Data acquisition for meta-analysis of genome-wide linkage studies using the genome search meta-analysis method

BACKGROUND: The Genome Search Meta-Analysis (GSMA) method enables researchers to pool results across genome-wide linkage studies, to increase the power to detect linkage. Results from individual studies must be extracted, with the maximum evidence for linkage placed into bins, usually of 30 cM width, and ranked within the study. Ranks are then summed across studies, with high summed ranks potentially showing evidence for linkage in the meta-analysis. OBJECTIVES: In this paper we study the properties of the GSMA method considering two different issues: (1) data binning from genome-wide results when indexed markers or graphs are available, based on either predefined boundary markers, or equal-length bins; (2) the use of selected instead of genome-wide results, using simulation to estimate power and type I error rates of GSMA. This is relevant when published papers show only summary results (e.g. with NPL score >1). Results: Using digitizing software to extract linkage statistics from graphs and assigning equal bin length is accurate, with the resulting ranking of bins similar to those defined through boundary markers. Simulation results show that power can fall substantially when genome-wide results are not available, particularly when only results from a single marker are available in a linked region. However there is no increase in false positive findings. CONCLUSIONS: The GSMA method is robust across different bin definitions and methods of data presentation and extraction. Using studies based on only the top ranked bins does not produce false positive results, but lacks power to detect genes conferring a modest increase in risk. Therefore, we advise that effort should be made to obtain genome-wide results from investigators or from published papers to avoid limiting the utility of the GSMA.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part II: Schizophrenia

Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (R(avg)) and then weighted for sample size (N(sqrt)[affected casess]). A permutation test was used to compute the probability of observing, by chance, each bin's average rank (P(AvgRnk)) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (P(ord)). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (PAvgRnk<.000417). Two aggregate criteria for linkage were also met (clusters of nominally significant P values that did not occur in 1,000 replicates of the entire data set with no linkage present): 12 consecutive bins with both P(AvgRnk) and P(ord)<.05, including regions of chromosomes 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, and 14p, and 19 consecutive bins with P(ord)<.05, additionally including regions of chromosomes 16q, 18q, 10p, 15q, 6q, and 17q. There is greater consistency of linkage results across studies than has been previously recognized. The results suggest that some or all of these regions contain loci that increase susceptibility to schizophrenia in diverse populations.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part III: Bipolar disorder

Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for “very narrow” (i.e., BP-I and schizoaffective disorder–BP) and “narrow” (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A “broad” model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part I: Methods and power analysis

This is the first of three articles on a meta-analysis of genome scans of schizophrenia (SCZ) and bipolar disorder (BPD) that uses the rank-based genome scan meta-analysis (GSMA) method. Here we used simulation to determine the power of GSMA to detect linkage and to identify thresholds of significance. We simulated replicates resembling the SCZ data set (20 scans; 1,208 pedigrees) and two BPD data sets using very narrow (9 scans; 347 pedigrees) and narrow (14 scans; 512 pedigrees) diagnoses. Samples were approximated by sets of affected sibling pairs with incomplete parental data. Genotypes were simulated and nonparametric linkage (NPL) scores computed for 20 180-cM chromosomes, each containing six 30-cM bins, with three markers/bin (or two, for some scans). Genomes contained 0, 1, 5, or 10 linked loci, and we assumed relative risk to siblings (lambda(sibs)) values of 1.15, 1.2, 1.3, or 1.4. For each replicate, bins were ranked within-study by maximum NPL scores, and the ranks were averaged (R(avg)) across scans. Analyses were repeated with weighted ranks ((sqrt)N[genotyped cases] for each scan). Two P values were determined for each R(avg): P(AvgRnk) (the pointwise probability) and P(ord) (the probability, given the bin's place in the order of average ranks). GSMA detected linkage with power comparable to or greater than the underlying NPL scores. Weighting for sample size increased power. When no genomewide significant P values were observed, the presence of linkage could be inferred from the number of bins with nominally significant P(AvgRnk), P(ord), or (most powerfully) both. The results suggest that GSMA can detect linkage across multiple genome scans.     

