bluuebunny/crossref_metadata_2025_split huggingface dataset:
Local false discovery rate and minimum total error rate approaches to identifying interesting chromosomal regions
Authors: Sinha Ritwik , Sinha Moumita , Mathew George , Elston Robert C , Luo Yuqun 
Published: January 2006

AbstractThe simultaneous testing of a large number of hypotheses in a genome scan, using individual thresholds for significance, inherently leads to inflated genome-wide false positive rates. There exist various approaches to approximating the correct genomewidep-values under various assumptions, either by way of asymptotics or simulations. We explore a philosophically different criterion, recently proposed in the literature, which controls the false discovery rate. The test statistics are assumed to arise from a mixture of distributions under the null and non-null hypotheses. We fit the mixture distribution using both a nonparametric approach and commingling analysis, and then apply the local false discovery rate to select cut-off points for regions to be declared interesting. Another criterion, the minimum total error, is also explored. Both criteria seem to be sensible alternatives to controlling the classical type I and type II error rates.
Linkage and association analyses of microsatellites and single-nucleotide polymorphisms in nuclear families
Authors: Lin Jennifer , Liu Kuang-Yu 
Published: January 2006

Abstract
Several simulation studies have suggested that a high-density single-nucleotide polymorphisms (SNPs) marker set may be as useful as a traditional microsatellites (MS) marker set in performing whole-genome linkage analysis. However, very few studies have directly tested the SNPs-based genome-wide scan. In the present study, we compared the linkage results from the SNPs-based scan with a map density of 3-cM spacing with those from the MS scan using a 10-cM marker set among 300 nuclear families each from the Aipotu (AI), Danacaa (DA), and Karangar (KA) populations from the simulated Genetic Analysis Workshop 14 Problem 2 data. We found that information contents obtained from the SNPs scan were somewhat lower than those from the MS scan. However, the linkage results obtained from the two scans showed a high degree of similarity. Both scans identified a similar number of chromosomal regions attaining nominal significance (p < 0.05). Specifically, both scans detected confirmed evidence for linkage (NPL ≥ 4.07, p = 2 × 10-5) to chromosome 1 in the AI families, chromosomes 1 and 3 in the DA families, and chromosomes 3, 5, and 9 in the KA families. An additional confirmed linkage to chromosome 5 in the AI families was detected only by the MS scan. We also observed slightly wider 1-LOD intervals for more of the SNP peaks than for the MS peaks, which is likely due to lower information contents for the SNPs. Subsequent fine-mapping association analysis further identified 2 to 3 markers significantly associated with disease status in each population; B03T3056, B03T3058, and B05T4139 in the AI population, B03T3056 and B03T3058 in the KA population, and B03T3056, B03T3057, and B03T3058 in the DA population. Among the four markers, three were chosen based on results obtained from the two scans, but one was solely from the SNP scan. In summary, our finding suggests that the SNP-based genome scan has the potential to be as powerful as the traditional MS-based scan and offers good identification of peak location for further fine-mapped association analysis.
Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure
Authors: Liu Nianjun , Chen Liang , Wang Shuang , Oh Cheongeun , Zhao Hongyu 
Published: January 2006

Abstract
Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.
A comparison between microsatellite and single-nucleotide polymorphism markers with respect to two measures of information content
Authors: Thalamuthu Anbupalam , Mukhopadhyay Indranil , Ray Amrita , Weeks Daniel E 
Published: January 2006

Abstract
Using the Genetic Analysis Workshop 14 (GAW14) simulated dataset, we compare microsatellite and single-nucleotide polymorphism (SNP) markers in terms of two measures of information content, the traditional entropy-based information content measure, and a new "relative information" measure. Both attempt to measure the amount of information contained in the markers about the identity-by-descent (IBD) sharing among relatives. The performance of the two information measures are compared based on their variability and ability to predict change in the LOD score (ΔLOD) as map density increases for SNP markers. Although in a linked region, LOD scores are correlated with measures of information, we observe that none of the measures predict the LOD score itself very well. In an unlinked region, the LOD score is not related to either measures of information. The information content of microsatellite markers with 7.5-cM spacing is slightly higher than that of SNP markers with 3-cM spacing. At these map densities, microsatellites are found to be uniformly more informative than SNPs irrespective of their level of heterozygosity. For SNPs, we found that as the level of heterozygosity increases, the information content increases. As reported in all other previous studies, we also found that high-density SNPs have higher information content compared to low-density microsatellites. Performance of both the two information measures considered here are similar, but the relative information measure predicts ΔLOD as marker density increases better than the traditional entropy-based information measure.
Whole-genome linkage analysis in mapping alcoholism genes using single-nucleotide polymorphisms and microsatellites
Authors: Wang Shuang , Huang Song , Liu Nianjun , Chen Liang , Oh Cheongeun , Zhao Hongyu 
Published: January 2006

