Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels

Genomic Architecture Predicts Tree Topology, Population Structuring, and Demographic History in Amazonian Birds

Geographic barriers are frequently invoked to explain genetic structuring across the landscape. However, inferences on the spatial and temporal origins of population variation have been largely limited to evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known as genomic architecture in producing heterogeneity in differentiation across the genome. To test how variation in genomic characteristics (e.g. recombination rate) impacts our ability to reconstruct general patterns of differentiation between species that cooccur across geographic barriers, we sequenced the whole genomes of multiple bird populations that are distributed across rivers in southeastern Amazonia. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with species tree support. Gene flow was less pervasive in genomic regions of low recombination, making these windows more likely to retain patterns of population structuring that matched the species tree. We further found that approximately a third of the genome showed evidence of selective sweeps and linked selection, skewing genome-wide estimates of effective population sizes and gene flow between populations toward lower values. In sum, we showed that the effects of intrinsic genomic characteristics and selection can be disentangled from neutral processes to elucidate spatial patterns of population differentiation.

Identifying the Genetic Basis of Mineral Elements in Rice Grain Using Genome-Wide Association Mapping

Mineral malnutrition is a major problem in many rice-consuming countries. It is essential to know the genetic mechanisms of accumulation of mineral elements in the rice grain to provide future solutions for this issue. This study was conducted to identify the genetic basis of six mineral elements (Cu, Fe, K, Mg, Mn, and Zn) by using three models for single-locus and six models for multi-locus analysis of a genome-wide association study (GWAS) using 174 diverse rice accessions and 6565 SNP markers. To declare a SNP as significant, -log10(P) ≥ 3.0 and 15% FDR significance cut-off values were used for single-locus models, while LOD ≥ 3.0 was used for multi-locus models. Using these criteria, 147 SNPs were detected by one or two GWAS methods at -log10(P) ≥ 3.0, 48 of which met the 15% FDR significance cut-off value. Single-locus models outperformed multi-locus models before applying multi-test correction, but once applied, multi-locus models performed better. While 14 (~29%) of the identified quantitative trait loci (QTLs) after multiple test correction co-located with previously reported genes/QTLs and marker associations, another 34 trait-associated SNPs were novel. After mining genes within 250 kb of the 48 significant SNP loci, in silico and gene enrichment analyses were conducted to predict their potential functions. These shortlisted genes with their functions could guide future experimental validation, helping us to understand the complex molecular mechanisms controlling rice grain mineral elements.

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Simple Summary

The interactions between SNPs, which are known as epistasis, can strongly influence the phenotype. Their detection is still a challenge, which is made even more difficult through the existence of background associations that can hide correct epistatic interactions. To address the limitations of existing methods, we present in this study our novel method MIDESP for the detection of epistatic SNP pairs. It is the first mutual information-based method that can be applied to both qualitative and quantitative phenotypes and which explicitly accounts for background associations in the dataset.

Abstract

The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

Dissection of the genetic variation and candidate genes of lint percentage by a genome-wide association study in upland cotton

A genome-wide associated study identified six novel QTLs for lint percentage. Two candidate genes underlying this trait were also detected. Increasing lint percentage (LP) is a core goal of cotton breeding. To better understand the genetic basis of LP, a genome-wide association study (GWAS) was conducted using 276 upland cotton accessions planted in multiple environments and genotyped with a CottonSNP63K array. After filtering, 10,660 high-quality single-nucleotide polymorphisms (SNPs) were retained. Population structure, principal component and neighbor-joining phylogenetic tree analyses divided the accessions into two subpopulations. These results along with linkage disequilibrium decay indicated accessions were not highly structured and exhibited weak relatedness. GWAS uncovered 23 polymorphic SNPs and 15 QTLs significantly associated with LP, with six new QTLs identified. Two candidate genes, Gh_D05G0313 and Gh_D05G1124, both contained one significant SNP, highly expressed during ovule and fiber development stages, implying that the two genes may act as the most promising regulators of LP. Furthermore, the phenotypic value of LP was found to be positively correlated with the number of favorable SNP alleles. These favorable alleles for LP identified in the study may be useful for improving lint yield.

Identifying favorable alleles for improving key agronomic traits in upland cotton

BACKGROUND

Gossypium hirsutum L. is grown worldwide and is the largest source of natural fiber crop. We focus on exploring the favorable alleles (FAs) for upland cotton varieties improvement, and further understanding the history of accessions selection and acumination of favorable allele during breeding.

RESULTS

The genetic basis of phenotypic variation has been studied. But the accumulation of favorable alleles in cotton breeding history in unknown, and potential favorable alleles to enhance key agronomic traits in the future cotton varieties have not yet been identified. Therefore, 419 upland cotton accessions were screened, representing a diversity of phenotypic variations of 7362 G. hirsutum, and 15 major traits were investigated in 6 environments. These accessions were categorized into 3 periods (early, medium, and modern) according to breeding history. All accessions were divided into two major groups using 299 polymorphic microsatellite markers: G1 (high fiber yield and quality, late maturity) and G2 (low fiber yield and quality, early maturity). The proportion of G1 genotype gradually increased from early to modern breeding periods. Furthermore, 21 markers (71 alleles) were significantly associated (-log P > 4) with 15 agronomic traits in multiple environments. Seventeen alleles were identified as FAs; these alleles accumulated more in the modern period than in other periods, consistent with their phenotypic variation trends in breeding history. Our results demonstrate that the favorable alleles accumulated through breeding effects, especially for common favorable alleles. However, the potential elite accessions could be rapidly screened by rare favorable alleles.

