Genetic diversity and linkage disequilibrium analysis in elite sugar beet breeding lines and wild beet accessions

Key message

Linkage disequilibrium decay in sugar beet is strongly affected by the breeding history, and varies extensively between and along chromosomes, allowing identification of known and unknown signatures of selection.

Abstract

Genetic diversity and linkage disequilibrium (LD) patterns were investigated in 233 elite sugar beet breeding lines and 91 wild beet accessions, using 454 single nucleotide polymorphisms (SNPs) and 418 SNPs, respectively. Principal coordinate analysis suggested the existence of three groups of germplasm, corresponding to the wild beets, the seed parent and the pollen parent breeding pool. LD was investigated in each of these groups, with and without correction for genetic relatedness. Without correction for genetic relatedness, in the pollen as well as the seed parent pool, LD persisted beyond 50 centiMorgan (cM) on four (2, 3, 4 and 5) and three chromosomes (2, 4 and 6), respectively; after correction for genetic relatedness, LD decayed after <6 cM on all chromosomes in both pools. In the wild beet accessions, there was a strong LD decay: on average LD disappeared after 1 cM when LD was calculated with a correction for genetic relatedness. Persistence of LD was not only observed between distant SNPs on the same chromosome, but also between SNPs on different chromosomes. Regions on chromosomes 3 and 4 that harbor disease resistance and monogermy loci showed strong genetic differentiation between the pollen and seed parent pools. Other regions, on chromosomes 8 and 9, for which no a priori information was available with respect to their contribution to the phenotype, still contributed to clustering of lines in the elite breeding material.     

Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis

GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.     

Evaluation of microsatellite markers in association studies: a search for an immune-related susceptibility gene in sarcoidosis

Association studies using linkage disequilibrium (LD) between candidate loci and nearby markers have been proposed to identify susceptibility genes for complex diseases. We analyzed polymorphisms of microsatellites (MSs) and LD patterns of the regions in which candidate genes related to the Th1 immune response have been annotated and attempted to identify a susceptibility gene for sarcoidosis in a marker-based association study. Nineteen MSs were identified in six Th1-related genes (IFNGR1, IFNGR2, IL12RB1, IL12RB2, STAT1 and STAT4) and then eight were further characterized as useful polymorphic markers. Most of these MSs showed LD with single nucleotide polymorphisms (SNPs) on both 5 and 3 ends of these candidate genes, in which r² values between at least one of the MS marker alleles and the SNPs were higher than 0.1. A significant association with one MS allele near STAT4 was shown and a cluster of SNPs in LD with the MS marker was associated with sarcoidosis. These results suggest that association studies using not only SNPs but also multi-allelic MS within or near candidate loci would be useful markers to search for a disease susceptibility gene, especially in populations with unknown LD structure.     

An association analysis for 14 candidate genes mapping to meat quality quantitative trait loci in a Duroc pig population reveals that the ATP1A2 genotype is highly associated with muscle electric conductivity

In previous GWAS carried out in a Duroc commercial line (Lipgen population), we detected on pig chromosomes 3, 4 and 14 several QTL for gluteus medius muscle redness (GM a*), electric conductivity in the longissimus dorsi muscle (LD CE) and vaccenic acid content in the LD muscle (LD C18:1 n − 7), respectively. We have genotyped, in the Lipgen population, 19 SNPs mapping to 14 genes located within these QTL. Subsequently, association analyses have been performed. After correction for multiple testing, two SNPs in the TGFBRAP1 (rs321173745) and SELENOI (rs330820437) genes were associated with GM a*, whereas ACADSB (rs81449951) and GPR26 (rs343087568) genotypes displayed significant associations with LD vaccenic content. Moreover, the polymorphisms located at the ATP1A2 (rs344748241), ATP8B2 (rs81382410) and CREB3L4 (rs321278469 and rs330133789) genes showed significant associations with LD CE. We made a second round of association analyses including the SNPs mentioned above as well as other SNPs located in the chromosomes to which they map. After performing a correction for multiple testing, the only association that remained significant at the chromosome-wide level was that between the ATP1A2 genotype and LD CE. From a functional point of view, this association is meaningful because this locus encodes a subunit of the Na⁺/K⁺-ATPase responsible for maintaining an electrochemical gradient across the plasma membrane.     