Abstract
There is currently a great interest in using single-nucleotide polymorphisms (SNPs) in genetic linkage and association studies because of the abundance of SNPs as well as the availability of high-throughput genotyping technologies. In this study, we compared the performance of whole-genome scans using SNPs with microsatellites on 143 pedigrees from the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workhsop 14. A total of 315 microsatellites and 10,081 SNPs from Affymetrix on 22 autosomal chromosomes were used in our analyses. We found that the results from the two scans had good overall concordance. One region on chromosome 2 and two regions on chromosome 7 showed significant linkage signals (i.e., NPL ≥ 2) for alcoholism from both the SNP and microsatellite scans. The different results observed between the two scans may be explained by the difference observed in information content between the SNPs and the microsatellites.
Comparison of microsatellites, single-nucleotide polymorphisms (SNPs) and composite markers derived from SNPs in linkage analysis
Authors: Xing Chao , Schumacher Fredrick R , Xing Guan , Lu Qing , Wang Tao , Elston Robert C 
Published: January 2006

Abstract
There is growing evidence that a map of dense single-nucleotide polymorphisms (SNPs) can outperform a map of sparse microsatellites for linkage analysis. There is also argument as to whether a clustered SNP map can outperform an evenly spaced SNP map. Using Genetic Analysis Workshop 14 simulated data, we compared for linkage analysis microsatellites, SNPs, and composite markers derived from SNPs. We encoded the composite markers in a two-step approach, in which the maximum identity length contrast method was employed to allow for recombination between loci. A SNP map 2.3 times as dense as a microsatellite map (~2.9 cM compared to ~6.7 cM apart) provided slightly less information content (~0.83 compared to ~0.89). Most inheritance information could be extracted when the SNPs were spaced < 1 cM apart. Comparing the linkage results on using SNPs or composite markers derived from them based on both 3 cM and 0.3 cM resolution maps, we showed that the inter-SNP distance should be kept small (< 1 cM), and that for multipoint linkage analysis the original markers and the derived composite markers had similar power; but for single point linkage analysis the resulting composite markers lead to more power. Considering all factors, such as information content, flexibility of analysis method, map errors, and genotyping errors, a map of clustered SNPs can be an efficient design for a genome-wide linkage scan.
A genome-wide scanning and fine mapping study of COGA data
Authors: Yang Hsin-Chou , Chang Chien-Ching , Lin Chin-Yu , Chen Chun-Liang , Lin Chin-Yu , Fann Cathy SJ 
Published: January 2006

Abstract
A thorough genetic mapping study was performed to identify predisposing genes for alcoholism dependence using the Collaborative Study on the Genetics of Alcoholism (COGA) data. The procedure comprised whole-genome linkage and confirmation analyses, single locus and haplotype fine mapping analyses, and gene × environment haplotype regression. Stratified analysis was considered to reduce the ethnic heterogeneity and simultaneously family-based and case-control study designs were applied to detect potential genetic signals. By using different methods and markers, we found high linkage signals at D1S225 (253.7 cM), D1S547 (279.2 cM), D2S1356 (64.6 cM), and D7S2846 (56.8 cM) with nonparametric linkage scores of 3.92, 4.10, 4.44, and 3.55, respectively. We also conducted haplotype and odds ratio analyses, where the response was the dichotomous status of alcohol dependence, explanatory variables were the inferred individual haplotypes and the three statistically significant covariates were age, gender, and max drink (the maximum number of drinks consumed in a 24-hr period). The final model identified important AD-related haplotypes within a candidate region of NRXN1 at 2p21 and a few others in the inter-gene regions. The relative magnitude of risks to the identified risky/protective haplotypes was elucidated.
Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism
Authors: Doan Betty Q , Frangakis Constantine E , Shugart Yin Y , Bailey-Wilson Joan E 
Published: January 2006

Abstract

Background
Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated.