Systemic lupus erythematosus susceptibility loci defined by genome scan meta-analysis

To date, several susceptibility loci for systemic lupus erythematosus (SLE) have been identified by individual genome-wide scans, but many of these loci have shown inconsistent results across studies. Additionally, many individual studies are at the lower limit of acceptable power recommended for declaring significant linkage. The genome search meta-analysis (GSMA) has been proposed as a valid and robust method for combining several genome scan results. The aim of this study is to investigate whether there is any consistent evidence of linkage across multiple studies, and to identify novel SLE susceptibility loci by using GSMA method. Twelve genome scan results generated from nine independent studies have been used for the present GSMA. All together, the data consists of 605 families with 1,355 SLE affected individuals from three self-reported ethnicities; Caucasian, African-American, and Hispanic. For each study, the genome was divided into 120 bins (30 cM) and ranked according to the maximum evidence of linkage within each bin. The ranks were summed and averaged across studies following which the significance was assessed by the permutation tests. The present study identified two genomic locations at 6p22.3–6p21.1 and 16p12.3–16q12.2 that met genome-wide significance (p<0.000417). The identified region at 6p22.3–6p21.1 contains the HLA region. The combined p-values using Fisher’s method also supported the significance in these regions. Clustering of significant adjacent bins was observed for chromosomes 6 and 16. Additionally, there are 12 other bins with two point-wise p-values (Psumrnk and Pord) <0.05, suggesting that these bin regions are highly likely to contain SLE susceptibility loci. Among them, present GSMA also identified two novel regions at 4q32.1–4q34.3 and 13q13.2–13q22.2. However, separate analysis using only Caucasian populations identified the strongest evidence for linkage at chromosome 6p21.1–6q15 (Psumrnk=0.00021). One interesting novel region suggests that 3q22.1–3q25.33 (Psumrnk=0.01376) may be an ethnicity-specific SLE linkage. In summary, the present GSMA have identified two statistically significant genomic regions that reconfirmed the SLE linkage at chromosomes 6 and 16.     

GSMA: software implementation of the genome search meta-analysis method

Meta-analysis can be used to pool results of genome-wide linkage scans. This is of great value in complex diseases, where replication of linked regions occurs infrequently. The genome search meta-analysis (GSMA) method is widely used for this analysis, and a computer program is now available to implement the GSMA.     

Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6-7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160-167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287-294 cM, including D2), chromosome 5 (p-value < 0.001 for 0-7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7-14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage.     

Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.     

Genome scan linkage analysis comparing microsatellites and single-nucleotide polymorphisms markers for two measures of alcoholism in chromosomes 1, 4, and 7

Background

We analyzed 143 pedigrees (364 nuclear families) in the Collaborative Study on the Genetics of Alcoholism (COGA) data provided to the participants in the Genetic Analysis Workshop 14 (GAW14) with the goal of comparing results obtained from genome linkage analysis using microsatellite and with results obtained using SNP markers for two measures of alcoholism (maximum number of drinks -MAXDRINK and an electrophysiological measure from EEG -TTTH1). First, we constructed haplotype blocks by using the entire set of single-nucleotide polymorphisms (SNP) in chromosomes 1, 4, and 7. These chromosomes have shown linkage signals for MAXDRINK or EEG-TTTH1 in previous reports. Second, we randomly selected one, two, three, four, and five SNPs from each block (referred to as Rep1 – Rep5, respectively) to conduct linkage analysis using variance component approach. Finally, results of all SNP analyses were compared with those obtained using microsatellite markers.

Results

The LOD scores obtained from SNPs were slightly higher but the curves were not radically different from those obtained from microsatellite analyses. The peaks of linkage regions from SNP sets were slightly shifted to the left when compared to those from microsatellite markers. The reduced sets of SNPs provide signals in the same linkage regions but with a smaller LOD score suggesting a significant impact of the decrease in information content on linkage results. The widths of 1 LOD support interval of linkage regions from SNP sets were smaller when compared to those of microsatellite markers. However, two linkage regions obtained from the microsatellite linkage analysis on chromosome 7 for LOG of TTTH1 were not detected in the SNP based analyses.

