Search

Gait speed is a heritable, robust predictor of longevity in older adults. Using genome-wide linkage analysis in 2379 individuals from 509 families (64±12 years; 45% men), we identified a locus on chromosome 16p linked to gait speed change over 7±1 years (logarithm of the odds score [LOD]=4.2). Gait speed change was calculated using a two-stage growth curve mixed-model. DNA sequencing was completed to identify single nucleotide variants (SNVs) in the linkage region. Association analyses between the 24039 SNVs in the ~1.6mBP region (3.7-5.3mBP) and gait speed change were performed adjusting for age, age2, sex, height, field center, familial relatedness and population substructure. Eleven families (188 individuals) accounted for most of the linkage signal (LOD=6.06). Associations between SNVs flanking the Mesothelin (MSLN) gene and gait speed change were identified (lead SNV rs56850119: β = -0.5±0.1, p = 6.4*10-7). Thus, MSLN is a potential positional candidate gene for mobility decline with aging.

Pulmonary function progressively declines with aging. Forced expiratory volume 1-second (FEV1) and forced vital capacity (FVC) are predictors of morbidity of cardiovascular diseases and all-cause mortality. Reduced pulmonary function has shown association with elevated chronic low-grade systemic inflammation and low muscle strength, and glycosylated hemoglobin (HbA1c). We evaluated whether FEV1 and FVC share common genetic factors with interleukin-6 (IL-6), high-sensitivity C-reactive protein (hsCRP), body mass index (BMI), handgrip strength, plasma glucose, and HbA1c employing correlated meta-analysis (CMA) in up to 3888 individuals (age range: 26-106). CMA tests whether combining genome-wide association (GWA) P-values from two or more traits enhances the ability to detect variants concomitantly influencing such traits. We considered a variant pleiotropic if the univariate GWA P≤1.0x10-02 and CMA P≤5.0x10-08. We identified pleiotropic loci for FEV1 (or FVC), IL-6 and hsCRP within CYCS, CALN1, TRIM9, AXIN2, MICAL3, and CMIP; FEV1 (or FVC) and BMI within CYP2U1, LINC00871-RPL10L-MDGA2, and LOC105372472; FEV1 (or FVC) and grip strength within XXYLT1 and MAL2; FEV1 and FPG within MAF; and FVC and HbA1c within HDHD3. Some loci were reported as GWAS suggestive (P

The GAW20 simulation data set is based upon the companion Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study fenofibrate clinical trial data set that forms the real data example for GAW20. The simulated data problem consists of 200 simulated replications of what might happen if we were to repeat the GOLDN clinical trial 200 independent times, for these exact same subjects, but using a new fictitious drug (called “genomethate”) that has a pharmaco-epigenetic effect on triglyceride response. For each replication, the pre-genomethate values at visits 1 and 2 are constant (ie, pedigree structures, age, sex, all phenotypes, covariates, genome-wide association study (GWAS) genotypes, and visit 2 methylation values), the same as the real GOLDN data across all 200 replications. Only the post-genomethate treatment data (ie, methylation and triglyceride levels for visits 3 and 4) change across the 200 replications. We postulate a growth curve pharmaco-epigenetic response model, in which each patient’s response to genomethate treatment is individualized, and is dependent upon their genotype as well as the methylation state for key genes.

Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

Background Because a significant number of patients with prostate cancer (PCa) are diagnosed with disease unlikely to cause harm, genetic markers associated with clinically aggressive PCa have potential clinical utility. Since cell cycle checkpoint dysregulation is crucial for the development and progression of cancer, we tested the hypothesis that common germ-line variants within cell cycle genes were associated with aggressive PCa. Methods Via a two-stage design, 364 common sequence variants in 88 genes were tested. The initial stage consisted of 258 aggressive PCa patients and 442 controls, and the second stage added 384 aggressive PCa Patients and 463 controls. European-American and African-American samples were analyzed separately. In the first stage, SNPs were typed by Illumina Goldengate assay while in the second stage SNPs were typed by Pyrosequencing assays. Genotype frequencies between cases and controls were compared using logistical regression analysis with additive, dominant and recessive models. Results Eleven variants within 10 genes (CCNC, CCND3, CCNG1, CCNT2, CDK6, MDM2, SKP2, WEE1, YWHAB, YWHAH) in the European-American population and nine variants in 7 genes (CCNG1, CDK2, CDK5, MDM2, RB1, SMAD3, TERF2) in the African-American population were found to be associated with aggressive PCa using at least one model. Of particular interest, CCNC (rs3380812) was associated with risk in European-American cohorts from both institutions. CDK2 (rs1045435) and CDK5 (rs2069459) were associated with risk in the African-American cohorts from both institutions. Lastly, variants within MDM2 and CCNG1 were protective for aggressive PCa in both ethnic groups. Conclusions This study confirms that polymorphisms within cell cycle genes are associated with clinically aggressive PCa. Validation of these markers in additional populations is necessary, but these markers may help identify patients at risk for potentially lethal carcinoma. Prostate © 2015 Wiley Periodicals, Inc.

This study compares methods of imputing genetic markers, given a typed GWAS scaffold from the Long Life Family Study (LLFS) and latest reference panel of 1000-Genomes. We examined two programs for ...

