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.     

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.     

Guidelines for genotyping in genomewide linkage studies: single-nucleotide-polymorphism maps versus microsatellite maps

Genomewide linkage scans have traditionally employed panels of microsatellite markers spaced at intervals of approximately 10 cM across the genome. However, there is a growing realization that a map of closely spaced single-nucleotide polymorphisms (SNPs) may offer equal or superior power to detect linkage, compared with low-density microsatellite maps. We performed a series of simulations to calculate the information content associated with microsatellite and SNP maps across a range of different marker densities and heterozygosities for sib pairs (with and without parental genotypes), sib trios, and sib quads. In the case of microsatellite markers, we varied density across 11 levels (1 marker every 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 cM) and marker heterozygosity across 6 levels (2, 3, 4, 5, 10, or 20 equally frequent alleles), whereas, in the case of SNPs, we varied marker density across 4 levels (1 marker every 0.1, 0.2, 0.5, or 1 cM) and minor-allele frequency across 7 levels (0.5, 0.4, 0.3, 0.2, 0.1, 0.05, and 0.01). When parental genotypes were available, a map consisting of microsatellites spaced every 2 cM or a relatively sparse map of SNPs (i.e., at least 1 SNP/cM) was sufficient to extract most of the inheritance information from the map (>95% in most cases). However, when parental genotypes were unavailable, it was important to use as dense a map of markers as possible to extract the greatest amount of inheritance information. It is important to note that the information content associated with a traditional map of microsatellite markers (i.e., 1 marker every ~10 cM) was significantly lower than the information content associated with a dense map of SNPs or microsatellites. These results strongly suggest that previous linkage studies that employed sparse microsatellite maps could benefit substantially from reanalysis by use of a denser map of markers.     

The linkage information content value of polymorphism genetic markers in model-free linkage analysis

Guo and Elston [Hum Hered 1999;49:112-118] developed a linkage information content (LIC) value to measure the informativeness of a marker for identity-by-descent (IBD) sharing status of relative pairs. LIC values were derived for five types of relative pairs: full sib, half sib, grandparent-grandchild, first cousin and avuncular. In this paper, we give corrected LIC values for full sib, grandparent-grandchild, first cousin and avuncular pairs, and indicate the availability of a computer program to calculate them.     

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.     

Construction of two genetic linkage maps of taro using single nucleotide polymorphism and microsatellite markers

Linkage maps are needed for genetic studies and molecular breeding of taro (Colocasia esculenta). In this study, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphism (SNP) loci on two mapping populations: F31 (HLB11 × VU006) composed of 266 progenies and F32 [HLB01 × (VU370×ID316)] composed of 292 progenies. SNP calling generated an initial set of 22,734 SNPs for F31 and 16,744 for F32. A large proportion of individuals and loci were later removed by filtering on the proportion of missing data and segregation distortions. Linkage maps were constructed with filtered SNPs in association with 14 simple sequence repeat (SSR) markers, using the maximum likelihood method. In both populations, loci were successfully grouped into 14 linkage groups (LGs) with an independence logarithm of odds (LOD) threshold of 11.0 and 8.0 for F31 and F32, respectively. LGs ranged in size from 90 to 15 markers for F31 and from 92 to 12 markers for F32. Bridge markers (459 SNPs and 9 SSRs) were identified and revealed homologous groups between families. Although our maps presented unprecedented chromosome coverage, the colinearity between homologous groups was low (except for LG07), and map lengths were globally inflated. Putative effects of missing data, segregation distortion, and genotyping errors on map accuracy are discussed. This research work led to the identification of a reliable set of SNPs potentially useful as a tool for a wide range of genetic studies in taro.     