Conclusion

The linkage results from SNPs showed narrower linkage regions and slightly higher LOD scores when compared to those of microsatellite markers. The different builds of the genetic maps used in microsatellite and SNPs markers or/and errors in genotyping may account for the microsatellite linkage signals on chromosome 7 that were not identified using SNPs. Also, unresolved map issues between SNPs and microsatellite markers may be partly responsible for the shifted linkage peaks when comparing the two types of markers.

     

Analyzing large-scale proteomics projects with latent semantic indexing

Since the advent of public data repositories for proteomics data, readily accessible results from high-throughput experiments have been accumulating steadily. Several large-scale projects in particular have contributed substantially to the amount of identifications available to the community. Despite the considerable body of information amassed, very few successful analyses have been performed and published on this data, leveling off the ultimate value of these projects far below their potential. A prominent reason published proteomics data is seldom reanalyzed lies in the heterogeneous nature of the original sample collection and the subsequent data recording and processing. To illustrate that at least part of this heterogeneity can be compensated for, we here apply a latent semantic analysis to the data contributed by the Human Proteome Organization's Plasma Proteome Project (HUPO PPP). Interestingly, despite the broad spectrum of instruments and methodologies applied in the HUPO PPP, our analysis reveals several obvious patterns that can be used to formulate concrete recommendations for optimizing proteomics project planning as well as the choice of technologies used in future experiments. It is clear from these results that the analysis of large bodies of publicly available proteomics data by noise-tolerant algorithms such as the latent semantic analysis holds great promise and is currently underexploited.     

Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes

Most common genetic disorders have a complex inheritance and may result from variants in many genes, each contributing only weak effects to the disease. Pinpointing these disease genes within the myriad of susceptibility loci identified in linkage studies is difficult because these loci may contain hundreds of genes. However, in any disorder, most of the disease genes will be involved in only a few different molecular pathways. If we know something about the relationships between the genes, we can assess whether some genes (which may reside in different loci) functionally interact with each other, indicating a joint basis for the disease etiology. There are various repositories of information on pathway relationships. To consolidate this information, we developed a functional human gene network that integrates information on genes and the functional relationships between genes, based on data from the Kyoto Encyclopedia of Genes and Genomes, the Biomolecular Interaction Network Database, Reactome, the Human Protein Reference Database, the Gene Ontology database, predicted protein-protein interactions, human yeast two-hybrid interactions, and microarray co-expressions. We applied this network to interrelate positional candidate genes from different disease loci and then tested 96 heritable disorders for which the Online Mendelian Inheritance in Man database reported at least three disease genes. Artificial susceptibility loci, each containing 100 genes, were constructed around each disease gene, and we used the network to rank these genes on the basis of their functional interactions. By following up the top five genes per artificial locus, we were able to detect at least one known disease gene in 54% of the loci studied, representing a 2.8-fold increase over random selection. This suggests that our method can significantly reduce the cost and effort of pinpointing true disease genes in analyses of disorders for which numerous loci have been reported but for which most of the genes are unknown.     

Population structure and genetic differentiation associated with breeding history and selection in tomato (Solanum lycopersicum L.)

Tomato (Solanum lycopersicum L.) has undergone intensive selection during and following domestication. We investigated population structure and genetic differentiation within a collection of 70 tomato lines representing contemporary (processing and fresh-market) varieties, vintage varieties and landraces. The model-based Bayesian clustering software, STRUCTURE, was used to detect subpopulations. Six independent analyses were conducted using all marker data (173 markers) and five subsets of markers based on marker type (single-nucleotide polymorphisms, simple sequence repeats and insertion/deletions) and location (exon and intron sequences) within genes. All of these analyses consistently separated four groups predefined by market niche and age into distinct subpopulations. Furthermore, we detected at least two subpopulations within the processing varieties. These subpopulations correspond to historical patterns of breeding conducted for specific production environments. We found no subpopulation within fresh-market varieties, vintage varieties and landraces when using all marker data. High levels of admixture were shown in several varieties representing a transition in the demarcation between processing and fresh-market breeding. The genetic clustering detected by using the STRUCTURE software was confirmed by two statistics, pairwise F(st) (θ) and Nei's standard genetic distance. We also identified a total of 19 loci under positive selection between processing, fresh-market and vintage germplasm by using an F(st)-outlier method based on the deviation from the expected distribution of F(st) and heterozygosity. The markers and genome locations we identified are consistent with known patterns of selection and linkage to traits that differentiate the market classes. These results demonstrate how human selection through breeding has shaped genetic variation within cultivated tomato.     