To examine whether single-nucleotide polymorphism (SNP) by methylation interactions can be detected, we analyzed GAW20 simulated triglycerides at visits 3 and 4 against baseline (visits 1 and 2) under 4 general linear models and 2 tree-based models in 200 replications of a sample of 680 individuals. Effects for SNPs, methylation cytosine-phosphate-guanine (CpG) effects, and interactions for SNP/CpG pairs were included. Causative SNPs/CpG pairs distributed on autosomal chromosomes 1 to 20 were tested to examine sensitivity. We also tested noncausative SNP/CpG pairs on chromosomes 21 and 22 to estimate the empirical null. We found reasonable power to detect the main causative loci, with the exact power depending on sample size and strength of effects at the SNP and CpG sites.

Poster presented at OHBM 2018.

BACKGROUND: The Healthy Aging Index (HAI) is a tool for measuring the extent of health and disease across multiple systems. METHODS: We conducted a genome-wide association study and a genome-wide linkage analysis to map quantitative trait loci associated with the HAI and a modified HAI weighted for mortality risk in 3,140 individuals selected for familial longevity from the Long Life Family Study. The genome-wide association study used the Long Life Family Study as the discovery cohort and individuals from the Cardiovascular Health Study and the Framingham Heart Study as replication cohorts. RESULTS: There were no genome-wide significant findings from the genome-wide association study; however, several single-nucleotide polymorphisms near ZNF704 on chromosome 8q21.13 were suggestively associated with the HAI in the Long Life Family Study (p < 10(-) (6)) and nominally replicated in the Cardiovascular Health Study and Framingham Heart Study. Linkage results revealed significant evidence (log-odds score = 3.36) for a quantitative trait locus for mortality-optimized HAI in women on chromosome 9p24-p23. However, results of fine-mapping studies did not implicate any specific candidate genes within this region of interest. CONCLUSIONS: ZNF704 may be a potential candidate gene for studies of the genetic underpinnings of longevity. BACKGROUND: The Healthy Aging Index (HAI) is a tool for measuring the extent of health and disease across multiple systems.METHODS: We conducted a genome-wide association study and a genome-wide linkage analysis to map quantitative trait loci associated with the HAI and a modified HAI weighted for mortality risk in 3,140 individuals selected for familial longevity from the Long Life Family Study. The genome-wide association study used the Long Life Family Study as the discovery cohort and individuals from the Cardiovascular Health Study and the Framingham Heart Study as replication cohorts.RESULTS: There were no genome-wide significant findings from the genome-wide association study; however, several single-nucleotide polymorphisms near ZNF704 on chromosome 8q21.13 were suggestively associated with the HAI in the Long Life Family Study (p < 10(-) (6)) and nominally replicated in the Cardiovascular Health Study and Framingham Heart Study. Linkage results revealed significant evidence (log-odds score = 3.36) for a quantitative trait locus for mortality-optimized HAI in women on chromosome 9p24-p23. However, results of fine-mapping studies did not implicate any specific candidate genes within this region of interest.CONCLUSIONS: ZNF704 may be a potential candidate gene for studies of the genetic underpinnings of longevity.

Non-high-density lipoprotein cholesterol (NHDL) is an independent and superior predictor of CVD risk as compared to LDL alone. It represents a spectrum of atherogenic lipid fractions with possibly a distinct genomic signature. We performed genome-wide association studies (GWAS) to identify loci influencing baseline NHDL and its postprandial lipemic (PPL) response. We carried out GWAS in 4,241 participants of European descent. Our discovery cohort included 928 subjects from the Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN) Study. Our replication cohorts included 3,313 subjects from the Heredity and Phenotype Intervention (HAPI) Heart Study and Family Heart Study (FamHS). A linear mixed model using the kinship matrix was used for association tests. The best association signal was found in a tri-genic region at RHOQ-PIGF-CRIPT for baseline NHDL (lead SNP rs6544903, discovery p = 7e-7, MAF = 2%; validation p = 6e-4 at 0.1 kb upstream neighboring SNP rs3768725, and 5e-4 at 0.7 kb downstream neighboring SNP rs6733143, MAF = 10%). The lead and neighboring SNPs were not perfect surrogate proxies to each other (D′ = 1, r2 = 0.003) but they seemed to be partially dependent (likelihood ration test p = 0.04). Other suggestive loci (discovery p < 1e-6) included LOC100419812 and LOC100288337 for baseline NHDL, and LOC100420502 and CDH13 for NHDL PPL response that were not replicated (p > 0.01). The current and first GWAS of NHDL yielded an interesting common variant in RHOQ-PIGF-CRIPT influencing baseline NHDL levels. Another common variant in CDH13 for NHDL response to dietary high fat intake challenge was also suggested. Further validations for both loci from large independent studies, especially interventional studies, are warranted.

BACKGROUND: The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes.METHODS: We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests.RESULTS: We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3' UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL).CONCLUSIONS: Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants. BACKGROUND: The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes.METHODS: We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests.RESULTS: We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3' UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL).CONCLUSIONS: Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants.

