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Plant cell wall formation is a complex, coordinated and developmentally regulated process. Cellulose is the most dominant constituent of plant cell walls. Because of its paracrystalline structure, cellulose is the main determinant of mechanical strength of plant tissues. As the most abundant polysaccharide on earth, it is also the focus of cellulosic biofuel industry. To reduce culm lodging in wheat and for improved ethanol production, delineation of the variation for stem cellulose content could prove useful. We present results on the analysis of the stem cellulose content of 288 diverse wheat accessions and its genome-wide association study (GWAS). Cellulose concentration ranged from 35 to 52% (w/w). Cellulose content was normally distributed in the accessions around a mean and median of 45% (w/w). Genome-wide marker-trait association study using 21,073 SNPs helped identify nine SNPs that were associated (p < 1E-05) with cellulose content. Four strongly associated (p < 8.17E-05) SNP markers were linked to wheat unigenes, which included β-tubulin, Auxin-induced protein 5NG4, and a putative transmembrane protein of unknown function. These genes may be directly or indirectly involved in the formation of cellulose in wheat culms. GWAS results from this study have the potential for genetic manipulation of cellulose content in bread wheat and other small grain cereals to enhance culm strength and improve biofuel production. [ABSTRACT FROM AUTHOR]

Accuracy of genomic prediction is commonly calculated as the Pearson correlation coefficient between the predicted and observed phenotypes in the inference population by using cross-validation analysis. More frequently than expected, significant negative accuracies of genomic prediction have been reported in genomic selection studies. These negative values are surprising, given that theminimumvalue for prediction accuracy should hover around zero when randomly permuted data sets are analyzed.We reviewed the two common approaches for calculating the Pearson correlation and hypothesized that these negative accuracy values reflect potential bias owing to artifacts caused by themathematical formulas used to calculate prediction accuracy. The first approach, Instant accuracy, calculates correlations for each fold and reports prediction accuracy as themean of correlations across fold. The other approach, Hold accuracy, predicts all phenotypes in all fold and calculates correlation between the observed and predicted phenotypes at the end of the cross-validation process. Using simulated and real data, we demonstrated that our hypothesis is true. Both approaches are biased downward under certain conditions. The biases become larger when more fold are employed and when the expected accuracy is low. The bias of Instant accuracy can be corrected using amodified formula. [ABSTRACT FROM AUTHOR]

Background: Maize requires more water than most other crops; therefore, the water use efficiency of this crop must be improved for maize production under undesirable land and changing environmental conditions. Results: To elucidate the genetic control of drought in maize, we evaluated approximately 5000 inbred lines from 30 linkage-association joint mapping populations under two contrasting water regimes for seven drought-related traits, including yield and anthesis-silking interval (ASI). The joint linkage analysis was conducted to identify 220 quantitative trait loci (QTLs) under well-watered conditions and 169 QTLs under water-stressed conditions. The genome-wide association analysis identified 365 single nucleotide polymorphisms (SNPs) associated with drought-related traits, and these SNPs were located in 354 candidate genes. Fifty-two of these genes showed significant differential expression in the inbred line B73 under the well-watered and water-stressed conditions. In addition, genomic predictions suggested that the moderate-density SNPs obtained through genotyping-by-sequencing were able to make accurate predictions in the nested association mapping population for drought-related traits with moderate-to-high heritability under the water-stressed conditions. Conclusions: The results of the present study provide important information that can be used to understand the genetic basis of drought stress responses and facilitate the use of beneficial alleles for the improvement of drought tolerance in maize. [ABSTRACT FROM AUTHOR]

Background: A genome-wide association study (GWAS) is the foremost strategy used for finding genes that control human diseases and agriculturally important traits, but it often reports false positives. In contrast, its complementary method, linkage analysis, provides direct genetic confirmation, but with limited resolution. A joint approach, using multiple linkage populations, dramatically improves resolution and statistical power. For example, this approach has been used to confirm that many complex traits, such as flowering time controlling adaptation in maize, are controlled by multiple genes with small effects. In addition, genotyping by sequencing (GBS) at low coverage not only produces genotyping errors, but also results in large datasets, making the use of high-throughput sequencing technologies computationally inefficient or unfeasible. Results: In this study, we converted raw SNPs into effective recombination bins. The reduced bins not only retain the original information, but also correct sequencing errors from low-coverage genomic sequencing. To further increase the statistical power and resolution, we merged a new temperate maize nested association mapping (NAM) population derived in China (CN-NAM) with the existing maize NAM population developed in the US (US-NAM). Together, the two populations contain 36 families and 7,000 recombinant inbred lines (RILs). One million SNPs were generated for all the RILs with GBS at low coverage. We developed high-quality recombination maps for each NAM population to correct genotyping errors and improve the computational efficiency of the joint linkage analysis. The original one million SNPs were reduced to 4,932 and 5,296 recombination bins with average interval distances of 0.34 cM and 0.28 cM for CN-NAM and US-NAM, respectively. The quantitative trait locus (QTL) mapping for flowering time (days to tasseling) indicated that the high-density, recombination bin map improved resolution of QTL mapping by 50 % compared with that using a medium-density map. We also demonstrated that combining the CN-NAM and US-NAM populations improves the power to detect QTL by 50 % compared to single NAM population mapping. Among the QTLs mapped by joint usage of the US-NAM and CN-NAM maps, 25 % of the QTLs overlapped with known flowering-time genes in maize. Conclusion: This study provides directions and resources for the research community, especially maize researchers, for future studies using the recombination bin strategy for joint linkage analysis. Available resources include efficient usage of low-coverage genomic sequencing, detailed positions for genes controlling maize flowering, and recombination bin maps and flowering- time data for both CN and US NAMs. Maize researchers even have the opportunity to grow both CN and US NAM populations to study the traits of their interest, as the seeds of both NAM populations are available from the seed repository in China and the US. [ABSTRACT FROM AUTHOR]

