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.     

Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits

Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.     

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.

     

Modeling the effect of an associated single-nucleotide polymorphism in linkage studies

For linkage analysis in affected sibling pairs, we propose a regression model to incorporate information from a disease-associated single-nucleotide polymorphism located under the linkage peak. This model can be used to study if the associated single-nucleotide polymorphism marker partly explains the original linkage peak. Two sources of information are used for performing this task, namely the genotypes of the parents and the genotypes of the siblings. We applied the methods to three significantly disease-associated single-nucleotide polymorphisms and five microsatellite markers at the end of chromosome 3 of replicate 1 of Aipotu population. Two out of five of the microsatellite markers showed a LOD score higher than 3. The question to be answered was whether one of the single-nucleotide polymorphisms partly explains these high LOD scores. We did not have the answers when we analyzed the data.     

Linkage analysis of the GAW14 simulated dataset with microsatellite and single-nucleotide polymorphism markers in large pedigrees

Recent studies have suggested that a high-density single nucleotide polymorphism (SNP) marker set could provide equivalent or even superior information compared with currently used microsatellite (STR) marker sets for gene mapping by linkage. The focus of this study was to compare results obtained from linkage analyses involving extended pedigrees with STR and single-nucleotide polymorphism (SNP) marker sets. We also wanted to compare the performance of current linkage programs in the presence of high marker density and extended pedigree structures. One replicate of the Genetic Analysis Workshop 14 (GAW14) simulated extended pedigrees (n = 50) from New York City was analyzed to identify the major gene D2. Four marker sets with varying information content and density on chromosome 3 (STR [7.5 cM]; SNP [3 cM, 1 cM, 0.3 cM]) were analyzed to detect two traits, the original affection status, and a redefined trait more closely correlated with D2. Multipoint parametric and nonparametric linkage analyses (NPL) were performed using programs GENEHUNTER, MERLIN, SIMWALK2, and S.A.G.E. SIBPAL. Our results suggested that the densest SNP map (0.3 cM) had the greatest power to detect linkage for the original trait (genetic heterogeneity), with the highest LOD score/NPL score and mapping precision. However, no significant improvement in linkage signals was observed with the densest SNP map compared with STR or SNP-1 cM maps for the redefined affection status (genetic homogeneity), possibly due to the extremely high information contents for all maps. Finally, our results suggested that each linkage program had limitations in handling the large, complex pedigrees as well as a high-density SNP marker set.     

Comparison of single-nucleotide polymorphisms and microsatellites in detecting quantitative trait loci for alcoholism: The Collaborative Study on the Genetics of Alcoholism

Background

The feasibility of effectively analyzing high-density single nucleotide polymorphism (SNP) maps in whole genome scans of complex traits is not known. The purpose of this study was to compare variance components linkage results using different density marker maps in data from the Collaborative Study on the Genetics of Alcoholism (COGA). Marker maps having an average spacing of 10 cM (microsatellite), 0.78 cM (SNP1), and 0.31 cM (SNP2) were used to identify quantitative trait loci (QTLs) affecting maximum number of alcoholic drinks consumed in a 24-hour period (lnmaxalc).

Results

Heritability of lnmaxalc was estimated to be 15%. Multipoint variance components linkage analysis revealed similar linkage patterns among the three marker panels, with the SNP maps consistently yielding higher LOD scores. Robust LOD scores > 1.0 were observed on chromosomes 1 and 13 for all three marker maps. Additional LODs > 1.0 were observed on chromosome 4 with both SNP maps and on chromosomes 18 and 21 with the SNP2 map. Peak LOD scores for lnmaxalc were observed on chromosome 1, although none reached genome-wide statistical significance. Quantile-quantile plots revealed that the multipoint distribution of SNP results appeared to fit the asymptotic null distribution better than the twopoint results.

Conclusion

In conclusion, variance-components linkage analysis using high-density SNP maps provided higher LOD scores compared with the standard microsatellite map, similar to studies using nonparametric linkage methods. Widespread application of SNP maps will depend on further improvements in the computational methods implemented in current software packages.