Because a significant number of patients with prostate cancer (PCa) are diagnosed with disease unlikely to cause harm, genetic markers associated with clinically aggressive PCa have potential clinical utility. Since cell cycle checkpoint dysregulation is crucial for the development and progression of cancer, we tested the hypothesis that common germ-line variants within cell cycle genes were associated with aggressive PCa.Via a two-stage design, 364 common sequence variants in 88 genes were tested. The initial stage consisted of 258 aggressive PCa patients and 442 controls, and the second stage added 384 aggressive PCa Patients and 463 controls. European-American and African-American samples were analyzed separately. In the first stage, SNPs were typed by Illumina Goldengate assay while in the second stage SNPs were typed by Pyrosequencing assays. Genotype frequencies between cases and controls were compared using logistical regression analysis with additive, dominant and recessive models.Eleven variants within 10 genes (CCNC, CCND3, CCNG1, CCNT2, CDK6, MDM2, SKP2, WEE1, YWHAB, YWHAH) in the European-American population and nine variants in 7 genes (CCNG1, CDK2, CDK5, MDM2, RB1, SMAD3, TERF2) in the African-American population were found to be associated with aggressive PCa using at least one model. Of particular interest, CCNC (rs3380812) was associated with risk in European-American cohorts from both institutions. CDK2 (rs1045435) and CDK5 (rs2069459) were associated with risk in the African-American cohorts from both institutions. Lastly, variants within MDM2 and CCNG1 were protective for aggressive PCa in both ethnic groups.This study confirms that polymorphisms within cell cycle genes are associated with clinically aggressive PCa. Validation of these markers in additional populations is necessary, but these markers may help identify patients at risk for potentially lethal carcinoma.

Next-generation sequencing of large numbers of individuals presents challenges in data preparation, quality control, and statistical analysis because of the rarity of the variants. The Genetic Analysis Workshop 17 (GAW17) data provide an opportunity to survey existing methods and compare these methods with novel ones. Specifically, the GAW17 Group 2 contributors investigate existing and newly proposed methods and study design strategies to identify rare variants, predict functional variants, and/or examine quality control. We introduce the eight Group 2 papers, summarize their approaches, and discuss their strengths and weaknesses. For these investigations, some groups used only the genotype data, whereas others also used the simulated phenotype data. Although the eight Group 2 contributions covered a wide variety of topics under the general idea of identifying rare variants, they can be grouped into three broad categories according to their common research interests: functionality of variants and quality control issues, family-based analyses, and association analyses of unrelated individuals. The aims of the first subgroup were quite different. These were population structure analyses that used rare variants to predict functionality and examine the accuracy of genotype calls. The aims of the family-based analyses were to select which families should be sequenced and to identify high-risk pedigrees; the aim of the association analyses was to identify variants or genes with regression-based methods. However, power to detect associations was low in all three association studies. Thus this work shows opportunities for incorporating rare variants into the genetic and statistical analyses of common diseases.

Objective: While multiple lines of evidence suggest the importance of genetic contributors to risk of preterm birth, the nature of the genetic component has not been identified. We perform segregation analyses to identify the best fitting genetic model for gestational age, a quantitative proxy for preterm birth. Methods: Because either mother or infant can be considered the proband from a preterm delivery and there is evidence to suggest that genetic factors in either one or both may influence the trait, we performed segregation analysis for gestational age either attributed to the infant (infant’s gestational age), or the mother (by averaging the gestational ages at which her children were delivered), using 96 multiplex preterm families. Results: These data lend further support to a genetic component contributing to birth timing since sporadic (i.e. no familial resemblance) and nontransmission (i.e. environmental factors alone contribute to gestational age) models are strongly rejected. Analyses of gestational age attributed to the infant support a model in which mother’s genome and/or maternally-inherited genes acting in the fetus are largely responsible for birth timing, with a smaller contribution from the paternally-inherited alleles in the fetal genome. Conclusion: Our findings suggest that genetic influences on birth timing are important and likely complex.

Hypertrophic cardiomyopathy (HCM) is a disease of mutant sarcomeric proteins (except for phenocopy). Cardiac hypertrophy is the clinical diagnostic hallmark of HCM and a major determinant of morbidity and mortality in various cardiovascular diseases. However, there is remarkable variability in expression of hypertrophy, even among HCM patients with identical causal mutations. We hypothesized modifier genes are partly responsible for the variation in hypertrophic expressivity. To map the modifier loci, we typed 811 short-tandem repeat markers (∼5 cMdense) in 100 members of an HCM family including 36 with the InsG791 mutation in MYBPC3. We performed oligogenic simultaneous segregation and linkage analyses using Markov Chain Monte Carlo methods and detected linkage on 3q26.2 (180 cM), 10p13 (41 cM), 17q24 (108 cM) with log of the posterior placement probability ratio (LOP) of 3.51, 4.86 and 4.17, respectively, and suggestive linkage (LOP of 2.40) on 16q12.2 (73 cM). The effect sizes varied according to the modifier locus, age and sex. It ranged from ∼8 g shift in left ventricular mass for 10p13 locus heterozygosity for the common allele to ∼90 g shift for 3q26.2 locus homozygosity for the uncommon allele. Refining the 10p13 locus restricted the candidate modifier genes to ITGA8 , C10orf97 (CARP) and PTER. ITGA8 and CARP are biologically plausible candidates as they are implicated in cardiac fibrosis and apoptosis, respectively. Since cardiac hypertrophy is a major determinant of total and cardiovascular mortality and morbidity, regardless of the etiology, identification of the specific modifier genes could have significant prognostic and therapeutic implications for various cardiovascular diseases. © The Author 2007. Published by Oxford University Press. All rights reserved.

