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.     

The effect of linkage disequilibrium on linkage analysis of incomplete pedigrees

Dense SNP maps can be highly informative for linkage studies. But when parental genotypes are missing, multipoint linkage scores can be inflated in regions with substantial marker-marker linkage disequilibrium (LD). Such regions were observed in the Affymetrix SNP genotypes for the Genetic Analysis Workshop 14 (GAW14) Collaborative Study on the Genetics of Alcoholism (COGA) dataset, providing an opportunity to test a novel simulation strategy for studying this problem. First, an inheritance vector (with or without linkage present) is simulated for each replicate, i.e., locations of recombinations and transmission of parental chromosomes are determined for each meiosis. Then, two sets of founder haplotypes are superimposed onto the inheritance vector: one set that is inferred from the actual data and which contains the pattern of LD; and one set created by randomly selecting parental alleles based on the known allele frequencies, with no correlation (LD) between markers. Applying this strategy to a map of 176 SNPs (66 Mb of chromosome 7) for 100 replicates of 116 sibling pairs, significant inflation of multipoint linkage scores was observed in regions of high LD when parental genotypes were set to missing, with no linkage present. Similar inflation was observed in analyses of the COGA data for these affected sib pairs with parental genotypes set to missing, but not after reducing the marker map until r2 between any pair of markers was 相似文献   

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.     

Evaluating outlier loci and their effect on the identification of pedigree errors

Homozygosity outlier loci, which show patterns of variation that are extremely divergent from the rest of the genome, can be evaluated by comparison of the homozygosity under Hardy-Weinberg proportions (the sum of the squares of allele frequencies) with the expected homozygosity under neutrality. Such outlier loci are potentially under selection (balancing selection or directional selection) when genome-wide effects (such as bottleneck and rapid population growth) are excluded. Outlier loci show skewed allele frequencies with respect to neutrality and may therefore affect the identification of pedigree errors. However, choosing neutral markers (excluding outlier loci) for the identification of pedigree errors has been neglected thus far. Our results showed that 4.1%, 5.5%, and 1.5% of the microsatellite markers, Illumina single-nucleotide polymorphisms (SNPs), and Affymetrix SNPs, respectively, on the autosomes appear to be under balancing selection (p or=40%) appear to be under balancing selection. Pedigree structure errors in 15 of 143 pedigrees were detected using microsatellite markers from the autosomes and/or selected SNPs from chromosomes 1 to 18 of the Illumina and/or selected SNPs from chromosomes 1 to 16 of the Affymetrix. Outlier loci did not make a major difference to the identification of pedigree errors. The Collaborative Study on the Genetics of Alcoholism data has pedigree errors and some of them may be due to sample mix up.     

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.     

Estimation of the extent of linkage disequilibrium in seven regions of the porcine genome

Linkage disequilibrium (LD) refers to the correlation among neighboring alleles, reflecting non-random patterns of association between alleles at (nearby) loci. A better understanding of LD in the porcine genome is of direct relevance for identification of genes and mutations with a certain effect on the traits of interest. Here, 215 SNPs in seven genomic regions were genotyped in individuals of three breeds. Pairwise linkage disequilibrium was calculated for all marker pairs. To estimate the extent of LD, all pairwise LD values were plotted against the distance between the markers. Based on SNP markers in four genomic regions analyzed in three panels from populations of Large White, Dutch Landrace, and Meishan origin, useful LD is estimated to extend for approximately 40 to 60 kb in the porcine genome.     

Linkage disequilibrium across two different single-nucleotide polymorphism genome scans

Linkage disequilibrium (LD) content was calculated for the Genetic Analysis Workshop 14 Affymetrix and Illumina single-nucleotide polymorphism (SNP) genome scans of the Collaborative Study on the Genetics of Alcoholism samples. Pair-wise LD was measured as both D' and r2 on 505 pedigree founder individuals. The r2 estimates were then used to correct the multipoint identity by descent matrix (MIBD) calculation to account for LD and LOD scores on chromosomes 3 and 18 were calculated for COGA's ttdt3 electrophysiological trait using those MIBDs. Extensive LD was observed throughout both marker sets, and it was higher in Affymetrix's more dense SNP map. However, SNP density did not solely account for Affymetrix's higher LD. MIBD estimation procedures assume linkage equilibrium to construct genotypes of non-genotyped pedigree founder individuals, and dense SNP genotyping maps are likely to contain moderate to high LD between markers. LOD score plots calculated after correction for LD followed the same general pattern as uncorrected ones. Since in our study almost half of the pedigree founders were genotyped, it is possible that LD had a minor impact on the LOD scores. Caution should probably be taken when using high density SNP maps when many non-genotyped founders are present in the study pedigrees.     

