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Autor     Naheif Ebraheim Mohamed Mohamed
Titel    Association mapping for drought stress related traits in a structured population with wild and cultivated barley
Ort    Bonn
Jahr    2009
URL    http://hss.ulb.uni-bonn.de/2009/1669/1669.pdf

Literaturverz.   

yes
Fußnoten    yes
Fragmente    21


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As a complement to traditional linkage studies, association mapping or linkage disequilibrium (LD) mapping offers a powerful alternative approach for fine-scale mapping of flowring time in maize (Thornsberry et al. 2001), yield traits in barley (Kraakman et al. 2004), Iron deficiency in soybean (Wang et al. 2008), and disease resistance in rice (Garris et al. 2003), potato (Gebhardt et al. 2004; Simko et al. 2004) , corn (Szalma et al. 2005) and fusarium head blight resistance in barley (Massman et al. 2011). As a complement to traditional linkage studies, association mapping or linkage disequilibrium (LD) mapping offers a powerful alternative approach for fine-scale mapping of flowering time in maize (Thornsberry et al. 2001), yield traits in barley (Kraakman et al. 2004), Iron deficiency in soybean (Wang et al. 2008), and disease resistance in rice (Garris et al. 2003), potato (Gebhardt et al. 2004; Simko et al. 2004) and corn (Szalma et al. 2005).
Anmerkungen

Source is mentioned on p. 14 - but not here.

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It is particularly common in the Near East Fertile Crescent (Zohary 1969). Generally, wild barley is not tolerant to extreme low temperatures.

Wild barley has a quite similar morphology to cultivated 2-rowed barley. The most marked differences are wild barley’s brittle rachis and its hulled grain. Six-rowed barley has evolved during domestication, the trait being controlled by a single gene on chromosome 2 (Komatsuda et al. 1999, Tanno et al. 2002). Wild barley is the only wild Hordeum species that can produce fully fertile hybrids (with normal chromosome pairing and segregation in meiosis) when crossed with cultivated barley. Hybrids can also be formed in nature when these two occur at the same location (Asfaw & Von Bothmer 1990). Studies with wild and cultivated barley have reported that there is more variation within the wild than in the [cultivated barley (Saghai Maroof et al. 1995), although in some cases the opposite has been reported for some isozymes and mitochondrial DNA (Nevo, 1992).]


Asfaw Z, von Bothmer R (1990) Hybridization between landrace varieties of Ethiopian barley (Hordeum vulgare subsp. vulgare) and the progenitor of barley (H. vulgare subsp. spontaneum). Hereditas 112: 57-64

Komatsuda T, Li WB, Takaiwa F, Oka S (1999) High resolution map around the vrs1 locus controlling two- and six-rowed spike in barley, Hordeum vulgare. Genome 42: 248-253

Nevo E (1992) Origin, evolution, population genetics and resources for breeding of wild barley, Hordeum spontaneum, in the Fertile Crescent. In: Barley, genetics, biochemtry, molecular biology and biotechology (ed. R. Shewry). C. A. B. International, Wallindford, Oxon, UK CAB International: 19-43

Saghai Maroof MA, Zhang Q, Biyashev R (1995) Comparison of restriction fragment length polymorphisms in wild and cultivated barley. Genome 38: 298-306

Tanno K, Taketa S, Takeda K, Komatsuda T (2002) A DNA marker closely linked to the vrs1 locus (row-type gene) indicates multiple origins of six-rowed cultivated barley (Hordeum vulgare L.). Theor Appl Genet 104: 54-60

Zohary D (1969) The progenitors of wheat and barley in relation to domestication and agricultural dispersal in the old world. In Ucko PJ, Dimbleby GW (eds) The domestication and exploitation of plants and animals. General Duckworth and Co. Ltd., London, pp 47-66. http://faostat.fao.org [sic!]

It is particularly common in the Near East Fertile Crescent (Zohary 1969). In general, wild barley is not tolerant to extreme low temperatures and is rarely found above 1500 m altitude.

[...]

Wild barley and cultivated 2-rowed barley have quite similar morphology. The most notable differences are wild barley’s brittle rachis and its hulled grain. Six-rowed barley

[page 3]

has evolved during domestication, the trait being controlled by a single gene on chromosome 2 (Komatsuda et al. 1999, Tanno et al. 2002). Wild barley subspecies spontaneum is the only wild Hordeum species that can produce fully fertile hybrids (with normal chromosome pairing and segregation in meiosis) when crossed with cultivated barley. Hybrids can also be formed in nature when these two occur at the same location (Asfaw & Von Bothmer 1990).

Studies with wild and cultivated barley have shown that there is more variation within the wild than in the cultivated barley (Saghai Maroof et al. 1995), although in some cases the opposite has been reported for some isozymes and mitochondrial DNA (Nevo, 1992).


Asfaw Z, von Bothmer R (1990) Hybridization between landrace varieties of Ethiopian barley (Hordeum vulgare ssp. vulgare) and the progenitor of barley (H. vulgare ssp. spontaneum). Hereditas 112: 57-64.

Komatsuda T, Li WB, Takaiwa F, Oka S (1999) High resolution map around the vrs1 locus controlling two- and six-rowed spike in barley, Hordeum vulgare. Genome 42: 248-253.

Nevo E (1992) Origin, evolution, population genetics and resources for breeding of wild barley, Hordeum spontaneum, in the Fertile Crescent. In: Barley, genetics, biochemtry, molecular biology and biotechology (ed. R. Shewry). C. A. B. International, Wallindford, Oxon, UK CAB International: 19-43.

Saghai Maroof MA, Zhang Q, Biyashev R (1995) Comparison of restriction fragment length polymorphisms in wild and cultivated barley. Genome 38: 298-306

Tanno K, Taketa S, Takeda K, Komatsuda T (2002) A DNA marker closely linked to the vrs1 locus (row-type gene) indicates multiple origins of six-rowed cultivated barley (Hordeum vulgare L.). Theor Appl Genet 104: 54-60.

Zohary D (1969) The progenitors of wheat and barley in relation to domestication and agricultural dispersal in the old world. In Ucko PJ, Dimbleby GW (eds) The domestication and exploitation of plants and animals. General Duckworth and Co. Ltd., London, pp 47-66.

Anmerkungen

The source is not given. The URL given after Zohary 1969 does not contain the text.

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[Studies with wild and cultivated barley have reported that there is more variation within the wild than in the] cultivated barley (Saghai Maroof et al. 1995), although in some cases the opposite has been reported for some isozymes and mitochondrial DNA (Nevo, 1992). The larger genetic variation within wild barley gives good opportunity to use this variation for breeding purposes like drought, salinity and diseases resistance.