Results
Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12.

Conclusion
These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets.
Multipoint identity-by-descent computations for single-point polymorphism and microsatellite maps
Authors: Hinrichs Anthony L , Bertelsen Sarah , Bierut Laura J , Dunn Gerald , Jin Carol H , Kauwe John S , Suarez Brian K 
Published: January 2006

Abstract
We used the LOKI software to generate multipoint identity-by-descent matrices for a microsatellite map (with 31 markers) and two single-nucleotide polymorphism (SNP) maps to examine information content across chromosome 7 in the Collaborative Study on the Genetics of Alcoholism dataset. Despite the lower information provided by a single SNP, SNP maps overall had higher and more uniform information content across the chromosome. The Affymetrix map (578 SNPs) and the Illumina map (271 SNPs) provided almost identical information. However, increased information has a computational cost: SNP maps require 100 times as many iterations as microsatellites to produce stable estimates.
Simulation-based homozygosity mapping with the GAW14 COGA dataset on alcoholism
Authors: Libiger Ondrej , Schork Nicholas J 
Published: January 2006

Abstract

Background
We have developed a simulation-based approach to the analysis of shared homozygous chromosomal segments and have applied it to data on allele sharing among alcoholics in a single Collaborative Study on the Genetics of Alcoholism pedigree. Our assessment of sharing involved the use of a single-nucleotide polymorphism (SNP) marker map provided by Affymetrix.

Results
All 11 affected individuals in the selected pedigree shared 2 copies of an allele at 4 adjacent SNPs in a region on chromosome 5. Via simulation, we determined that the probability that such sharing is caused by mere chance is less than 0.0000001. After correcting for undocumented inbreeding, this probability rose to 0.0016. The probability that the shared segment emanates from a single ancestor and is unrelated to the affection status is less than 0.0000001 in the corrected pedigree. Haplotype association analysis and a search for a protective locus using unaffected individuals yielded no significant results.

Conclusion
Homozygosity mapping results on chromosome 5 provide suggestive evidence of the region's role as one that may harbor a genetic determinant of alcoholism. Furthermore, the probabilities of chance homozygous allele sharing for the original and for the inbreeding-corrected pedigree provide insight into the impact that inbreeding can have on such calculations.
Mutation Scanning of the RET Protooncogene Using High-Resolution Melting Analysis
Authors: Margraf Rebecca L , Mao Rong , Highsmith W Edward , Holtegaard Leonard M , Wittwer Carl T 
Published: January 2006

Abstract
Background: Single-base pair missense mutations in exons 10, 11, 13, 14, 15, and 16 of the RET protooncogene are associated with the autosomal dominant multiple endocrine neoplasia type 2 (MEN2) syndromes: MEN2A, MEN2B, and familial medullary thyroid carcinoma. The current widely used approach for RET mutation detection is sequencing of the exons.
Methods: Because RET mutations are rare and the majority are heterozygous mutations, we investigated RET mutation detection by high-resolution amplicon melting analysis. This mutation scanning technique uses a saturating double-stranded nucleic acid binding dye, LCGreen®, and the high-resolution melter, HR-1™, to detect heterozygous and homozygous sequence variations. Mutant genotypes are distinguished from the wild-type genotype by an altered amplicon melting curve shape or position.
Results: Samples of 26 unique RET mutations, 4 nonpathogenic polymorphisms, or the wild-type genotype were available for this study. The developed RET mutation-scanning assay differentiated RET sequence variations from the wild-type genotype by altered derivative melting curve shape or position. A blinded study of 80 samples (derived from the 35 mutant, polymorphism, or wild-type samples) demonstrated that 100% of RET sequence variations were differentiated from wild-type samples. For exons 11 and 13, the nonpathogenic polymorphisms could be distinguished from the pathogenic RET mutations. Some RET mutations could be directly genotyped by the mutation scanning assay because of unique derivative melting curve shapes.
Conclusion: RET high-resolution amplicon melting analysis is a sensitive, closed-tube assay that can detect RET protooncogene sequence variations.
Interchangeability of Measurements of Total and Free Prostate-Specific Antigen in Serum with 5 Frequently Used Assay Combinations: An Update
Authors: Stephan Carsten , Klaas Moritz , Müller Christian , Schnorr Dietmar , Loening Stefan A , Jung Klaus 
Published: January 2006