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.     

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.     

Extent and genome-wide distribution of linkage disequilibrium in commercial maize germplasm

Association mapping is based on linkage disequilibrium (LD) resulting from historical recombinations and helps understanding the genetic basis of complex traits. Many factors affect LD and, therefore, it must be determined empirically in the germplasm under investigation to examine the prospects of successful genome-wide association mapping. The objectives of our study were to (1) examine the extent of LD with simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers in 1,537 commercial maize inbred lines belonging to four heterotic pools, (2) compare the LD patterns determined by these two marker types, (3) evaluate the number of SNP markers needed to perform genome-wide association analyses, and (4) investigate temporal trends of LD. Mean values of the squared correlation coefficient ( $ \bar{R} $ ) were almost identical for unlinked, linked, and adjacent SSR marker pairs. In contrast, $ \bar{R} $ values were lowest for the unlinked SNP loci and highest for the SNPs within amplicons. LD decay varied across the different heterotic pools and the individual chromosomes. The SSR markers employed in the present study are not adequate for association analysis, because of insufficient marker density for the germplasm evaluated. Based on the decay of LD in the various heterotic pools, we would need between 4,000 and 65,000 SNP markers to detect with a reasonable power associations with rather large quantitative trait loci (QTL). A much higher marker density is required to identify QTL with smaller effects. However, not only the total number of markers but also their distribution among and along the chromosomes are primordial for undertaking powerful association analyses.     

An analysis of identical single-nucleotide polymorphisms genotyped by two different platforms

The overlap of 94 single-nucleotide polymorphisms (SNP) among the 4,720 and 11,120 SNPs contained in the linkage panels of Illumina and Affymetrix, respectively, allows an assessment of the discrepancy rate produced by these two platforms. Although the no-call rate for the Affymetrix platform is approximately 8.6 times greater than for the Illumina platform, when both platforms make a genotypic call, the agreement is an impressive 99.85%. To determine if disputed genotypes can be resolved without sequencing, we studied recombination in the region of the discrepancy for the most discrepant SNP rs958883 (typed by Illumina) and tsc02060848 (typed by Affymetrix). We find that the number of inferred recombinants is substantially higher for the Affymetrix genotypes compared to the Illumina genotypes. We illustrate this with pedigree 10043, in which 3 of 7 versus 0 of 7 offspring must be double recombinants using the genotypes from the Affymetrix and the Illumina platforms, respectively. Of the 36 SNPs with one or more discrepancies, we identified a subset that appears to cluster in families. Some of this clustering may be due to the presence of a second segregating SNP that obliterates a XbaI site (the restriction enzyme used in the Affymetrix platform), resulting in a fragment too long (>1,000 bp) to be amplified.     

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.     

Nucleotide diversity and linkage disequilibrium in cold-hardiness- and wood quality-related candidate genes in Douglas fir

Nuclear sequence variation and linkage disequilibrium (LD) were studied in 15 cold-hardiness- and 3 wood quality-related candidate genes in Douglas fir [Pseudotsuga menziesii (Mirb.) Franco]. This set of genes was selected on the basis of its function in other plants and collocation with cold-hardiness-related quantitative trait loci (QTL). The single-nucleotide polymorphism (SNP) discovery panel represented 24 different trees from six regions in Washington and Oregon plus parents of a segregating population used in the QTL study. The frequency of SNPs was one SNP per 46 bp across coding and noncoding regions on average. Haplotype and nucleotide diversities were also moderately high with H(d) = 0.827 +/- 0.043 and pi = 0.00655 +/- 0.00082 on average, respectively. The nonsynonymous (replacement) nucleotide substitutions were almost five times less frequent than synonymous ones and substitutions in noncoding regions. LD decayed relatively slowly but steadily within genes. Haploblock analysis was used to define haplotype tag SNPs (htSNPs). These data will help to select SNPs for association mapping, which is already in progress.     

Single-nucleotide polymorphism versus microsatellite markers in a combined linkage and segregation analysis of a quantitative trait

Increasingly, single-nucleotide polymorphism (SNP) markers are being used in preference to microsatellite markers. However, methods developed for microsatellites may be problematic when applied to SNP markers. We evaluated the results of using SNPs vs. microsatellites in Monte Carlo Markov chain (MCMC) oligogenic combined segregation and linkage analysis methods. These methods were developed with microsatellite markers in mind. We selected chromosome 7 from the Collaborative Study on the Genetics of Alcoholism dataset for analysis because linkage to an electrophysiological trait had been reported there. We found linkage in the same region of chromosome 7 with the Affymetrix SNP data, the Illumina SNP data, and the microsatellite marker data. The MCMC sampler appears to mix with both types of data. The sampler implemented in this MCMC oligogenic combined segregation and linkage analysis appears to handle SNP data as well as microsatellite data and it is possible that the localizations with the SNP data are better.     