Novel rank-based approaches for discovery and replication in genome-wide association studies

In recent years, genome-wide association studies (GWAS) have uncovered a large number of susceptibility variants. Nevertheless, GWAS findings provide only tentative evidence of association, and replication studies are required to establish their validity. Due to this uncertainty, researchers often focus on top-ranking SNPs, instead of considering strict significance thresholds to guide replication efforts. The number of SNPs for replication is often determined ad hoc. We show how the rank-based approach can be used for sample size allocation in GWAS as well as for deciding on a number of SNPs for replication. The basis of this approach is the "ranking probability": chances that at least j true associations will rank among top u SNPs, when SNPs are sorted by P-value. By employing simple but accurate approximations for ranking probabilities, we accommodate linkage disequilibrium (LD) and evaluate consequences of ignoring LD. Further, we relate ranking probabilities to the proportion of false discoveries among top u SNPs. A study-specific proportion can be estimated from P-values, and its expected value can be predicted for study design applications.     

Linkage disequilibrium grouping of single nucleotide polymorphisms (SNPs) reflecting haplotype phylogeny for efficient selection of tag SNPs

Single nucleotide polymorphisms (SNPs) have been proposed to be grouped into haplotype blocks harboring a limited number of haplotypes. Within each block, the portion of haplotypes is expected to be tagged by a selected subset of SNPs; however, none of the proposed selection algorithms have been definitive. To address this issue, we developed a tag SNP selection algorithm based on grouping of SNPs by the linkage disequilibrium (LD) coefficient r(2) and examined five genes in three ethnic populations--the Japanese, African Americans, and Caucasians. Additionally, we investigated ethnic diversity by characterizing 979 SNPs distributed throughout the genome. Our algorithm could spare 60% of SNPs required for genotyping and limit the imprecision in allele-frequency estimation of nontag SNPs to 2% on average. We discovered the presence of a mosaic pattern of LD plots within a conventionally inferred haplotype block. This emerged because multiple groups of SNPs with strong intragroup LD were mingled in their physical positions. The pattern of LD plots showed some similarity, but the details of tag SNPs were not entirely concordant among three populations. Consequently, our algorithm utilizing LD grouping allows selection of a more faithful set of tag SNPs than do previous algorithms utilizing haplotype blocks.     

Linkage-disequilibrium-based binning affects the interpretation of GWASs

Genome-wide association studies (GWASs) are critically dependent on detailed knowledge of the pattern of linkage disequilibrium (LD) in the human genome. GWASs generate lists of variants, usually SNPs, ranked according to the significance of their association to a trait. Downstream analyses generally focus on the gene or genes that are physically closest to these SNPs and ignore their LD profile with other SNPs. We have developed a flexible R package (LDsnpR) that efficiently assigns SNPs to genes on the basis of both their physical position and their pairwise LD with other SNPs. We used the positional-binning and LD-based-binning approaches to investigate whether including these "LD-based" SNPs would affect the interpretation of three published GWASs on bipolar affective disorder (BP) and of the imputed versions of two of these GWASs. We show how including LD can be important for interpreting and comparing GWASs. In the published, unimputed GWASs, LD-based binning effectively "recovered" 6.1%-8.3% of Ensembl-defined genes. It altered the ranks of the genes and resulted in nonnegligible differences between the lists of the top 2,000 genes emerging from the two binning approaches. It also improved the overall gene-based concordance between independent BP studies. In the imputed datasets, although the increases in coverage (>0.4%) and rank changes were more modest, even greater concordance between the studies was observed, attesting to the potential of LD-based binning on imputed data as well. Thus, ignoring LD can result in the misinterpretation of the GWAS findings and have an impact on subsequent genetic and functional studies.     

Extent and consistency of linkage disequilibrium and identification of DNA markers for production and egg quality traits in commercial layer chicken populations

Background

The genome sequence and a high-density SNP map are now available for the chicken and can be used to identify genetic markers for use in marker-assisted selection (MAS). Effective MAS requires high linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), and sustained marker-QTL LD over generations. This study used data from a 3,000 SNP panel to assess the level and consistency of LD between single nucleotide polymorphisms (SNPs) over consecutive years in two egg-layer chicken lines, and analyzed one line by two methods (SNP-wise association and genome-wise Bayesian analysis) to identify markers associated with egg-quality and egg-production phenotypes.