Data capture in bioinformatics: requirements and experiences with Pedro

Background  

The systematic capture of appropriately annotated experimental data is a prerequisite for most bioinformatics analyses. Data capture is required not only for submission of data to public repositories, but also to underpin integrated analysis, archiving, and sharing – both within laboratories and in collaborative projects. The widespread requirement to capture data means that data capture and annotation are taking place at many sites, but the small scale of the literature on tools, techniques and experiences suggests that there is work to be done to identify good practice and reduce duplication of effort.     

Meta-analysis of genome-wide linkage studies of asthma and related traits

Background

Asthma and allergy are complex multifactorial disorders, with both genetic and environmental components determining disease expression. The use of molecular genetics holds great promise for the identification of novel drug targets for the treatment of asthma and allergy. Genome-wide linkage studies have identified a number of potential disease susceptibility loci but replication remains inconsistent. The aim of the current study was to complete a meta-analysis of data from genome-wide linkage studies of asthma and related phenotypes and provide inferences about the consistency of results and to identify novel regions for future gene discovery.

Methods

The rank based genome-scan meta-analysis (GSMA) method was used to combine linkage data for asthma and related traits; bronchial hyper-responsiveness (BHR), allergen positive skin prick test (SPT) and total serum Immunoglobulin E (IgE) from nine Caucasian asthma populations.

Results

Significant evidence for susceptibility loci was identified for quantitative traits including; BHR (989 pedigrees, n = 4,294) 2p12-q22.1, 6p22.3-p21.1 and 11q24.1-qter, allergen SPT (1,093 pedigrees, n = 4,746) 3p22.1-q22.1, 17p12-q24.3 and total IgE (729 pedigrees, n = 3,224) 5q11.2-q14.3 and 6pter-p22.3. Analysis of the asthma phenotype (1,267 pedigrees, n = 5,832) did not identify any region showing genome-wide significance.

Conclusion

This study represents the first linkage meta-analysis to determine the relative contribution of chromosomal regions to the risk of developing asthma and atopy. Several significant results were obtained for quantitative traits but not for asthma confirming the increased phenotype and genetic heterogeneity in asthma. These analyses support the contribution of regions that contain previously identified asthma susceptibility genes and provide the first evidence for susceptibility loci on 5q11.2-q14.3 and 11q24.1-qter.     

Genome-Wide Linkage Scan for Primary Open Angle Glaucoma: Influences of Ancestry and Age at Diagnosis

Primary open-angle glaucoma (POAG) is the most common form of glaucoma and one of the leading causes of vision loss worldwide. The genetic etiology of POAG is complex and poorly understood. The purpose of this work is to identify genomic regions of interest linked to POAG. This study is the largest genetic linkage study of POAG performed to date: genomic DNA samples from 786 subjects (538 Caucasian ancestry, 248 African ancestry) were genotyped using either the Illumina GoldenGate Linkage 4 Panel or the Illumina Infinium Human Linkage-12 Panel. A total of 5233 SNPs was analyzed in 134 multiplex POAG families (89 Caucasian ancestry, 45 African ancestry). Parametric and non-parametric linkage analyses were performed on the overall dataset and within race-specific datasets (Caucasian ancestry and African ancestry). Ordered subset analysis was used to stratify the data on the basis of age of glaucoma diagnosis. Novel linkage regions were identified on chromosomes 1 and 20, and two previously described loci—GLC1D on chromosome 8 and GLC1I on chromosome 15—were replicated. These data will prove valuable in the context of interpreting results from genome-wide association studies for POAG.     

