bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain event‐related potentials, dopamine D2 receptor gene polymorphism, and smoking
Authors: Anokhin A.P. , Todorov A.A. , Madden P.A.F. , Grant J.D. , Heath A.C. 
Published: November 2013

AbstractThis paper explores the relationship between the DRD2 gene polymorphism, P300, and smoking. Both smoking and DRD2 have significant reducing effects on P300 amplitude. The effect of smoking is apparent only in the presence of the A1 allele of the DRD2 locus. Transmission/disequilibrium analyses show a negative association between the A2 allele and smoking initiation, suggesting a protective effect of this allele. When the sample is stratified into lower‐ and higher‐P300 categories, we find a significant association between A1 and current smoking only in individuals with lower P300. Both concordance for smoking and DRD2 genotype are significant predictors of sib‐pair similarity in P300 amplitude. These results suggest a synergistic effect of different neurogenetic risk factors contributing to nicotine dependence. Neurocognitive variation (P300) may moderate the association between DRD2 and smoking. Alternatively, DRD2 genotype may modulate the long‐term impact of nicotine on neurocognitive functioning.
A genome‐wide search for loci contributing to smoking and alcoholism
Authors: Bergen Andrew W. , Korczak Jeannette F. , Weissbecker Karen A. , Goldstein Alisa M. 
Published: November 2013

AbstractUsing the Collaborative Study on the Genetics of Alcoholism (COGA) data, we performed a sib‐pair linkage analysis of two smoking‐related traits and one alcoholism phenotype. The first trait, EVRNVR, was a dichotomous one we constructed based on epidemiological definitions of smoking. The second trait, PKYRS, used the quantitative pack‐year history provided, and the third trait was the COGA alcoholism classification, ALDX1. There was some evidence for linkage of the EVRNVR trait to regions on chromosomes 6, 9, and 14. Smaller numbers of loci provided nominal evidence for linkage to PKYRS, although some candidate gene regions were identified. The number of loci identified using EVRNVR suggests that a threshold‐based phenotype may better identify loci affecting smoking history. Approximately one‐third of the loci that showed evidence for linkage to EVRNVR at a nominal significance level (p < 0.01) also showed evidence for linkage to ALDX1. Some of these regions may represent loci increasing vulnerability to both smoking and alcoholism.
Markov chain monte carlo linkage analysis of a complex qualitative phenotype
Authors: Hinrichs Anthony , Lin Jennifer H. , Reich Theodore , Bierut Laura , Suarez Brian K. 
Published: November 2013

AbstractWe tested a new computer program, LOKI, that implements a reversible jump Markov chain Monte Carlo (MCMC) technique for segregation and linkage analysis. Our objective was to determine whether this software, designed for use with continuously distributed phenotypes, has any efficacy when applied to the discrete disease states of the simulated data from the Mordor data from GAW Problem 1. Although we were able to identify the genomic location for two of the three quantitative trait loci by repeated application of the software, the MCMC sampler experienced significant mixing problems indicating that the method, as currently formulated in LOKI, was not suitable for the discrete phenotypes in this data set.
Genetic dissection of a complex trait
Authors: Lin Jennifer H. , Hinrichs Anthony , Liu Kuang‐Yu , Bierut Laura , Suarez Brian K. 
Published: November 2013

AbstractA number of genetic and statistical tools were applied to various partitions of the simulated data to identify susceptibility loci, relevant environmental factors, and their interaction(s). The distribution of genotypes at D1G24 among affected children in the first population was found to differ significantly from Hardy‐Weinberg expectation. Two transmission/disequilibrium tests identified the preferential transmission of allele 1 as the source of the disequilibrium. Simple contingency table analysis revealed a positive association between exposure to environmental factor E1 and disease phenotype. Multipoint linkage analyses on various subsets of the data identified three “signal” regions (in addition to the aforementioned D1G24) localized at D1G9–10, D3G45, and D5G38. The even numbered chromosomes appeared to be devoid of susceptibility loci. Further analyses of subsamples of affected sib pairs, selected according to their disease phenotype and their exposure to E1, clarified some linkage relationships, particularly for D3G45, thereby suggesting the presence of a specific gene×environment interaction. Logistic analysis designed to clarify the relationship between disease phenotype and two risk factors (El exposure and the presence of allele 1 at D1G24) in the first population, revealed a significantly negative interaction which, upon learning the details of the generating model, we now attribute to the presence of heterogeneity.
A comparison of some allele‐sharing based linkage analysis methods for detecting complex trait loci
Authors: Liu Ying , Mirea Lucia , Pinnaduwage Dushanthi , Fallah Shafagh , Tritchler David 
Published: November 2013

