal or repeated measurement studies. The popular approach FBAT-PC is based on a linear combination of the individual traits. We propose a one-sided modification, FBAT-PCM, which has a closed-form expression and is always more powerful. We also present two approaches FBAT-LC and FBAT-LCC based on linear combination of the univariate test statistics. Furthermore, all three approaches are shown to be unified to a general form. Through simulation studies, we compare the power of these tests under different models of genetic effect sizes. Compared to original FBAT-PC, our modification achieves a power gain of up to 50%. In addition, all three new approaches gain substantial power compared to the ordinary approach of Bonferroni correction, with the relative performance depending upon the underlying model. Application of these approaches for testing an association between Body Mass Index and a previously reported candidate SNP confirms our results.

Several family-based approaches have been previously proposed to enhance the power for testing genetic association when the traits are measured longitudinally or repeatedly. In this paper, we show that some of these FBAT approaches can be easily extended to accommodate incomplete data and remain unbiased tests. We also show that because of the nature of FBAT approaches, we can impute the missing phenotypes without biasing our tests and achieve higher power. We propose two imputation techniques based on E-M algorithm and the conditional mean model, respectively. Through simulation studies, these two imputation techniques are shown to have correct false positive rate and generally achieve higher power than complete case analysis or simple mean-imputation. Application of these approaches for testing an association between Body Mass Index and a previously reported candidate SNP confirms our results.

Attention deficit hyperactivity disorder (ADHD) is a complex condition with environmental and genetic etiologies. Up to this point, research has identified genetic associations with candidate genes from known biological pathways. In order to identify novel ADHD susceptibility genes, 600,000 SNPs were genotyped in 958 ADHD proband-parent trios. After applying data cleaning procedures we examined 429,981 autosomal SNPs in 909 family trios. We generated six quantitative phenotypes from 18 ADHD symptoms to be used in genome-wide association analyses. With the PBAT screening algorithm, we identified 2 SNPs, rs6565113 and rs552655 that met the criteria for significance within a specified phenotype. These SNPs are located in intronic regions of genes CDH13 and GFOD1, respectively. CDH13 has been implicated previously in substance use disorders. We also evaluated the association of SNPs from a list of 37 ADHD candidate genes that was specified a priori. These findings, along with association P-values with a magnitude less than 10(-5), are discussed in this manuscript. Seventeen of these candidate genes had association P-values lower then 0.01: SLC6A1, SLC9A9, HES1, ADRB2, HTR1E, DDC, ADRA1A, DBH, DRD2, BDNF, TPH2, HTR2A, SLC6A2, PER1, CHRNA4, SNAP25, and COMT. Among the candidate genes, SLC9A9 had the strongest overall associations with 58 association test P-values lower than 0.01 and multiple association P-values at a magnitude of 10(-5) in this gene. In sum, these findings identify novel genetic associations at viable ADHD candidate genes and provide confirmatory evidence for associations at previous candidate genes. Replication of these results is necessary in order to confirm the proposed genetic variants for ADHD., (Copyright 2008 Wiley-Liss, Inc.)

Results of behavioral genetic and molecular genetic studies have converged to suggest that genes substantially contribute to the development of attention deficit/hyperactivity disorder (ADHD), a common disorder with an onset in childhood. Yet, despite numerous linkage and candidate gene studies, strongly consistent and replicable association has eluded detection. To search for ADHD susceptibility genes, we genotyped approximately 600,000 SNPs in 958 ADHD affected family trios. After cleaning the data, we analyzed 438,784 SNPs in 2,803 individuals comprising 909 complete trios using ADHD diagnosis as phenotype. We present the initial TDT findings as well as considerations for cleaning family-based TDT data. None of the SNP association tests achieved genome-wide significance, indicating that larger samples may be required to identify risk loci for ADHD. We additionally identify a systemic bias in family-based association, and suggest that variable missing genotype rates may be the source of this bias., (Copyright 2008 Wiley-Liss, Inc.)