The research presented in group 11 of the Genetic Analysis Workshop 15 (GAW15) falls into two major themes: Model selection approaches for gene mapping (both Bayesian and Frequentist); and other Bayesian methods. These methods either allow relaxation of some of the common assumptions, such as mode of inheritance, for studying complicated genetic systems, or allow incorporation of additional information into the model. Over half of the groups applied model selection methods on all three data sets, using models in which genetic markers were used as predictors for linkage, phenotype expression, or transmission to an affected offspring. Most groups employed variations of Stochastic Search Variable Selection as the model selection method of choice. A brief review of this class of methods is given in this summary paper, followed by highlights of other methods and overall summaries of each contribution to the GAW15 presentation group 11. These group contributions exhibit the value of framing genetic problems in terms of model selection, and highlight the impact of variable selection for gene mapping.

Presentation Group 4 participants analyzed the Collaborative Study on the Genetics of Alcoholism data provided for Genetic Analysis Workshop 14. This group examined various aspects of linkage analysis and related issues. Seven papers included linkage analyses, while the eighth calculated identity-by-descent (IBD) probabilities. Six papers analyzed linkage to an alcoholism phenotype: ALDX1 (four papers), ALDX2 (one paper), or a combination both (one paper). Methods used included Bayesian variable selection coupled with Haseman-Elston regression, recursive partitioning to identify phenotype and covariate groupings that interact with evidence for linkage, nonparametric linkage regression modeling, affected sib-pair linkage analysis with discordant sib-pair controls, simulation-based homozygosity mapping in a single pedigree, and application of a propensity score to collapse covariates in a general conditional logistic model. Alcoholism linkage was found with ≥2 of these approaches on chromosomes 2, 4, 6, 7, 9, 14, and 21. The remaining linkage paper compared the utility of several single-nucleotide polymorphism (SNP) and microsatellite marker maps for Monte Carlo Markov chain combined oligogenic segregation and linkage analysis, and analyzed one of the electrophysiological endophenotypes, ttth1, on chromosome 7. Linkage was found with all marker sets. The last paper compared the multipoint IBD information content of several SNP sets and the microsatellite set, and found that while all SNP sets examined contained more information than the microsatellite set, most of the information contained in the SNP sets was captured by a subset of the SNP markers with ∼1-cM marker spacing. From these papers, we highlight three points: a 1-cM SNP map seems to capture most of the linkage information, so denser maps do not appear necessary; careful and appropriate use of covariates can aid linkage analysis; and sources of increased gene-sharing between relatives should be accounted for in analyses. Genet. Epidemiol. 29(Suppl. 1):S29–S34, 2005. © 2005 Wiley-Liss, Inc.

OBJECTIVE: Glycated hemoglobin (HbA1c) is a stable index of chronic glycemic status and hyperglycemia associated with progressive development of insulin resistance and frank diabetes. It is also associated with premature aging and increased mortality. To uncover novel loci for HbA1c that are associated with healthy aging, we conducted a genome-wide association study (GWAS) using non-diabetic participants in the Long Life Family Study (LLFS), a study with familial clustering of exceptional longevity in the US and Denmark.METHODS: A total of 4088 non-diabetic subjects from the LLFS were used for GWAS discoveries, and a total of 8231 non-diabetic subjects from the Atherosclerosis Risk in Communities Study (ARIC, in the MAGIC Consortium) and the Health, Aging, and Body Composition Study (HABC) were used for GWAS replications. HbA1c was adjusted for age, sex, centers, 20 principal components, without and with BMI. A linear mixed effects model was used for association testing.RESULTS: Two known loci at GCK rs730497 (or rs2908282) and HK1 rs17476364 were confirmed (pCONCLUSIONS: The analysis reconfirmed two known GWAS loci (GCK, HK1) and identified 25 suggestive loci including one reconfirmed variant in G6PC2 and one replicated variant near OR10R3P/SPTA1. Future focused survey of sequence elements containing mainly functional and regulatory variants may yield additional findings. Glycated hemoglobin (HbA1c) is a stable index of chronic glycemic status and hyperglycemia associated with progressive development of insulin resistance and frank diabetes. It is also associated with premature aging and increased mortality. To uncover novel loci for HbA1c that are associated with healthy aging, we conducted a genome-wide association study (GWAS) using non-diabetic participants in the Long Life Family Study (LLFS), a study with familial clustering of exceptional longevity in the US and Denmark.

Multipoint linkage analysis methods are often used in human genetic studies. Although multipoint methods increase power for a linkage analysis and will become essential if use of diallelic markers becomes widespread, the methods in use assume an accurate meiotic marker map. Unfortunately, uncertainties in estimates of between-marker meiotic distances are large. Also, sex-averaged maps are generally used, but recombination rates differ in males and females. Both these types of map misspecification can lead to lod score bias, but such bias has not previously been systematically quantified. We examine multipoint lod score bias arising from these map misspecifications, in both the presence and absence of actual linkage. We define bias as the expected difference between the lod score computed under the misspecified map and that computed under the true map. With actual linkage, any map misspecification causes negative bias in lod scores, resulting in loss of power to detect linkage. In most cases, bias is modest, only reaching clearly detectable levels when both types of misspecification are substantial. In the absence of linkage, map misspecification can cause positive or negative bias: falsely assuming a 1:1 female:male ratio always causes positive bias; using too large a distance gives a positive bias; using too small a distance gives a negative bias. This bias can inflate the false-positive rate, especially when the sample size is modest. We conclude that although current sex-averaged maps are suitable for a first-pass multipoint screen, the potential for bias from map misspecification should be evaluated in following up results from such an analysis.