     

A genome-wide linkage analysis of alcoholism on microsatellite and single-nucleotide polymorphism data,using alcohol dependence phenotypes and electroencephalogram measures

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism and alcohol-related phenotypes. We performed genome-wide linkage analyses on the COGA data made available to participants in the Genetic Analysis Workshop 14 (GAW 14). The dataset comprised 1,350 participants from 143 families. The samples were analyzed on three technologies: microsatellites spaced at 10 cM, Affymetrix GeneChip Human Mapping 10 K Array (HMA10K) and Illumina SNP-based Linkage III Panel. We used ALDX1 and ALDX2, the COGA definitions of alcohol dependence, as well as electrophysiological measures TTTH1 and ECB21 to detect alcoholism susceptibility loci. Many chromosomal regions were found to be significant for each of the phenotypes at a p-value of 0.05. The most significant region for ALDX1 is on chromosome 7, with a maximum LOD score of 2.25 for Affymetrix SNPs, 1.97 for Illumina SNPs, and 1.72 for microsatellites. The same regions on chromosome 7 (96-106 cM) and 10 (149-176 cM) were found to be significant for both ALDX1 and ALDX2. A region on chromosome 7 (112-153 cM) and a region on chromosome 6 (169-185 cM) were identified as the most significant regions for TTTH1 and ECB21, respectively. We also performed linkage analysis on denser maps of markers by combining the SNPs datasets from Affymetrix and Illumina. Adding the microsatellite data to the combined SNP dataset improved the results only marginally. The results indicated that SNPs outperform microsatellites with the densest marker sets performing the best.     

Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14

The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided.     

Inclusion of unaffected sibs increases power in model-free linkage analysis of a behavioral trait

Study design strategies are of critical importance in the search for genes underlying complex diseases. Two important design choices in planning gene mapping studies are the analytic strategy to be used, which will have an impact on the type of data to be collected, and the choice of genetic markers. In the present paper, we used the simulated behavioral trait data provided in the Genetic Analysis Workshop 14 to: 1) investigate the usefulness of incorporating unaffected sibs in model-free linkage analysis and, 2) compare linkage results of genome scans using a 7-cM microsatellite map with a 3-cM single nucleotide polymorphisms map. To achieve these aims, we used the maximum-likelihood-binomial method with two different coding approaches. We defined the unaffected sibs as those totally free of phenotypes correlated to the disease. Without prior knowledge of the answers, we were able to correctly localize 2 out of 5 loci (LOD > 3) in a sample of 200 families that included the unaffected sibs but only one locus when based on an affected-only strategy, using either microsatellite or SNPs genome scan. LOD scores were considerably higher using the analytic strategy which incorporated the unaffected sibs. In conclusion, including unaffected sibs in model-free linkage analysis of complex binary traits is helpful, at least when complete parental data are available, whereas there are no striking advantages in using single nucleotide polymorphisms over microsatellite map at marker densities used in the current study.     

Standard linkage and association methods identify the mechanism of four susceptibility genes for a simulated complex disease

The simulated dataset of the Genetic Analysis Workshop 14 provided affection status and the presence or absence of 12 traits. It was determined that all affected individuals must have traits E, F and H (EFH phenotype) and they must also have either trait B (B subtype) or traits C, D, and G (CDG subtype). A genome screen was performed, and linkage peaks were identified on chromosomes 1, 3, 5, and 9 using microsatellite markers. Dense panels of single-nucleotide polymorphism (SNP) markers were ordered for each of the four linkage peaks. In each case, association analyses identified a single SNP that accounted for the linkage evidence. The SNP on chromosome 1 appeared to primarily influence the B subtype, while the SNPs on chromosomes 5 and 9 primarily influenced the CDG subtype. The chromosome 3 SNP had the strongest effect and influenced both subtypes, as well as the requisite EFH phenotype. Recognizing the two subtypes prior to linkage analysis was key to identifying these loci using only a single replicate. This highlights the need in real life situations for careful examination of the phenotypic data prior to genetic analysis.     

Optimizing the evidence for linkage by permuting marker order

We developed a new marker-reordering algorithm to find the best order of fine-mapping markers for multipoint linkage analysis. The algorithm searches for the best order of fine-mapping markers such that the sum of the squared differences in identity-by-descent distribution between neighboring markers is minimized. To test this algorithm, we examined its effect on the evidence for linkage in the simulated and the Collaborative Studies on Genetics of Alcoholism (COGA) data. We found enhanced evidence for linkage with the reordered map at the true location in the simulated data (p-value decreased from 1.16 x 10(-9) to 9.70 x 10(-10)). Analysis of the White population from the COGA data with the reordered map for alcohol dependence led to a significant change of the linkage signal (p = 0.0365 decreased to p = 0.0039) on chromosome 1 between marker D1S1592 and D1S1598. Our results suggest that reordering fine-mapping markers in candidate regions when the genetic map is uncertain can be a critical step when considering a dense map.     