Abstract
Background: The comparability of total and free prostate-specific antigen (tPSA and fPSA) results among commercial PSA assays has been suggested to be improved by calibration to WHO PSA reference materials and the development of equimolar-response assays. To characterize the current situation, we assessed 5 frequently used commercial assay combinations for tPSA and fPSA regarding the interchangeability of the PSA values and the ratio of fPSA to tPSA (%fPSA), equimolar characteristics, and diagnostic accuracy.
Methods: Sera from 314 patients with prostate cancer (PCa) and 282 men with no evidence of prostate cancer (NPCa) were measured with tPSA and fPSA assays from Abbott (AxSYM), Beckman Coulter (Access), Diagnostic Products Corporation (Immulite 2000), and Roche (Elecsys 2010) and with tPSA and complexed PSA (cPSA) assays from Bayer (ADVIA Centaur).
Results: Method comparisons (Passing and Bablok regressions; Bland–Altman plots) showed assay-dependent results for tPSA, fPSA, and %fPSA. With the Access tPSA values taken as 100%, tPSA concentrations varied from 87% (AxSYM and ADVIA Centaur) to 115% (Immulite), leading to different numbers of patients classified according to the commonly recommended tPSA cutoffs for performing a biopsy. Different %fPSA values also led to assay-dependent ROC analysis results, a finding that shows the importance for the diagnostic accuracy.
Conclusion: Interchangeability of tPSA, fPSA, and %fPSA values obtained by commercial PSA assays remains inadequate, but attention to this issue may minimize the misinterpretation of PSA results obtained by different assays.
Comparison of Solution‐Based versus Laser Ablation Inductively Coupled Plasma Mass Spectrometry for Analysis of Larval Fish Otolith Microelemental Composition
Authors: Ludsin Stuart A. , Fryer Brian J. , Gagnon Joel E. 
Published: January 2006

AbstractOtolith microchemistry has become a widely used tool for fisheries‐based research in marine systems. However, its application to systems without well‐defined juvenile nursery areas in which distinct otolith elemental signatures can develop (i.e., most freshwater systems) remains limited. In large part, this deficiency is due to unsuitable protocols for reliably processing and analyzing small otoliths of larvae. Herein, we evaluate the abilities of solution‐based (SO) and laser ablation (LA) inductively coupled plasma mass spectrometry (ICPMS) to quantify the otolith elemental composition of larval yellow perch Perca flavescens captured in three distinct spawning locations in Lake Erie (USA–Canada). Analysis of otolith pairs by each technique demonstrated that both SO‐ and LA‐ICPMS could be used to reliably quantify the more abundant elements, such as Sr and Ba. Magnesium and zinc, analyzed by use of both SO‐ and LA‐ICPMS, also met the criteria for inclusion in our analyses (i.e., the coefficients of variation of standards were <10.5%, and over 90% of samples were above detection limits at a single location). Upon closer inspection of the data, however, we found that estimates of Mg and Zn were only reliable for LA‐ICPMS. Estimates of these two elements using SO‐ICPMS were unrealistically high, probably owing to contamination during the otolith dissolution and handling phases. We also found that LA‐ICPMS provided more precise estimates than did SO‐ICPMS for nearly all elements explored, but LA‐ICPMS was somewhat limited by high limits of detection for some elements. Despite these differences, both techniques could accurately discriminate among larvae produced in different Lake Erie spawning locations, primarily because of the significant variation in Sr among larval otoliths. Ultimately, although both methods are appropriate for analysis of otoliths from larvae, we recommend the use of LA‐ICPMS in future otolith microchemical applications involving larvae.