The study objective was to examine the temporal stability of the antisocial personality disorder (ASPD) diagnosis based on whether specific antisocial symptoms were considered to be related to substance abuse. A total of 407 adults who were initially part of a family study of alcoholism and sociopathy were blindly reassessed an average of 8 years later, using the Home Environment and Lifetime Psychiatric Evaluation Record (HELPER) and basing diagnoses on the clinician's best final estimate using all sources of data. "Narrow" and "broad" ASPD diagnoses were made at both times based on whether individual symptoms were counted toward diagnosis if they occurred in the setting of significant substance abuse. kappa values varied from 0.31 to 0.68, with more restrictive methods of diagnosis being less stable. After deriving estimates of sensitivity and specificity of diagnosis, the probability of being a "case" could be assigned based on the reported number of conduct problems occurring before age 15 as a clinical covariate for diagnosis. We conclude that diagnosing ASPD without attempting to attribute the cause of individual symptoms to substance abuse results in substantially greater temporal stability. Using a broader definition, the diagnosis of ASPD is highly sensitive (P = .97) and specific (q = 0.93). These findings may allow more accurate diagnosis of ASPD in drug-abusing individuals.

Personality traits have been shown to be associated with longevity and healthy aging. In order to discover novel genetic modifiers associated with personality traits as related with longevity, we performed a genome-wide association study (GWAS) on personality factors assessed by NEO-five-factor inventory in individuals enrolled in the Long Life Family Study (LLFS), a study of 583 families (N up to 4595) with clustering for longevity in the United States and Denmark. Three SNPs, in almost perfect LD, associated with agreeableness reached genome-wide significance (p 10(-8)) and replicated in an additional sample of 1279 LLFS subjects, although one (rs9650241) failed to replicate and the other two were not available in two independent replication cohorts, the Baltimore Longitudinal Study of Aging and the New England Centenarian Study. Based on 10,000,000 permutations, the empirical p-value of 2 × 10(-7) was observed for the genome-wide significant SNPs. Seventeen SNPs that reached marginal statistical significance in the two previous GWASs (p-value10(-4) and 10(-5)), were also marginally significantly associated in this study (p-value0.05), although none of the associations passed the Bonferroni correction. In addition, we tested age-by-SNP interactions and found some significant associations. Since scores of personality traits in LLFS subjects change in the oldest ages, and genetic factors outweigh environmental factors to achieve extreme ages, these age-by-SNP interactions could be a proxy for complex gene-gene interactions affecting personality traits and longevity.

Despite evidence from family studies that there is a strong genetic influence upon exceptional longevity, relatively few genetic variants have been associated with this trait. One reason could be that many genes individually have such weak effects that they cannot meet standard thresholds of genome wide significance, but as a group in specific combinations of genetic variations, they can have a strong influence. Previously we reported that such genetic signatures of 281 genetic markers associated with about 130 genes can do a relatively good job of differentiating centenarians from non-centenarians particularly if the centenarians are 106 years and older. This would support our hypothesis that the genetic influence upon exceptional longevity increases with older and older (and rarer) ages. We investigated this list of markers using similar genetic data from 5 studies of centenarians from the USA, Europe and Japan. The results from the meta-analysis show that many of these variants are associated with survival to these extreme ages in other studies. Since many centenarians compress morbidity and disability towards the end of their lives, these results could point to biological pathways and therefore new therapeutics to increase years of healthy lives in the general population.

We describe an extension to the TDT (transmission/disequilibrium test) which allows for more than two marker alleles and for covariates measured on the parent or offspring. We also describe a systematic genomic search where the mod score (maximized lod score) is computed for each marker under constraints on the population prevalence or penetrances of a single locus.

Variance components (VC) and the Bayesian Markov chain Monte Carlo (MCMC) analysis are two of the widely used linkage analysis approaches to mapping genes for complex quantitative traits. Both approaches can handle extended pedigrees and multiple markers and do not require a prespecified genetic model. In this study, we used simulated data to compare the performance of these two approaches with the traditional parametric linkage analysis. Using simulated data sets without linkage between a quantitative trait and the markers, we estimated a critical value for various test scores used in VC or MCMC and the location (LOC) score at a fixed level of significance (5%). These critical values were then used to determine the power for the three methods for simulated data sets with linkage. We found that both the VC and MCMC approaches worked well, compared with the LOC score, when there was only one gene underlying the quantitative trait; however, VC had higher power than the other methods in a simulation study of a complex phenotype influenced by more than one gene. We also compared two implementations of MCMC analysis, finding interpretation of results using the log of placement score was more accurate for linkage inference than the Bayes factor but required much more intensive simulation studies.

