Association Mapping Of Traits Research Articles

Genome-Wide Association Mapping of Virulence Genes in Wheat Karnal Bunt Fungus Tilletia indica Using Double Digest Restriction-Site Associated DNA-Genotyping by Sequencing Approach.

Tilletia indica is a quarantine fungal pathogen that poses a serious biosecurity threat to wheat-exporting countries. Acquiring genetic data for the pathogenicity characters of T. indica is still a challenge for wheat breeders and geneticists. In the current study, double digest restriction-site associated-DNA genotyping by sequencing was carried out for 39 T. indica isolates collected from different locations in India. The generated libraries upon sequencing were with 3,346,759 raw reads on average, and 151 x 2 nucleotides read length. The obtained bases per read ranged from 87 Mb in Ti 25 to 1,708 Mb in Ti 39, with 505 Mb on average per read. Trait association mapping was performed using 41,473 SNPs, infection phenotyping data, population structure, and Kinship matrix, to find single nucleotide polymorphisms (SNPs) linked to virulence genes. Population structure analysis divided the T. indica population in India into three subpopulations with genetic mixing in each subpopulation. However, the division was not in accordance with the degree of virulence. Trait association mapping revealed the presence of 13 SNPs associated with virulence. Using sequences analysis tools, one gene (g4132) near a significant SNP was predicted to be an effector, and its relative expression was assessed and found upregulated upon infection.

Molecular characterization of China aster (Callistephus chinensis (L.) Nees) genotypes using SSR markers

Abstract Understanding genetic diversity in target populations is of great importance in breeding and a prerequisite for association mapping of traits. In this study, 57 cross species simple sequence repeat (SSR) markers were screened for amplification in China aster. Twenty six polymorphic markers were used to estimate the genetic diversity in forty two China aster genotypes. The observed and expected heterozygosities within the genotypes were ranged from 0.00 to 0.80 and 0.17 to 0.50, respectively. Weighted Neighbor Joining method, grouped China aster genotypes into five major clusters which coincided for morphological traits mostly flower color and form, but not correlated for their geographical locations. The results suggested that, population may be useful for the genome-wide marker–trait association mapping. These set of cross species transferable SSR markers would enable the application of the SSR technique in China aster crop improvement.

Association mapping of traits related to leaf blast disease in rice (Oryza sativa L.)

The present research was conducted to validate and verify the association of SSR markers with traits related to leaf blast disease in 121 rice cultivars. Number of spots, number of spore-producing spots, spot size, infection type, infection percentage, and the area under the disease progress curve (AUDPC) were the blast-related traits measured in the rice blast nursery. Evaluation of genetic diversity of markers indicated that RM166 had the highest level of diversity indices including observed number of alleles, effective number of alleles, polymorphism information content (PIC) value, gene diversity, and Shannon’s index. Thus, RM166 is the more valuable marker which can be used in association mapping because of the high opportunity of linkage to valuable disease resistance traits. Association mapping was performed using population structure matrix through the general linear model (GLM) and the mixed linear model (MLM). Based on a structure analysis, three subpopulations were observed in the rice population of this study. Among the set of selected markers, 30 and 29 markers exhibited a significant linkage with traits related to blast disease in GLM and MLM, respectively. RM224 had the most significant relationship with evaluated traits in both GLM and MLM, suggesting that this marker can be used in breeding programs.

Applications and efficiencies of the first cat 63K DNA array

The development of high throughput SNP genotyping technologies has improved the genetic dissection of simple and complex traits in many species including cats. The properties of feline 62,897 SNPs Illumina Infinium iSelect DNA array are described using a dataset of over 2,000 feline samples, the most extensive to date, representing 41 cat breeds, a random bred population, and four wild felid species. Accuracy and efficiency of the array’s genotypes and its utility in performing population-based analyses were evaluated. Average marker distance across the array was 37,741 Kb, and across the dataset, only 1% (625) of the markers exhibited poor genotyping and only 0.35% (221) showed Mendelian errors. Marker polymorphism varied across cat breeds and the average minor allele frequency (MAF) of all markers across domestic cats was 0.21. Population structure analysis confirmed a Western to Eastern structural continuum of cat breeds. Genome-wide linkage disequilibrium ranged from 50–1,500 Kb for domestic cats and 750 Kb for European wildcats (Felis silvestris silvestris). Array use in trait association mapping was investigated under different modes of inheritance, selection and population sizes. The efficient array design and cat genotype dataset continues to advance the understanding of cat breeds and will support monogenic health studies across feline breeds and populations.

Recent developments in statistical methods for GWAS and high-throughput sequencing association studies of complex traits

ABSTRACTThe advent of large-scale genetic studies has helped bring a new era of biomedical research on dissecting the genetic architecture of complex human disease. Genome-wide association studies (GWASs) and next-generation sequencing studies are two popular tools for identifying genetic variants that are associated with complex traits. This article overviews some of the most important statistical tools for analyzing data from these two types of studies, with an emphasis on single-SNP tests for common variants and region-based tests for rare variants. We compare various statistical methods for common and rare variants in humans, and describe some critical considerations to guide the choice of an analysis method. Also discussed are the related topics of sample ascertainment, missing heritability, and multiple testing correction, as well as some remaining analytical challenges presented by complex trait association mapping using genomic data obtained via high-throughput technologies.