AbstractUsing randomly selected sib pairs from a subset of the GAW11 simulated data in Problem 2, we compared the results of some linkage analysis methods based on allele sharing. One method was the Haseman‐Elston test for a binary disease outcome (unaffected vs. mild or severe). The other methods, which analyzed the trinary ordered outcome unaffected/mild/severe were the Haseman‐Elston test, an extended Haseman‐Elston incorporating sib‐pair sums, variance components analysis, and regression analysis. Our analysis was done without knowledge of the generating model.
A comparison of two algorithms, multimap and gene mapping system, for automated construction of genetic linkage maps
Authors: Marinov Marin , Matise Tara Cox , Lathrop G. Mark , Weeks Daniel E. 
Published: November 2013

AbstractUsing the GAW11 Problem 2 data set, we compared the performance of two automated map construction algorithms, MultiMap and GMS (Gene Mapping System). The MultiMap algorithm iteratively adds markers in a stepwise manner to the map, while the GMS algorithm seeks to find the best order of the whole set of markers by selective permutations of logically formed subgroups of the markers. While it is difficult to compare these two rather different algorithms, we found that, on these data, GMS performed better than MultiMap, placing more markers in their true order on average, with little order ambiguity. In addition, as the number of markers increased, GMS was less computationally demanding than MultiMap. However, if MultiMap placed a marker, it was almost always in the correct order. In contrast, GMS often placed a group of markers on the wrong end of the map; such incorrect placements occur when the evidence for placement on one end or the other is not strong. Thus, there is room for further algorithmic developments that combine the strengths of both the MultiMap and GMS approaches.
Departure from the triangle constraints in discordant sib pairs: A test for genetic heterogeneity
Authors: Quesneville Hadi , Dizier Marie‐Hélène , Clerget‐Darpoux Françoise 
Published: November 2013

AbstractFor any genetic model, Holmans showed that the proportions of affected sibs sharing 2, 1, or 0 identical‐by‐descent parental marker alleles are constrained to belong to a specific triangle. The triangle constraints do not hold when the sib phenotypes are determined by different models. We test the rejection of triangle constraints on affected sib pairs discordant for severity, to determine whether different models control the severe and mild forms of the disease in the simulated data. With this method we show that a locus on chromosome 5 plays a different role in the two forms of the disease.
Mapping genotype to phenotype for linkage analysis
Authors: Saccone Nancy L. , Downey Thomas J. , Meyer Donald J. , Neuman Rosalind J. , Rice John P. 
Published: November 2013

AbstractWe model functions that use genetic information as input and trait information as output to understand genetic linkage in complex diseases. Using simulated data from GAW11, we have applied categorical classification methods and neural network analysis. We use sharing at selected markers as input, and the classification of the sib pair (for example, affected‐affected or affected‐unaffected) as output. In addition, our methods include environmental risk factors as predictors of phenotype. Categorical and neural network methods each led to results consistent with findings from other methods such as the logistic regression method of Rice et al. [this issue]. Post‐analysis comparison with the GAW11 answers showed that these methods are capable of detecting correct signals in a single replicate. One advantage of our methods is that they allow analysis of the entire genome at once, so that interactions among multiple trait‐influencing loci may be detected. Furthermore, these methods can use a variety of sib pairs rather than affected pairs only.
Systematic search for susceptibility genes in different populations
Authors: Schmidt Silke , Scholz Michael , Loesgen Sabine , Bickeböller Heike 
Published: November 2013

AbstractWe analyzed the first replicate of each of the four simulated population samples from three distinct populations by linkage and association genome scans and could identify three regions with susceptibility loci for the disease: on chromosome 1, marker D1G024, with strong evidence for gene×environment interaction; on chromosome 3, around marker D3G045; and on chromosome 5, markers D5G035–D5G042. Our results were obtained without knowing the true disease model and are compared with this model in the discussion.
Exploring the role of environmental factors in association and linkage studies
Authors: Seillier‐Moiseiwitsch Françoise , Ramaswami Anand , Tripputi Mark , Pankow James , Newman Beth 
Published: November 2013