Although extended pedigrees are often sampled through probands with extreme levels of a quantitative trait, Markov chain Monte Carlo (MCMC) methods for segregation and linkage analysis have not been able to perform ascertainment corrections. Further, the extent to which ascertainment of pedigrees leads to biases in the estimation of segregation and linkage parameters has not been previously studied for MCMC procedures. In this paper, we studied these issues with a Bayesian MCMC approach for joint segregation and linkage analysis, as implemented in the package Loki. We first simulated pedigrees ascertained through individuals with extreme values of a quantitative trait in spirit of the sequential sampling theory of Cannings and Thompson [Cannings and Thompson [1977] Clin. Genet. 12:208–212]. Using our simulated data, we detected no bias in estimates of the trait locus location. However, in addition to allele frequencies, when the ascertainment threshold was higher than or close to the true value of the highest genotypic mean, bias was also found in the estimation of this parameter. When there were multiple trait loci, this bias destroyed the additivity of the effects of the trait loci, and caused biases in the estimation all genotypic means when a purely additive model was used for analyzing the data. To account for pedigree ascertainment with sequential sampling, we developed a Bayesian ascertainment approach and implemented Metropolis-Hastings updates in the MCMC samplers used in Loki. Ascertainment correction greatly reduced biases in parameter estimates. Our method is designed for multiple, but a fixed number of trait loci. Genet. Epidemiol. 2007. © 2007 Wiley-Liss, Inc.

This issue of BMC Proceedings contains the proceedings of Genetic Analysis Workshop (GAW) 15, which was held November 11–15, 2006, in St. Pete Beach, Florida, USA. The GAWs began in 1982 and are now held in even-numbered years. They provide a forum for investigators interested in identifying genetic effects on complex diseases to evaluate and compare novel and existing statistical methods. The purpose of these Workshops is to allow the comparison of statistical methodologies for genetic epidemiology using common, well described data sets. Prior to each GAW, topics are chosen, one or more existing data sets are selected, and a set of simulated data is created that permits investigation of current questions of broad interest in statistical genetics. These data are made available to any scientist who requests them, and their analyses of these data are presented at the Workshop. Participation in the Workshop is open to anyone who submits an analysis of one of these data sets, provides data, or participates in Workshop organization. More information about GAW, including details of upcoming Workshops, may be found at http://www.gaworkshop.org.

Increasingly, single-nucleotide polymorphism (SNP) markers are being used in preference to microsatellite markers. However, methods developed for microsatellites may be problematic when applied to SNP markers. We evaluated the results of using SNPs vs. microsatellites in Monte Carlo Markov chain (MCMC) oligogenic combined segregation and linkage analysis methods. These methods were developed with microsatellite markers in mind. We selected chromosome 7 from the Collaborative Study on the Genetics of Alcoholism dataset for analysis because linkage to an electrophysiological trait had been reported there. We found linkage in the same region of chromosome 7 with the Affymetrix SNP data, the Illumina SNP data, and the microsatellite marker data. The MCMC sampler appears to mix with both types of data. The sampler implemented in this MCMC oligogenic combined segregation and linkage analysis appears to handle SNP data as well as microsatellite data and it is possible that the localizations with the SNP data are better.

Several kindreds of Volga German (VG) ancestry have a single PS2 mutation that causes an autosomal dominant form of Alzheimer's disease (AD). These families show a wide range in age-at-onset, which suggests the existence of modifying factors other than the PS2 mutation. To examine evidence for a genetic basis of variation in onset age, we performed a Bayesian oligogenic segregation and linkage analysis on nine VG families confirmed to have at least one affected PS2 carrier. This analysis simultaneously estimated the effects of APOE and PS2 and the number and effects of additional loci affecting AD age-at-onset. In addition, a family effect accounted for shared environmental effects. This analysis approach has the advantage of full use of the complete pedigree structure, as well as use of information on unsampled individuals with phenotypic data. These analyses provide evidence that APOE plays a small, but significant, role in modifying the age-at-onset in these VG families. The effects estimated for the APOE epsilon3 and epsilon4 genotypes were consistent with those estimated in previous analysis of late-onset AD families, with evidence for a dose-dependent relationship between number of epsilon4 alleles and age-at-onset. We estimated an approximately 83% posterior probability of at least one modifier locus in addition to APOE, and that the fraction of the variance in age-at-onset attributable to PS2, APOE, other loci, and family effects is approximately 70, approximately 2, approximately 6.5, and approximately 8.5%, respectively. These results provide evidence that APOE and other loci modify onset in AD caused by PS2 mutation.

Background Often, multiple measures of a trait are available in a genetic linkage analysis. We compare Monte Carlo Markov chain analysis of two very different measures of hypertension in the simulated Genetic Analysis Workshop 13 data to examine how choice of measure affects the results. The measures selected were age-of-onset of hypertension and systolic blood pressure at first visit. Results In combined segregation and linkage analysis of the complete pedigrees using the first replicate of the simulated data with missing values, we found that the age-of-onset analysis was better at identifying "slope" genes, while the systolic blood pressure analysis was better at identifying "baseline" genes. Conclusion Analysis of different trait measures may identify different trait-related genes. When linkage analysis is conducted on multiple trait measures, a linkage signal found for only one measure can represent a true trait locus.

The Genetic Analysis Workshop 13 simulated data aimed to mimic the major features of the real Framingham Heart Study data that formed Problem 1, but under a known inheritance model and with 100 replicates, so as to allow evaluation of the statistical properties of various methods. The pedigrees used were the 330 real pedigree structures (comprising 4692 individuals) with some minor changes to protect confidentiality. Fifty trait genes and 399 microsatellite markers were simulated by gene dropping on 22 autosomal chromosomes. Assuming random ascertainment of families, a system of eight longitudinal quantitative traits (designed to be similar to those in the real data) was generated with a wide range of heritabilities, including some pleiotropic and interactive effects. Genes could affect either the baseline level or the rate of change of the phenotype. Hypertension diagnosis and treatment were simulated with treatment availability, compliance, and efficacy depending on calendar year. Nongenetic traits of smoking and alcohol were generated as covariates for other traits. Death was simulated as a hazard rate depending upon age, sex, smoking, cholesterol, and systolic blood pressure. After the complete data were simulated, missing data indicators were generated based on logistic models fitted to the real data, involving the subject's history of previous missing values, together with that of their spouses, parents, siblings, and offspring, as well as marital status, only-child indicators, current value at certain simulated traits, and the data collection pattern on the cohort into which each subject was ascertained.