Microsatellite linkage analysis, single-nucleotide polymorphisms, and haplotype associations with ECB21 in the COGA data

This study, part of the Genetic Analysis Workshop 14 (GAW14), explored real Collaborative Study on the Genetics of Alcoholism data for linkage and association mapping between genetic polymorphisms (microsatellite and single-nucleotide polymorphisms (SNPs)) and beta (16.5-20 Hz) oscillations of the brain rhythms (ecb21). The ecb21 phenotype underwent the statistical adjustments for the age of participants, and for attaining a normal distribution. A total of 1,000 subjects' available phenotypes were included in linkage analysis with microsatellite markers. Linkage analysis was performed only for chromosome 4 where a quantitative trait locus with 5.01 LOD score had been previously reported. Previous findings related this location with the gamma-aminobutyric acid type A (GABAA) receptor. At the same location, our analysis showed a LOD score of 2.2. This decrease in the LOD score is the result of a drastic reduction (one-third) of the available GAW14 phenotypic data. We performed SNP and haplotype association analyses with the same phenotypic data under the linkage peak region on chromosome 4. Seven Affymetrix and two Illumina SNPs showed significant associations with ecb21 phenotype. A haplotype, a combination of SNPs TSC0044171 and TSC0551006 (the latter almost under the region of GABAA genes), showed a significant association with ecb21 (p = 0.015) and a relatively high frequency in the sample studied. Our results affirmed that the GABA region has potential of harboring genes that contribute quantitatively to the beta oscillation of the brain rhythms. The inclusion of the remaining 614 subjects, which in the GAW14 had missing data for the ecb21, can improve the strength of the associations as they have already shown that they contribute quite important information in the linkage analysis.     

Inheritance of the number and thickness of cell layers in barley aleurone tissue (<Emphasis Type="Italic">Hordeum vulgare </Emphasis>L.): an approach using F2–F3 progeny

The aleurone tissue of cereal grains, nutritionally rich in minerals and vitamins, is an important target for the improvement of cereals. Inheritance of the thickness and the number of cell layers in barley aleurone was studied on the F2–F3 progeny of an Erhard Frederichen × Criolla Negra cross in which the parental lines have three or two aleurone layers, respectively. F3 grain was sampled from each F2 plant and 96.8% of the entire F3 grain population was classified as being either the 2- or 3-layer type. Using microsatellite, single nucleotide polymorphism (SNP) and morphological markers on 190 F2 plants, a linkage map was built. Three quantitative trait loci (QTLs) affecting aleurone traits were revealed on chromosome 5H (max. LOD = 5.83) and chromosome 7H (max. LOD = 4.45) by interval mapping, and on chromosome 2H by marker analysis with an unmapped marker. These QTLs were consistent with genetic sub-models involving either 2-cell type dominance for 7H and 2H, or putative partial dominance for 5H where 2-cell-layer dominance and additivity gave similar LODs. The number of aleurone cell layers and aleurone thickness were strongly correlated and QTL results for these traits were alike. An SNP marker of sal1, an orthologue of the maize multilayer aleurone gene was mapped to the 7HL chromosome arm. However, the 7H QTL did not co-locate with the barley sal1 SNP, suggesting that an additional gene is involved in determining aleurone traits. These new mapping data allow comparisons to be made with related studies.     

A comparison of univariate, bivariate, and trivariate whole-genome linkage screens of genetically correlated electrophysiological endophenotypes

We used a maximum-likelihood based multipoint linkage approach implemented in SOLAR to examine simultaneously linkage for three electrophysiological endophenotypes from the Collaborative Study of the Genetics of Alcoholism: TTTH1, TTTH2, and TTTH3. These endophenotypes have been identified as markers of alcohol dependence susceptibility. Data were from 905 individuals in 143 families. Measured covariates considered included sex, age at electrophysiology data collection, habitual smoking status, and the maximum number of drinks consumed in a 24-hour period. Comparisons were made among genome-wide univariate, bivariate, and trivariate linkage analyses using genotypes based on microsatellite markers supplied by the Center for Inherited Disease Research, and genotypes based on single-nucleotide polymorphism markers provided by Illumina. All LODs were corrected to a standard equivalent to 1 degree of freedom. Using the trivariate approach and the microsatellite-based genotypes, we estimated a maximum multipoint linkage signal of LOD = 2.66 on chromosome 7q at 157 cM. Analyses using the Illumina SNP genotypes produced similar results, yielding a maximum multipoint LOD of 2.95 on 7q at 174 cM. These regions of interest correspond to those identified in the univariate and bivariate linkage screens. Our results suggest that trivariate multipoint linkage analyses have utility in the further characterization of chromosomal regions potentially containing genes influencing the phenotypes being examined. Based on a comparison of the number of LOD scores achieving statistical significance, our results suggest that the microsatellite- and Illumina SNP-based genotypes have similar utility for detecting genomic regions of interest.     