1.3.3 Wild barley (H. vulgare ssp. spontaneum)

Wild barley represents an important genetic resource for cultivated barley, which has a narrowed gene pool due to intensive breeding. Therefore, it is inevitably to study the genetics of different traits in wild barley, if it can use for cultivars improvement. Hordeum vulgar ssp. spontaneum (wild barley) is the ancestor of cultivated barley. It belongs to the Poaceae-family of grasses and within it to the Triticeae-tribe. Triticeae is a temperate plant group mainly concentrated around central and South-eastern Asia, although the species belonging to it are distributed around the world. Triticeae includes many economically important cultivated cereals and forages but also about 350 wild species. The wild species are of great interest as potential gene donors for commercial breeding (Vanhala 2004).

Wild ancestry: The wild ancestor of the cultivated barley is well known. The crop shows close affinities to a group of wild and weedy barley forms which are traditionally grouped in Hordeum spontaneous C. Koch, but which are in fact, the wild race or subspecies of the cultivated crop. The correct name for this wild is therefore H. vulgare L. ssp. spontaneum (C. Koch). These are annual, brittle, two-rowed, diploid (2n = 14), predominantly self-pollinated barley forms and the only wild Hordeum stock that is cross compatible and fully interceptive with the cultivated barley, spontaneum X vulgare hybrids show normal chromosome pairing in meiosis (Bothmer 1992). Morphologically, the similarity between wild spontaneous and cultivated two-rowed distichal varieties is rather striking. They differ mainly in their modes of seed dispersal. Wild barley ears are brittle and maturity disarticulates into individual arrow-like triplets. These are highly specialized organs, which ensure the survival of the plant under wild conditions. Under cultivation this specialization broke down and non-brittle mutants were automatically selected for in the man-made system of sowing, reaping and threshing (Harlan and Zohary 1966, Zohary 1969).

The development of new barley cultivars tolerant of abiotic and biotic stress is an essential part of the continued improvement of the crop. The domestication of barley, as in many crops, resulted in a marked truncation of the genetic variation present in wild populations. This process is significant to agronomists and scientists because a lack of allelic variation will prevent the development of adapted cultivars and hinder the investigation of the genetic [mechanisms underlying performance.]

Studies with wild and cultivated barley have shown that there is more variation within

the wild than in the cultivated barley (Saghai Maroof et al. 1995), although in some cases the opposite has been reported for some isozymes and mitochondrial DNA (Nevo, 1992). The larger genetic variation within wild barley gives the opportunity to use this variation for breeding purposes.

1.2.3 Wild barley

Wild barley represents an important genetic resource for cultivated barley, which has a narrowed gene pool due to intensive breeding. Therefore, it is imperative to study the genetics of different traits in wild barley, if it is to be used for cultivar improvement. Hordeum vulgare ssp. spontaneum (wild barley) is the ancestor of cultivated barley. It belongs to the poaceae-family of grasses and within it to the triticeae-tribe. Triticeae is a temperate plant group mainly concentrated around central and South-eastern Asia, although the species belonging to it are distributed around the world. Triticeae includes many economically important cultivated cereals and forages but also about 350 wild species. The wild species are of great interest as potential gene donors for commercial breeding (Vanhala 2004).

Wild ancestry: The wild ancestor of the cultivated barley is well known. The crop shows close affinities to a group of wild and weedy barley forms which are traditionally grouped in Hordeum spontaneous C. Koch, but which are in fact, the wild race or subspecies of the cultivated crop. The correct name for this wild is therefore H. vulgare L. ssp. spontaneum (C. Koch). These are annual, brittle, two-rowed, diploid (2n = 14), predominantly self-pollinated barley forms and the only wild Hordeum stock that is cross compatible and fully interceptive with the cultivated barley, vulgare x spontaneum hybrids show normal chromosome pairing in meiosis (von Bothmer 1992). Also

[p.4:] morphologically, the similarity between wild spontaneous and cultivated two-rowed distichal varieties is rather striking. They differ mainly in their modes of seed dispersal. Spontaneous ears are brittle and maturity disarticulates into individual arrow-like triplets. These are highly specialized devices, which ensure the survival of the plant under wild conditions. Under cultivation this specialization broke down and non-brittle mutants were automatically selected for in the man-made system of sowing, reaping and threshing (Harlan and Zohary 1966, Zohary 1969).

The development of new barleys tolerant of abiotic and biotic stress is an essential part of the continued improvement of the crop. The domestication of barley, as in many crops, resulted in a marked truncation of the genetic variation present in wild populations. This process is significant to agronomists and scientists because a lack of allelic variation will prevent the development of adapted cultivars and hinder the investigation of the genetic mechanisms underlying performance.

Anmerkungen
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Ellis et al. (2000) reported that wild barley would be a useful source of new genetic variation for abiotic stress tolerance if surveys identify appropriate genetic variation and the development of marker-assisted selection allows efficient manipulation in cultivar development.

The close genetic links between the cultivated crop and wild spontaneum barleys are indicated also by spontaneous hybridizations that occur sporadically when wild and cultivated forms grow side by side. Some of such hybridization products, combining brittle ears and fertile lateral spikelets, were in the past erroneously regarded as genuinely wild types and even given a specific rank (H. agriocrithon Åberg). Seed storage proteins, extensive isozyme and DNA tests have already been carried out in barley (Nevo 1992). The results confirm the close relationships between the wild and cultivated entities grouped in the H. vulgare complex.

Wild barley would be a useful source of new genetic variation for abiotic stress tolerance if surveys identify appropriate genetic variation and the development of marker-assisted selection allows efficient manipulation in cultivar development, there are many wild barley collections from all areas of its natural distribution, but the largest are derived from the Mediterranean region (Ellis et al. 2000).

The close genetic affinities between the cultivated crop and wild spontaneum barleys are indicated also by spontaneous hybridizations that occur sporadically when wild and cultivated forms grow side by side. Some of such hybridization products, combining brittle ears and fertile lateral spikelets, were in the past erroneously regarded as genuinely wild types and even given a specific rank (H. agriocrithon Åberg). Extensive isozyme, seed storage proteins, and DNA tests have already been carried out in barley (Nevo 1992). The results confirm the close relationships between the wild and cultivated entities grouped in the H. vulgare complex.

Anmerkungen
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The main breeding objectives are high yield, and resistance to biotic and abiotic stresses. Furthermore, malting cultivars need to have high malting quality, which includes plump kernels, rapid and uniform germination, and optimal values for protein content and enzymatic activity (Kraakman 2005).