The use of single-nucleotide polymorphism maps in pharmacogenomics

Single-nucleotide polymorphisms (SNPs), common variations among the DNA of individuals, are being uncovered and assembled into large SNP databases that promise to enable the dissection of the genetic basis of disease and drug response (i.e., pharmacogenomics). Although great strides have been made in understanding the diversity of the human genome, such as the frequency, distribution, and type of genetic variation that exists, the feasibility of applying this information to uncover useful pharmacogenomic markers is uncertain. The health care industry is clamoring for access to SNP databases for use in research in the hope of revolutionizing the drug development process. As the reality of using SNPs to uncover drug response markers is rarely addressed, this review discusses practical issues, such as patient sample size, SNP density and genome coverage, and data interpretation, that will be important for determining the applicability of pharmacogenomic information to medical practice.     

Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data

We introduce a new statistical model for patterns of linkage disequilibrium (LD) among multiple SNPs in a population sample. The model overcomes limitations of existing approaches to understanding, summarizing, and interpreting LD by (i) relating patterns of LD directly to the underlying recombination process; (ii) considering all loci simultaneously, rather than pairwise; (iii) avoiding the assumption that LD necessarily has a "block-like" structure; and (iv) being computationally tractable for huge genomic regions (up to complete chromosomes). We examine in detail one natural application of the model: estimation of underlying recombination rates from population data. Using simulation, we show that in the case where recombination is assumed constant across the region of interest, recombination rate estimates based on our model are competitive with the very best of current available methods. More importantly, we demonstrate, on real and simulated data, the potential of the model to help identify and quantify fine-scale variation in recombination rate from population data. We also outline how the model could be useful in other contexts, such as in the development of more efficient haplotype-based methods for LD mapping.     

Mapping quantitative trait loci using molecular marker linkage maps

Summary High-density restriction fragment length polymorphism (RFLP) and allozyme linkage maps have been developed in several plant species. These maps make it technically feasible to map quantitative trait loci (QTL) using methods based on flanking marker genetic models. In this paper, we describe flanking marker models for doubled haploid (DH), recombinant inbred (RI), backcross (BC), F₁ testcross (F₁TC), DH testcross (DHTC), recombinant inbred testcross (RITC), F₂, and F₃ progeny. These models are functions of the means of quantitative trait locus genotypes and recombination frequencies between marker and quantitative trait loci. In addition to the genetic models, we describe maximum likelihood methods for estimating these parameters using linear, nonlinear, and univariate or multivariate normal distribution mixture models. We defined recombination frequency estimators for backcross and F₂ progeny group genetic models using the parameters of linear models. In addition, we found a genetically unbiased estimator of the QTL heterozygote mean using a linear function of marker means. In nonlinear models, recombination frequencies are estimated less efficiently than the means of quantitative trait locus genotypes. Recombination frequency estimation efficiency decreases as the distance between markers decreases, because the number of progeny in recombinant marker classes decreases. Mean estimation efficiency is nearly equal for these methods.     

Evaluation of linkage disequilibrium and its effect on non-parametric multipoint linkage analysis using two high density single-nucleotide polymorphism mapping panels

Genotype data from the Illumina Linkage III SNP panel (n = 4,720 SNPs) and the Affymetrix 10 k mapping array (n = 11,120 SNPs) were used to test the effects of linkage disequilibrium (LD) between SNPs in a linkage analysis in the Collaborative Study on the Genetics of Alcoholism pedigree collection (143 pedigrees; 1,614 individuals). The average r2 between adjacent markers across the genetic map was 0.099 +/- 0.003 in the Illumina III panel and 0.17 +/- 0.003 in the Affymetrix 10 k array. In order to determine the effect of LD between marker loci in a nonparametric multipoint linkage analysis, markers in strong LD with another marker (r2 > 0.40) were removed (n = 471 loci in the Illumina panel; n = 1,804 loci in the Affymetrix panel) and the linkage analysis results were compared to the results using the entire marker sets. In all analyses using the ALDX1 phenotype, 8 linkage regions on 5 chromosomes (2, 7, 10, 11, X) were detected (peak markers p < 0.01), and the Illumina panel detected an additional region on chromosome 6. Analysis of the same pedigree set and ALDX1 phenotype using short tandem repeat markers (STRs) resulted in 3 linkage regions on 3 chromosomes (peak markers p < 0.01). These results suggest that in this pedigree set, LD between loci with spacing similar to the SNP panels tested may not significantly affect the overall detection of linkage regions in a genome scan. Moreover, since the data quality and information content are greatly improved in the SNP panels over STR genotyping methods, new linkage regions may be identified due to higher information content and data quality in a dense SNP linkage panel.     