Bayesian Monte Carlo Markov chain (MCMC) techniques have shown promise in dissecting complex genetic traits. The methods introduced by Heath ([1997], Am. J. Hum. Genet. 61:748-760), and implemented in the program Loki, have been able to localize genes for complex traits in both real and simulated data sets. Loki estimates the posterior probability of quantitative trait loci (QTL) at locations on a chromosome in an iterative MCMC process. Unfortunately, interpretation of the results and assessment of their significance have been difficult. Here, we introduce a score, the log of the posterior placement probability ratio (LOP), for assessing oligogenic QTL detection and localization. The LOP is the log of the posterior probability of linkage to the real chromosome divided by the posterior probability of linkage to an unlinked pseudochromosome, with marker informativeness similar to the marker data on the real chromosome. Since the LOP cannot be calculated exactly, we estimate it in simultaneous MCMC on both real and pseudochromosomes. We investigate empirically the distributional properties of the LOP in the presence and absence of trait genes. The LOP is not subject to trait model misspecification in the way a lod score may be, and we show that the LOP can detect linkage for loci of small effect when the lod score cannot. We show how, in the absence of linkage, an empirical distribution of the LOP may be estimated by simulation and used to provide an assessment of linkage detection significance.

Monoamine oxidase (MAO) activity levels have been suggested as a possible biological marker for alcohol dependence and abuse, as well as for schizophrenia and other psychiatric conditions. Using platelet MAO activities in the Collaborative Study on the Genetics of Alcoholism data set, we applied bootstrapping methods as a novel way to test for admixture in families. This bootstrapping involved resampling in family units and hypothesis testing of the resampled datasets for commingling in the distribution of MAO activity levels. Prior to commingling analysis, we used linear models to find covariates of greatest effect on MAO activity levels. While an alcoholism diagnosis was significant in men (n = 1151, P < 0.0001) and women (n = 1254, P = 0.0003), the effect lost significance after controlling for cigarette smoking, indicating alcoholism and smoking behavior to be highly confounded. When smoking histories were compared, former smokers had levels (mean = 7.1) closer to those who never smoked (mean = 7.0) than to current smokers (mean = 5.4). Furthermore, current daily smoking and time since smoking cessation were significantly related to MAO levels, indicating smoking probably has a direct effect on MAO levels, rather than the reverse. These results suggest that studies using MAO levels as a biological marker should consider smoking as an important covariate. Finally, admixture was found in MAO levels controlled for smoking and sex, possibly indicating a major genetic locus; this confirms previous evidence for admixture.

SummaryIt is usually difficult to localize genes that cause diseases with late ages at onset. These diseases frequently exhibit complex modes of inheritance, and only recent generations are available to be genotyped and phenotyped. In this situation, multipoint analysis using traditional exact linkage analysis methods, with many markers and full pedigree information, is a computationally intractable problem. Fortunately, Monte Carlo Markov chain sampling provides a tool to address this issue. By treating age at onset as a right-censored quantitative trait, we expand the methods used by Heath (1997) and illustrate them using an Alzheimer disease (AD) data set. This approach estimates the number, sizes, allele frequencies, and positions of quantitative trait loci (QTLs). In this simultaneous multipoint linkage and segregation analysis method, the QTLs are assumed to be diallelic and to interact additively. In the AD data set, we were able to localize correctly, quickly, and accurately two known genes, despite the existence of substantial genetic heterogeneity, thus demonstrating the great promise of these methods for the dissection of late-onset oligogenic diseases.

PURPOSE: The primary goal of the HERITAGE Family Study was to document the role of the genotype in the response to aerobic exercise training. Toward this end, nuclear families were enrolled in a 20-week exercise training program, with a large variety of tests performed before and after the training. Since study drift has the potential to adversely affect the results, reproducibility and potential bias over six consecutive 4-month periods were examined for selected test. METHODS: Intraclass correlations (ICC), technical errors (TE), coefficients of variation within subject (CV), and means were calculated with use of the pretraining test results for each of the six time periods. To check for homogeneity, hypothesis tests were performed on the intraclass correlations and means. If homogeneity was not found across all six periods, further tests were performed to assess differences between pairs of time periods. RESULTS: There was little evidence for real drifts in reproducibility, with most tests having ICCs of 0.8 or better. Only a few tests showed any change over time, and in no case was there evidence of a systematic drift in mean values. CONCLUSIONS: Overall, the reproducibility of the HERITAGE Family Study tests and assays considered in this paper was found to be very good, with no evidence of any systematic drift over time.

We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We conducted linkage only, linkage and association, and association only tests under this framework. We also compared these results with variance-component linkage analysis and regression analyses. The results indicate that the method shows some promise, but finding genes that have very small (h2 of 0.0015 to 0.0002).