Kraakman ATW (2005) Mapping of yield, yield stability, yield adaptability and other traits in barley using linkage disequilibrium mapping and linkage analysis. PhD thesis Wageningen University, The Netherlands

The main breeding objectives are high yield, and resistance to biotic and abiotic stresses. Furthermore, malting cultivars need to have high malting quality, which includes plump kernels, rapid and uniform germination, and optimal values for protein content and enzymatic activity (Kraakman 2005).

Kraakman ATW (2005) Mapping of yield, yield stability, yield adaptability and other traits in barley using linkage disequilibrium mapping and linkage analysis. PhD thesis Wageningen University, The Netherlands

Anmerkungen

The source is not given.

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I. It does not need prior sequence information for the species to be studied; this makes the method applicable to all species regardless of how much DNA sequence information is available for that species.

II. It is a high throughput, quick, and highly reproducible method.

III. It is cost effective, with an estimated cost per data point tenfold lower than SSR markers (Xia et al. 2005).

IV. The genetic scope of analysis is defined by the user and easily expandable.

1- It does not need prior sequence information for the species to be studied; this

makes the method applicable to all species regardless of how much DNA sequence information is available for that species.

[p.17:]

2- It is high throughput, quick and highly reproducible method.

3- It is cost effective, with an estimated cost per data point tenfold lower than SSR markers (Xia et al. 2005).

4- The genetic scope of analysis is defined by the user and easily expandable.

Anmerkungen
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V. It is not covered by exclusive patent rights, but on the contrary open-source (i.e. it is designed for open use and shared improvement). (Semagn et al. 2006)

1.3.10.2 This technique has also its own limitations:

I. DArT is a microarray-based technique that involves several steps, including preparation of genomic representation for the target species, cloning, and data management and analysis. The latter requires dedicated software’s such as DArTsoft and DArTdb. The establishment of DArT system, therefore, is highly likely to demand an extensive investment both in laboratory facility and skilled manpower.

II. DArT assays for the presence of a specific DNA fragment in a representation. Hence, DArT markers are primarily dominant (present or absent) or differences in intensity, which limits its value in some applications.

For quantitative trait analysis, DArT has many potential applications. Till now, DArT marker patterns have been principally applied to the assessment of genetic variability in a group of organisms, such the development of wild barley (Hordeum chilense) by Suárez et al. (2012). Wenzl et al. (2004, 2006) gives an example of such a map, showing how the standard techniques of map construction using linkage disequilibrium can be applied using DArT markers. DArT is especially appropriated to QTL mapping (Wittenburg et al. 2005), and can be used to construct medium-density linkage maps relatively quickly. As these studies clarified, the most accurate diversity analysis requires proportional amounts of clones from all individuals tested to be present on the array. If alleles from a genotype are under-represented on an array, then DArT will indicate potentially greater differences from the population average.

DArT markers can be used to track phenotypic traits in breeding like other molecular markers, and the high throughput and low cost nature of the technology makes DArT more affordable for marker assisted selection. Multiple loci can be involved in the selection process, but using an array means all loci is dealt with simultaneously. Such markers can then be tracked though an introgression or crossing program, and used to supplement phenotyping to reduce potential miss-identification of a trait due to environmental effects (Lande & Thompson 1990), as per any other marker-aided selection tool. Even though DArT can be applied in the absence of sequence information, individual DArT markers are sequence-ready and can be used in the development of probe-based markers for further research (Kilian, 2004). One shortcoming of DArT is the number of positions on a DArT array that are consistently non-polymorphic, i.e. non-marker clones. This has been recognised since the inception of this technology (Jaccoud [et al. 2001), and recent studies detail how polymorphic markers can be identified in an initial discovery array process, then re-arrayed for genotypic applications as polymorphism-enriched arrays (Wenzl et al. 2004, Xia et al. 2005).]

5- It is not covered by exclusive patent rights, but on the contrary open-source (i.e., it

is designed for open use and shared improvement).

This technique, however, has also its own limitations:

1- DArT is a microarray-based technique that involves several steps, including preparation of genomic representation for the target species, cloning, management and analysis. The latter requires dedicated software’s such as DArTsoft and DArTdb. The establishment of DArT system, therefore, is highly likely to demand an extensive investment both in laboratory facility and skilled manpower.

2- DArT assays for the presence (or amount) of a specific DNA fragment in a presentation. Hence, DArT markers are primarily dominant (present or absent) or differences in intensity, which limits its value in some applications.

3- [...]

[p.15:]

For quantitative trait analysis, DArT has many potential applications. So far, DArT marker patterns have been principally applied to the assessment of genetic variability in a group of organisms, such the assessment of cassava diversity by Xia et al. (2005), and barley diversity by

[p.16:] Wenzl et al. (2004). As these studies illustrate, the most accurate diversity analysis require proportional amounts of clones from all individuals tested to be present on the array. If alleles from a genotype are under-represented on an array, then DArT will indicate potentially greater differences from the population average. DArT is especially suited to QTL mapping (Wittenburg et al. 2005), and can be used to construct mediumdensity linkage maps relatively quickly. Wenzl et al. (2004) gives an example of such a map, showing how the standard techniques of map construction using linkage disequilibrium can be applied using DArT markers. DArT markers can be used to track phenotypic traits in breeding like other molecular markers, and the high throughput and low cost nature of the technology makes DArT more affordable for marker assisted selection. Multiple loci can be involved in the selection process, but using an array means all loci is dealt with simultaneously. Such markers can then be tracked though an introgression or crossing program, and used to supplement phenotyping to reduce potential miss-identification of a trait due to environmental effects (Lande & Thompson 1990), as per any other marker-aided selection tool. Even though DArT can be applied in the absence of sequence information, individual DArT markers are sequence-ready and can be used in the development of probe-based markers for further research (Kilian 2004). One shortcoming of DArT is the number of positions on a DArT array that are consistently non-polymorphic, i.e. non-marker clones. This has been recognised since the inception of this technology (Jaccoud et al. 2001), and recent studies detail how polymorphic markers can be identified in an initial discovery array process, then re-arrayed for genotypic applications as polymorphism-enriched arrays (Wenzl et al. 2004, Xia et al. 2005).