Bias of allele-sharing linkage statistics in the presence of intermarker linkage disequilibrium

Current genome-wide linkage-mapping single-nucleotide polymorphism (SNP) panels with densities of 0.3 cM are likely to have increased intermarker linkage disequilibrium (LD) compared to 5-cM microsatellite panels. The resulting difference in haplotype frequencies versus that predicted may affect multipoint linkage analysis with ungenotyped founders; a common haplotype may be assumed to be rare, leading to inflation of identical-by-descent (IBD) allele-sharing estimates and evidence for linkage. Using data simulated for the Genetic Analysis Workshop 14, we assessed bias in allele-sharing measures and nonparametric linkage (NPL all) and Kong and Cox LOD (KC-LOD) scores in a targeted analysis of regions with and without LD and with and without genes. Using over 100 replicates, we found that if founders were not genotyped, multipoint IBD estimates and delta parameters were modestly inflated and NPL all and KC-LOD scores were biased upwards in the region with LD and no gene; rather than centering on the null, the mean NPL all and KC-LOD scores were 0.51 +/- 0.91 and 0.19 +/- 0.38, respectively. Reduction of LD by dropping markers reduced this upward bias. These trends were not seen in the non-LD region with no gene. In regions with genes (with and without LD), a slight loss in power with dropping markers was suggested. These results indicate that LD should be considered in dense scans; removal of markers in LD may reduce false-positive results although information may also be lost. Methods to address LD in a high-throughput manner are needed for efficient, robust genomic scans with dense SNPs.     

Genetic Structure,Linkage Disequilibrium and Association Mapping of Verticillium Wilt Resistance in Elite Cotton (Gossypium hirsutum L.) Germplasm Population

Understanding the population structure and linkage disequilibrium in an association panel can effectively avoid spurious associations and improve the accuracy in association mapping. In this study, one hundred and fifty eight elite cotton (Gossypium hirsutum L.) germplasm from all over the world, which were genotyped with 212 whole genome-wide marker loci and phenotyped with an disease nursery and greenhouse screening method, were assayed for population structure, linkage disequilibrium, and association mapping of Verticillium wilt resistance. A total of 480 alleles ranging from 2 to 4 per locus were identified from all collections. Model-based analysis identified two groups (G1 and G2) and seven subgroups (G1a–c, G2a–d), and differentiation analysis showed that subgroup having a single origin or pedigree was apt to differentiate with those having a mixed origin. Only 8.12% linked marker pairs showed significant LD (P<0.001) in this association panel. The LD level for linked markers is significantly higher than that for unlinked markers, suggesting that physical linkage strongly influences LD in this panel, and LD level was elevated when the panel was classified into groups and subgroups. The LD decay analysis for several chromosomes showed that different chromosomes showed a notable change in LD decay distances for the same gene pool. Based on the disease nursery and greenhouse environment, 42 marker loci associated with Verticillium wilt resistance were identified through association mapping, which widely were distributed among 15 chromosomes. Among which 10 marker loci were found to be consistent with previously identified QTLs and 32 were new unreported marker loci, and QTL clusters for Verticillium wilt resistanc on Chr.16 were also proved in our study, which was consistent with the strong linkage in this chromosome. Our results would contribute to association mapping and supply the marker candidates for marker-assisted selection of Verticillium wilt resistance in cotton.     

Handling marker-marker linkage disequilibrium: pedigree analysis with clustered markers

Single-nucleotide polymorphisms (SNPs) are rapidly replacing microsatellites as the markers of choice for genetic linkage studies and many other studies of human pedigrees. Here, we describe an efficient approach for modeling linkage disequilibrium (LD) between markers during multipoint analysis of human pedigrees. Using a gene-counting algorithm suitable for pedigree data, our approach enables rapid estimation of allele and haplotype frequencies within clusters of tightly linked markers. In addition, with the use of a hidden Markov model, our approach allows for multipoint pedigree analysis with large numbers of SNP markers organized into clusters of markers in LD. Simulation results show that our approach resolves previously described biases in multipoint linkage analysis with SNPs that are in LD. An updated version of the freely available Merlin software package uses the approach described here to perform many common pedigree analyses, including haplotyping and haplotype frequency estimation, parametric and nonparametric multipoint linkage analysis of discrete traits, variance-components and regression-based analysis of quantitative traits, calculation of identity-by-descent or kinship coefficients, and case selection for follow-up association studies. To illustrate the possibilities, we examine a data set that provides evidence of linkage of psoriasis to chromosome 17.     

