Mapping Genes for Complex Traits: Obesity, Diabetes, Hypertension, and Dyslipidemia on the Pacific Island of Kosrae

One of the current challenges in human genetics is to map genes for common, complex diseases. For powerful mapping of such phenotypes, the suggestions are to analyze the underlying quantitative traits with covariate corrections in large extended pedigrees with multipoint analysis. This is virtually impossible to do with the current linkage programs, due to the computation difficulties of exactly calculating every possibility. Instead, sampling methods that sample the most likely data configuration from all the possibilities need to be used. This has been implemented in the reversible jump Markov chain Monte Carlo method Loki, which can carry out segregation and linkage analysis on quantitative traits in large pedigrees with multipoint analysis. Loki can model the trait with covariates, identify the number of quantitative trait loci, linked loci, and estimate allele frequencies and gene effects. This method has a lot promise but has not been vigorously tested for complete genome scans. The first part of this study was to develop a strategy for carrying out genome scans using Loki and to evaluate the output. This was first done using the Genetic Analysis Workshop 12 simulated dataset with known answers. This resulted in a number of suggestions, such as initial single chromosome analysis, correction for polygenic effect, joint analysis of positive signals, and convergence analysis. Next these suggestions were applied to a real dataset from the population of Kosrae, the Federated States of Micronesia. This is a study of the population on the island of Kosrae, which one large extended pedigree and high prevalence of the common complex disorders that are known as Syndrome X: obesity, type II diabetes, hypertension, and dyslipidemia. This resulted in a number of additional suggestions, such as phenotypic and genotypic corrections, dealing with mixing issues, and inspection of L-graphs for signal reliability. Once this strategy was developed, the second part of this study was to use Loki to identify quantitative trait loci for the continuous traits associated with Syndrome X and stature. This resulted in quantitative trait loci for body mass index, hip circumference, weight, fasting blood sugar, systolic blood pressure, arterial blood pressure, apolipoprotein B, total cholesterol, and height. This also identified interesting chromosomal regions with slight signals for correlated traits on chromosomes 1, 2, 7, 9, 13, and 16. This study shows that Loki is a program that can powerfully and reliably carry out linkage analysis on quantitative traits that was previously impossible to do and finds loci for many of the quantitative traits related to common metabolic disorders as well as height

Alcohol metabolizing genes and alcohol phenotypes in an Israeli household sample

BACKGROUND: Alcohol dehydrogenase 1B and 1C (ADH1B and ADH1C) variants have been robustly associated with alcohol phenotypes in East Asian populations, but less so in non-Asian populations where prevalence of the most protective ADH1B allele is low (generally <5%). Further, the joint effects of ADH1B and ADH1C on alcohol phenotypes have been unclear. Therefore, we tested the independent and joint effects of ADH1B and ADH1C on alcohol phenotypes in an Israeli sample, with higher prevalence of the most protective ADH1B allele than other non-Asian populations. METHODS: A structured interview assessed lifetime drinking and alcohol use disorders (AUDs) in adult Israeli household residents. Four single nucleotide polymorphisms (SNPs) were genotyped: ADH1B (rs1229984, rs1229982, and rs1159918) and ADH1C (rs698). Regression analysis examined the association between alcohol phenotypes and each SNP (absence vs. presence of the protective allele) as well as rs698/rs1229984 diplotypes (also indicating absence or presence of protective alleles) in lifetime drinkers (n = 1,129). RESULTS: Lack of the ADH1B rs1229984 protective allele was significantly associated with consumption- and AUD-related phenotypes (OR = 1.77 for AUD; OR = 1.83 for risk drinking), while lack of the ADH1C rs698 protective allele was significantly associated with AUD-related phenotypes (OR = 2.32 for AUD). Diplotype analysis indicated that jointly ADH1B and ADH1C significantly influenced AUD-related phenotypes. For example, among those without protective alleles for ADH1B or ADH1C, OR for AUD was 1.87 as compared to those without the protective allele for ADH1B only and was 3.16 as compared to those with protective alleles for both ADH1B and ADH1C. CONCLUSIONS: This study adds support for the relationship of ADH1B and ADH1C and alcohol phenotypes in non-Asians. Further, these findings help clarify the mixed results from previous studies by showing that ADH1B and ADH1C jointly effect AUDs, but not consumption. Studies of the association between alcohol phenotypes and either ADH1B or ADH1C alone may employ an oversimplified model, masking relevant information