Anmerkungen
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One of the first well developed classical genetic maps for barley included isozymes and morphological markers (Sogaard and von-Wettstein-Knowles 1987). Genetic mapping of barley accelerated with the application of molecular markers to doubled haploid (DH) populations (Chen and Hayes, 1989). Subsequently, molecular markers were added, beginning with RFLP and PCR markers (Shin et al. 1990), and these maps became more dense (Graner et al. 1991, and Kleinhofs et al. 1993) enabling the mapping of many important agronomic qualitative and quantitative traits. New molecular markers were developed, improving the barley genetic map with AFLP markers (Waugh et al. 1997, Qi et al. 1998, Yin et al. 1999 and Hori et al. 2003). One of the first well developed classical genetic maps for barley included isozymes and morphological markers (Sogaard and von-Wettstein-Knowles 1987). Later on, molecular markers were added, beginning with RFLP and PCR markers (Shin et al. 1990), and these maps became more dense (Graner et al. 1991, Heun et al. 1991, and Kleinhofs et al. 1993) enabling the mapping

[p.18:]

of many important agronomic qualitative and quantitative traits. New molecular markers were developed, improving the barley genetic map with AFLP markers (Waugh et al. 1997, Qi et al. 1998a, and Yin et al. 1999), and with microsatellite markers (Ramsay et al. 2000, Pillen et al. 2000, and Holton et al. 2002).

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Yu et al. (2006) developed new methodology, a mixed linear model (MLM) that combines both population structure information (Q-matrix) and level of pairwise relatedness coefficients—“kinship” (K-matrix) in the analysis, where the mixed linear model (MLM) approach found to be effective in removing the confounding effects of the population in association.

Overall approach of population-based association mapping in plants varies based on the methodology chosen, assuming structured population samples, the performance of association mapping includes the following steps as described by Abdurakhmonov and Abdukarimov (2008).

(1) Selection of a group of individuals from a natural population or germplasm collection with wide coverage of genetic diversity. (2) Recording or measuring the phenotypic characteristics (yield, quality, tolerance, or resistance) of selected population groups. (3) Genotyping a mapping population individuals with available molecular markers. (4) Assessment of the population structure (the level of genetic differentiation among groups within sampled population individuals) and kinship (coefficient of relatedness between pairs of each individual within a sample). And (5) based on information gained through population structure, correlation of phenotypic and genotypic/haplotypic data with the application of an appropriate statistical approach that reveals, consequently a specific gene(s) controlling a [QTL of interest can be cloned using the marker tags and annotated for an exact biological function. Association mapping offers three main advantages: increased mapping resolution, reduced research time, and greater allele numbers (Reich et al. 2001).]

4) recently, Yu et al. (2006) developed new methodology, a mixed linear model (MLM)

that combines both population structure information (Q-matrix) and level of pairwise relatedness coefficients—“kinship” (K-matrix) in the analysis, where the mixed linear model (MLM) approach found to be effective in removing the confounding effects of the population in association.

Although the overall approach of population-based association mapping in plants varies based on the methodology chosen (as above), assuming structured population samples, the performance of association mapping includes the following steps as described by Abdurakhmonov and Abdukarimov 2008. (1) Selection of a group of individuals from a natural population or germplasm collection with wide coverage of genetic diversity. (2) Recording or measuring the phenotypic characteristics (yield, quality, tolerance, or resistance) of selected population groups. (3) Genotyping a mapping population individuals with available molecular markers. (4) Assessment of the population structure (the level of genetic differentiation among groups within sampled population individuals) and kinship (coefficient of relatedness between pairs of each individual within a sample). And (5) based on information gained through population structure, correlation of phenotypic and genotypic/haplotypic data with the application of an appropriate statistical approach that reveals, consequently a specific gene(s) controlling a QTL of interest can be cloned using the marker tags and annotated for an exact biological function. [...] Compared to linkage mapping in traditional bioparental populations, association mapping offers three main advantages: increased mapping resolution, reduced research time, and greater allele numbers (Reich et al. 2001).

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[And (5) based on information gained through population structure, correlation of phenotypic and genotypic/haplotypic data with the application of an appropriate statistical approach that reveals, consequently a specific gene(s) controlling a] QTL of interest can be cloned using the marker tags and annotated for an exact biological function. Association mapping offers three main advantages: increased mapping resolution, reduced research time, and greater allele numbers (Reich et al. 2001).

Association mapping, also known as linkage disequilibrium mapping, is a relatively new and promising genetic method for complex trait dissection. Association mapping has the promise of higher mapping resolution through exploitation of historical recombination events at the population level that may enable gene level mapping on non-model organisms where linkage based approaches would not be feasible (Varshney and Tuberosa 2007).


Reich DE, Cargill M, Bolk S et al. (2001) Linkage disequilibrium in the human genome. Nature, vol. 411, No. 6834: 199–204

Varshney RK and Tuberosa R (2007) Application of linkage disequilibrium and association mapping in crop plants. Genomics Approaches and Platforms, Vol 1: 97 – 119

And (5) based on information gained through population structure, correlation of phenotypic and genotypic/haplotypic data with the application of an appropriate statistical approach that reveals, consequently a specific gene(s) controlling a QTL of interest can be cloned using the marker tags and annotated for an exact biological function.

[...]

Compared to linkage mapping in traditional bioparental populations, association mapping offers three main advantages: increased mapping resolution, reduced research time, and greater allele numbers (Reich et al. 2001).

[page 20]

Association mapping, also known as linkage disequilibrium mapping, is a relatively new and promising genetic method for complex trait dissection. Association mapping has the promise of higher mapping resolution through exploitation of historical recombination events at the population level that may enable gene level mapping on non-model organisms where linkage based approaches would not be feasible (Varshney and Tuberosa. 2007).


Reich DE, Cargill M, Bolk S, Ireland J, Sabeti PC, Richter DJ, Lavery T, Kouyoumjian R, Farhadian S, Ward R, Lander ES (2001) Linkage disequilibrium in the human genome. Nature, vol. 411, No. 6834: 199–204

Varshney RK, Tuberosa R (2007) Application of linkage disequilibrium and association mapping in crop plants. Genomics Approaches and Platforms, Vol 1: 97 – 119.

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The most difficulties and problems with association mapping is that population structure can lead to spurious association between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favour of family-based test of association, but this comes at a considerable cost in the need collect DNA from close relative of affected individuals (Pritchared et al. 2000).

Breseghello and Sorrells (2006) studied AM in wheat for identification of genetic markers associated with kernel morphology and milling quality. They used in their study a population of 149 cultivars of soft winter wheat (Triticum aestivum L.), were genotyping with 93 SSR markers, Association between markers and traits was tested using a linear mixed-effect model, where the marker being tested was considered as a fixed effects factor and subpopulation was considered as a random-effects factor. Significant markers were detected in the three chromosomes tested; kernel width was associated with the locus Xwmc111-2D in both Ohio (OH) and New York (NY) and with Xgwm30-2D in NY only. A tow-marker model including both loci was significantly (P = 0.0002) more informative for KW in NY than either marker separately. The locus Xgwm539-2D was associated with kernel length in NY, although in this location it did not achieve the corrected threshold. Six loci in the LD block near the centromere of 5A were associated with kernel area, length, and weight, but not with kernel width.