CONCLUSION

In our study, genetic variation and genome-wide associations for 419 upland cotton accessions were analyzed. Two favorable allele types were identified during three breeding periods, providing important information for yield/quality improvement of upland cotton germplasm.

Genome-wide association study discovered favorable single nucleotide polymorphisms and candidate genes associated with ramet number in ramie (Boehmeria nivea L.)

BACKGROUND

Ramie (Boehmeria nivea L.) is one of the most important natural fiber crops and an important forage grass in south China. Ramet number, which is a quantitative trait controlled by multigenes, is one of the most important agronomic traits in plants because the ramet number per plant is a key component of grain yield and biomass. However, the genetic variation and genetic architecture of ramie ramet number are rarely known.

RESULTS

A genome-wide association study was performed using a panel of 112 core germplasms and 108,888 single nucleotide polymorphisms (SNPs) detected using specific-locus amplified fragment sequencing technology. Trait-SNP association analysis detected 44 significant SNPs that were associated with ramet number at P < 0.01. The favorable SNP Marker20170-64 emerged at least twice in the three detected stages and was validated to be associated with the ramie ramet number using genomic DNA polymerase chain reaction with an F₁ hybrid progeny population. Comparative genome analysis predicted nine candidate genes for ramet number based on Marker20170-64. Real-time quantitative polymerase chain reaction analysis indicated that six of the genes were specific to upregulation in the ramie variety with high ramet number. These results suggest that these genes could be considered as ramet number-associated candidates in ramie.

CONCLUSIONS

The identified loci or genes may be promising targets for genetic engineering and selection for modulating the ramet number in ramie. Our work improves understanding of the genetics of ramet number in ramie core germplasms and provides tools for marker-assisted selection for improvement of agricultural traits.

Haplotype Loci Under Selection in Canadian Durum Wheat Germplasm Over 60 Years of Breeding: Association With Grain Yield, Quality Traits, Protein Loss, and Plant Height

Durum wheat was introduced in the southern prairies of western Canada in the late nineteenth century. Breeding efforts have mainly focused on improving quality traits to meet the pasta industry demands. For this study, 192 durum wheat lines were genotyped using the Illumina 90K Infinium iSelect assay, and resulted in a total of 14,324 polymorphic SNPs. Genetic diversity changed over time, declining during the first 20 years of breeding in Canada, then increased in the late 1980s and early 1990s. We scanned the genome for signatures of selection, using the total variance Fst-based outlier detection method (Lositan), the hierarchical island model (Arlequin) and the Bayesian genome scan method (BayeScan). A total of 407 outliers were identified and clustered into 84 LD-based haplotype loci, spanning all 14 chromosomes of the durum wheat genome. The association analysis detected 54 haplotype loci, of which 39% contained markers with a complete reversal of allelic state. This tendency to fixation of favorable alleles corroborates the success of the Canadian durum wheat breeding programs over time. Twenty-one haplotype loci were associated with multiple traits. In particular, hap_4B_1 explained 20.6, 17.9 and 16.6% of the phenotypic variance of pigment loss, pasta b∗ and dough extensibility, respectively. The locus hap_2B_9 explained 15.9 and 17.8% of the variation of protein content and protein loss, respectively. All these pleiotropic haplotype loci offer breeders the unique opportunity for further improving multiple traits, facilitating marker-assisted selection in durum wheat, and could help in identifying genes as functional annotations of the wheat genome become available.

Genome-Wide Association Study Identifies Candidate Genes Related to Seed Oil Composition and Protein Content in Gossypium hirsutum L

Cotton (Gossypium spp.) is a leading natural fiber crop and an important source of vegetable protein and oil for humans and livestock. To investigate the genetic architecture of seed nutrients in upland cotton, a genome-wide association study (GWAS) was conducted in a panel of 196 germplasm resources under three environments using a CottonSNP80K chip of 77,774 loci. Relatively high genetic diversity (average gene diversity being 0.331) and phenotypic variation (coefficient of variation, CV, exceeding 3.9%) were detected in this panel. Correlation analysis revealed that the well-documented negative association between seed protein (PR) and oil may be to some extent attributable to the negative correlation between oleic acid (OA) and PR. Linkage disequilibrium (LD) was unevenly distributed among chromosomes and subgenomes. It ranged from 0.10-0.20 Mb (Chr19) to 5.65-5.75 Mb (Chr25) among the chromosomes and the range of Dt-subgenomes LD decay distances was smaller than At-subgenomes. This panel was divided into two subpopulations based on the information of 41,815 polymorphic single-nucleotide polymorphism (SNP) markers. The mixed linear model considering both Q-matrix and K-matrix [MLM(Q+K)] was employed to estimate the association between the SNP markers and the seed nutrients, considering the false positives caused by population structure and the kinship. A total of 47 SNP markers and 28 candidate quantitative trait loci (QTLs) regions were found to be significantly associated with seven cottonseed nutrients, including protein, total fatty acid, and five main fatty acid compositions. In addition, the candidate genes in these regions were analyzed, which included three genes, Gh_D12G1161, Gh_D12G1162, and Gh_D12G1165 that were most likely involved in the control of cottonseed protein concentration. These results improved our understanding of the genetic control of cottonseed nutrients and provided potential molecular tools to develop cultivars with high protein and improved fatty acid compositions in cotton breeding programs through marker-assisted selection.