Alcohol consumption mediates the relationship between ADH1B and DSM-IV alcohol use disorder and criteria

OBJECTIVE: A single nucleotide variation in the alcohol dehydrogenase 1B (ADH1B) gene, rs1229984, produces an ADH1B enzyme with faster acetaldehyde production. This protective variant is associated with lower alcohol consumption and lower risk for alcohol use disorders (AUDs). Based on the premise that faster ADH1B kinetics decreases alcohol consumption, we formally tested if the association between ADH1B variant rs1229984 and AUDs occurs through consumption. We also tested whether the association between rs1229984 and each of the 11 Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), AUD criteria occurs through consumption. METHOD: A total of 1,130 lifetime drinkers from an Israeli household sample were assessed with a structured interview and genotyped for rs1229984 (protective allele frequency = 0.28). Logistic regression evaluated the association between rs1229984 and each phenotype (AUDs, 11 individual DSM-IV criteria). For phenotypes significantly related to rs1229984, the effect through consumption was tested with logistic regression and bootstrapping. RESULTS: ADH1B rs1229984 was significantly associated with AUDs and six criteria, with odds ratios ranging from 1.32 to 1.96. The effect through consumption was significant for these relationships, explaining 23%-74% of the total ADH1B effect. CONCLUSIONS: This is the first study to show that ADH1B rs1229984 is related to 6 of the 11 DSM-IV AUD criteria and that alcohol consumption explained a significant proportion of these associations and the association of ADH1B with AUDs. Better understanding of the relationship between ADH1B and the DSM-IV AUD criteria, including effects through consumption, will enhance our understanding of the etiologic model through which AUDs can occur

Trends in Marijuana Use Among Pregnant and Nonpregnant Reproductive-Aged Women, 2002-2014

Between 2001 and 2013, marijuana use among US adults more than doubled, many states legalized marijuana use, and attitudes toward marijuana became more permissive. In aggregated 2007-2012 data, 3.9% of pregnant women and 7.6% of nonpregnant reproductive-aged women reported past-month marijuana use. Although the evidence is mixed, human and animal studies suggest that prenatal marijuana exposure may be associated with poor offspring outcomes (eg, low birth weight, impaired neurodevelopment). The American College of Obstetricians and Gynecologists recommends that pregnant women and women contemplating pregnancy be screened for and discouraged from using marijuana and other substances. Whether marijuana use has changed over time among pregnant and nonpregnant reproductive-aged women is unknown

Clinical evaluation, imaging studies, indications for cytologic study, and preprocedural requirements for duct brushing studies and pancreatic FNA: The papanicolaou society of cytopathology recommendations for pancreatic and biliary cytology

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106723/1/dc23095.pd

Network analysis of Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) 3.1 items for non-medical use of alcohol, tobacco, cannabis, prescription sedatives, prescription stimulants, and prescription opioids

BackgroundAt-risk substance use is a leading cause of preventable morbidity and mortality worldwide. The Alcohol, Smoking and Substance Involvement Screening Test 3.1 (ASSIST) is widely used to screen for such use.ObjectivesUsing network analysis to reframe risky substance use as a web of interacting ASSIST symptoms to provide important suggestions about potential mechanisms underlying risky use.MethodsCross-sectional data on the ASSIST was collected via an online survey from a general population sample of Jewish adults in Israel (N=4,002; 50.4% women). Network analysis was carried out for ASSIST symptoms for non-medical use of alcohol, tobacco, cannabis, prescription sedatives, prescription stimulants, and prescription opioids. First, networks were modeled for each substance, to explore the following research questions: which symptoms were most strongly related? and what are the key symptoms that compose the networks? Second, networks were compared to determine if symptom relationships differed between substances.ResultsBasic similarities were observed across substances, e.g., strongest direct associations between frequency of use and craving, and frequency of substance related problems and role interference. Role interference and craving appeared to play important roles in the networks. Differences were observed between substances in strength of associations between symptoms.ConclusionNetwork structures were similar across substances, suggesting that similar intervention approaches may be appropriate, with substance-specific strategies as warranted. Among those who use substances, addressing the effects of role interference and craving in risky substance use may help reduce substance-related harms and limit progression to full blown disorder