Yu et al. (2006) observed six gene expression phenotypes as phenotypic traits in mapping expression quantitative trait loci (eQTL). For the sample containing complex familial relationships and population structure, and they studied three quantitative traits measured on 277 diverse maize inbred lines, representing the diversity present in public breeding programs around the world. The population differentiation (Fst) among the major subgroups in our sample ranged from 0.047 (SSR) to 0.073 (SNP)., Although 80% of the pair-wise kinship estimates were close to 0, the remaining estimates were distributed from 0.05 to 1.0, as expected from complex familial relationship and population structure. Furthermore they found 37.6% of SNPs were associated with flowering time at P < 0.05 by the simple model, compared with 14.1% by the Q model, 6.1% by the K model and only 6.0% by the Q+K model. For flowering time and ear height, the Q+k model had the highest power. For ear diameter, the k model yielded a slightly higher power than the Q+k model did. The most benefit of the Q+K model is able to systematically account for multiple levels of relatedness among individuals.

The most difficulties and problems with association mapping is that population structure

can lead to spurious association between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favour of family-based test of association, but this comes at a considerable cost in the need collect DNA from close relative of affected individuals (Pritchared et al. 2000).

[...]

Yu et al. (2006) observed six gene expression phenotypes as phenotypic traits in mapping expression quantitative trait loci (eQTL). For the sample containing complex familial relationships and population structure, and they studied three quantitative traits measured on 277 diverse maize inbred lines, representing the diversity present in public breeding programs around the world. The population differentiation (Fst) among the major subgroups in our sample ranged from 0.047 (SSR) to 0.073 (SNP)., Although 80% of the pair-wise kinship estimates were close to 0, the remaining estimates were distributed from 0.05 to 1.0, as expected from complex familial relationship and population structure. Furthermore they found 37.6% of SNPs were associated with flowering time at P < 0.05 by the simple model, compared with 14.1% by the Q model, 6.1% by the K model and only 6.0% by the Q+K model. For flowering time and ear height, the Q+k model had the highest power. For ear diameter, the k model yielded a slightly higher power than the Q+k model did. The most benefit of the Q+K model is able to systematically account for multiple levels of relatedness among individuals.

[p.22:]

Breseghello and Sorrells (2006) studied Association mapping (AM) in wheat for identification of genetic markers associated with kernel morphology and milling quality. They used in their study a population of 149 cultivars of soft winter wheat (Triticum aestivum L.), were genotyping with 93 SSR markers, Association between markers and traits was tested using a linear mixed-effect model, where the marker being tested was considered as a fixed effects factor and subpopulation was considered as a randomeffects factor. Significant markers were detected in the three chromosomes tested; kernel width was associated with the locus Xwmc111-2D in both Ohio (OH) and New York (NY) and with Xgwm30-2D in NY only. A tow-marker model including both loci was significantly (P = 0.0002) more informative for KW in NY than either marker separately. The locus Xgwm539-2D was associated with kernel length in NY, although in this location it did not achieve the corrected threshold. Six loci in the LD block near the centromere of 5A were associated with kernel area, length, and weight, but not with kernel width.

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Kantartzi and Stewart (2008) and analysed of genetic distancepopulation structure provided evidence of significant population structure in the Gossypium arboreum accessions and identified the highest likelihood at k=6 . A total of 30 marker-trait association were identified with 19 SSR markers located on 11 chromosomes, the association analysis identified marker-trait associations (P=0.05) for all traits evaluated. Lint%, lint colour, elongation, micronaire and perimeter were associated with four markers each, length with three markers, and strength and maturity with tow and five markers respectively, Furthermore the LD (R2 values) between markers ranged from 10% to 20%. Of the 30 marker-trait associations, four identified 15% or more of the total variation for lint% (BNL0256 and BNL1122), lint colour (BNL0542) and length (BNL1122).

[...]

Linkage disequilibrium (LD) is the non-random association of alleles in a sample population and forms the basis for the construction of genetic maps and the localization of genetic loci for a variety of traits. The principles leading to LD apply to both biparental mapping populations (F2, RILs, etc) and natural populations. Because of its inherent advantages, LD mapping approaches are increasingly being applied for plant species, in particular maize. Due to the out-breeding character of this species, LD extends only over a few kb and thus leads to [gh genetic resolution, up to the level of individual candidate genes that can be associated with a given trait (Rafalski and Morgante 2004, Gupta et al. 2005).]

Analysis of genetic distance and population structure provided evidence of significant population structure in the G. arboretum accessions and identified the highest likelihood at k=6 . A total of 30 marker-trait association were identified with 19 SSR markers located on 11 chromosomes, the association analysis identified marker-trait associations (P=0.05) for all traits evaluated. Lint%, lint colour, elongation, micronaire and perimeter were associated with four markers each, length with three markers, and strength and maturity with tow and five markers respectively, Furthermore the LD (R2 values) between markers ranged from 10% to 20%. Of the 30 marker-trait associations, four identified 15% or more of the total variation for lint% (BNL0256 and BNL1122), lint colour (BNL0542) and length (BNL1122) (Kantartzi and Stewart. 2008).

[page 22:]

Linkage disequilibrium is the non-random association of alleles in a sample population and forms the basis for the construction of genetic maps and the localization of genetic loci for a variety of traits. The principles leading to LD apply to both biparental mapping populations (F2, RILs, etc) and natural populations.

[page 23:]

Because of its inherent advantages, LD mapping approaches are increasingly being applied for plant species, in particular maize. Due to the out-breeding character of this species, LD extends only over a few kb and thus leads to a high genetic resolution, up to the level of individual candidate genes that can be associated with a given trait (Rafalski and Morgante 2004, Gupta et al. 2005).

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[Due to the out-breeding character of this species, LD extends only over a few kb and thus leads to] a high genetic resolution, up to the level of individual candidate genes that can be associated with a given trait (Rafalski and Morgante 2004, Gupta et al. 2005).

A European germplasm collection of 146 two-rowed spring barley cultivars was used to carry out LD mapping of yield traits using 236 AFLP markers (Kraakman et al. 2004). Associated markers were identified that are located in similar regions where QTLs for yield had been found in barley (Romagosa et al. 1999 and Li et al. 2006).

A survey of 953 cultivated barley accessions representing a broad spectrum of the genetic diversity in barley genetic resources revealed that LD extends up to 50 cM but is highly dependent on population structure (Kraakman et al. 2004 and Malysheva-Otto et al. 2006). On the one hand, the high level of LD in barley is due to the inbreeding mating type of this species; on the other hand, the selection of germplasm plays an important role analysis of a germplasm collection of European cultivars, land races and wild barley accession from the Fertile Crescent region provided hints that the level of LD increases from cultivars to landraces to wild barley (Caldwell et al. 2006). Similarly, Morell et al. (2005) reported low levels of LD in wild barley by examining LD within and between 18 genes from 25 accessions.