Codifying Collegiality: Recent Developments in Data Sharing Policy in the Life Sciences

Over the last decade, there have been significant changes in data sharing policies and in the data sharing environment faced by life science researchers. Using data from a 2013 survey of over 1600 life science researchers, we analyze the effects of sharing policies of funding agencies and journals. We also examine the effects of new sharing infrastructure and tools (i.e., third party repositories and online supplements). We find that recently enacted data sharing policies and new sharing infrastructure and tools have had a sizable effect on encouraging data sharing. In particular, third party repositories and online supplements as well as data sharing requirements of funding agencies, particularly the NIH and the National Human Genome Research Institute, were perceived by scientists to have had a large effect on facilitating data sharing. In addition, we found a high degree of compliance with these new policies, although noncompliance resulted in few formal or informal sanctions. Despite the overall effectiveness of data sharing policies, some significant gaps remain: about one third of grant reviewers placed no weight on data sharing plans in their reviews, and a similar percentage ignored the requirements of material transfer agreements. These patterns suggest that although most of these new policies have been effective, there is still room for policy improvement.     

Genome-wide linkage analysis for alcohol dependence: a comparison between single-nucleotide polymorphism and microsatellite marker assays

Both theoretical and applied studies have proven that the utility of single nucleotide polymorphism (SNP) markers in linkage analysis is more powerful and cost-effective than current microsatellite marker assays. Here we performed a whole-genome scan on 115 White, non-Hispanic families segregating for alcohol dependence, using one 10.3-cM microsatellite marker set and two SNP data sets (0.33-cM, 0.78-cM spacing). Two definitions of alcohol dependence (ALDX1 and ALDX2) were used. Our multipoint nonparametric linkage analysis found alcoholism was nominal linked to 12 genomic regions. The linkage peaks obtained by using the microsatellite marker set and the two SNP sets had a high degree of correspondence in general, but the microsatellite marker set was insufficient to detect some nominal linkage peaks. The presence of linkage disequilibrium between markers did not significantly affect the results. Across the entire genome, SNP datasets had a much higher average linkage information content (0.33 cM: 0.93, 0.78 cM: 0.91) than did microsatellite marker set (0.57). The linkage peaks obtained through two SNP datasets were very similar with some minor differences. We conclude that genome-wide linkage analysis by using approximately 5,000 SNP markers evenly distributed across the human genome is sufficient and might be more powerful than current 10-cM microsatellite marker assays.     

Optimizing copy number variation analysis using genome-wide short sequence oligonucleotide arrays

The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39–77% and 86–100% for non-segmental duplication regions, respectively, and 18–55% and 39–77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives.     

EST pipeline system: detailed and automated EST data processing and mining

Expressed sequence tags (ESTs) are widely used in gene survey research these years. The EST Pipeline System, software developed by Hangzhou Genomics Institute (HGI), can automatically analyze different scalar EST sequences by suitable methods. All the analysis reports, including those of vector masking, sequence assembly, gene annotation, Gene Ontology classification, and some other analyses, can be browsed and searched as well as downloaded in the Excel format from the web interface, saving research efforts from routine data processing for biological rules embedded in the data.     

Comparing single-nucleotide polymorphism marker-based and microsatellite marker-based linkage analyses

We compared linkage analysis results for an alcoholism trait, ALDX1 (DSM-III-R and Feigner criteria) using a nonparametric linkage analysis method, which takes into account allele sharing among several affected persons, for both microsatellite and single-nucleotide polymorphism (SNP) markers (Affymetrix and Illumina) in the Collaborative Study on the Genetics of Alcoholism (COGA) dataset provided to participants at the Genetic Analysis Workshop 14 (GAW14). The two sets of linkage results from the dense Affymetrix SNP markers and less densely spaced Illumina SNP markers are very similar. The linkage analysis results from microsatellite and SNP markers are generally similar, but the match is not perfect. Strong linkage peaks were found on chromosome 7 in three sets of linkage analyses using both SNP and microsatellite marker data. We also observed that for SNP markers, using the given genetic map and using the map by converting 1 megabase pair (1 Mb) to 1 centimorgan (cM), did not change the linkage results. We recommend the use of the 1 Mb-to-1 cM converted map in a first round of linkage analysis with SNP markers in which map integration is an issue.