SNPMace – A meta-analysis to estimate SNP effects by combining results from multiple countries

Selection of bulls and cows is increasingly made on genomic estimated breeding values (GEBVs) calculated from their SNP genotypes and the estimated effects of each SNP. To obtain the most accurate GEBVs a large training population of animals with phenotypes and genotypes is needed. For some traits, some breeds and some countries such a large training population is not available. In these cases it would increase the accuracy of GEBVs if information from multiple countries and breeds were combined. This paper describes a meta-analysis to combine SNP effects from multiple countries. A project to test this procedure is under way and, if successful, may result in a new Interbull service

Genome-wide association study of root-lesion nematodes Pratylenchus species and crown rot Fusarium culmorum in bread wheat

Triticum aestivum L., also known as common wheat, is affected by many biotic stresses. Root diseases are the most difficult to tackle due to the complexity of phenotypic evaluation and the lack of resistant sources compared to other biotic stress factors. Soil-borne pathogens such as the root-lesion nematodes caused by the Pratylenchus species and crown rot caused by various Fusarium species are major wheat root diseases, causing substantial yield losses globally. A set of 189 advanced spring bread wheat lines obtained from the International Maize and Wheat Improvement Center (CIMMYT) were genotyped with 4056 single nucleotide polymorphisms (SNP) markers and screened for root-lesion nematodes and crown rot resistance. Population structure revealed that the genotypes could be divided into five subpopulations. Genome-Wide Association Studies were carried out for both resistances to Pratylenchus and Fusarium species. Based on our results, 11 different SNPs on chromosomes 1A, 1B, 2A, 3A, 4A, 5B, and 5D were significantly associated with root-lesion nematode resistance. Seven markers demonstrated association with P. neglectus, while the remaining four were linked to P. thornei resistance. In the case of crown rot, eight different markers on chromosomes 1A, 2B, 3A, 4B, 5B, and 7D were associated with Fusarium crown rot resistance. Identification and screening of root diseases is a challenging task; therefore, the newly identified resistant sources/genotypes could be exploited by breeders to be incorporated in breeding programs. The use of the identified markers in marker-assisted selection could enhance the selection process and cultivar development with root-lesion nematode and crown rot resistance

Genome-Wide Association Mapping of Yield and Grain Quality Traits in Winter Wheat Genotypes

<div><p>The main goal of this study was to investigate the genetic basis of yield and grain quality traits in winter wheat genotypes using association mapping approach, and identify linked molecular markers for marker assisted selection. A total of 120 elite facultative/winter wheat genotypes were evaluated for yield, quality and other agronomic traits under rain-fed and irrigated conditions for two years (2011–2012) at the Tel Hadya station of ICARDA, Syria. The same genotypes were genotyped using 3,051 Diversity Array Technologies (DArT) markers, of which 1,586 were of known chromosome positions. The grain yield performance of the genotypes was highly significant both in rain-fed and irrigated sites. Average yield of the genotypes ranged from 2295 to 4038 kg/ha and 4268 to 7102 kg/ha under rain-fed and irrigated conditions, respectively. Protein content and alveograph strength (W) ranged from 13.6–16.1% and 217.6–375 Jx10-4, respectively. DArT markers wPt731910 (3B), wPt4680 (4A), wPt3509 (5A), wPt8183 (6B), and wPt0298 (2D) were significantly associated with yield under rain-fed conditions. Under irrigated condition, tPt4125 on chromosome 2B was significantly associated with yield explaining about 13% of the variation. Markers wPt2607 and wPt1482 on 5B were highly associated with protein content and alveograph strength explaining 16 and 14% of the variations, respectively. The elite genotypes have been distributed to many countries using ICARDA’s International system for potential direct release and/or use as parents after local adaptation trials by the NARSs of respective countries. The QTLs identified in this study are recommended to be used for marker assisted selection after through validation using bi-parental populations.</p></div

Additional file 7: of Assessment of genetic diversity and yield performance in Jordanian barley (Hordeum vulgare L.) landraces grown under Rainfed conditions

Population structure of 154 Jordanian barley genotypes used in this study. (XLSX 34 kb

Decline of LD as measured by R² against genetic distance

<p>Decline of LD as measured by R² against genetic distance</p

Mean grain yield and quality performance of the top 20 high yielding genotypes at Tel Hadya, Syria, 2011–2012.

<p>Mean grain yield and quality performance of the top 20 high yielding genotypes at Tel Hadya, Syria, 2011–2012.</p

Population structure among genotypes.

<p>A) Plot of the average logarithm of the probability of data likelihood [Ln P(D)], as a function of the number of assumed subgroups (k), with K allowed to range from 2 to 12. B) Plot of the Bayesian Information Criterion for each population number from 1 to 50 C) The proportion of the genome of each individual originating from each inferred population (a total of 11 and each color represent a single population)</p

Association mapping profiles of wheat agronomic performance and quality attributes.

<p>Consensus linkage maps are based on information from Huang et al. (2012). On the left, values are genetic distance in centimorgans (cM). On the right, diversity array technology markers (underlined) are significantly associated with resistance to wheat quality attributes. On the far right side, the agronomic performance and quality attributes associated are indicated. Protein: Grain protein content, FDT: Farinograph development time, FST: Farinograph stability time, L: Alveograph extensibility, P/L: Alveograph configuration ratio, W: Alveograph strength. DH2011IRR: Days to heading recorded in 2011 under irrigated conditions; DH2011RF: Days to heading in 2011 under rainfed conditions; DH2012IRR: Days to heading in 2012 under irrigated conditions, DHIRRAv: Average days to heading in the irrigated trials, DHRFAv: Average days to heading in the rainfed trials, DHAv: Average days to heading accross environments; PLH2011IRR: Plant height in 2011 under irrigated conditions; PH2012RF:Plant height in 2012 under rainfed conditions, PHIRRAv: Average plant height in the irrigated trials, PLHAv: Average plant height accross environments; YLD2011IRR: Yield in 2011 under irrigated conditions; YLD2011RF: Yield in 2011 under rainfed conditions, YLD2012RF: Yield in 2012 under rainfed conditions. YieldAv: Average yield accross environments.</p

Mean, minimum and maximum values of the different agronomic and quality traits measured on 120 FWW genotypes at Tel Hadya, Syria, 2011–2012.

<p>^a: SED (Standard error of the differences of the means) is indicated for yield, days to heading, days to maturity, plant height; and SD (Standard deviation) is indicated for quality traits: grain protein content, FDT: Farinograph development time, FST: Farinograph stability time, FAB: Farinograph water absorption, MTI: Mixing tolerance index, W: Alveograph strength, P: Alveograph tenacity, L: Alveograph extensibility, P/L: Alveograph configuration ratio, TKW: Thousand kernel weight, TW: Test weight and PSI: Particle size index.</p><p>^b P-Value: the significance of the differences among the agronomical/quality scores, P > 0.05 means no significant difference among the 120 genotypes for the described trait.</p><p>Mean, minimum and maximum values of the different agronomic and quality traits measured on 120 FWW genotypes at Tel Hadya, Syria, 2011–2012.</p