Linkage and association mapping reveals the genetic basis of brown fibre (Gossypium hirsutum)

Brown fibre cotton is an environmental-friendly resource that plays a key role in the textile industry. However, the fibre quality and yield of natural brown cotton are poor, and fundamental research on brown cotton is relatively scarce. To understand the genetic basis of brown fibre cotton, we constructed linkage and association populations to systematically examine brown fibre accessions. We fine-mapped the brown fibre region, Lc1 , and dissected it into 2 loci, qBF-A07-1 and qBF-A07-2. The qBF-A07-1 locus mediates the initiation of brown fibre production, whereas the shade of the brown fibre is affected by the interaction between qBF-A07-1 and qBF-A07-2. Gh_A07G2341 and Gh_A07G0100 were identified as candidate genes for qBF-A07-1 and qBF-A07-2, respectively. Haploid analysis of the signals significantly associated with these two loci showed that most tetraploid modern brown cotton accessions exhibit the introgression signature of Gossypium barbadense. We identified 10 quantitative trait loci (QTLs) for fibre yield and 19 QTLs for fibre quality through a genome-wide association study (GWAS) and found that qBF-A07-2 negatively affects fibre yield and quality through an epistatic interaction with qBF-A07-1. This study sheds light on the genetics of fibre colour and lint-related traits in brown fibre cotton, which will guide the elite cultivars breeding of brown fibre cotton.

Population structure and genetic basis of the agronomic traits of upland cotton in China revealed by a genome-wide association study using high-density SNPs

Gossypium hirsutum L. represents the largest source of textile fibre, and China is one of the largest cotton-producing and cotton-consuming countries in the world. To investigate the genetic architecture of the agronomic traits of upland cotton in China, a diverse and nationwide population containing 503 G. hirsutum accessions was collected for a genome-wide association study (GWAS) on 16 agronomic traits. The accessions were planted in four places from 2012 to 2013 for phenotyping. The CottonSNP63K array and a published high-density map based on this array were used for genotyping. The 503 G. hirsutum accessions were divided into three subpopulations based on 11 975 quantified polymorphic single-nucleotide polymorphisms (SNPs). By comparing the genetic structure and phenotypic variation among three genetic subpopulations, seven geographic distributions and four breeding periods, we found that geographic distribution and breeding period were not the determinants of genetic structure. In addition, no obvious phenotypic differentiations were found among the three subpopulations, even though they had different genetic backgrounds. A total of 324 SNPs and 160 candidate quantitative trait loci (QTL) regions were identified as significantly associated with the 16 agronomic traits. A network was established for multieffects in QTLs and interassociations among traits. Thirty-eight associated regions had pleiotropic effects controlling more than one trait. One candidate gene, Gh_D08G2376, was speculated to control the lint percentage (LP). This GWAS is the first report using high-resolution SNPs in upland cotton in China to comprehensively investigate agronomic traits, and it provides a fundamental resource for cotton genetic research and breeding.

General and specific combining abilities in a maize (Zea mays L.) test-cross hybrid panel: relative importance of population structure and genetic divergence between parents

General and specific combining abilities of maize hybrids between 288 inbred lines and three tester lines were highly related to population structure and genetic distance inferred from SNP data. Many studies have attempted to provide reliable and quick methods to identify promising parental lines and combinations in hybrid breeding programs. Since the 1950s, maize germplasm has been organized into heterotic groups to facilitate the exploitation of heterosis. Molecular markers have proven efficient tools to address the organization of genetic diversity and the relationship between lines or populations. The aim of the present work was to investigate to what extent marker-based evaluations of population structure and genetic distance may account for general (GCA) and specific (SCA) combining ability components in a population composed of 800 inter and intra-heterotic group hybrids obtained by crossing 288 inbred lines and three testers. Our results illustrate a strong effect of groups identified by population structure analysis on both GCA and SCA components. Including genetic distance between parental lines of hybrids in the model leads to a significant decrease of SCA variance component and an increase in GCA variance component for all the traits. The latter suggests that this approach can be efficient to better estimate the potential combining ability of inbred lines when crossed with unrelated lines, and limits the consequences of tester choice. Significant residual GCA and SCA variance components of models taking into account structure and/or genetic distance highlight the variation available for breeding programs within structure groups.

Single Marker and Haplotype-Based Association Analysis of Semolina and Pasta Colour in Elite Durum Wheat Breeding Lines Using a High-Density Consensus Map

Association mapping is usually performed by testing the correlation between a single marker and phenotypes. However, because patterns of variation within genomes are inherited as blocks, clustering markers into haplotypes for genome-wide scans could be a worthwhile approach to improve statistical power to detect associations. The availability of high-density molecular data allows the possibility to assess the potential of both approaches to identify marker-trait associations in durum wheat. In the present study, we used single marker- and haplotype-based approaches to identify loci associated with semolina and pasta colour in durum wheat, the main objective being to evaluate the potential benefits of haplotype-based analysis for identifying quantitative trait loci. One hundred sixty-nine durum lines were genotyped using the Illumina 90K Infinium iSelect assay, and 12,234 polymorphic single nucleotide polymorphism (SNP) markers were generated and used to assess the population structure and the linkage disequilibrium (LD) patterns. A total of 8,581 SNPs previously localized to a high-density consensus map were clustered into 406 haplotype blocks based on the average LD distance of 5.3 cM. Combining multiple SNPs into haplotype blocks increased the average polymorphism information content (PIC) from 0.27 per SNP to 0.50 per haplotype. The haplotype-based analysis identified 12 loci associated with grain pigment colour traits, including the five loci identified by the single marker-based analysis. Furthermore, the haplotype-based analysis resulted in an increase of the phenotypic variance explained (50.4% on average) and the allelic effect (33.7% on average) when compared to single marker analysis. The presence of multiple allelic combinations within each haplotype locus offers potential for screening the most favorable haplotype series and may facilitate marker-assisted selection of grain pigment colour in durum wheat. These results suggest a benefit of haplotype-based analysis over single marker analysis to detect loci associated with colour traits in durum wheat.