Genome-wide scan for bovine twinning rate QTL using linkage disequilibrium

Twinning is a complex trait with negative impacts on health and reproduction, which cause economic loss in dairy production. Several twinning rate quantitative trait loci (QTL) have been detected in previous studies, but confidence intervals for QTL location are broad and many QTL are unreplicated. To identify genomic regions or genes associated with twinning rate, QTL analysis based on linkage combined with linkage disequilibrium (LLD) and individual marker associations was conducted across the genome using high-throughput single nucleotide polymorphism (SNP) genotypes. A total of 9919 SNP markers were genotyped with 200 sires and sons in 19 half-sib North American Holstein dairy cattle families. After SNPs were genotyped, informative markers were selected for genome-wide association tests and QTL searches. Evidence for twinning rate QTL was found throughout the genome. Thirteen markers significantly associated with twinning rate were detected on chromosomes 2, 5 and 14 ( P  < 2.3 × 10⁻⁵). Twenty-six regions on fourteen chromosomes were identified by LLD analysis at P  < 0.0007. Seven previously reported ovulation or twinning rate QTL were supported by results of single marker association or LLD analyses. Single marker association analysis and LLD mapping were complementary tools for the identification of putative QTL in this genome scan.     

Comparative informativeness for linkage of multiple SNPs and single microsatellites

Single nucleotide polymorphisms (SNPs), or biallelic markers, are popular in genetic linkage studies due to their abundance in the genome, stability, and ease of scoring. We determined the 'information ratio' (IR) of closely spaced SNPs in simulated nuclear families and affected sib pairs (ASPs). (The IR is the ratio of actual average maximum lod score to the maximum lod score attainable if the marker were fully informative.) The nuclear families included parental information, whereas the ASPs did not. We analyzed these SNPs in two ways: (1) using multipoint analysis, and (2) treating the SNPs as 'composite markers' (i.e., haplotypes, as assigned by GENEHUNTER). (3) We also calculated the IR of a single microsatellite marker with multiple alleles and compared with the IR from the SNPs. For each set of input conditions, we simulated 1000 nuclear families, of 2, 3, 4, or 5 children each, as well as 1000 ASPs. We generated SNP marker data for strings of k = 1, 2, 3, 5, 7, and 10 SNP loci, with no recombination (theta = 0) and no linkage disequilibrium among the SNPs. The MAF (minor allele frequency) was either 0.5 or 0.25, and allele frequencies were the same for all k loci in any analysis. We also generated marker data for one single-locus microsatellite marker, with m = 3, 4, 5, 6, 7, and 9 equally frequent alleles. In all simulations, the disease was fully penetrant dominant, and there was no recombination or linkage disequilibrium among markers or between marker and disease. When multipoint analysis was used, we found that 5-7 closely spaced SNPs were usually enough to yield an IR of approximately 100%, for nuclear families of any size. However, for the ASPs, even 7-10 SNPs yielded an IR of only 70-80%. A microsatellite with 9 equally frequent alleles yielded about the same IR (86-88%) as a string of 4-5 SNPs, in nuclear families. SNPs analyzed as 'composite markers' analyses performed worse, due to the inherent ambiguity of SNP haplotyping.     

Population structure, genetic diversity and linkage disequilibrium in elite winter wheat assessed with SNP and SSR markers

Modern genomics approaches rely on the availability of high-throughput and high-density genotyping platforms. A major breakthrough in wheat genotyping was the development of an SNP array. In this study, we used a diverse panel of 172 elite European winter wheat lines to evaluate the utility of the SNP array for genomic analyses in wheat germplasm derived from breeding programs. We investigated population structure and genetic relatedness and found that the results obtained with SNP and SSR markers differ. This suggests that additional research is required to determine the optimum approach for the investigation of population structure and kinship. Our analysis of linkage disequilibrium (LD) showed that LD decays within approximately 5–10 cM. Moreover, we found that LD is variable along chromosomes. Our results suggest that the number of SNPs needs to be increased further to obtain a higher coverage of the chromosomes. Taken together, SNPs can be a valuable tool for genomics approaches and for a knowledge-based improvement of wheat.