Results

The LD between markers pairs was high at short distances (r² > 0.2 at < 2 Mb) and remained high after one generation (correlations of 0.80 to 0.92 at < 5 Mb) in both lines. Single- and 3-SNP regression analyses using a mixed model with SNP as fixed effect resulted in 159 and 76 significant tests (P < 0.01), respectively, across 12 traits. A Bayesian analysis called BayesB, that fits all SNPs simultaneously as random effects and uses model averaging procedures, identified 33 SNPs that were included in the model >20% of the time (φ > 0.2) and an additional ten 3-SNP windows that had a sum of φ greater than 0.35. Generally, SNPs included in the Bayesian model also had a small P-value in the 1-SNP analyses.

Conclusion

High LD correlations between markers at short distances across two generations indicate that such markers will retain high LD with linked QTL and be effective for MAS. The different association analysis methods used provided consistent results. Multiple single SNPs and 3-SNP windows were significantly associated with egg-related traits, providing genomic positions of QTL that can be useful for both MAS and to identify causal mutations.

     

HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms

Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed with regions of low LD. Such LD patterns make it possible to select a set of single nucleotide polymorphism (SNPs; tag SNPs) for genome-wide association studies. We have developed a suite of computer programs to analyze the block-like LD patterns and to select the corresponding tag SNPs. Compared to other programs for haplotype block partitioning and tag SNP selection, our program has several notable features. First, the dynamic programming algorithms implemented are guaranteed to find the block partition with minimum number of tag SNPs for the given criteria of blocks and tag SNPs. Second, both haplotype data and genotype data from unrelated individuals and/or from general pedigrees can be analyzed. Third, several existing measures/criteria for haplotype block partitioning and tag SNP selection have been implemented in the program. Finally, the programs provide flexibility to include specific SNPs (e.g. non-synonymous SNPs) as tag SNPs. AVAILABILITY: The HapBlock program and its supplemental documents can be downloaded from the website http://www.cmb.usc.edu/~msms/HapBlock.     

Genome-wide association study for soybean cyst nematode resistance in Chinese elite soybean cultivars

Soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) is a highly recalcitrant endoparasite of soybean roots, causing more yield loss than any other pest. To identify quantitative trait loci (QTL) controlling resistance to SCN (HG type 2.5.7, race 1), a genome-wide association study (GWAS) was performed. The association panel, consisting of 120 Chinese soybean cultivars, was genotyped with 7189 single nucleotide polymorphism (SNPs). A total of 6204 SNPs with minor allele frequency >0.05 were used to estimate linkage disequilibrium (LD) and population structure. The mean level of LD measured by r ² declined very rapidly to half its maximum value (0.51) at 220 kb. The overall population structure was approximately coincident with geographic origin. The GWAS results identified 13 SNPs in 7 different genomic regions significantly associated with SCN resistance. Of these, three SNPs were localized in previously mapped QTL intervals, including rhg1 and Rhg4. The GWAS results also detected 10 SNPs in 5 different genomic regions associated with SCN resistance. The identified loci explained an average of 95.5% of the phenotypic variance. The proportion of phenotypic variance was due to additive genetic variance of the validated SNPs. The present study identified multiple new loci and refined chromosomal regions of known loci associated with SCN resistance. The loci and trait-associated SNPs identified in this study can be used for developing soybean cultivars with durable resistance against SCN.     

Linkage disequilibrium-based quality control for large-scale genetic studies

Quality control (QC) is a critical step in large-scale studies of genetic variation. While, on average, high-throughput single nucleotide polymorphism (SNP) genotyping assays are now very accurate, the errors that remain tend to cluster into a small percentage of "problem" SNPs, which exhibit unusually high error rates. Because most large-scale studies of genetic variation are searching for phenomena that are rare (e.g., SNPs associated with a phenotype), even this small percentage of problem SNPs can cause important practical problems. Here we describe and illustrate how patterns of linkage disequilibrium (LD) can be used to improve QC in large-scale, population-based studies. This approach has the advantage over existing filters (e.g., HWE or call rate) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors. Applying this LD-based QC procedure to data from The International HapMap Project, we identify over 1,500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes. Our method is implemented in the software package fastPHASE, available from the Stephens Lab website (http://stephenslab.uchicago.edu/software.html).     