AbstractWe analyzed some simulated data to assess the success of statistical methodologies to establish the role of the environmental factors (EF) and to identify associated and linked markers. We considered five replicates for each of the four studies, and, with the knowledge of the generating model, concentrated our analyses on chromosomes (CH) 1,3, and 5. To determine the influence of EF and associated markers on the affection status (AS), we utilized chi‐square tests for independence and recursive partitioning (via the CART software). To identify linked markers, we scanned the relevant chromosomes with nonparametric multipoint linkage (NPL) and transmission/disequilibrium tests. These analyses were performed on the whole data set as well as on subsets of individuals and families defined by exposure to EF. CART correctly selected the associated marker (D1G024) and EF1 for Study (ST) 1 and did not generate trees for the other studies. NPL identified the relevant regions on CH3 and CH5 but failed to do so for CH1, except in ST4. Stratifying families by exposure to EF1 did not consistently increase sensitivity of NPL to the relevant CH3 markers, but did help characterize the genetic heterogeneity and identify linked families.
Detecting interactions between gene, site, and environmental variables using GAP
Authors: Shin Janey , Corey Mary 
Published: November 2013

AbstractRegressive models that incorporate measured variables and assumed genetic parameters were used to detect interactions between gene, research site, and environmental variables in GAW11 Problem 2. Replicates 1 to 5 were used in the analyses. Significant three‐way gene×environment×site interactions were seen for all models, regardless of what assumptions were made about genetic transmission. Therefore, regressive models within each of the four sites were examined for significant gene×environment interactions. At one site, there was a pattern of gene×environment interaction that was consistent in most of the genetic models assumed. Joint and separate segregation and linkage analyses were compared in this site. No patterns of gene×environment interaction were seen in the other sites. Results from this analysis show that regressive modeling can identify complex interactions in data from heterogeneous populations even when ascertainment assumptions are violated.
A bayesian markov chain monte carlo approach to map disease genes in simulated GAW11 data
Authors: Uimari Pekka , Pitkäniemi Janne , Onkamo Päivi 
Published: November 2013

AbstractA Bayesian method for multipoint mapping of disease genes based on Markov chain Monte Carlo algorithms was applied to the simulated GAW11 data (Study 2). The method is based on repeated Gibbs and more general Metropolis‐Hastings steps. For simplicity we assumed a single disease locus model with two alleles. A normal distribution for the underlying latent variable of the qualitative phenotype was assumed. Based on a single replicate of the data no clear evidence of any of the genes underlying the simulated disease was found. However, when three replicates were combined the method was able to locate the locus C correctly on chromosome 3.
Modeling linkage and association with evaluation of common sampling schemes
Authors: Wicks J. , Wilson S. R. 
Published: November 2013

AbstractThe probability model of parental transmission (of a multiallelic marker) to affected offspring has been reparameterized in such a way as to distinguish the recombination fraction parameter from the association parameters. The advantage of this reparameterization is that statistical tests can be developed which disentangle linkage and association. It is shown that these tests perform well compared with a standard approach that is commonly used. Further, the simulated data (Problem 2) enable a comparison to be made of the performance of these tests under different family sampling schemes.
Using case‐control designs for genome‐wide screening for associations between genetic markers and disease susceptibility loci
Authors: Yang Quanhe , Khoury Muin J. , Atkinson Michael , Sun Fengzhu , Cheng Rong , Flanders W. Dana 
Published: November 2013