The CEPH samples are an invaluable resource for mapping genes that contribute to traits that can be measured in cell lines. With the many markers that have already been genotyped for the Centre d'Etude du Polymorphisme Humain (CEPH) pedigrees and are readily available, one need only obtain phenotypes to conduct a linkage analysis. For Genetic Analysis Workshop 15 (GAW15), over 3000 expression levels of genes in lymphoblastoid cells in 14 of the CEPH pedigrees were provided. For this study, eight of these expression levels were selected to obtain a spectrum of heritabilities, three were selected based on linkage results with traditional LOD scores >3, and one trait was selected at random. A Bayesian Monte Carlo Markov chain oligogenic segregation and linkage analysis was conducted on each of these 12 traits using the genome-wide single-nucleotide polymorphism linkage markers provided for GAW15. Our goal was to assess the ability of these methods to map genes in the CEPH pedigrees. Surprisingly, positive linkage signals were found for all 12 traits, even those with a very small traditionally calculated heritability. However, the portion of the variance attributed to genetic sources by the oligogenic segregation analysis differed substantially in some cases from the traditional heritability. It appears that genetic variance estimated from oligogenic segregation analysis may be a better indicator of whether genes can be mapped for complex traits than traditional heritability.

ss Open Acce Introduction Genetic Analysis Workshop 14: microsatellite and single-nucleotide polymorphism marker loci for genome-wide scans Joan E Bailey-Wilson*1, Laura Almasy2, Mariza de Andrade3, Julia Bailey4, Heike Bickeboller5, Heather J Cordell6, E Warwick Daw7, Lynn Goldin8, Ellen L Goode9, Courtney Gray-McGuire10, Wayne Hening11, Gail Jarvik12, Brion S Maher13, Nancy Mendell14, Andrew D Paterson15, John Rice16, Glen Satten17, Brian Suarez16, Veronica Vieland18, Marsha Wilcox19, Heping Zhang20, Andreas Ziegler21 and Jean W MacCluer2

Late-onset familial Alzheimer disease (LOFAD) is a genetically heterogeneous and complex disease for which only one locus, APOE, has been definitively identified. Difficulties in identifying additional loci are likely to stem from inadequate linkage analysis methods. Nonparametric methods suffer from low power because of limited use of the data, and traditional parametric methods suffer from limitations in the complexity of the genetic model that can be feasibly used in analysis. Alternative methods that have recently been developed include Bayesian Markov chain–Monte Carlo methods. These methods allow multipoint linkage analysis under oligogenic trait models in pedigrees of arbitrary size; at the same time, they allow for inclusion of covariates in the analysis. We applied this approach to an analysis of LOFAD on five chromosomes with previous reports of linkage. We identified strong evidence of a second LOFAD gene on chromosome 19p13.2, which is distinct from APOE on 19q. We also obtained weak evidence of linkage to chromosome 10 at the same location as a previous report of linkage but found no evidence for linkage of LOFAD age-at-onset loci to chromosomes 9, 12, or 21.

Although it is clear that apoE plays an important role in the genetics of late-onset Alzheimer disease (AD), evidence exists that additional genes may play a role in AD, and estimates of the total contribution of apoE to the variance in onset of AD vary widely. Unfortunately, little information is available on the number and contribution of additional genes. We estimated the number of additional quantitative-trait loci and their contribution to the variance in age at onset of AD, as well as the contribution of apoE and sex, in an oligogenic segregation analysis of 75 families (742 individuals) ascertained for members with late-onset AD. We found evidence that four additional loci make a contribution to the variance in age at onset of late-onset AD that is similar to or greater in magnitude than that made by apoE, with one locus making a contribution several times greater than that of apoE. Additionally, we confirmed previous findings of a dose effect for the apoE varepsilon4 allele, a protective effect for the varepsilon2 allele, evidence for allelic interactions at the apoE locus, and a small protective effect for males. Furthermore, although we estimate that the apoE genotype can make a difference of

National Institutes of Health (AR44422, N01-AR-7-2232); Genome Canada and Associations AFP; Polyarctique-Groupe Taitbout; Rhumatisme et Travail; Arthritis Research Campaign; National Institute of General Medical Sciences (R01 GM31575)

The Genetic Analysis Workshop (GAW) 16 Problem 3 comprises simulated phenotypes emulating the lipid domain and its contribution to cardiovascular disease risk. For each replication there were 6,476 subjects in families from the Framingham Heart Study (FHS), with their actual genotypes for Affymetrix 550 k single-nucleotide polymorphisms (SNPs) and simulated phenotypes. Phenotypes are simulated at three visits, 10 years apart. There are up to 6 "major" genes influencing variation in high- and low-density lipoprotein cholesterol (HDL, LDL), and triglycerides (TG), and 1,000 "polygenes" simulated for each trait. Some polygenes have pleiotropic effects. The locus-specific heritabilities of the major genes range from 0.1 to 1.0%, under additive, dominant, or overdominant modes of inheritance. The locus-specific effects of the polygenes ranged from 0.002 to 0.15%, with effect sizes selected from negative exponential distributions. All polygenes act independently and have additive effects. Individuals in the LDL upper tail were designated medicated. Subjects medicated increased across visits at 2%, 5%, and 15%. Coronary artery calcification (CAC) was simulated using age, lipid levels, and CAC-specific polymorphisms. The risk of myocardial infarction before each visit was determined by CAC and its interactions with smoking and two genetic loci. Smoking was simulated to be commensurate with rates reported by the Centers for Disease Control. Two hundred replications were simulated.