Whole-genome linkage analysis in mapping alcoholism genes using single-nucleotide polymorphisms and microsatellites

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 Workshop 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 >or= 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.     

Whole-genome association studies on alcoholism comparing different phenotypes using single-nucleotide polymorphisms and microsatellites

Alcoholism is a complex disease. As with other common diseases, genetic variants underlying alcoholism have been illusive, possibly due to the small effect from each individual susceptible variant, gene x environment and gene x gene interactions and complications in phenotype definition. We conducted association tests, the family-based association tests (FBAT) and the backward haplotype transmission association (BHTA), on the Collaborative Study of the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop (GAW) 14. Efron's local false discovery rate method was applied to control the proportion of false discoveries. For FBAT, we compared the results based on different types of genetic markers (single-nucleotide polymorphisms (SNPs) versus microsatellites) and different phenotype definitions (clinical diagnoses versus electrophysiological phenotypes). Significant association results were found only between SNPs and clinical diagnoses. In contrast, significant results were found only between microsatellites and electrophysiological phenotypes. In addition, we obtained the association results for SNPs and microsatellites using COGA diagnosis as phenotype based on BHTA. In this case, the results for SNPs and microsatellites are more consistent. Compared to FBAT, more significant markers are detected with BHTA.     

Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites

Recombination during meiosis is one of the most important biological processes, and the level of recombination rates for a given individual is under genetic control. In this study, we conducted genome-wide association studies to identify chromosomal regions associated with recombination rates. We analyzed genotype data collected on the pedigrees in the Collaborative Study on the Genetics on Alcoholism data provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 single-nucleotide polymorphisms from Affymetrix on 22 autosomal chromosomes were used in our association analysis. Genome-wide gender-specific recombination counts for family founders were inferred first and association analysis was performed using multiple linear regressions. We used the positive false discovery rate (pFDR) to account for multiple comparisons in the two genome-wide scans. Eight regions showed some evidence of association with recombination counts based on the single-nucleotide polymorphism analysis after adjusting for multiple comparisons. However, no region was found to be significant using microsatellites.     

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.     

Comparison of microsatellites versus single-nucleotide polymorphisms in a genome linkage screen for prostate cancer-susceptibility Loci

Prostate cancer is one of the most common cancers among men and has long been recognized to occur in familial clusters. Brothers and sons of affected men have a 2-3-fold increased risk of developing prostate cancer. However, identification of genetic susceptibility loci for prostate cancer has been extremely difficult. Although the suggestion of linkage has been reported for many chromosomes, the most promising regions have been difficult to replicate. In this study, we compare genome linkage scans using microsatellites with those using single-nucleotide polymorphisms (SNPs), performed in 467 men with prostate cancer from 167 families. For the microsatellites, the ABI Prism Linkage Mapping Set version 2, with 402 microsatellite markers, was used, and, for the SNPs, the Early Access Affymetrix Mapping 10K array was used. Our results show that the presence of linkage disequilibrium (LD) among SNPs can lead to inflated LOD scores, and this seems to be an artifact due to the assumption of linkage equilibrium that is required by the current genetic-linkage software. After excluding SNPs with high LD, we found a number of new LOD-score peaks with values of at least 2.0 that were not found by the microsatellite markers: chromosome 8, with a maximum model-free LOD score of 2.2; chromosome 2, with a LOD score of 2.1; chromosome 6, with a LOD score of 4.2; and chromosome 12, with a LOD score of 3.9. The LOD scores for chromosomes 6 and 12 are difficult to interpret, because they occurred only at the extreme ends of the chromosomes. The greatest gain provided by the SNP markers was a large increase in the linkage information content, with an average information content of 61% for the SNPs, versus an average of 41% for the microsatellite markers. The strengths and weaknesses of microsatellite versus SNP markers are illustrated by the results of our genome linkage scans.     

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.     

Linkage analysis of alcoholism-related electrophysiological phenotypes: genome scans with microsatellites compared to single-nucleotide polymorphisms

P300 amplitude is an electrophysiological quantitative trait that is correlated with both alcoholism and smoking status. Using the Collaborative Study on the Genetics of Alcoholism data, we performed model-free linkage analysis to investigate the relationship between alcoholism, P300 amplitude, and habitual smoking. We also analyzed the effect of parent-of-origin on alcoholism, and utilized both microsatellites (MS) markers and single-nucleotide polymorphisms (SNPs). We found significant evidence of linkage for alcoholism to chromosome 10; inclusion of P300 amplitude as a covariate provided additional evidence of linkage to chromosome 12. This same region on chromosome 12 showed some evidence for a parent-of-origin effect. We found evidence of linkage for the P300 phenotype to chromosome 7 in non-smokers, and to chromosome 17 in alcoholics. The effects of alcoholism and habitual smoking on P300 amplitude appear to have separate genetic determinants. Overall, there were few differences between MS and SNP genome scans. The use of covariates and parent-of-origin effects allowed detection of linkage not seen otherwise.     