A time-to-onset analysis for family-based samples was performed on the genomewide association (GWAS) data for attention deficit hyperactivity disorder (ADHD) to determine if associations exist with the age at onset of ADHD. The initial dataset consisted of 958 parent-offspring trios that were genotyped on the Perlegen 600,000 SNP array. After data cleaning procedures, 429,981 autosomal SNPs and 930 parent-offspring trios were used found suitable for use and a family-based logrank analysis was performed using that age at first ADHD symptoms as the quantitative trait of interest. No SNP achieved genome-wide significance, and the lowest P-values had a magnitude of 10(-7). Several SNPs among a pre-specified list of candidate genes had nominal associations including SLC9A9, DRD1, ADRB2, SLC6A3, NFIL3, ADRB1, SYT1, HTR2A, ARRB2, and CHRNA4. Of these findings SLC9A9 stood out as a promising candidate, with nominally significant SNPs in six distinct regions of the gene., (Copyright 2008 Wiley-Liss, Inc.)

Alzheimer's disease (AD) is a genetically complex and heterogeneous disorder. To date four genes have been established to either cause early-onset autosomal-dominant AD (APP, PSEN1, and PSEN2(1-4)) or to increase susceptibility for late-onset AD (APOE5). However, the heritability of late-onset AD is as high as 80%, (6) and much of the phenotypic variance remains unexplained to date. We performed a genome-wide association (GWA) analysis using 484,522 single-nucleotide polymorphisms (SNPs) on a large (1,376 samples from 410 families) sample of AD families of self-reported European descent. We identified five SNPs showing either significant or marginally significant genome-wide association with a multivariate phenotype combining affection status and onset age. One of these signals (p = 5.7 x 10(-14)) was elicited by SNP rs4420638 and probably reflects APOE-epsilon4, which maps 11 kb proximal (r2 = 0.78). The other four signals were tested in three additional independent AD family samples composed of nearly 2700 individuals from almost 900 families. Two of these SNPs showed significant association in the replication samples (combined p values 0.007 and 0.00002). The SNP (rs11159647, on chromosome 14q31) with the strongest association signal also showed evidence of association with the same allele in GWA data generated in an independent sample of approximately 1,400 AD cases and controls (p = 0.04). Although the precise identity of the underlying locus(i) remains elusive, our study provides compelling evidence for the existence of at least one previously undescribed AD gene that, like APOE-epsilon4, primarily acts as a modifier of onset age.

The failure of researchers to replicate genetic-association findings is most commonly attributed to insufficient statistical power, population stratification, or various forms of between-study heterogeneity or environmental influences.(1) Here, we illustrate another potential cause for nonreplications that has so far not received much attention in the literature. We illustrate that the strength of a genetic effect can vary by age, causing "age-varying associations." If not taken into account during the design and the analysis of a study, age-varying genetic associations can cause nonreplication. By using the 100K SNP scan of the Framingham Heart Study, we identified an age-varying association between a SNP in ROBO1 and obesity and hypothesized an age-gene interaction. This finding was followed up in eight independent samples comprising 13,584 individuals. The association was replicated in five of the eight studies, showing an age-dependent relationship (one-sided combined p = 3.92 x 10(-9), combined p value from pediatric cohorts = 2.21 x 10(-8), combined p value from adult cohorts = 0.00422). Furthermore, this study illustrates that it is difficult for cross-sectional study designs to detect age-varying associations. If the specifics of age- or time-varying genetic effects are not considered in the selection of both the follow-up samples and in the statistical analysis, important genetic associations may be missed.

The objective of this study was to determine whether single nucleotide polymorphisms (SNPs) within candidate genes for ADHD are associated with quantitative phenotypes generated from inattentive and hyperactive-impulsive symptoms. One hundred forty-three SNPs were genotyped in and around five ADHD candidate genes. A highly heritable quantitative phenotype was generated at each SNP by weighting inattentive and hyperactive-impulsive symptoms. Once these phenotypes were generated, a screening procedure was used to select and test the five SNP/phenotype combinations with the greatest power to detect an association for each candidate gene. Adjacent SNPs in the promoter region of DRD4, hCV26775267 and hCV26775266, were associated with the quantitative phenotypes generated from the ADHD symptoms (corrected P-values = 0.012 for both SNPs). The correlations between the ADHD symptoms and quantitative phenotype revealed that inattentive symptoms had a strong influence on the generated phenotype. Subsequent family-based association test-principal components (FBAT-PC) analyses using inattentive symptoms only also had significant associations. SNPs in the promoter region of DRD4 are associated with the phenotypes generated from ADHD symptoms. The strong correlation of the inattentive symptoms with these quantitative phenotypes and the subsequent FBAT-PC analyses suggest this region is primarily associated with inattentive symptoms. This analysis adds to previous findings by suggesting that variants at these loci may be specifically associated with inattentive symptoms., ((c) 2007 Wiley-Liss, Inc.)