Association mapping for traits related to nitrogen use efficiency in tropical maize lines under field conditions

Nitrogen deficiency is one of the most critical abiotic stresses for maize (Zea mays) cultivation worldwide. For a productive and sustainable scenario, developing genotypes more efficient in nitrogen use is essential. This study aimed to identify single nucleotide polymorphism (SNP) markers associated with nitrogen use efficiency (NUE) traits under field conditions and candidate genes related to these markers by genome-wide association study (GWAS). Sixty-four tropical maize inbred lines were evaluated in ideal and low nitrogen conditions for total root length (TRL) and low nitrogen tolerance index (LNTI). GWAS was performed using a fixed and random model circulating probability unification method, with marker-based principal components to correct for population stratification. Genotypic values were predicted using mixed model equations. Seven significant markers were identified. Among the primary biological processes, candidate genes are related to transcription control and regulation, detected to all evaluated traits, and the synthesis of Guanosine Monophosphate Synthetase, enzyme directly involved in the provision and recycling of nitrogen. GWAS analysis revealed genomic regions in tropical maize associated with NUE under field conditions. The main biological process identified as related to these markers/regions evidence cellular processes and functions associated with different process of nitrogen synthesis and recycling.

Population structure and association mapping of traits related to reproductive development in field pea

Field pea (Pisum sativum) is an important pulse crop globally for human consumption and livestock feed. A panel of 92 diverse pea cultivars was evaluated across nine environments and genotyped using 1536 single nucleotide polymorphisms (SNPs) arranged in a GoldenGate array. Population structure analysis revealed three subpopulations roughly consistent with the cultivar origin. Phenotyping included days to flowering (DTF), duration of flowering (DOF), number of reproductive nodes, number of pods on the main stem, percentage of pods set, percentage of pods retained with seed and pollen germination reduction due to heat stress. Association analyses identified a total of 60 SNPs significantly associated (−log10 p ≥ 4.3) with these seven reproductive development-related traits. Among these 60 marker-trait associations, 33 SNPs were associated with the onset of flowering, 8 SNPs with pod development and 19 SNPs with the number of reproductive nodes. No SNP marker was significantly associated with in vitro pollen germination reduction caused by high temperature stress. We found that 12 SNPs associated with DTF and 2 SNPs associated with DOF overlapped with the SNP markers associated with the number of reproductive nodes. Genomic regions associated with variation for reproductive development-related traits identified in this study provide grounds for future genetic improvement in pea.

An efficient and cost-effective approach for genic microsatellite marker-based large-scale trait association mapping: identification of candidate genes for seed weight in chickpea

The large-scale validation and high-throughput genotyping of numerous informative genic microsatellite markers are required for association mapping to identify candidate genes for complex quantitative traits in chickpea. However, the screening and genotyping of such informative markers in individual genotypes/whole association panels for trait association mapping involves massive costs in terms of resources, time and labour due to low genetic polymorphism in chickpea. We have developed an alternative time-saving and cost-effective pool-based trait association mapping approach by combining pooled DNA analysis (with 616 genic microsatellite markers) and individual genotype (large structured association panel) genotyping. Using this approach we have identified seven seed weight-associated transcription factor gene-derived microsatellite markers (with minor allele frequency >15 %) in desi and kabuli chickpea. Strong marker allele effects of these five transcription factors with increasing seed weight in the contrasting desi and kabuli genotypes were evident. Bi-parental linkage mapping using 241 of the informative gene-based microsatellite markers resulted in the identification and mapping of nine such markers linked with three major quantitative trait loci (explaining a total phenotypic variance of 23.5–34.7 %) on chromosomes 1 (CaqSW1.1: 73.5–74.5 cM and CaqSW1.2: 79.3–81.3 cM) and 2 (CaqSW2.1: 65.7–67.5 cM) controlling 100-seed weight in chickpea. The integration of pool-based trait association mapping with differential expression profiling, traditional bi-parental linkage mapping and high-resolution microsatellite-single nucleotide polymorphism marker-based haplotyping/linkage disequilibrium mapping delineated four transcription factor genes (DUF3594, bZIP, DUF1635 and SBP) controlling seed weight in desi and kabuli chickpea. The strategies implemented in our study can be used in large-scale trait association mapping for the rapid identification of candidate genes and in the development of functional markers for traits of agricultural importance in crop species including chickpea.