Comparison of microsatellites, single-nucleotide polymorphisms (SNPs) and composite markers derived from SNPs in linkage analysis

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 (approximately 2.9 cM compared to approximately 6.7 cM apart) provided slightly less information content (approximately 0.83 compared to approximately 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.     

Linkage analysis using single nucleotide polymorphisms

We performed multipoint linkage analysis using 83 markers from the SNP Consortium (TSC) SNP linkage map in 3 regions covering 190 cM previously scanned with microsatellite markers and found to be linked to type 2 diabetes. Since the average linkage disequilibrium present in the TSC SNP marker clusters is relatively low, we assumed the intracluster genetic distances were a reasonable small nonzero distance (0.03 cM) and performed linkage analysis using GENEHUNTER PLUS and ASM linkage analysis software. We found that for the pedigree structures and missing data patterns in our samples the average information content in all three regions and the LOD score curves in two regions obtained from the TSC SNP markers were similar to results obtained from microsatellite marker maps with 10 cM average spacing. We also give an algorithm which extends the Lander-Green algorithm to permit multipoint linkage analysis of clusters of tightly linked markers with arbitrarily high levels of intracluster linkage disequilibrium.     

Neuropsychological endophenotype approach to genome-wide linkage analysis identifies susceptibility loci for ADHD on 2q21.1 and 13q12.11

ADHD linkage findings have not all been consistently replicated, suggesting that other approaches to linkage analysis in ADHD might be necessary, such as the use of (quantitative) endophenotypes (heritable traits associated with an increased risk for ADHD). Genome-wide linkage analyses were performed in the Dutch subsample of the International Multi-Center ADHD Genetics (IMAGE) study comprising 238 DSM-IV combined-type ADHD probands and their 112 affected and 195 nonaffected siblings. Eight candidate neuropsychological ADHD endophenotypes with heritabilities > 0.2 were used as quantitative traits. In addition, an overall component score of neuropsychological functioning was used. A total of 5407 autosomal single-nucleotide polymorphisms (SNPs) were used to run multipoint regression-based linkage analyses. Two significant genome-wide linkage signals were found, one for Motor Timing on chromosome 2q21.1 (LOD score: 3.944) and one for Digit Span on 13q12.11 (LOD score: 3.959). Ten suggestive linkage signals were found (LOD scores > or = 2) on chromosomes 2p, 2q, 3p, 4q, 8q, 12p, 12q, 14q, and 17q. The suggestive linkage signal for the component score that was found at 2q14.3 (LOD score: 2.878) overlapped with the region significantly linked to Motor Timing. Endophenotype approaches may increase power to detect susceptibility loci in ADHD and possibly in other complex disorders.     

A comparison between microsatellite and single-nucleotide polymorphism markers with respect to two measures of information content

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 (Delta LOD) 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 Delta LOD as marker density increases better than the traditional entropy-based information measure.     

A genetic linkage map and comparative genome analysis of common carp (Cyprinus carpio L.) using microsatellites and SNPs

A genetic linkage map is a powerful research tool for mapping traits of interest and is essential to understanding genome evolution. The aim of this study is to provide an expanded genetic linkage map of common carp to effectively carry out quantitative trait loci analysis and conduct comparative mapping analysis between lineages. Here, we constructed a genetic linkage map of common carp (Cyprinus carpio L.) using microsatellite and single-nucleotide polymorphism (SNP) markers in a 159 sibling family. A total of 246 microsatellites and 306 SNP polymorphic markers were genotyped in this family. Linkage analysis using JoinMap 4.0 organized 427 markers (186 microsatellites and 241 SNPs) to 50 linkage groups, ranging in size from 1.4 to 130.1 cM. Each group contained 2-30 markers. The linkage map covered a genetic distance of 2,039.2 cM and the average interval for markers within the linkage groups was approximately 6.4 cM. In addition, comparative genome analysis within five model teleost fish revealed a high percentage (74.7%) of conserved loci corresponding to zebrafish chromosomes. In most cases, each zebrafish chromosome comprised two common carp linkage groups. The comparative analysis also revealed independent chromosome rearrangements in common carp and zebrafish. The linkage map will be of great assistance in mapping genes of interest and serve as a reference to approach comparative mapping and enable further insights into the comprehensive investigations of genome evolution of common carp.