Childhood Maltreatment, Stressful Life Events, and Alcohol Craving in Adult Drinkers

Background: Little is known about the relationship between stressful life events and alcohol craving in the general population, and whether a history of childhood maltreatment sensitizes individuals to crave alcohol after adult stressors. Methods: Participants were 22,147 past-year drinkers from Wave 2 (2004 to 2005) of the National Epidemiologic Survey on Alcohol and Related Conditions. A structured, face-to-face interview assessed past-year stressful life events, alcohol craving, and history of childhood maltreatment. Logistic regression was used to generate adjusted odds ratios (aOR) to evaluate the relationship between stressful life events and craving, adjusting for demographic characteristics and parental history of alcoholism. Interaction between stressful life events and childhood maltreatment was also assessed. Results: Compared to participants with no stressful life events, those with ≥3 events had increased odds of moderate alcohol craving (aOR = 3.15 [95% CI = 2.30 to 4.33]) and severe craving (aOR = 8.47 [95% CI = 4.78 to 15.01]). Stressful life events and childhood maltreatment interacted in predicting severe craving (p = 0.017); those with ≥3 events were at higher risk of craving if they had been exposed to childhood maltreatment. Conclusions: A direct relationship between stressful life events and risk of alcohol craving was observed. Further, history of childhood maltreatment increased the salience of stressful life events in adulthood. Future studies should examine the role of psychiatric comorbidity in more complex models of stress sensitization and alcohol craving

Construction of the model for the Genetic Analysis Workshop 14 simulated data...

The Genetic Analysis Workshop 14 simulated dataset was designed 1) To test the ability to find genes related to a complex disease (such as alcoholism). Such a disease may be given a variety of definitions by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. 2) To observe the effect on genetic analysis and gene discovery of a complex set of gene × gene interactions. 3) To allow comparison of microsatellite vs. large-scale single-nucleotide polymorphism (SNP) data. 4) To allow testing of association to identify the disease gene and the effect of moderate marker × marker linkage disequilibrium. 5) To observe the effect of different ascertainment/disease definition schemes on the analysis. Data was distributed in two forms. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Internet-obtainable data consisted of a finer 10,000 SNP map, which also contained data on controls. While disease characteristics and parameters were constant, four "studies" used varying ascertainment schemes based on differing beliefs about disease characteristics. One of the studies contained multiplex two- and three-generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different phenotypes, which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. We also simulated some marker × marker linkage disequilibrium around some of the genes and also in areas without disease genes. We tried two different methods to simulate the linkage disequilibrium

Pain, cannabis use, and physical and mental health indicators among veterans and nonveterans: results from the National Epidemiologic Survey on Alcohol and Related Conditions-III

AbstractChronic pain is associated with mental and physical health difficulties and is prevalent among veterans. Cannabis has been put forth as a treatment for chronic pain, and changes in laws, attitudes, and use patterns have occurred over the past 2 decades. Differences in prevalence of nonmedical cannabis use and cannabis use disorder (CUD) were examined across 2 groups: veterans or nonveterans and those reporting or not reporting recent pain. Data from the National Epidemiologic Survey on Alcohol and Related Conditions-III (2012-2013; n = 36,309) were analyzed using logistic regression. Prevalence differences (PDs) for 3 cannabis outcomes (1) past-year nonmedical cannabis use, (2) frequent (≥3 times a week) nonmedical use, and (3) DSM-5 CUD were estimated for those reporting recent moderate to severe pain (veterans or nonveterans) and veterans reporting or not reporting recent pain. Difference in differences was calculated to investigate PDs on outcomes associated with residence in a state with medical cannabis laws (MCLs). Associations between physical and mental health and cannabis variables were tested. The results indicated that the prevalence of recent pain was greater among veterans (PD = 7.25%, 95% confidence interval (CI) [4.90-9.60]). Among veterans, the prevalence of frequent cannabis use was greater among those with pain (PD = 1.92%, 98% CI [0.21-3.63]), and among veterans residing in a state with MCLs, the prevalence of CUD was greater among those reporting recent pain (PD = 3.88%, 98% CI [0.36-7.39]). Findings failed to support the hypothesis that cannabis use improves mental or physical health for veterans with pain. Providers treating veterans with pain in MCL states should monitor such patients closely for CUD