The identification of SNPs with indeterminate positions using the Equine SNP50 BeadChip

We have used linkage disequilibrium (LD) to identify single nucleotide polymorphisms (SNPs) on the Illumina Equine SNP50 BeadChip, which may be incorrectly positioned on the genome map. A total of 1201 Thoroughbred horses were genotyped using the Illumina Equine SNP50 BeadChip. LD was evaluated in a pairwise fashion between all autosomal SNPs, both within and across chromosomes. Filters were then applied to the data, firstly to identify SNPs that may have been mapped to the wrong chromosome and secondly to identify SNPs that may have been incorrectly positioned within chromosomes. We identified a single SNP on ECA28, which showed low LD with neighbouring SNPs but considerable LD with a group of SNPs on ECA10. Furthermore, a cluster of SNPs on ECA5 showed unusually low LD with surrounding SNPs. A total of 39 SNPs met the criteria for unusual within-chromosome LD. The results of this study indicate that some SNPs may be misplaced. This finding is significant, as misplaced SNPs may lead to difficulties in the application of genomic methods, such as homozygosity mapping, for which SNP order is important.     

A Graphical Weighted Power Improving Multiplicity Correction Approach for SNP Selections

Controlling for the multiplicity effect is an essential part of determining statistical significance in large-scale single-locus association genome scans on Single Nucleotide Polymorphisms (SNPs). Bonferroni adjustment is a commonly used approach due to its simplicity, but is conservative and has low power for large-scale tests. The permutation test, which is a powerful and popular tool, is computationally expensive and may mislead in the presence of family structure. We propose a computationally efficient and powerful multiple testing correction approach for Linkage Disequilibrium (LD) based Quantitative Trait Loci (QTL) mapping on the basis of graphical weighted-Bonferroni methods. The proposed multiplicity adjustment method synthesizes weighted Bonferroni-based closed testing procedures into a powerful and versatile graphical approach. By tailoring different priorities for the two hypothesis tests involved in LD based QTL mapping, we are able to increase power and maintain computational efficiency and conceptual simplicity. The proposed approach enables strong control of the familywise error rate (FWER). The performance of the proposed approach as compared to the standard Bonferroni correction is illustrated by simulation and real data. We observe a consistent and moderate increase in power under all simulated circumstances, among different sample sizes, heritabilities, and number of SNPs. We also applied the proposed method to a real outbred mouse HDL cholesterol QTL mapping project where we detected the significant QTLs that were highlighted in the literature, while still ensuring strong control of the FWER.     

Improved Heritability Estimation from Genome-wide SNPs

Estimation of narrow-sense heritability, h², from genome-wide SNPs genotyped in unrelated individuals has recently attracted interest and offers several advantages over traditional pedigree-based methods. With the use of this approach, it has been estimated that over half the heritability of human height can be attributed to the ∼300,000 SNPs on a genome-wide genotyping array. In comparison, only 5%–10% can be explained by SNPs reaching genome-wide significance. We investigated via simulation the validity of several key assumptions underpinning the mixed-model analysis used in SNP-based h² estimation. Although we found that the method is reasonably robust to violations of four key assumptions, it can be highly sensitive to uneven linkage disequilibrium (LD) between SNPs: contributions to h² are overestimated from causal variants in regions of high LD and are underestimated in regions of low LD. The overall direction of the bias can be up or down depending on the genetic architecture of the trait, but it can be substantial in realistic scenarios. We propose a modified kinship matrix in which SNPs are weighted according to local LD. We show that this correction greatly reduces the bias and increases the precision of h² estimates. We demonstrate the impact of our method on the first seven diseases studied by the Wellcome Trust Case Control Consortium. Our LD adjustment revises downward the h² estimate for immune-related diseases, as expected because of high LD in the major-histocompatibility region, but increases it for some nonimmune diseases. To calculate our revised kinship matrix, we developed LDAK, software for computing LD-adjusted kinships.     

Single-nucleotide polymorphism frequency in a set of selected lines of bread wheat (Triticum aestivum L.).