Genome-wide SSR-based association mapping for fiber quality in nation-wide upland cotton inbreed cultivars in China

Background

Since upland cotton was introduced into China during the 1920s–1950s, hundreds of inbreed cultivars have been developed. To explore the molecular diversity, population structure and elite alleles, 503 inbred cultivars developed in China and some foreign cultivars from the United States and the Soviet Union were collected and analyzed by 494 genome-wide SSRs (Simple Sequence Repeats).

Methods

Four hundred and ninety-four pairs of SSRs with high polymorphism and uniform distribution on 26 chromosomes were used to scan polymorphisms in 503 nation-wide upland cottons. The programming language R was used to make boxplots for the phenotypic traits in different environments. Molecular marker data and 6 fiber quality traits were analyzed by the method of MLM (mixed linear model) (P + G + Q + K) in the TASSEL software package on the basis of the population structure and linkage disequilibrium analysis. The loci of elite allelic variation and typical materials carrying elite alleles were identified based on phenotypic effect values.

Results

A total of 179 markers were polymorphic and generated 426 allele loci; the population based on molecular diversity was classified into seven subpopulations corresponding to pedigree origin, ecological and geographical distribution. The attenuation distance of linkage disequilibrium dropped significantly up to 0–5 cM. Association mapping for fiber quality showed that 216 marker loci were associated with fiber quality traits (P < 0.05) explaining 0.58 % ~ 5.12 % of the phenotypic variation, with an average of 2.70 %. Thirteen marker loci were coincident with other studies, and three were detected for the same trait. Seven quantitative trait loci were related to known genes in fiber development. Based on phenotypic effects, 48 typical materials that contained the elite allele loci related to fiber quality traits were identified and are widely used in practical breeding.

Conclusions

The molecular diversity and population structure of 503 nation-wide upland cottons in China were evaluated by 494 genome-wide SSRs, and association mapping for fiber quality revealed known and novel elite alleles. The molecular diversity provides a guide for parental mating in cotton breeding, and the association mapping results will aid in the fine-mapping genes related to fiber quality traits and facilitate further studies on candidate genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2662-x) contains supplementary material, which is available to authorized users.

Breeding patterns and cultivated beets origins by genetic diversity and linkage disequilibrium analyses

Genetic diversity in worldwide population of beets is strongly affected by the domestication history, and the comparison of linkage disequilibrium in worldwide and elite populations highlights strong selection pressure. Genetic relationships and linkage disequilibrium (LD) were evaluated in a set of 2035 worldwide beet accessions and in another of 1338 elite sugar beet lines, using 320 and 769 single nucleotide polymorphisms, respectively. The structures of the populations were analyzed using four different approaches. Within the worldwide population, three of the methods gave a very coherent picture of the population structure. Fodder beet and sugar beet accessions were grouped together, separated from garden beets and sea beets, reflecting well the origins of beet domestication. The structure of the elite panel, however, was less stable between clustering methods, which was probably because of the high level of genetic mixing in breeding programs. For the linkage disequilibrium analysis, the usual measure (r (2)) was used, and compared with others that correct for population structure and relatedness (r S (2) , r V (2) , r VS (2)). The LD as measured by r (2) persisted beyond 10 cM within the elite panel and fell below 0.1 after less than 2 cM in the worldwide population, for almost all chromosomes. With correction for relatedness, LD decreased under 0.1 by 1 cM for almost all chromosomes in both populations, except for chromosomes 3 and 9 within the elite panel. In these regions, the larger extent of LD could be explained by strong selection pressure.

Intensive field phenotyping of maize (Zea mays L.) root crowns identifies phenes and phene integration associated with plant growth and nitrogen acquisition

Root architecture is an important regulator of nitrogen (N) acquisition. Existing methods to phenotype the root architecture of cereal crops are generally limited to seedlings or to the outer roots of mature root crowns. The functional integration of root phenes is poorly understood. In this study, intensive phenotyping of mature root crowns of maize was conducted to discover phenes and phene modules related to N acquisition. Twelve maize genotypes were grown under replete and deficient N regimes in the field in South Africa and eight in the USA. An image was captured for every whorl of nodal roots in each crown. Custom software was used to measure root phenes including nodal occupancy, angle, diameter, distance to branching, lateral branching, and lateral length. Variation existed for all root phenes within maize root crowns. Size-related phenes such as diameter and number were substantially influenced by nodal position, while angle, lateral density, and distance to branching were not. Greater distance to branching, the length from the shoot to the emergence of laterals, is proposed to be a novel phene state that minimizes placing roots in already explored soil. Root phenes from both older and younger whorls of nodal roots contributed to variation in shoot mass and N uptake. The additive integration of root phenes accounted for 70% of the variation observed in shoot mass in low N soil. These results demonstrate the utility of intensive phenotyping of mature root systems, as well as the importance of phene integration in soil resource acquisition.

