Linkage Disequilibrium Mapping of Morphological, Resistance, and Other Agronomically Relevant Traits in Modern Spring Barley Cultivars

A set of 148 modern spring barley cultivars was explored for the extent of linkage disequilibrium (LD) between genes governing traits and nearby marker alleles. Associations of agronomically relevant traits (days to heading, plant height), resistance traits (leaf rust, barley yellow dwarf virus (BYD)), and morphological traits (rachilla hair length, lodicule size) with AFLP markers and SSR markers were found. Known major genes and QTLs were confirmed, but also new putative QTLs were found. The LD mapping clearly indicated the common occurrence of Rph3, a gene for hypersensitivity resistance against Puccinia hordei, and also confirmed the QTL Rphq2 for prolonging latency period of P. hordei in seedlings. We also found strong indication for a hitherto not reported gene for resistance or tolerance to BYD on chromosome 2, linked to SSR marker HVM054. Our conclusion is that LD mapping is a valuable additional tool in the search for applicable marker associations with major genes and QTLs. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

Association mapping of agronomic traits on chromosome 2A of wheat

Association mapping is a method to test the association between molecular markers and quantitative trait loci (QTL) based on linkage disequilibrium (LD). In this study, the collection of 108 wheat germplasm accessions form China were evaluated for their plant heights, spike length, spikelets per spike, grains per spike, thousand kernel weight and spikelets density in 3 years at three locations. And they were genotyped with 85 SSR markers and 40 EST-SSR markers. The population structure was inferred on the basis of unlinked 48 SSR markers and 40 EST-SSR markers. The extent of LD on chromosome 2A was 2.3 cM. Association of 37 SSR loci on chromosomes 2A with six agronomic traits was analysed with a mixed linear model. A total of 14 SSR loci were significantly associated with agronomic traits. Some of the associated markers were located in the QTL region detected in previous linkage mapping analysis. Our results demonstrated that association mapping can enhance QTL information and achieves higher resolution with short LD extent.     

Linkage disequilibrium based association mapping of fiber quality traits in <Emphasis Type="Italic">G. hirsutum</Emphasis> L. variety germplasm

Cotton is the world’s leading cash crop, but it lags behind other major crops for marker-assisted breeding due to limited polymorphisms and a genetic bottleneck through historic domestication. This underlies a need for characterization, tagging, and utilization of existing natural polymorphisms in cotton germplasm collections. Here we report genetic diversity, population characteristics, the extent of linkage disequilibrium (LD), and association mapping of fiber quality traits using 202 microsatellite marker primer pairs in 335 G. hirsutum germplasm grown in two diverse environments, Uzbekistan and Mexico. At the significance threshold (r ² ≥ 0.1), a genome-wide average of LD extended up to genetic distance of 25 cM in assayed cotton variety accessions. Genome wide LD at r ² ≥ 0.2 was reduced to ~5–6 cM, providing evidence of the potential for association mapping of agronomically important traits in cotton. Results suggest linkage, selection, inbreeding, population stratification, and genetic drift as the potential LD-generating factors in cotton. In two environments, an average of ~20 SSR markers was associated with each main fiber quality traits using a unified mixed liner model (MLM) incorporating population structure and kinship. These MLM-derived significant associations were confirmed in general linear model and structured association test, accounting for population structure and permutation-based multiple testing. Several common markers, showing the significant associations in both Uzbekistan and Mexican environments, were determined. Between 7 and 43% of the MLM-derived significant associations were supported by a minimum Bayes factor at ‘moderate to strong’ and ‘strong to very strong’ evidence levels, suggesting their usefulness for marker-assisted breeding programs and overall effectiveness of association mapping using cotton germplasm resources. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Population structure and linkage disequilibrium of a mini core set of maize inbred lines in China

Understanding genetic diversity, population structure, and the level and distribution of linkage disequilibrium (LD) in target populations are of great importance and the prerequisite for association mapping. In the present study, 145 genome-wide SSR markers were used to assess the genetic diversity, population structure, and LD of a set of 95 maize inbred lines which represented the Chinese maize inbred lines. Results showed that the population included a diverse genetic variation. A model-based population structure analysis subdivided the inbred lines into four subgroups that correspond to the four major empirical germplasm origins in China, i.e., Lancaster, Reid, Tangsipingtou and P. Among all of the inbred lines, 65.3% were assigned into the corresponding subgroups; others were assigned into a “mixed” subgroup. LD was significant at a 0.01 level between 63.89% of the SSR pairs in the entire sample and with a range of 18.75–40.28% in the subgroups. Among factors influencing LD, linkage was the major cause for LD of SSR loci. The results suggested that the population may be used in the detection of genome-wide SSR marker–phenotype association. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. R. Wang and Y. Yu contributed equally to the work.     