Comparison of the power between microsatellite and single-nucleotide polymorphism markers for linkage and linkage disequilibrium mapping of an electrophysiological phenotype

We performed linkage and linkage disequilibrium (LD) mapping analyses to compare the power between microsatellite and single nucleotide polymorphism (SNP) markers. Chromosome-wide analyses were performed for a quantitative electrophysiological phenotype, ttth1, on chromosome 7. Multipoint analysis of microsatellite markers using the variance component (VC) method showed the highest LOD score of 4.20 at 162 cM, near D7S509 (163.7 cM). Two-point analysis of SNPs using the VC method yielded the highest LOD score of 3.98 in the Illumina SNP data and 3.45 in the Affymetrix SNP data around 152-153 cM. In family-based single SNP and SNP haplotype LD analysis, we identified seven SNPs associated with ttth1. We searched for any potential candidate genes in the location of the seven SNPs. The SNPs rs1476640 and rs768055 are located in the FLJ40852 gene (a hypothetical protein), and SNP rs1859646 is located in the TAS2R5 gene (a taste receptor). The other four SNPs are not located in any known or annotated genes. We found the high density SNP scan to be superior to microsatellites because it is effective in downstream fine mapping due to a better defined linkage region. Our study proves the utility of high density SNP in genome-wide mapping studies.     

Construction of bovine whole-genome radiation hybrid and linkage maps using high-throughput genotyping

High-density whole-genome maps are essential for ordering genes or markers and aid in the assembly of genome sequence. To increase the density of markers on the bovine radiation hybrid map, and hence contribute to the assembly of the bovine genome sequence, an Illumina BeadStation was used to simultaneously type large numbers of markers on the Roslin-Cambridge 3000 rad bovine-hamster whole-genome radiation hybrid panel (WGRH3000). In five multiplex reactions, 6738 sequence tagged site (STS) markers were successfully typed on the WGRH3000 panel DNA. These STSs harboured SNPs that were developed as a result of the bovine genome sequencing initiative. Typically, the most time consuming and expensive part of creating high-density radiation hybrid (RH) maps is genotyping the markers on the RH panel with conventional approaches. Using the method described in this article, we have developed a high-density whole-genome RH map with 4690 loci and a linkage map with 2701 loci, with direct comparison to the bovine whole-genome sequence assembly (Btau_2.0) in a fraction of the time it would have taken with conventional typing and genotyping methods.     

High-density single-nucleotide polymorphism maps of the human genome

Here we report a large, extensively characterized set of single-nucleotide polymorphisms (SNPs) covering the human genome. We determined the allele frequencies of 55,018 SNPs in African Americans, Asians (Japanese-Chinese), and European Americans as part of The SNP Consortium's Allele Frequency Project. A subset of 8333 SNPs was also characterized in Koreans. Because these SNPs were ascertained in the same way, the data set is particularly useful for modeling. Our results document that much genetic variation is shared among populations. For autosomes, some 44% of these SNPs have a minor allele frequency > or =10% in each population, and the average allele frequency differences between populations with different continental origins are less than 19%. However, the several percentage point allele frequency differences among the closely related Korean, Japanese, and Chinese populations suggest caution in using mixtures of well-established populations for case-control genetic studies of complex traits. We estimate that approximately 7% of these SNPs are private SNPs with minor allele frequencies <1%. A useful set of characterized SNPs with large allele frequency differences between populations (>60%) can be used for admixture studies. High-density maps of high-quality, characterized SNPs produced by this project are freely available.     