AbstractWe used a case‐control design to scan the genome for any associations between genetic markers and disease susceptibility loci using the first two replicates of the Mycenaean population from the GAW11 (Problem 2) data. Using a case‐control approach, we constructed a series of 2‐by‐3 tables for each allele of every marker on all six chromosomes. Odds ratios (ORs) and 95% confidence intervals (95% CI) were estimated for all alleles of every marker. We selected the one allele for which the estimated OR had the minimum p‐value to plot in the graph. Among these selected ORs, we calculated 95% CI for those that had a p‐value ≤ adjusted α level. Significantly high ORs were taken to indicate an association between a marker locus and a suspected disease‐susceptibility gene. For the Mycenaean population, the case‐control design identified allele number 1 of marker 24 on chromosome 1 to be associated with a disease susceptibility gene, OR = 2.10 (95% CI 1.66–2.62). Our approach failed to show any other significant association between case‐control status and genetic markers. Stratified analysis on the environmental risk factor (E1) provided no further evidence of significant association other than allele 1 of marker 24 on chromosome 1. These data indicate the absence of linkage disequilibrium for markers flanking loci A, B, and C.Finally, we examined the effect of gene×environment (G×E) interaction for the identified allele. Our results provided no evidence of G×E interaction, but suggested that the environmental exposure alone was a risk factor for the disease.
Comparison of GENEHUNTER and MFLINK for analysis of COGA linkage data
Authors: Curtis David , Zhao Jing Hua , Sham Pak C. 
Published: November 2013

AbstractWe compared the NPLALL statistic from GENEHUNTER with two‐point and three‐point MALODs and MFLODs from MFLINK for all autosomal markers in the Collaborative Study on the Genetics of Alcoholism (COGA) data set. In general MFLINK produced more significant results than GENEHUNTER and implicated two regions containing candidate genes (ADH3 and DRD2). Many regions of interest identified in other studies reported at this workshop produced MALODs significant at p ≤ 0.05, but these would not have been picked up by GENEHUNTER unless a less significant threshold were used.
Nonparametric linkage analysis of alcohol dependence with chromosome 1 and 7 markers
Authors: D'Alfonso Sandra 
Published: November 2013

AbstractWe analyzed 105 multiplex families from the Collaborative Study on the Genetics of Alcoholism (COGA) for markers on chromosome 1 (n = 20) and chromosome 7 (n = 19) with the GENEHUNTER program. On chromosome 1, three markers (D1S532, D1S1588, and D1S534) in single‐point analysis and five markers in multipoint analysis (D1S532, D1S1588, D1S1675, D1S534, and D1S1595) showed a nonparametric linkage (NPL) p‐value < 0.05. They mapped in two different but nearby regions, spanning, respectively, 18 cM (D1S532, D1S1588) and 19 cM (D1S1675, D1S534, and D1S1595) and separated by a 20‐cM interval. The highest NPL score was obtained for D1S534 (NPL = 2.26, p = 0.013 for single‐point analysis and NPL = 2.81, p = 0.0029, for multipoint analysis). The maximum NPL score on chromosome 7 was observed for D7S1793 (NPL = 1.72, p = 0.044 for single‐point analysis and NPL = 2.02, p = 0.023 for multipoint analysis). The markers which showed NPL p‐values < 0.05 on chromosome 1 (D1S532, D1S1588, D1S1675, D1S534, and D1S1595) and on chromosome 7 (D7S1793) were analyzed for association using the transmission/disequilibrium test. None of the alleles at these six loci showed a distorted transmission pattern. These data indicate that the two loci “suggestively” linked to alcohol dependence are either in linkage equilibrium with a susceptibility locus or are too far from the susceptibility locus to show disequilibrium, if present.
Smoking behavior is under the influence of a major quantitative trait locus on human chromosome 5q
Authors: Duggirala Ravindranath , Almasy Laura , Blangero John 
Published: November 2013

AbstractDespite some evidence that smoking behavior is influenced by both genetic and environmental factors, efforts at identifying specific genes that influence smoking are extremely limited. Using information on smoking behavior history for 973 individuals distributed across 105 COGA families and a multipoint variance components method, we tested for linkage between smoking behavior (as defined by number of cigarette packs per day for 1 year) and chromosomal locations across the genome using information from 296 markers. We found strong evidence (lod = 3.2) for linkage of smoking behavior to a genetic location on chromosome 5q (D5S1354). Weaker evidence was found for linkage of smoking behavior to genetic locations on chromosomes 4 (between markers D4S244 and D4S2393), 15 (D15S642), and 17 (GATA193). Oligogenic linkage analyses suggest the putative locus on chromosome 5q is the primary determinant of genetic variation in smoking. Although the magnitude of the lod score is compelling, the large gap between the markers D5S1456 and D5S1354 (which is also at the end of chromosome 5q) in the COGA data set reduces the enthusiasm for this putative quantitative trait locus. However, typing of additional markers in this region may provide greater support for the localization of a susceptibility locus at this chromosomal location, which is not far from the DRD1 (D1 dopamine receptor gene) locus.
The impact of redefining affection status for alcoholism on affected‐sib‐pair analysis
Authors: Fann Cathy S.J. , Shugart Yin Y. , Lachman Herb , Collins Andrew , Chang C.J. 
Published: November 2013