Background: Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is one of the most fatal pests of soybean (Glycine max (L.) Merr.) worldwide and causes huge loss of soybean yield each year. Multiple sources of resistance are urgently needed for effective management of SCN via the development of resistant cultivars. The aim of the present study was to investigate the genetic architecture of resistance to SCN HG Type 0 (race 3) and HG Type 1.2.3.5.7 (race 4) in landraces and released elite soybean cultivars mostly from China. Results: A total of 440 diverse soybean landraces and elite cultivars were screened for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7. Exactly 131 new sources of SCN resistance were identified. Lines were genotyped by SNP markers detected by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) approach. A total of 36,976 SNPs were identified with minor allele frequencies (MAF) > 4 % that were present in 97 % of all the genotypes. Genome-wide association mapping showed that a total of 19 association signals were significantly related to the resistance for the two HG Types. Of the 19 association signals, eight signals overlapped with reported QTL including Rhg1 and Rhg4 genes. Another eight were located in the linked regions encompassing known QTL. Three QTL were found that were not previously reported. The average value of female index (FI) of soybean accessions with resistant alleles was significantly lower than those with susceptible alleles for each peak SNP. Disease resistance proteins with leucine rich regions, cytochrome P450s, protein kinases, zinc finger domain proteins, RING domain proteins, MYB and WRKY transcription activation families were identified. Such proteins may participate in the resistant reaction to SCN and were frequently found in the tightly linked genomic regions of the peak SNPs. Conclusions: GWAS extended understanding of the genetic architecture of SCN resistance in multiple genetic backgrounds. Nineteen association signals were obtained for the resistance to the two Hg Types of SCN. The multiple beneficial alleles from resistant germplasm sources will be useful for the breeding of cultivars with improved resistance to SCN. Analysis of genes near association signals may facilitate the recognition of the causal gene(s) underlying SCN resistances. [ABSTRACT FROM AUTHOR]

Background The inheritance of most human diseases and agriculturally important traits is controlled by many genes with small effects. Identifying these genes, while simultaneously controlling false positives, is challenging. Among available statistical methods, the mixed linear model (MLM) has been the most flexible and powerful for controlling population structure and individual unequal relatedness (kinship), the two common causes of spurious associations. The introduction of the compressed MLM (CMLM) method provided additional opportunities for optimization by adding two new model parameters: grouping algorithms and number of groups. Results This study introduces another model parameter to develop an enriched CMLM (ECMLM). The parameter involves algorithms to define kinship between groups (that is, kinship algorithms). The ECMLM calculates kinship using several different algorithms and then chooses the best combination between kinship algorithms and grouping algorithms. Conclusion Simulations show that the ECMLM increases statistical power. In some cases, the magnitude of power gained by using ECMLM instead of CMLM is larger than the improvement found by using CMLM instead of MLM. [ABSTRACT FROM AUTHOR]