Linkage analysis of complex diseases using microsatellites and single-nucleotide polymorphisms: application to alcoholism

The efficacy of linkage studies using microsatellites and single-nucleotide polymorphisms (SNPs) was evaluated. Analyzed data were supplied by the Collaborative Study on the Genetics of Alcoholism (COGA). Alcoholism was analyzed together with a simulated trait caused by a gene of known position, through a nonparametric linkage test (NPL). For the alcoholism trait, four densities of SNPs (1 SNP per 0.2 cM, 0.5 cM, 1 cM and 2 cM) showed higher peaks of NPL z scores and smaller significant p-values than the usual 10-cM density of microsatellites. However, the two highest densities of SNPs had unstable z score signals, and therefore were difficult to interpret. Analyzing a simulated trait with the same markers in the same pedigrees, we confirmed the higher power of all four densities of SNPs compared to the 10-cM microsatellites panel, although the existence of other confounding peaks was confirmed for maps that are denser than 1 SNP/cM. We further showed that estimating the gene position using SNPs is far less biased than using the usual panel of microsatellites (biases of 0-2 cM for SNPs vs. 8.9 cM for microsatellites). We conclude that using dense maps of SNPs in linkage analysis is more powerful and less biased than using the 10-cM maps of microsatellites. However, linkage signals can be unstable and difficult to interpret when several SNPs are genotyped per centimorgan. The power and accuracy of 1 SNP/cM or 1 SNP/2 cM may be sufficient in a genome-wide linkage scan while denser maps may be most useful in fine-gene mapping studies exploiting linkage disequilibrium.     

Genome-wide linkage analysis of age at onset of alcohol dependence: a comparison between microsatellites and single-nucleotide polymorphisms

Background

Using the dataset provided for Genetic Analysis Workshop 14 by the Collaborative Study on the Genetics of Alcoholism, we performed genome-wide linkage analysis of age at onset of alcoholism to compare the utility of microsatellites and single-nucleotide polymorphisms (SNPs) in genetic linkage study.

Methods

A multipoint nonparametric variance component linkage analysis method was applied to the survival distribution function obtained from semiparametric proportional hazards model of the age at onset phenotype of alcoholism. Three separate linkage analyses were carried out using 315 microsatellites, 2,467 and 9,467 SNPs, spanning the 22 autosomal chromosomes.

Results

Heritability of age at onset was estimated to be approximately 12% (p < 0.001). We observed weak correlation, both in trend and strength, of genome-wide linkage signals between microsatellites and SNPs. Results from SNPs revealed more and stronger linkage signals across the genome compared with those from microsatellites. The only suggestive evidence of linkage from microsatellites was on chromosome 1 (LOD of 1.43). Differences in map densities between the two sets of SNPs used in this study did not appear to confer an advantage in terms of strength of linkage signals.

Conclusion

Our study provided support for better performance of dense SNP maps compared with the sparse mirosatellite maps currently available for linkage analysis of quantitative traits. This better performance could be attributable to precise definition and high map resolutions achievable with dense SNP maps, thus resulting in increased power to detect possible loci affecting given trait or disease.

     

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.

     

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.

     

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.     

A score test for the linkage analysis of qualitative and quantitative traits based on identity by descent data from sib-pairs