Background: Family-based association tests are important tools for investigating genetic risk factors of complex diseases. These tests are especially valuable for being robust to population structure. We introduce a tool, EFBAT, which performs exact family-based tests of association for X-chromosome and autosomal biallelic markers., Results: The program EFBAT extends a network algorithm previously applied to autosomal markers to include the X-chromosome and to perform tests of association under the null hypotheses "no association, no linkage" and "no association in the presence of linkage" under additive, dominant and recessive genetic models. These tests are valid regardless of patterns of missing familial data., Conclusion: The general framework for performing exact family-based association tests has been usefully extended to the X-chromosome, particularly for the hypothesis of "no association in the presence of linkage" and for different genetic models.

The purpose of this study was to determine whether the single nucleotide polymorphisms (SNPs) within candidate genes for attention deficit hyperactivity disorder (ADHD) are associated with the age at onset for ADHD. One hundred and forty-three SNPs were genotyped across five candidate genes (DRD5, SLC6A3, HTR1B, SNAP25, DRD4) for ADHD in 229 families with at least one affected offspring. SNPs with the highest estimated power to detect an association with age at onset were selected for each candidate gene, using a power-based screening procedure that does not compromise the nominal significance level. A time-to-onset analysis for family-based samples was performed on these SNPs to determine if an association exists with age at onset for ADHD. Seven consecutive SNPs surrounding the D5 dopamine receptor gene (DRD5), were associated with the age at onset for ADHD; FDR adjusted q-values ranged from 0.008 to 0.023. This analysis indicates that individuals with the risk genotype develop ADHD earlier than individuals with any other genotype. A haplotype analysis across the 6 significant SNPs that were in linkage disequilibrium with one another, CTCATA, was also found to be significant (p-value = 0.02). We did not observe significant associations with age at onset for the other candidate loci tested. Although definitive conclusions await independent replication, these results suggest that a variant in DRD5 may affect age at onset for ADHD.

Recent animal research suggests that brain-derived neurotrophic factor (BDNF), may mediate response to different environmental stimuli. In this paper, we evaluated the possible role of BDNF as a moderator of attention deficit hyperactivity disorder (ADHD) in the context of different socioeconomic classes. We genotyped ten single nucleotide polymorphisms (SNPs) in and around BDNF in 229 families and evaluate whether there are SNP-by-socioeconomic status (SES) interactions for attention deficit hyperactivity. We developed three quantitative phenotypes for ADHD from nine inattentive and nine hyperactive-impulsive symptoms that were used in SNP-by-SES interaction analyses using a new methodology implemented in the computer program PBAT. Findings were adjusted for multiple comparisons using the false discovery rate. We found multiple significant SNP-by-SES interactions using the inattentive symptom count. This study suggests that different SES classes may modify the effect of the functional variant(s) in and around BDNF to have an impact on the number of ADHD symptom counts that are observed. The two exons within BDNF represent potential functional variants that may be causing the observed associations.

INTRODUCTIONThe ammoniacal silver staining method is one of the most sensitive methods used to detect proteins on an SDS-PAGE gel. However, this and other standard silver staining methods are not compatible with mass spectrometry (MS), which is fast becoming the best way to identify proteins isolated on 2D gels. Because the proteins in gels to be analyzed by mass spectroscopy cannot be modified, many of the common sensitizing agents (e.g., glutaraldehyde and strong oxidizing agents) cannot be used. This method is compatible with MALDI and ESI-MS, and it shows an increased ability to deal with semipreparative protein loads without negative staining as compared with other silver staining methods. However, this process is less sensitive than standard silver staining methods.

A SNP upstream of the INSIG2 gene, rs7566605, was recently found to be associated with obesity as measured by body mass index (BMI) by Herbert and colleagues. The association between increased BMI and homozygosity for the minor allele was first observed in data from a genome-wide association scan of 86,604 SNPs in 923 related individuals from the Framingham Heart Study offspring cohort. The association was reproduced in four additional cohorts, but was not seen in a fifth cohort. To further assess the general reproducibility of this association, we genotyped rs7566605 in nine large cohorts from eight populations across multiple ethnicities (total n = 16,969). We tested this variant for association with BMI in each sample under a recessive model using family-based, population-based, and case-control designs. We observed a significant (p < 0.05) association in five cohorts but saw no association in three other cohorts. There was variability in the strength of association evidence across examination cycles in longitudinal data from unrelated individuals in the Framingham Heart Study Offspring cohort. A combined analysis revealed significant independent validation of this association in both unrelated (p = 0.046) and family-based (p = 0.004) samples. The estimated risk conferred by this allele is small, and could easily be masked by small sample size, population stratification, or other confounders. These validation studies suggest that the original association is less likely to be spurious, but the failure to observe an association in every data set suggests that the effect of SNP rs7566605 on BMI may be heterogeneous across population samples., Competing Interests: Competing interests. The authors have declared that no competing interests exist.