Candidate gene association mapping for winter survival and spring regrowth in perennial ryegrass

Perennial ryegrass (Lolium perenne L.) is a widely cultivated cool-season grass species because of its high quality for forage and turf. Susceptibility to freezing damage limits its further use in temperate zones. The objective of this study was to identify candidate genes significantly associated with winter survival and spring regrowth in a global collection of 192 perennial ryegrass accessions. Significant differences in winter survival (WS), percentage of canopy green cover (CGC), chlorophyll index (Chl), and normalized difference vegetation index (NDVI) were found among accessions. After controlling population structure, LpLEA3 encoding a late embryogenesis abundant group 3 protein and LpCAT encoding a catalase were associated with CGC and Chl, while LpMnSOD encoding a magnesium superoxide dismutase and LpChl Cu-ZnSOD encoding a chlorophyll copper-zinc superoxide dismutase were associated with NDVI or Chl. Significant association was also discovered between C-repeat binding factor LpCBF1b and WS. Three sequence variations identified in LpCAT, LpMnSOD, and LpChl Cu-ZnSOD were synonymous substitutions, whereas one pair of adjacent single nucleotide polymorphisms (SNPs) in LpLEA3 and one SNP in LpCBF1b resulted in amino acid change. The results demonstrated that allelic variation in LpLEA3 and LpCBF1b was closely related to winter survival and spring regrowth in perennial ryegrass.

The biodiversity and genetic structure of Balearic sheep breeds

The Balearic sheep breeds, Mallorquina, Menorquina, Roja Mallorquina, Ibicenca and one possible new genetic group, Formentera, constitute a unique genetic resource in the Mediterranean farming landscape, displaying high genetic diversity levels and being well differentiated among themselves and with respect to the continental sheep breeds. We used a microsatellite panel of markers to study genetic diversity and relationships with other Spanish breeds. The results reported in this study have important implications for the use, conservation and breeding of Balearic sheep stocks. A mean number of 7.59 alleles was found among the Balearic sheep breeds for the microsatellites scored. The whole mean value of observed heterozygosity amounted to 0.62, whereas the expected heterozygosity value was 0.69, suggesting the presence of a great degree of genetic variability, although a significant deficit of heterozygotes was detected for some markers. Genetic distance estimates showed that Balearic sheep are differentiated from the other Spanish breeds and in particular, from the Merino type. The Ibicenca breed showed the highest distance value from other breeds. The neighbour-net method of analysis clustered the Roja Mallorquina, Menorquina and Mallorquina breeds. The Structure results clearly demonstrated the genetic differentiation among the four Balearic sheep breeds, with the Ibicenca and Formentera races joined, with slight migration among them. Few external genetic influences from the Spanish mainland breeds were detected.

Identification of associated SSR markers for yield component and fiber quality traits based on frame map and Upland cotton collections

Detecting QTLs (quantitative trait loci) that enhance cotton yield and fiber quality traits and accelerate breeding has been the focus of many cotton breeders. In the present study, 359 SSR (simple sequence repeat) markers were used for the association mapping of 241 Upland cotton collections. A total of 333 markers, representing 733 polymorphic loci, were detected. The average linkage disequilibrium (LD) decay distances were 8.58 cM (r2 > 0.1) and 5.76 cM (r2 > 0.2). 241 collections were arranged into two subgroups using STRUCTURE software. Mixed linear modeling (MLM) methods (with population structure (Q) and relative kinship matrix (K)) were applied to analyze four phenotypic datasets obtained from four environments (two different locations and two years). Forty-six markers associated with the number of bolls per plant (NB), boll weight (BW), lint percentage (LP), fiber length (FL), fiber strength (FS) and fiber micornaire value (FM) were repeatedly detected in at least two environments. Of 46 associated markers, 32 were identified as new association markers, and 14 had been previously reported in the literature. Nine association markers were near QTLs (at a distance of less than 1-2 LD decay on the reference map) that had been previously described. These results provide new useful markers for marker-assisted selection in breeding programs and new insights for understanding the genetic basis of Upland cotton yields and fiber quality traits at the whole-genome level.

Application of genomic SSR locus polymorphisms on the identification and classification of chrysanthemum cultivars in China

The Chinese traditional chrysanthemum is a notable group of chrysanthemums (Chrysanthemum×morifolium Ramat.) in which the phenotypic characteristics richly vary. At present, there is a serious controversy regarding homonyms and synonyms within this group. Moreover, the current international chrysanthemum classification systems are not comprehensive enough to be used on Chinese traditional chrysanthemums. Thus, we first identified a broad collection of 480 Chinese traditional chrysanthemum cultivars using the unique DNA fingerprints and molecular identities that were established by 20 simple sequence repeat markers. Five loci, which distinguished all of the selected cultivars, were identified as the core loci to establish unique fingerprints and molecular identities with 19 denary digits for each cultivar. A cluster analysis based on Nei's genetic distance indicated that the selected cultivars were clustered according to their horticultural classification. Population structure analysis was subsequently performed with K values ranging from 2 to 14, and the most likely estimate for the population structure was ten subpopulations, which was nearly consistent with the clustering result. Principal component analysis was further performed to verify the classification results. On the basis of the Q-matrices of K = 10, a total of 19 traits were found to be associated with 42 markers. Taken together, these results can serve as starting points for the identification and classification of chrysanthemums based on the polymorphism of microsatellite markers, which is beneficial to promote the marker-assisted breeding and international communication of this marvelous crop.