AbstractThe analysis of a complex disease such as alcohol dependence requires a more precise definition of affection status. Collaborative Study on the Genetics of Alcoholism (COGA) provided a variety of qualitative and quantitative measures as well as genotype information, in addition to two criteria of affection status. To identify two groups of phenotypically “more homogeneous” individuals among alcoholics (COGA criterion), we redefined affection status by using cluster analysis and classification and regression tree, incorporating some important covariates such as event related potentials, monoamine oxidase B activity, status of smoking, age of onset, three variables of personality assessed with the Tridimensional Personality Questionnaire and three latent class variables. With redefined affection status, we repeated nonparametric analysis by three sib pair analysis programs (SIBPAL, SIBPAIR, and BETA) using nine candidate DNA markers identified by Reich et al. [1998] and Long et al. [1998]. The goals of our analysis are 1) to confirm previous results for these nine markers with redefined affection status and 2) to compare the performance from these three programs.
Familial analysis of event related potentials
Authors: King Terri M. , Barnholtz Jill , Page Grier P. 
Published: November 2013

AbstractThis report summarizes our analysis of the auditory and visual event related evoked potentials. These data were collected as a component of the Collaborative Study on the Genetics of Alcoholism and distributed as a part of the data available for the Genetic Analysis Workshop 11. For this analysis, we collapsed the data collected from eight leads using principal components methods. Using four collapsed variables derived from the principal components, we used regression analysis to adjust for environmental and demographic variables. We then fit the best fitting regression model and calculated the residuals. Using the residuals, we performed segregation analysis using S.A.G.E. Finally, we applied Markov chain Monte Carlo reverse jump methods to identify areas with potential quantitative trait loci. Our findings indicate that there may be an underlying genetic component to the potentials.
Sib‐pair linkage analyses of alcoholism: Dichotomous and quantitative measures
Authors: Korczak Jeannette F. , Bergen Andrew W. , Goldstein Alisa M. , Weissbecker Karen A. 
Published: November 2013

AbstractWe hypothesized that a quantitative alcoholism trait would have greater power than the Collaborative Study on the Genetics of Alcoholism (COGA) dichotomous alcoholism traits, ALDX1 and ALDX2, to detect putative alcoholism loci. To test this, we performed nonparametric sib‐pair linkage analysis to screen 285 polymorphic autosomal markers for evidence of linkage to ALDX1, ALDX2, and a quantitative trait, QUANT, defined from the 11 COGA latent class variables. We also examined the effects on the analyses of including covariates (sex, age, and pack‐years of smoking) and of transforming QUANT (log and square root). ALDX1 and ALDX2 showed the greatest evidence for linkage to markers on chromosome 1, by both the affected sib‐pair and the Haseman‐Elston tests. Regions of interest were also identified on chromosomes 4, 8, 16, and 17. QUANT showed little evidence for linkage to any chromosomal region, having no more significant results than were expected by chance. Including covariates or transforming QUANT had little effect on the analyses. A quantitative trait based on all 37 latent class variables, with each variable appropriately weighted, may have had more power than QUANT to detect genomic regions of relevance to alcoholism.
Incorporating larger families in identity‐by‐descent based linkage analysis
Authors: Loesgen Sabine , Scholz Michael , Schmidt Silke , Bickeböller Heike 
Published: November 2013

AbstractGenome scans for alcoholism susceptibility genes were carried out using identity‐by‐descent‐based statistics for qualitative traits. We compared the results when 1) multipoint information was used for all families, where some had to be truncated, 2) multipoint information was used only for small families while large (untruncated) pedigrees were analyzed with a single‐point approach, and 3) single‐point analysis was used for all pedigrees. Differences between the methods were observed, but neither method could identify regions related to the susceptibility for alcoholism.