QTLs conferring FOV 7 resistance detected by linkage and association mapping in Upland cotton

Fusarium wilt is a worldwide disease that affects cotton production. Molecular markers tightly linked to resistance genes can be used for marker-assisted and/or genomic selection. We performed both family-based linkage mapping and population-based association mapping (AM) to detect quantitative trait loci (QTLs) conferring resistance against Fusarium oxysporum f. sp. vasinfectum race 7 (FOV 7) in Upland cotton. To identify QTLs underlying FOV 7 resistance by linkage mapping, three Upland cotton cultivars/lines, Xuzhou 142, Yumian 21 and Shang 9901, were used to obtain the composite cross population, designated as Xuzhou 142/Yumian 21//Xuzhou 142/Shang 9901. A linkage map containing 185 simple sequence repeat loci and 40 linkage groups was constructed with an average distance of 7.5 cM between adjacent markers. Seven QTLs were detected by linkage mapping, explaining 2.9–6.6 % of the total phenotypic variance. We also performed marker–trait AM with the MLM model (Q + K) in a panel composed of 356 Upland cotton cultivars. In total, 27 loci were significantly associated with FOV 7 resistance at the α = 0.01 level (−log10 P ≥ 2), which were distributed on 16 chromosomes and explained 1.48–12.99 % of phenotypic variation. Three of the 7 QTLs identified by linkage mapping could be detected in AM. We identified the favorable allele for each of the 27 associated loci and investigated the number of favorable alleles in each accession. The results should increase our understanding of the genetic basis of FOV resistance and facilitate future resistance breeding in Upland cotton.

Population structure and linkage disequilibrium of ICRISAT foxtail millet (Setaria italica (L.) P. Beauv.) core collection

Use of diverse germplasm is a key factor which allows high level of resolution due to extensive recombination in the history. Therefore, population used in association mapping should posses as many phenotypes as possible. One of the methods to obtain most of the phenotypes is to construct the core collection. The ICRISAT foxtail millet core collection consisting of 155 accessions was genotyped using 72 simple sequence repeat (SSR) markers to investigate the genetic diversity, population structure and linkage disequilibrium (LD). A high degree of molecular diversity among the accessions was found, with an average of 16.69 alleles per locus. STRUCTURE analyses classify the accessions into four subpopulations (SP) based on SSR allelic diversity. The Neighbor joining clustering and the principal coordinate analysis were in accordance with the racial classification. The distribution of molecular genetic variation among and within the four SP and three races showed high degree of variability within each group, and low level of genetic distance (GD) among the groups. LD decay of <40 cM of GD in foxtail millet core collection was observed, which suggests that it could be possible to achieve resolution down to the 40 cM level. From this investigation, it is evident that the foxtail millet core collection developed at ICRISAT is very diverse and could be a valuable resource for trait association mapping, crop breeding and germplasm management.

GWAPP: A Web Application for Genome-Wide Association Mapping in Arabidopsis

Arabidopsis thaliana is an important model organism for understanding the genetics and molecular biology of plants. Its highly selfing nature, small size, short generation time, small genome size, and wide geographic distribution make it an ideal model organism for understanding natural variation. Genome-wide association studies (GWAS) have proven a useful technique for identifying genetic loci responsible for natural variation in A. thaliana. Previously genotyped accessions (natural inbred lines) can be grown in replicate under different conditions and phenotyped for different traits. These important features greatly simplify association mapping of traits and allow for systematic dissection of the genetics of natural variation by the entire A. thaliana community. To facilitate this, we present GWAPP, an interactive Web-based application for conducting GWAS in A. thaliana. Using an efficient implementation of a linear mixed model, traits measured for a subset of 1386 publicly available ecotypes can be uploaded and mapped with a mixed model and other methods in just a couple of minutes. GWAPP features an extensive, interactive, and user-friendly interface that includes interactive Manhattan plots and linkage disequilibrium plots. It also facilitates exploratory data analysis by implementing features such as the inclusion of candidate polymorphisms in the model as cofactors.

Statistical distributions of test statistics used for quantitative trait association mapping in structured populations

BackgroundSpurious associations between single nucleotide polymorphisms and phenotypes are a major issue in genome-wide association studies and have led to underestimation of type 1 error rate and overestimation of the number of quantitative trait loci found. Many authors have investigated the influence of population structure on the robustness of methods by simulation. This paper is aimed at developing further the algebraic formalization of power and type 1 error rate for some of the classical statistical methods used: simple regression, two approximate methods of mixed models involving the effect of a single nucleotide polymorphism (SNP) and a random polygenic effect (GRAMMAR and FASTA) and the transmission/disequilibrium test for quantitative traits and nuclear families. Analytical formulae were derived using matrix algebra for the first and second moments of the statistical tests, assuming a true mixed model with a polygenic effect and SNP effects.ResultsThe expectation and variance of the test statistics and their marginal expectations and variances according to the distribution of genotypes and estimators of variance components are given as a function of the relationship matrix and of the heritability of the polygenic effect. These formulae were used to compute type 1 error rate and power for any kind of relationship matrix between phenotyped and genotyped individuals for any level of heritability. For the regression method, type 1 error rate increased with the variability of relationships and with heritability, but decreased with the GRAMMAR method and was not affected with the FASTA and quantitative transmission/disequilibrium test methods.ConclusionsThe formulae can be easily used to provide the correct threshold of type 1 error rate and to calculate the power when designing experiments or data collection protocols. The results concerning the efficacy of each method agree with simulation results in the literature but were generalized in this work. The power of the GRAMMAR method was equal to the power of the FASTA method at the same type 1 error rate. The power of the quantitative transmission/disequilibrium test was low. In conclusion, the FASTA method, which is very close to the full mixed model, is recommended in association mapping studies.