Developing a common bean core collection suitable for association mapping studies

Because of the continuous introduction of germplasm from abroad, some collections have a high number of accessions, making it difficult to explore the genetic variability present in a germplasm bank for conservation and breeding purposes. Therefore, the aim of this study was to quantify and analyze the structure of genetic variability among 500 common bean accessions to construct a core collection. A total of 58 SSRs were used for this purpose. The polymorphism information content (PIC) in the 180 common bean accessions selected to compose the core collection ranged from 0.17 to 0.86, and the discriminatory power (DP) ranged from 0.21 to 0.90. The 500 accessions were clustered into 15 distinct groups and the 180 accessions into four distinct groups in the Structure analysis. According to analysis of molecular variance, the most divergent accessions comprised 97.2% of the observed genetic variability present within the base collection, confirming the efficiency of the selection criterion. The 180 selected accessions will be used for association mapping in future studies and could be potentially used by breeders to direct new crosses and generate elite cultivars that meet current and future global market needs.

Unraveling the genetic architecture of subtropical maize (Zea mays L.) lines to assess their utility in breeding programs

BACKGROUND

Maize is an increasingly important food crop in southeast Asia. The elucidation of its genetic architecture, accomplished by exploring quantitative trait loci and useful alleles in various lines across numerous breeding programs, is therefore of great interest. The present study aimed to characterize subtropical maize lines using high-quality SNPs distributed throughout the genome.

RESULTS

We genotyped a panel of 240 subtropical elite maize inbred lines and carried out linkage disequilibrium, genetic diversity, population structure, and principal component analyses on the generated SNP data. The mean SNP distance across the genome was 70 Kb. The genome had both high and low linkage disequilibrium (LD) regions; the latter were dominant in areas near the gene-rich telomeric portions where recombination is frequent. A total of 252 haplotype blocks, ranging in size from 1 to 15.8 Mb, were identified. Slow LD decay (200-300 Kb) at r(2) ≤ 0.1 across all chromosomes explained the selection of favorable traits around low LD regions in different breeding programs. The association mapping panel was characterized by strong population substructure. Genotypes were grouped into three distinct clusters with a mean genetic dissimilarity coefficient of 0.36.

CONCLUSIONS

The genotyped panel of subtropical maize lines characterized in this study should be useful for association mapping of agronomically important genes. The dissimilarity uncovered among genotypes provides an opportunity to exploit the heterotic potential of subtropical elite maize breeding lines.

Combined linkage and linkage disequilibrium QTL mapping in multiple families of maize (Zea mays L.) line crosses highlights complementarities between models based on parental haplotype and single locus polymorphism

Advancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits.

Efficacy of population structure analysis with breeding populations and inbred lines

The objective was to assess by simulation the efficacy of population structure analysis in plant breeding. Twelve populations and 300 inbred lines were simulated and genotyped using 100 microsatellite loci. The experimental material included populations with and without admixture, ancestry relationship and linkage disequilibrium, and with distinct levels of genetic differentiation and effective sizes. The analyses were performed using Structure software and employed all available models. For all the group number (K) tested, for both populations and inbred lines, the admixture model with correlated allelic frequencies provided the highest value for the logarithm of the marginal likelihood. Fitting appropriate model and using adequate sample size for individuals and markers, Structure was effective in identifying the correct population structure, migrants and individuals with genome from distinct populations. The linkage model did not result in an improvement in clustering relative to the admixture model with correlated allelic frequencies. The inclusion of prior information did not change the results; for some K values the analyses showed slight higher values of the marginal likelihood. The reduction in the number of individuals and markers negatively affected the results. There was a high variation in the most probable K value between the evaluated methods.

Nitrogen-metabolism related genes in barley - haplotype diversity, linkage mapping and associations with malting and kernel quality parameters

Background

Several studies report about intra-specific trait variation of nitrogen-metabolism related traits, such as N(itrogen)-use efficiency, protein content, N-storage and remobilization in barley and related grass species. The goal of this study was to assess the intra-specific genetic diversity present in primary N-metabolism genes of barley and to investigate the associations of the detected haplotype diversity with malting and kernel quality related traits.

Results

Partial sequences of five genes related to N-metabolism in barley (Hordeum vulgare L.) were obtained, i.e. nitrate reductase 1, glutamine synthetase 2, ferredoxin-dependent glutamate synthase, aspartate aminotransferase and asparaginase. Two to five haplotypes in each gene were discovered in a set of 190 various varieties. The development of 33 SNP markers allowed the genotyping of all these barley varieties consisting of spring and winter types. Furthermore, these markers could be mapped in several doubled haploid populations. Cluster analysis based on haplotypes revealed a more uniform pattern of the spring barleys as compared to the winter barleys. Based on linear model approaches associations to several malting and kernel quality traits including soluble N and protein were identified.

Conclusions

A study was conducted to investigate the presence of sequence variation of several genes related to the primary N-metabolism in barley. The detected diversity could be related to particular phenotypic traits. Specific differences between spring and winter barleys most likely reflect different breeding aims. The developed markers can be used as tool for further genetic studies and marker-assisted selection during breeding of barley.