Information on single-nucleotide polymorphisms (SNPs) in hexaploid bread wheat is still scarce. The goal of this study was to detect SNPs in wheat and examine their frequency. Twenty-six bread wheat lines from different origins worldwide were used. Specific PCR-products were obtained from 21 genes and directly sequenced. SNPs were discovered from the alignment of these sequences. The overall sequence polymorphism observed in this sample appears to be low; 64 single-base polymorphisms were detected in approximately 21.5 kb (i.e., 1 SNP every 335 bp). The level of polymorphism is highly variable among the different genes studied. Fifty percent of the genes studied contained no sequence polymorphism, whereas most SNPs detected were located in only 2 genes. As expected, taking into account a synthetic line created with a wild Triticum tauschii parent increases the level of polymorphism (101 SNPs; 1 SNP every 212 bp). The detected SNPs are available at http://urgi.versailles.inra.fr/GnpSNP">http://urgi.versailles.inra.fr/GnpSNP. Data on linkage disequilibrium (LD) are still preliminary. They showed a significant level of LD in the 2 most polymorphic genes. To conclude, the genome size of hexaploid wheat and its low level of polymorphism complicate SNP discovery in this species.     

A novel method combining linkage disequilibrium information and imputed functional knowledge for tagSNP selection

Analyses of high-density SNPs in genetic studies have the potential problems of prohibitive genotyping costs and inflated false discovery rates. Current methods select subsets of representative SNPs (tagSNPs) using information either on potential biologic functionality of the SNPs or on the underlying linkage disequilibrium (LD) structure, but not both. Combining the two types of information may lead to more effective tagSNP selection. The proposed method combines both functional and LD information using a weighted factor analysis (WFA) model. The WFA was applied to the dense SNP collection from 129 genes sequenced by the SeattleSNPs Program for Genomic Application. TagSNPs selected by WFA were compared with those selected by an LD-based method. WFA allowed prioritization of SNPs that would otherwise share equivalent ranking due to underlying LD structure alone. Furthermore, WFA consistently included SNPs not selected by function or by LD alone. A literature review of a subset of genes revealed that SNPs selected by WFA were more likely represented in published reports.     

Evaluation of LD decay and various LD-decay estimators in simulated and SNP-array data of tetraploid potato

Key message

The number of SNPs required for QTL discovery is justified by the distance at which linkage disequilibrium has decayed. Simulations and real potato SNP data showed how to estimate and interpret LD decay.

Abstract

The magnitude of linkage disequilibrium (LD) and its decay with genetic distance determine the resolution of association mapping, and are useful for assessing the desired numbers of SNPs on arrays. To study LD and LD decay in tetraploid potato, we simulated autotetraploid genotypes and used it to explore the dependence on: (1) the number of haplotypes in the population (the amount of genetic variation) and (2) the percentage of haplotype specific SNPs (hs-SNPs). Several estimators for short-range LD were explored, such as the average r ², median r ², and other percentiles of r ² (80, 90, and 95 %). For LD decay, we looked at LD_½,90, the distance at which the short-range LD is halved when using the 90 % percentile of r ² at short range, as estimator for LD. Simulations showed that the performance of various estimators for LD decay strongly depended on the number of haplotypes, although the real value of LD decay was not influenced very much by this number. The estimator LD_½,90 was chosen to evaluate LD decay in 537 tetraploid varieties. LD_½,90 values were 1.5 Mb for varieties released before 1945 and 0.6 Mb in varieties released after 2005. LD_½,90 values within three different subpopulations ranged from 0.7 to 0.9 Mb. LD_½,90 was 2.5 Mb for introgressed regions, indicating large haplotype blocks. In pericentromeric heterochromatin, LD decay was negligible. This study demonstrates that several related factors influencing LD decay could be disentangled, that no universal approach can be suggested, and that the estimation of LD decay has to be performed with great care and knowledge of the sampled material.