We propose a general likelihood-based approach to the linkage analysis of qualitative and quantitative traits using identity by descent (IBD) data from sib-pairs. We consider the likelihood of IBD data conditional on phenotypes and test the null hypothesis of no linkage between a marker locus and a gene influencing the trait using a score test in the recombination fraction theta between the two loci. This method unifies the linkage analysis of qualitative and quantitative traits into a single inferential framework, yielding a simple and intuitive test statistic. Conditioning on phenotypes avoids unrealistic random sampling assumptions and allows sib-pairs from differing ascertainment mechanisms to be incorporated into a single likelihood analysis. In particular, it allows the selection of sib-pairs based on their trait values and the analysis of only those pairs having the most informative phenotypes. The score test is based on the full likelihood, i.e. the likelihood based on all phenotype data rather than just differences of sib-pair phenotypes. Considering only phenotype differences, as in Haseman and Elston (1972) and Kruglyak and Lander (1995), may result in important losses in power. The linkage score test is derived under general genetic models for the trait, which may include multiple unlinked genes. Population genetic assumptions, such as random mating or linkage equilibrium at the trait loci, are not required. This score test is thus particularly promising for the analysis of complex human traits. The score statistic readily extends to accommodate incomplete IBD data at the test locus, by using the hidden Markov model implemented in the programs MAPMAKER/SIBS and GENEHUNTER (Kruglyak and Lander, 1995; Kruglyak et al., 1996). Preliminary simulation studies indicate that the linkage score test generally matches or outperforms the Haseman-Elston test, the largest gains in power being for selected samples of sib-pairs with extreme phenotypes.     

A test of quantitative genetic theory using Drosophila- effects of inbreeding and rate of inbreeding on heritabilities and variance components

Inbreeding is expected to decrease the heritability within populations. However, results from empirical studies are inconclusive. In this study, we investigated the effects of three breeding treatments (fast and slow rate of inbreeding - inbred to the same absolute level - and a control) on heritability, phenotypic, genetic and environmental variances of sternopleural bristle number in Drosophila melanogaster. Heritability, and phenotypic, genetic and environmental variances were estimated in 10 replicate lines within each of the three treatments. Standard least squares regression models and Bayesian methods were used to analyse the data. Heritability and additive genetic variance within lines were higher in the control compared with both inbreeding treatments. Heritabilities and additive genetic variances within lines were higher in slow compared with fast inbred lines, indicating that slow inbred lines retain more evolutionary potential despite the same expected absolute level of inbreeding. The between line variance was larger with inbreeding and more than twice as large in the fast than in the slow inbred lines. The different pattern of redistribution of genetic variance within and between lines in the two inbred treatments cannot be explained invoking the standard model based on selective neutrality and additive gene action. Environmental variances were higher with inbreeding, and more so with fast inbreeding, indicating that inbreeding and the rate of inbreeding affect environmental sensitivity. The phenotypic variance decreased with inbreeding, but was not affected by the rate of inbreeding. No inbreeding depression for mean sternopleural bristle number was observed in this study. Considerable variance between lines in additive genetic variance within lines was observed, illustrating between line variation in evolutionary potential.     

不同来源甜高粱种质资源的表型遗传多样性分析

通过对主要农艺性状的方差分析及聚类分析,对194份不同来源的甜高粱种质资源进行了表型性状的遗传多样性分析。结果表明,不同来源品种间的性状差异较大,与中国品种相比,国外品种具有植株高大、生物产量高及含糖量高等优异性状,可用于国内资源的种质创新及品种改良。遗传距离为0.66时将所有资源划分为6类,各类群主要按农艺性状进行了划分。研究结果将为杂交育种的亲本选择提供理论参考。     

Identification of quantitative trait loci associated with rice eating quality traits using a population of recombinant inbred lines derived from a cross between two temperate japonica cultivars

Improved eating quality is a major breeding target in japonica rice due to market demand. In this study, we performed genetic analysis to identify quantitative trait loci (QTLs) that control rice eating quality traits using 192 recombinant inbred lines (RILs) derived from a cross between two japonica cultivars, 'Suweon365' and 'Chucheongbyeo'. We evaluated the stickiness (ST) and overall evaluation (OE) of cooked rice using a sensory test, the glossiness of cooked rice (GCR) using a Toyo-taste meter, and measured the amylose content (AC), protein content (PC), alkali digestion value (ADV), and days to heading (DH) of the RILs in the years 2006 and 2007. Our analysis revealed 21 QTLs on chromosomes 1, 4, 6, 7, 8, and 11. QTLs on chromosomes 6, 7, and 8 were detected for three traits related to eating quality in both years. QTLs for ST and OE were identified by a sensory test in the same region of the QTLs for AC, PC, ADV, GCR and DH on chromosome 8. QTL effects on the GCR were verified using QTL-NILs (near-isogenic lines) of BC(3)F(4-6) in the Suweon365 background, a low eating quality variety, and some BC(1)F(3) lines. Chucheongbyeo alleles at QTLs on chromosomes 7 and 8 increased the GCR in the NILs and backcrossed lines. The QTLs identified by our analysis will be applicable to future marker-assisted selection (MAS) strategies for improving the eating quality of japonica rice.