Comprehensive genotyping of the USA national maize inbred seed bank

BACKGROUND

Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world.

RESULTS

The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits.

CONCLUSIONS

The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity.

Association mapping of Septoria speckled leaf blotch resistance in U.S. barley breeding germplasm

Septoria speckled leaf blotch (SSLB) is a sporadic but important disease of barley (Hordeum vulgare) in the Upper Midwest region of the United States and Prairie Provinces of Canada that is caused primarily by Septoria passerinii. Most of the widely grown cultivars in the region are susceptible to the disease. To identify and map SSLB resistance loci in U.S. barley breeding germplasm, we employed an association mapping approach using 3,840 breeding lines and cultivars and nearly 3,000 single-nucleotide polymorphism markers previously mapped to the seven barley chromosomes. SSLB infection responses (IRs) were assayed on seedling plants in the greenhouse using a 0-to-5 scale. From the analysis of four yearly panels consisting of 960 lines each, four quantitative trait loci (QTL) for SSLB resistance were identified: one on chromosome 1H (Rsp-qtl-H_12_31144), one on chromosome 3H (Rsp-qtl-3H_12_31488), and two on chromosome 6H (Rsp-qtl-6H_11_21032 and Rsp-qtl-6H_11_10064). Individual resistance QTL reduced the mean IR from 9 to 38% compared with lines lacking any resistance alleles. However, the combination of all four resistance QTL together reduced the mean IR by 83%. The markers found associated with these QTL will be valuable for programs utilizing marker-assisted selection for SSLB resistance.

Construction of core collections suitable for association mapping to optimize use of Mediterranean olive (Olea europaea L.) genetic resources

Phenotypic characterisation of germplasm collections is a decisive step towards association mapping analyses, but it is particularly expensive and tedious for woody perennial plant species. Characterisation could be more efficient if focused on a reasonably sized subset of accessions, or so-called core collection (CC), reflecting the geographic origin and variability of the germplasm. The questions that arise concern the sample size to use and genetic parameters that should be optimized in a core collection to make it suitable for association mapping. Here we investigated these questions in olive (Olea europaea L.), a perennial fruit species. By testing different sampling methods and sizes in a worldwide olive germplasm bank (OWGB Marrakech, Morocco) containing 502 unique genotypes characterized by nuclear and plastid loci, a two-step sampling method was proposed. The Shannon-Weaver diversity index was found to be the best criterion to be maximized in the first step using the Core Hunter program. A primary core collection of 50 entries (CC₅₀) was defined that captured more than 80% of the diversity. This latter was subsequently used as a kernel with the Mstrat program to capture the remaining diversity. 200 core collections of 94 entries (CC₉₄) were thus built for flexibility in the choice of varieties to be studied. Most entries of both core collections (CC₅₀ and CC₉₄) were revealed to be unrelated due to the low kinship coefficient, whereas a genetic structure spanning the eastern and western/central Mediterranean regions was noted. Linkage disequilibrium was observed in CC₉₄ which was mainly explained by a genetic structure effect as noted for OWGB Marrakech. Since they reflect the geographic origin and diversity of olive germplasm and are of reasonable size, both core collections will be of major interest to develop long-term association studies and thus enhance genomic selection in olive species.

Phenotypic diversity and association mapping for fruit quality traits in cultivated tomato and related species

Association mapping has been proposed as an efficient approach to assist in the identification of the molecular basis of agronomical traits in plants. For this purpose, we analyzed the phenotypic and genetic diversity of a large collection of tomato accessions including 44 heirloom and vintage cultivars (Solanum lycopersicum), 127 S. lycopersicum var. cerasiforme (cherry tomato) and 17 Solanum pimpinellifolium accessions. The accessions were genotyped using a SNPlex™ assay of 192 SNPs, among which 121 were informative for subsequent analysis. Linkage disequilibrium (LD) of pairwise loci and population structure were analyzed, and the association analysis between SNP genotypes and ten fruit quality traits was performed using a mixed linear model. High level of LD was found in the collection at the whole genome level. It was lower when considering only the 127 S. lycopersicum var. cerasiforme accessions. Genetic structure analysis showed that the population was structured into two main groups, corresponding to cultivated and wild types and many intermediates. The number of associations detected per trait varied, according to the way the structure was taken into account, with 0-41 associations detected per trait in the whole collection and a maximum of four associations in the S. lycopersicum var. cerasiforme accessions. A total of 40 associations (30 %) were co-localized with previously identified quantitative trait loci. This study thus showed the potential and limits of using association mapping in tomato populations.