     

Selection of genetic markers for association analyses,using linkage disequilibrium and haplotypes

The genotyping of closely spaced single-nucleotide polymorphism (SNP) markers frequently yields highly correlated data, owing to extensive linkage disequilibrium (LD) between markers. The extent of LD varies widely across the genome and drives the number of frequent haplotypes observed in small regions. Several studies have illustrated the possibility that LD or haplotype data could be used to select a subset of SNPs that optimize the information retained in a genomic region while reducing the genotyping effort and simplifying the analysis. We propose a method based on the spectral decomposition of the matrices of pairwise LD between markers, and we select markers on the basis of their contributions to the total genetic variation. We also modify Clayton's "haplotype tagging SNP" selection method, which utilizes haplotype information. For both methods, we propose sliding window-based algorithms that allow the methods to be applied to large chromosomal regions. Our procedures require genotype information about a small number of individuals for an initial set of SNPs and selection of an optimum subset of SNPs that could be efficiently genotyped on larger numbers of samples while retaining most of the genetic variation in samples. We identify suitable parameter combinations for the procedures, and we show that a sample size of 50-100 individuals achieves consistent results in studies of simulated data sets in linkage equilibrium and LD. When applied to experimental data sets, both procedures were similarly effective at reducing the genotyping requirement while maintaining the genetic information content throughout the regions. We also show that haplotype-association results that Hosking et al. obtained near CYP2D6 were almost identical before and after marker selection.     

Association of Polymorphisms and Haplotypes in the Insulin-Like Growth Factor 1 Receptor (IGF1R) Gene with the Risk of Breast Cancer in Korean Women

The insulin-like growth factor (IGF) signaling pathway plays an important role in cancer biology. The IGF 1 receptor (IGF1R) overexpression has been associated with a number of hematological neoplasias and solid tumors including breast cancer. However, molecular mechanism involving IGF1R in carcinogenic developments is clearly not known. We investigated the genetic variations across the IGF1R polymorphism and the risk of breast cancer risk in Korean women. A total of 1418 individuals comprising 1026 breast cancer cases and 392 age-matched controls of Korean were included for the analysis. Genomic DNA was extracted from whole blood and single nucleotide polymorphisms (SNPs) were analyzed on the GoldenGate Assay system by Illumina’s Custom Genetic Analysis service. SNPs were selected for linkage disequilibrium (LD) analysis by Haploview. We genotyped total 51 SNPs in the IGF1R gene and examined for association with breast cancer. All the SNPs investigated were in Hardy-Weinberg equilibrium. These SNPs tested were significantly associated with breast cancer risk, after correction for multiple comparisons by adjusting for age at diagnosis, BMI, age at menarche, and age at first parturition. Among 51 IGF1R SNPs, five intron located SNPs (rs8032477, rs7175052, rs12439557, rs11635251 and rs12916884) with homozygous genotype (variant genotype) were associated with decreased risk of breast cancer. Fisher’s combined p-value for the five SNPs was 0.00032. Three intron located SNPs with heterozygous genotypes also had decreased risk of breast cancer. Seven of the 51 IGF1R SNPs were in LD and in one haplotype block, and were likely to be associated with breast cancer risk. Overall, this case-control study demonstrates statistically significant associations between breast cancer risk and polymorphisms in IGF1R gene.     

Linkage disequilibrium and allele-frequency distributions for 114 single-nucleotide polymorphisms in five populations

Single-nucleotide polymorphisms (SNPs) may be extremely important for deciphering the impact of genetic variation on complex human diseases. The ultimate value of SNPs for linkage and association mapping studies depends in part on the distribution of SNP allele frequencies and intermarker linkage disequilibrium (LD) across populations. Limited information is available about these distributions on a genomewide scale, particularly for LD. Using 114 SNPs from 33 genes, we compared these distributions in five American populations (727 individuals) of African, European, Chinese, Hispanic, and Japanese descent. The allele frequencies were highly correlated across populations but differed by >20% for at least one pair of populations in 35% of SNPs. The correlation in LD was high for some pairs of populations but not for others (e.g., Chinese American or Japanese American vs. any other population). Regardless of population, average minor-allele frequencies were significantly higher for SNPs in noncoding regions (20%-25%) than for SNPs in coding regions (12%-16%). Interestingly, we found that intermarker LD may be strongest with pairs of SNPs in which both markers are nonconservative substitutions, compared to pairs of SNPs where at least one marker is a conservative substitution. These results suggest that population differences and marker location within the gene may be important factors in the selection of SNPs for use in the study of complex disease with linkage or association mapping methods.