Anti-primer quenching-based real-time PCR for simplex or multiplex DNA quantification and single-nucleotide polymorphism genotyping

Nucleic acid amplification and detection plays an increasingly important role in genetic analysis of clinical samples, medical diagnostics and drug discovery. We present a new quantitative PCR method that allows versatile and flexible nucleic acid target quantification. One of the PCR primers is modified by an oligonucleotide "tail" fluorescently labeled at the 5' end. An oligonucleotide complementary to this tail, carrying a 3'-quencher ("anti-primer"), is included in the PCR along with the two primers. Following primer extension, the reaction temperature is lowered such that the anti-primer hybridizes to and quenches the fluorescence of only the free primer and not the double-stranded PCR product, allowing real-time fluorescent quantification of the latter. This anti-primer-based quantitative real-time PCR (aQRT-PCR) allows simplex or multiplex quantification or single-nucleotide polymorphism genotyping in clinical samples of widely differing quality (e.g., fresh samples, formalin-fixed paraffin-embedded samples and plasma-circulating DNA) and provides a practical alternative to existing, more expensive approaches. The process of aQRT-PCR takes 1.5-2 h.     

Effects of misspecification of allele frequencies on the type I error rate of model-free linkage analysis

Linkage analyses of simulated quantitative trait data were performed using the Haseman-Elston (H-E) sib pair regression test to investigate the effects of inaccurate allele frequency estimates on the type I error rates of this test. Computer simulations generating a quantitative trait in nuclear families were performed using GASP [1]. Assuming no linkage, several data sets were simulated; they differed in marker allele numbers and frequencies, number of sib pairs and number of sibships. Each set of simulated data was analyzed using (1) all parental marker data, (2) half of the parental marker data, and (3) no parental marker data, using both correct and incorrect allele frequencies in the latter 2 cases. The H-E sib pair linkage method was found to be robust to misspecification of marker allele frequencies regardless of the number of alleles.     

Loss-of-heterozygosity analysis of small-cell lung carcinomas using single-nucleotide polymorphism arrays

Human cancers arise by a combination of discrete mutations and chromosomal alterations. Loss of heterozygosity (LOH) of chromosomal regions bearing mutated tumor suppressor genes is a key event in the evolution of epithelial and mesenchymal tumors. Global patterns of LOH can be understood through allelotyping of tumors with polymorphic genetic markers. Simple sequence length polymorphisms (SSLPs, or microsatellites) are reliable genetic markers for studying LOH, but only a modest number of SSLPs are used in LOH studies because the genotyping procedure is rather tedious. Here, we report the use of a highly parallel approach to genotype large numbers of single-nucleotide polymorphisms (SNPs) for LOH, in which samples are genotyped for nearly 1,500 loci by performing 24 polymerase chain reactions (PCR), pooling the resulting amplification products and hybridizing the mixture to a high-density oligonucleotide array. We characterize the results of LOH analyses on human small-cell lung cancer (SCLC) and control DNA samples by hybridization. We show that the patterns of LOH are consistent with those obtained by analysis with both SSLPs and comparative genomic hybridization (CGH), whereas amplifications rarely are detected by the SNP array. The results validate the use of SNP array hybridization for tumor studies.     

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.     

A microsatellite marker based linkage map of tobacco

We report the first linkage map of tobacco (Nicotiana tabacum L.) generated through microsatellite markers. The microsatellite markers were predominantly derived from genomic sequences of the Tobacco Genome Initiative (TGI) through bioinformatics screening for microsatellite motives. A total of 684 primer pairs were screened for functionality in a panel of 16 tobacco lines. Of those, 637 primer pairs were functional. Potential parents for mapping populations were evaluated for their polymorphism level through genetic similarity analysis. The similarity analysis revealed that the known groups of tobacco varieties (Burley, Flue-cured, Oriental and Dark) form distinct clusters. A mapping population, based on a cross between varieties Hicks Broad Leaf and Red Russian, and consisting of 186 F2 individuals, was selected for mapping. A total of 282 functional microsatellite markers were polymorphic in this population and 293 loci could be mapped together with the morphological trait flower color. Twenty-four tentative linkage groups spanning 1,920 cM could be identified. This map will provide the basis for the genetic mapping of traits in tobacco and for further analyses of the tobacco genome. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.