The genotyping database for 953 cultivated barley accessions profiled with 48 SSR markers was established. The principal coordinate analysis revealed structuring of the barley population with regard to (i) geographical regions and (ii) agronomic traits. Geographic origin contributed most to the observed molecular diversity. Genome-wide linkage disequilibrium (LD) was estimated as squared correlation of allele frequencies (r2). The values of LD for barley were comparable to other plant species (conifers, poplar and maize). The pattern of intrachromosomal LD with distances between the genomic loci ranging from 1 to 150 cM revealed that in barley LD extended up to distances as long as 50 cM with r2 > 0.05, or up to 10 cM with r2 > 0.2. Few loci mapping to different chromosomes showed significant LD with r2 > 0.05. The number of loci in significant LD as well as the pattern of LD was clearly dependent on the population structure. The LD in homogenous group of 207 European 2-rowed spring barleys compared to the highly structured worldwide barley population was increased in the number of loci pairs with r2 > 0.05 and had higher values of r2, although the percentage of intrachromosomal loci pairs in significant LD based on P < 0.001 was 100% in the whole set of varieties, but only 45% in the subgroup of European 2-rowed spring barley. The value of LD also varied depending on polymorphism of the loci selected for genotyping (Malysheva-Otto et al. 2006).

Due to the out-breeding character of this species, LD extends only over a few kb and thus leads to a high genetic resolution, up to the level of individual candidate genes that can be associated with a given trait (Rafalski and Morgante 2004, Gupta et al. 2005).

[...] A European germplasm collection of 146 tworowed spring barley cultivars was used to carry out LD mapping of yield traits using 236 AFLP markers (Kraakman et al. 2004). Associated markers were identified that are located in similar regions where QTLs for yield had been found in barley. (Romagosa et al. 1999 and Li et al. 2006).

A systematic survey of 953 gene bank accessions representing a broad spectrum of the genetic diversity in barley genetic resources revealed that LD extends up to 50 cM but is highly dependent on population structure (Kraakman et al. 2004 and Malysheva- Otto et al. 2006). On the one hand, the high level of LD in barley is due to the inbreeding mating type of this species; on the other hand, the selection of germplasm plays an important role Analysis of a germplasm collection of European cultivars, land races and wild barley accession from the Fertile Crescent region provided hints that the level of LD increases from cultivars to landraces to wild barley (Caldwell et al. 2006). Similarly, Morell et al. (2005) reported low levels of LD in wild barley by examining LD within and between 18 genes from 25 accessions.

The genotyping database for 953 cultivated barley accessions profiled with 48 SSR markers was established. The PCoA revealed structuring of the barley population with regard to (i) geographical regions and (ii) agronomic traits. Geographic origin contributed most to the observed molecular diversity. Genome-wide linkage disequilibrium (LD) was estimated as squared correlation of allele frequencies (r2). The values of LD for barley were comparable to other plant species (conifers, poplar and maize). The pattern of intrachromosomal LD with distances between the genomic loci ranging from 1 to 150 cM revealed that in barley LD extended up to distances as long as 50 cM with r2 > 0.05, or up to 10 cM with r2 > 0.2. Few loci mapping to different chromosomes showed significant LD with r2 > 0.05. The number of loci in significant LD as well as the pattern of LD was clearly dependent on the population structure. The LD in the homogenous group of 207 European 2-rowed spring barleys compared to the highly structured worldwide barley population was increased in the number of loci pairs with r2 > 0.05 and had higher values of r2, although the percentage of intrachromosomal loci pairs in significant LD based on P < 0.001 was 100% in the whole set of varieties, but only 45% in the subgroup of European 2-rowed spring barleys (Malysheva-Otto et al. 2006).

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The genotyping database for 953 cultivated barley accessions profiled with 48 SSR markers was established. The PCoA revealed structuring of the barley population with regard to (i) geographical regions and (ii) agronomic traits. Geographic origin contributed most to the observed molecular diversity. Genome-wide linkage disequilibrium (LD) was estimated as squared correlation of allele frequencies (r2). The values of LD for barley were comparable to other plant species (conifers, poplar and maize). The pattern of intrachromosomal LD with distances between the genomic loci ranging from 1 to 150 cM revealed that in barley LD extended up to distances as long as 50 cM with r2 > 0.05, or up to 10 cM with r2 > 0.2. Few loci mapping to different chromosomes showed significant LD with r2 > 0.05. The number of loci in significant LD as well as the pattern of LD was clearly dependent on the population structure.

The LD in the homogenous group of 207 European 2-rowed spring barleys compared to the highly structured worldwide barley population was increased in the number of loci pairs with r2 > 0.05 and had higher values of r2, although the percentage of intrachromosomal loci pairs in significant LD based on P < 0.001 was 100% in the whole set of varieties, but only 45% in the subgroup of European 2-rowed spring barleys (Malysheva-Otto et al. 2006).

The genotyping database for 953 cultivated barley accessions profiled with 48 SSR markers was established. The PCoA revealed structuring of the barley population with regard to (i) geographical regions and (ii) agronomic traits. Geographic origin contributed most to the observed molecular diversity. Genome-wide linkage disequilibrium (LD) was estimated as squared correlation of allele frequencies (r2). The values of LD for barley were comparable to other plant species (conifers, poplar and maize). The pattern of intrachromosomal LD with distances between the genomic loci ranging from 1 to 150 cM revealed that in barley LD extended up to distances as long as 50 cM with r2 > 0.05, or up to 10 cM with r2 > 0.2. Few loci mapping to different

chromosomes showed significant LD with r2 > 0.05. The number of loci in significant LD as well as the pattern of LD was clearly dependent on the population structure.

The LD in the homogenous group of 207 European 2-rowed spring barleys compared to the highly structured worldwide barley population was increased in the number of loci pairs with r2 > 0.05 and had higher values of r2, although the percentage of intrachromosomal loci pairs in significant LD based on P < 0.001 was 100% in the whole set of varieties, but only 45% in the subgroup of European 2-rowed spring barleys (Malysheva-Otto et al. 2006).

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2.10.4 Marker-Trait association analysis

A major problem with association mapping is the presence of a population structure, which can lead to false positives and failure to detect genuine associations (i.e., false negatives), particularly in highly selfing species (Iwata et al. 2007). The association analysis was performed with a mixed linear model (MLM) including Q and K matrix using SAS Software version 9.2. This analysis was achieved to identify DArT markers which are associated with the Fusarium graminearum stress tolerance traits and heading date trait considering the population based on population structure and the relatedness relationships.