Linkage disequilibrium clustering‐based approach for association mapping with tightly linked genomewide data

Genomewide association studies (GWAS) aim to identify genetic markers strongly associated with quantitative traits by utilizing linkage disequilibrium (LD) between candidate genes and markers. However, because of LD between nearby genetic markers, the standard GWAS approaches typically detect a number of correlated SNPs covering long genomic regions, making corrections for multiple testing overly conservative. Additionally, the high dimensionality of modern GWAS data poses considerable challenges for GWAS procedures such as permutation tests, which are computationally intensive. We propose a cluster‐based GWAS approach that first divides the genome into many large nonoverlapping windows and uses linkage disequilibrium network analysis in combination with principal component (PC) analysis as dimensional reduction tools to summarize the SNP data to independent PCs within clusters of loci connected by high LD. We then introduce single‐ and multilocus models that can efficiently conduct the association tests on such high‐dimensional data. The methods can be adapted to different model structures and used to analyse samples collected from the wild or from biparental F₂ populations, which are commonly used in ecological genetics mapping studies. We demonstrate the performance of our approaches with two publicly available data sets from a plant (Arabidopsis thaliana) and a fish (Pungitius pungitius), as well as with simulated data.     

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.     

Six new recombinant inbred populations for the study of quantitative traits in Arabidopsis thaliana

Quantitative approaches are now widely used to study the genetic architecture of complex traits. However, most studies have been conducted in single mapping populations, which sample only a fraction of the natural allelic variation available within a gene pool and can identify only a subset of the loci controlling the traits. To enable the progress towards an understanding of the global genetic architecture of a broad range of complex traits, we have developed and characterised six new Arabidopsis thaliana recombinant inbred populations. To evaluate the utility of these populations for integrating analyses from multiple populations, we identified quantitative trait loci (QTL) controlling flowering time in vernalized plants growing in 16 h days. We used the physical positions of markers to align the linkage maps of our populations with those of six existing populations. We identified seven QTL in genomic locations coinciding with those identified in previous studies and in addition a further eight QTL were identified. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. An erratum to this article can be found at     

A population-based LD map of the human chromosome 6p

The recent publication of the complete sequence of human chromosome 6 provides a platform from which to investigate genomic sequence variation. We report here a detailed linkage disequilibrium (LD) pattern map across the entire human chromosome 6p by using a set of 1152 single nucleotide polymorphisms (SNPs) in a population of 198 Singaporean Chinese, with 326 SNPs focused in the major histocompatibility complex (MHC) region. Our analysis shows some unexpectedly high segments of strong LD in a 10-Mb region that includes the extremely polymorphic and gene-rich MHC loci and many non-MHC genes. These include the telomeric peri-MHC region that harbors olfactory receptors, histones and zinc finger clusters, and the centromeric peri-MHC region that contains several unknown open reading frames. The data also help refine a human–mouse synteny break in the region between 28.6 and 29.4 Mb. The population-based LD map presented here will provide an essential resource for understanding the genomic sequence variation of chromosome 6p and LD mapping of disease genes of complex genetic traits. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. H. Yu and J.-M. Chia should be regarded as joint first authors.     

关联分析及其在植物遗传学研究中的应用

植物的很多重要经济性状均属于复杂性状。基于连锁分析的QTL作图是研究复杂性状的有效手段, 但其尚存在一定的局限性。随着现代生物学的发展, 一种基于连锁不平衡的新剖分复杂性状方法--关联分析法, 开始应用于植物遗传学研究。与QTL作图法相比, 应用关联分析法具有不需要构建特殊的群体, 可同时对多个等位基因进行分析, 定位QTL精度可达到单基因水平等优势。该文介绍了关联分析方法学的基础和特性, 简述了其在植物遗传学研究中的进展情况, 并对其未来发展和在植物遗传学研究中的应用进行了展望。     

Germplasm-regression-combined (GRC) marker-trait association identification in plant breeding: a challenge for plant biotechnological breeding under soil water deficit conditions

In the past 20 years, the major effort in plant breeding has changed from quantitative to molecular genetics with emphasis on quantitative trait loci (QTL) identification and marker assisted selection (MAS). However, results have been modest. This has been due to several factors including absence of tight linkage QTL, non-availability of mapping populations, and substantial time needed to develop such populations. To overcome these limitations, and as an alternative to planned populations, molecular marker–trait associations have been identified by the combination between germplasm and the regression technique. In the present preview, the authors (1) survey the successful applications of germplasm–regression–combined (GRC) molecular marker–trait association identification in plants; (2) describe how to do the GRC analysis and its differences from mapping QTL based on a linkage map reconstructed from the planned populations; (3) consider the factors that affect the GRC association identification, including selections of optimal germplasm and molecular markers and testing of identification efficiency of markers associated with traits; and (4) finally discuss the future prospects of GRC marker–trait association analysis used in plant MAS/QTL breeding programs, especially in long-juvenile woody plants when no other genetic information such as linkage maps and QTL are available.