The high throughput of data arising from the complete sequence of the human genome has left statistical geneticists with a rich and extensive information source. The wide availability of software and the increase in computing power has improved the possibilities to access and process such data. One problem is incompleteness of the data: unobserved or partially observed data points due to technical reasons or reasons associated with the patient's status or erroneous measurements of phenotype or genotype, to name a few. When not properly accounted for, these sources of incompleteness may seriously jeopardize the credibility of results from analyses. In this paper we provide some perspectives on the occurrence and analysis of different forms of incomplete data in family-based genetic association testing.

In genetic association studies, multiple markers are usually employed to cover a genomic region of interest for localizing a trait locus. In this report, we propose a novel multi-marker family-based association test (T(LC)) that linearly combines the single-marker test statistics using data-driven weights. We examine the type-I error rate in a numerical study and compare its power to identify a common trait locus using tag single nucleotide polymorphisms (SNPs) within the same haplotype block that the trait locus resides with three competing tests including a global haplotype test (T(H)), a multi-marker test similar to the Hotelling-T(2) test for the population-based data (T(MM)), and a single-marker test with Bonferroni's correction for multiple testing (T(B)). The type-I error rate of T(LC) is well maintained in our numeric study. In all the scenarios we examined, T(LC) is the most powerful, followed by T(B). T(MM) and T(H) are the poorest. T(H) and T(MM) have essentially the same power when parents are available. However, when both parents are missing, T(MM) is substantially more powerful than T(H). We also apply this new test on a data set from a previous association study on nicotine dependence., ((c) 2006 Wiley-Liss, Inc.)

INTRODUCTIONThis protocol describes a method for separating proteins based on their net charge using the technique of isoelectric focusing (IEF) on immobilized pH gradient (IPG) gels, providing the first dimension of the 2D separation. In this protocol, the IPG gels are focused using self-contained instruments for IEF. These high-voltage systems allow fewer manipulations of the IPG gels, resulting in less error, strip mix-up, contamination, air contact, or urea crystallization. Because rehydration and IEF can be performed consecutively within a single unit, these two steps can be performed unattended overnight. Finally, faster separations and sharper focusing are possible due to the higher voltage available in these instruments.

INTRODUCTIONCoomassie Blue R250 permanently stains membrane-bound proteins and is compatible with PVDF and nitrocellulose membranes, but it is incompatible with nylon membranes. This technique is relatively insensitive, with a detection limit of ~1.5 μg of protein. One drawback of Coomassie Blue staining is that it produces a high background that can make interpretation of results difficult.

INTRODUCTIONThe phosphorylation state of a protein has an important role in the regulation of a wide variety of cellular processes. As a result, there has been a great deal of interest in detecting phosphorylated proteins. The method presented here uses the GelCode phosphoprotein staining kit (Pierce Chemical Company). This method depends on the hydrolysis of the phosphoprotein phosphoester linkage using sodium hydroxide in the presence of calcium ions. The gel containing the newly formed insoluble calcium phosphate is then treated with ammonium molybdate in dilute nitric acid. The resultant insoluble nitrophospho-molybdate complex is stained with Methyl Green. After destaining, the phosphoproteins are colored green to green-blue. The detection limit is in the nanogram range, but depends on the degree of phosphorylation of the protein. This method will detect the phosphoproteins phosvitin and β-casein in the 40-80 ng/band and 80-160 ng/band range, respectively. The method presented here is for staining minigels. Volumes will need to be increased for larger gels.

INTRODUCTIONColloidal Gold is the most sensitive staining technique for proteins bound on membranes, detecting as little as 1-3 ng of protein. Protein spots are permanently stained a dark red after incubation with the Colloidal Gold solution. Colloidal Gold staining can detect proteins on both nitrocellulose and PVDF membranes, but it is not recommended for nylon membranes.