Two statistical models used as follows:

First one with year’s effect

Yijkmnf = μ + Yi + Mj + Yi*Mj + ΣPCAk + Am(Mj)Kn + Yi*Am(Mj)Kn + εf(ijkmn)

where μ is the general mean, Yi is the fixed effect of the i-th Year, Mj is the fixed effect of j-th marker, Yi*Mj is the fixed interaction of i-th year with j-th marker, PCAk is the fixed effect of k-th subgroup of the population structure (PC values), Am(Mj)Kn is the random effect of m-th accession nested in the j-th marker associated with n-th kinship coefficient, Yi*Am(Mj)Kn is the random interaction effect of j-th year with m-th accession nested in the j-th marker associated with nth kinship coefficient, εf(ijkmn) is the error.

Second one with the fungus isolates effect

Yijkmnf = μ + Ti + Mj + Ti*Mj+ ΣPCAk + Am(Mi)Kn + Ti*Am(Mj)Kn + εf(ijkmn)

where μ is the general mean, Ti is the fixed effect of the i-th disease isolates, Mj is the fixed effect of j-th marker, Ti*Mj is the fixed interaction effect of j-th disease isolates with l-th marker, PCAk is the fixed effect of k-th subgroup of the population structure (PC values), Mj is the fixed effect of l-th marker, Am(Ml)Kn is the random effect of mth accession nested in the l-th marker associated with n-th kinship coefficient , Ti*Am(Mj)Kn is the random interaction effect of i-th disease isolates with m-th accession nested in the j-th marker associated with n-th kinship coefficient, εf(ijkmn) is the error.

2.6.3 Marker-Trait association analysis

A major problem with association mapping is the presence of a population structure, which can lead to false positives and failure to detect genuine associations (i.e. false negatives), particularly in highly selfing species (Iwata et al. 2007). Therefore we include the relatedness relationships and population structure of the tested accessions using the Q and K matrixes in our analysis. The association analysis was performed with a mixed linear model (MLM) using “ASReml” Software version 2 according to Stich et al. (2008). The statistical model for the association analysis identifying DArT markers which are associated with the tested drought tolerance traits considering population structure and the relatedness relationships is as follows:

Yijklmnf = μ + Yi + Tj + Yi*Tj + Qk + ML + Yi*ML + Tj*ML + Yi*Tj*ML + Am(ML)Kn + Yi*Am(ML)Kn + Tj*Am(ML)Kn + εf(ijkmn)

where μ is the general mean, Yi is the fixed effect of the ith Year, Tj is the fixed effect of the jth Drought treatment, Yi*Tj is the fixed interaction effect of ith year with jth drought treatment, Qk is the fixed effect of kth subgroup of the population structure (Q matrix), ML is the fixed effect of Lth marker, Yi*ML is the fixed interaction of ith year with Lth marker, Tj*ML is the fixed interaction effect of jth drought treatment with Lth marker, Yi*Tj*ML is the fixed interaction effect of ith year with jth drought treatment and Lth marker, Am(ML)Kn is the random effect of mth accession nested in the Lth marker associated with nth kinship coefficient , Yi*Am(ML)Kn is the random interaction effect of ith year with mth accession nested in the Lth marker associated with nth kinship coefficient, Tj*Am(ML)Kn is the random interaction effect of jth drought treatment with mth accession nested in the Lth marker associated with nth kinship coefficient, εf(ijkmn) is the error.

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3. Results:

The barley population of wild and cultivated forms was evaluated under Fusarium head blight disease stress under greenhouse and field-potted conditions for three successive seasons (2009, 2010 and 2011). In parallel, the population was genotyped with 895 DArT Markers to identify DArT markers associated disease infection tolerance. Structure analysis was conducted using Structure software 2.2 and Principal Component Analysis (PCA), Kinship coefficients matrix calculated by SPAGeDi 1.3 software and then the association analysis was achieved including population structure (PC values) and relatedness relationship coefficients (K matrix) to avoid the superiors association to detect the marker genotype which associated with studied traits by SAS 9.2 software. The following part presents the phenotypic variation, phenotypic correlation among traits, and the markers which associated with each trait.

3.1 Phenotypic measurements

In this study 140 accession were evaluated for quantitative traits (Leaves Disease Scoring; visual and Image analysis software for plant disease quantification (APS Assess) scoring (VS and LDS), Spikes Disease scoring (SDS) and Heading date (HD)) for two different isolates from Fusarium graminarum (5.1 and 5.3) the phenotypic differences between genotypes in 2009, 2010 and 2011 seasons are shown in table (3).

3. Results:

The structured barley population was evaluated under well-watered and drought stress conditions under greenhouse for two successive seasons (2007 and 2008). In parallel, the population was genotyped with 1081 DArT Markers to identify DArT markers associated drought tolerance traits in structured barley population. Structure analysis was conducted using Structure software 2.2, and Kinship coefficients matrix calculated by TASSEL 2.0.1, and then the association analysis was achieved including population structure (Q-matrix) and relatedness relationship coefficients (K-matrix) to avoid the superiors association to detect the marker genotype which associated with studied traits by ASReml Software version 2. The following part presents the phenotypic variation, phenotypic correlation among traits, and the markers which associated with each trait.

3.1 Phenotypic measurements

In this study 119 accession were evaluated for quantitative traits (Wilting score, Shoot fresh weight, Shoot dry weight, Root traits, Root/Shoot ratio, Relative water content, Osmotic potential, and Proline content) the phenotypic differences between wellwatered and stress conditions in 2007 and 2008 seasons are shown in table 3.

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3.3 Population structure and Kinship coefficients

The Population structure analysis was conducted using genotypic data of 895 DArT markers by using Structure Software 2.2 (Pritchard et al 2000), and the accessions subdivided into 16 subpopulations, base on the suggestion of Pritchard and Wen (2007), by using the burn-in time 100 000 and the number of replications (MCMC) was 200 000, the individuals placed into k clusters, we set k (the number of subpopulations) from 1 to 22. To reach the appropriate K value, the estimated normal logharithm of the probability of fit (averaged for the two runs), the population structure matrix (Q) was defined by running structure at K = 16, where the highest likelihood has been obtained (Fig.10a).

Fig. (10) (a) presents the number of clusters, which have the highest maximum likelihood, and (b) presents the percentage of the accessions in each cluster.