Genetic diversity analysis of elite European maize (Zea mays L.) inbred lines using AFLP, SSR, and SNP markers reveals ascertainment bias for a subset of SNPs

Recent advances in high-throughput sequencing technologies have triggered a shift toward single-nucleotide polymorphism (SNP) markers. A systematic bias can be introduced if SNPs are ascertained in a small panel of genotypes and then used for characterizing a larger population (ascertainment bias). With the objective of evaluating a potential ascertainment bias of the Illumina MaizeSNP50 array with respect to elite European maize dent and flint inbred lines, we compared the genetic diversity among these materials based on 731 amplified fragment length polymorphisms (AFLPs), 186 simple sequence repeats (SSRs), 41,434 SNPs of the MaizeSNP50 array (SNP-A), and two subsets of it, i.e., 30,068 Panzea (SNP-P) and 11,366 Syngenta markers (SNP-S). We evaluated the bias effects on major allele frequency, allele number, gene diversity, modified Roger's distance (MRD), and on molecular variance (AMOVA). We revealed ascertainment bias in SNP-A, compared to AFLPs and SSRs. It affected especially European flint lines analyzed with markers (SNP-S) specifically developed to maximize differences among North American dent germplasm. The bias affected all genetic parameters, but did not substantially alter the relative distances between inbred lines within groups. For these reasons, we conclude that the SNP markers of the MaizeSNP50 array can be employed for breeding purposes in the investigated material. However, attention should be paid in case of comparisons between genotypes belonging to different heterotic groups. In this case, it is advisable to prefer a marker subset with potentially low ascertainment bias, like in our case the SNP-P marker set.

Population structure and linkage disequilibrium in elite barley breeding germplasm from the United States

Cultivated barley is known to have a complex population structure and extensive linkage disequilibrium (LD). To conduct robust association mapping (AM) studies of economically important traits in US barley breeding germplasm, population structure and LD decay were examined in a complete panel of US barley breeding germplasm (3840 lines) genotyped with 3072 single nucleotide polymorphisms (SNPs). Nine subpopulations (sp1‒sp9) were identified by the program STRUCTURE and subsequently confirmed by principle component analysis (PCA). Out of the nine subpopulations, seven were very similar to the respective subpopulations identified by Hamblin et al. (2010) which were based on half of the germplasm and half of the SNP markers, but two subpopulations were found to be new. One subpopulation was dominated by six-rowed spring lines from Utah State University (UT) and the other was composed of six-rowed spring lines from multiple breeding programs (USDA-ARS Aberdeen (AB), Busch Agricultural Resources Inc. (BA), UT, and Washington State University (WA)). LD was found to decay across a range from 4.0 to 19.8 cM. This result indicates that the germplasm genotyped with 3072 SNPs would be robust for mapping and possibly identifying the causal polymorphisms contributing to disease resistance and perhaps other traits.

Diversity and linkage disequilibrium features in a composite public/private dent maize panel: consequences for association genetics as evaluated from a case study using flowering time

Recent progress in genotyping and resequencing techniques have opened new opportunities for deciphering quantitative trait variation by looking for associations between traits of interest and polymorphisms in panels of diverse inbred lines. Association mapping raises specific issues related to the choice of appropriate (i) panels and marker-densities and (ii) statistical methods to capture associations. In this study, we used a panel of 314 maize inbred lines from the dent pool, composed of inbred material from public institutes (113 inbred lines) and a private company (201 inbred lines). We showed that local LD was higher and genetic diversity lower in the material of private origin than in the public material. We compared the results obtained by different software for identifying population structure and computing relatedness among lines, and ran association tests for earliness related traits. Our results confirmed the importance of the mite polymorphism of Vgt1 on flowering time, but also showed that its effect can be captured by zmRap2.7 polymorphisms located 70 kb apart. We also highlighted associations with polymorphisms within genes putatively involved in lignin biosynthesis pathway, which deserve further investigations.

Association genetics of the loblolly pine (Pinus taeda, Pinaceae) metabolome

The metabolome of a plant comprises all small molecule metabolites, which are produced during cellular processes. The genetic basis for metabolites in nonmodel plants is unknown, despite frequently observed correlations between metabolite concentrations and stress responses. A quantitative genetic analysis of metabolites in a nonmodel plant species is thus warranted. Here, we use standard association genetic methods to correlate 3563 single nucleotide polymorphisms (SNPs) to concentrations of 292 metabolites measured in a single loblolly pine (Pinus taeda) association population. A total of 28 single locus associations were detected, representing 24 and 20 unique SNPs and metabolites, respectively. Multilocus Bayesian mixed linear models identified 2998 additional associations for a total of 1617 unique SNPs associated to 255 metabolites. These SNPs explained sizeable fractions of metabolite heritabilities when considered jointly (56.6% on average) and had lower minor allele frequencies and magnitudes of population structure as compared with random SNPs. Modest sets of SNPs (n = 1-23) explained sizeable portions of genetic effects for many metabolites, thus highlighting the importance of multi-SNP models to association mapping, and exhibited patterns of polymorphism consistent with being linked to targets of natural selection. The implications for association mapping in forest trees are discussed.

Novel measures of linkage disequilibrium that correct the bias due to population structure and relatedness

Among the several linkage disequilibrium measures known to capture different features of the non-independence between alleles at different loci, the most commonly used for diallelic loci is the r(2) measure. In the present study, we tackled the problem of the bias of r(2) estimate, which results from the sample structure and/or the relatedness between genotyped individuals. We derived two novel linkage disequilibrium measures for diallelic loci that are both extensions of the usual r(2) measure. The first one, r(S)(2), uses the population structure matrix, which consists of information about the origins of each individual and the admixture proportions of each individual genome. The second one, r(V)(2), includes the kinship matrix into the calculation. These two corrections can be applied together in order to correct for both biases and are defined either on phased or unphased genotypes.We proved that these novel measures are linked to the power of association tests under the mixed linear model including structure and kinship corrections. We validated them on simulated data and applied them to real data sets collected on Vitis vinifera plants. Our results clearly showed the usefulness of the two corrected r(2) measures, which actually captured 'true' linkage disequilibrium unlike the usual r(2) measure.