3.3 Population structure and Kinship coefficients

The Population structure analysis was conducted using genotypic data of 1081 DArT markers by using Structure Software 2.2 (Pritchard et al 2000), and the accessions subdivided into 12 subpopulations, base on the suggestion of Pritchard and Wen (2007), we used the burn-in time 50 000 and the number of replications (MCMC) was 100 000, the individuals placed into k clusters, we set k (the number of subpopulations) from 2 to 15 and performed 14 runs for k values, the population structure matrix (Q) was defined by running structure at K = 12 , where the highest likelihood has been obtained (Fig.3a).

Fig.3 (a) presents the number of clusters, which have the highest maximum likelihood, and (b) presents the percentage of the accessions in each cluster.

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Fig. 11 represents that all genotypes were distributed within the 16 groups according to the relatively genetic distances using structure and cluster analysis, in the colored part above the diagram each individual is represented by a single vertical line broken into k colored segments, with lengths proportional to each of the k inferred clusters or subgroups. Whereas the part below of the diagram represents the cluster analysis based on the DICE dissimilarity index and the unweighted neighbour-jointing method was performed on the 895 DArT markers for 124 Accessions, 16 main clusters were identified which correspond well with genetic distances and origin of the genotypes. Figure 5 shows that all accessions were distributed within the 12 groups according to the relatively genetic distances using structure and cluster analysis, in the colored part above the diagram each individual is represented by a single vertical line broken into k colored segments, with lengths proportional to each of the k inferred clusters or subgroups. Whereas the part below of the diagram represents the cluster analysis based on the DICE dissimilarity index and the unweighted neighbour-jointing method was performed on the 1081 DArT markers for 119 Accessions, twelve main clusters were identified which correspond well with genetic distances and origin of the genotypes.
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Association mapping identifies (QTLs) by examining the marker-trait associations that can be attributed to the strength of linkage disequilibrium between markers and functional polymorphisms across a set of diverse germplasm (Zhu et al. 2008).

[...]

Association mapping analysis was applied using 895 DArT markers to identify favorable QTLs that related to FHB disease tolerance. Association analysis done by using QK mixed-model approach, which proposed by Yu et al. (2006) that promises to correct for linkage disequilibrium (LD) caused by population structure and relatedness relationship. The validity of this approach has to be evaluated in breeding germplasm of autogamous species, because the population structure is presumably high and levels of relatedness relationship are diverse (Garris et al. 2005).

Association mapping identifies quantitative trait loci (QTLs) by examining the markertrait

associations that can be attributed to the strength of linkage disequilibrium between markers and functional polymorphisms across a set of diverse germplasm (Zhu et al. 2008).

Association analysis was applied using QK mixed-model approach, which proposed by Yu et al. (2006) that promises to correct for linkage disequilibrium (LD) caused by population structure and relatedness relationship. The suitability of this approach has to be evaluated in breeding germplasm of autogamous species, because their population structure is presumably high and levels of relatedness relationship are diverse (Garris et al. 2005).

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[20.] Ib/Fragment 081 06 - Diskussion
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Association mapping of a trait is to identify chromosomal regions that contain genes affecting the trait. The discovery of dense polymorphic markers covering the entire genome provides us an opportunity to localize these regions by trying to find the markers closest to the genes of interest.

[...]

In the present study, 108 accessions of wild barley (H. vulgare ssp. spontaneum) and 7 landraces (H. vulgare ssp. vulgare) from the ICBB core collection (gene banks in Gatersleben and Braunschweig). 21 spring barley cultivars representative for the breeding pool of spring barley (H. vulgare ssp. vulgare) in the North Rhine Westphalia (NRW), Germany, (Reetz and Léon 2004) and 4 common cultivars (Scarlett, Lerche, Barke and Thuringia). The seeds of these cultivars were provided by the Institute of Crop Science and Resource Conservation (INRES), chair of plant breeding.

Association mapping of a trait is to identify chromosomal regions that contain genes affecting the trait. The discovery of dense polymorphic markers covering the entire genome provides us an opportunity to localize these regions by trying to find the markers closest to the genes of interest.

[...]

In the current study, 98 accessions of wild barley (H. vulgare ssp. spontaneum) from the ICBB core collection (gene banks in Gatersleben and Braunschweig) and 21 spring barley cultivars representative for the breeding pool of spring barley in the North Rhine Westphalia (NRW), Germany, (Reetz and Leon 2004). These cultivars were provided by the Institute of Crop Science and Resource Conservation (INRES), chair of plant breeding.

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[21.] Ib/Fragment 083 01 - Diskussion
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In parallel, DNA has been extracted from 10 mg freeze drying of each accession by using “Kit” procedure according DNeasy Plant Handbook 07/2006. The produced DNA of the accessions was sent to Australia and genotyped by using 1081 DArT markers (YarralumlaACT, Australia). The phenotypic data were analyzed each season separately as one way ANOVA using Proc GLM procedure and the Pearson correlation coefficients (r) between traits under disease infection condition were calculated by SAS version 9.2 (SAS institute 2008). PCA was carried out by using SAS 9.2 program PROC PRINCOMP, for study of the population structure. The significance for PCA was evaluated using Franklin et al. (1995) method. The relative kinship coefficients (K matrix) among all pairs of accessions were calculated using 895 DArT markers data by “SPAGeDi-1.3d” Software to calculate the pair-wise kinship coefficients for all accessions. The association analysis was performed in mixed linear model (MLM) including PCA values and K matrix. All studied traits LDS, VS, SDS and HD were exhibited highly significantly differences in three years. In parallel, DNA has been extracted from 10 mg freeze drying of each accession by using “Kit” procedure according DNeasy Plant Handbook 07/2006. The produced DNA of the accessions was sent to Australia and genotyped by using 1081 DArT markers (YarralumlaACT, Australia). The phenotypic data were analysed each season separately as one way ANOVA using Proc GLM procedure and the Pearson correlation coefficients (r) between traits under well-watered and drought stress condition were calculated by SAS version 9.1 (SAS institute 2003). [...] The relative kinship coefficients (K matrix) among all pairs of accessions were calculated using 1081 DArT markers data by TASSEL Software version 2.0.1 to calculate the pair-wise kinship coefficients for all accessions. For the results of ANOVA, the followed traits, WS, SFW, RWC, RL, FWa, FWb, FWc, RFW, DWc, RDW, and PC were exhibited highly significantly differences in both seasons, Pearson correlation coefficients (r) between 12 pairs of studied traits have been detected under droughtstress (D) and well-watered (W) treatment across two years (table 6 ). With regarding to the population structure, the accessions were subdivided into 12 subpopulations, which correspond well with genetic distances and origin of the genotypes (table 3 and figure 4). Seventy nine markers were correlated
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