Background Equine recurrent laryngeal neuropathy (RLN) is a bilateral mononeuropathy with an unknown pathogenesis that significantly affects performance in Thoroughbreds. A genetic contribution to the pathogenesis of RLN is suggested by the higher prevalence of the condition in offspring of RLN-affected than unaffected stallions. To better understand RLN pathogenesis and its genetic basis, we performed a genome-wide association (GWAS) of 282 RLN-affected and 268 control Thoroughbreds. Results We found a significant association of RLN with the LCORL/NCAPG locus on ECA3 previously shown to affect body size in horses. Using height at the withers of 505 of these horses, we confirmed the strong association of this locus with body size, and demonstrated a significant phenotypic and genetic correlation between height and RLN grade in this cohort. Secondary genetic associations for RLN on ECA18 and X did not correlate with withers height in our cohort, but did contain candidate genes likely influencing muscle physiology and growth: myostatin (MSTN) and integral membrane protein 2A (ITM2A). Conclusions This linkage between body size and RLN suggests that selective breeding to reduce RLN prevalence would likely reduce adult size in this population. However, our results do not preclude the possibility of modifier loci that attenuate RLN risk without reducing size or performance, or that the RLN risk allele is distinct but tightly linked to the body size locus on ECA3. This study is both the largest body size GWAS and the largest RLN GWAS within Thoroughbred horses to date, and suggests that improved understanding of the relationship between genetics, equine growth rate, and RLN prevalence may significantly advance our understanding and management of this disease. [ABSTRACT FROM AUTHOR]

In maize, water stress at flowering causes loss of kernel set and productivity. While changes in the levels of sugars and abscisic acid (ABA) are thought to play a role in this stress response, the mechanistic basis and genes involved are not known. A candidate gene approach was used with association mapping to identify loci involved in accumulation of carbohydrates and ABA metabolites during stress. A panel of single nucleotide polymorphisms (SNPs) in genes from these metabolic pathways and in genes for reproductive development and stress response was used to genotype 350 tropical and subtropical maize inbred lines that were well watered or water stressed at flowering. Pre-pollination ears, silks, and leaves were analysed for sugars, starch, proline, ABA, ABA-glucose ester, and phaseic acid. ABA and sugar levels in silks and ears were negatively correlated with their growth. Association mapping with 1229 SNPs in 540 candidate genes identified an SNP in the maize homologue of the Arabidopsis MADS-box gene, PISTILLATA, which was significantly associated with phaseic acid in ears of well-watered plants, and an SNP in pyruvate dehydrogenase kinase, a key regulator of carbon flux into respiration, that was associated with silk sugar concentration. An SNP in an aldehyde oxidase gene was significantly associated with ABA levels in silks of water-stressed plants. Given the short range over which decay of linkage disequilibrium occurs in maize, the results indicate that allelic variation in these genes affects ABA and carbohydrate metabolism in floral tissues during drought. [ABSTRACT FROM PUBLISHER]

Understanding the genetic basis of nitrogen and carbon metabolism will accelerate the development of plant varieties with high yield and improved nitrogen use efficiency. A robotized platform was used to measure the activities of 10 enzymes from carbon and nitrogen metabolism in the maize (Zea mays) intermated B73 x Mo17 mapping population, which provides almost a 4-fold increase in genetic map distance compared with conventional mapping populations. Seedling/juvenile biomass was included to identify its genetic factors and relationships with enzyme activities. All 10 enzymes showed heritable variation in activity. There were strong positive correlations between activities of different enzymes, indicating that they are coregulated. Negative correlations were detected between biomass and the activity of six enzymes. In total, 73 significant quantitative trait loci (QTL) were found that influence the activity of these 10 enzymes and eight QTL that influence biomass. While some QTL were shared by different enzymes or biomass, we critically evaluated the probability that this may be fortuitous. All enzyme activity QTL were in trans to the known genomic locations of structural genes, except for single cis-QTL for nitrate reductase, Glu dehydrogenase, and shikimate dehydrogenase; the low frequency and low additive magnitude compared with trans-QTL indicate that cis-regulation is relatively unimportant versus trans-regulation. Two-gene epistatic interactions were identified for eight enzymes and for biomass, with three epistatic QTL being shared by two other traits; however, epistasis explained on average only 2.8% of the genetic variance. Overall, this study identifies more QTL at a higher resolution than previous studies of genetic variation in metabolism. [ABSTRACT FROM AUTHOR]

Uncovering the genetic basis of agronomic traits in crop landraces that have adapted to various agro-climatic conditions is important to world food security. Here we have identified ∼3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method. We performed genome-wide association studies (GWAS) for 14 agronomic traits in the population of Oryza sativa indica subspecies. The loci identified through GWAS explained ∼36% of the phenotypic variance, on average. The peak signals at six loci were tied closely to previously identified genes. This study provides a fundamental resource for rice genetics research and breeding, and demonstrates that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice. [ABSTRACT FROM AUTHOR]

Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL. [ABSTRACT FROM AUTHOR]

The goal of many plant scientists' research is to explain natural phenotypic variation in terms of simple changes in DNA sequence. Traditionally, linkage mapping has been the most commonly employed method to reach this goal: experimental crosses are made to generate a family with known relatedness, and attempts are made to identify cosegregation of genetic markers and phenotypes within this family. In vertebrate systems, association mapping (also known as linkage disequilibrium mapping) is increasingly being adopted as the mapping method of choice. Association mapping involves searching for genotype-phenotype correlations in unrelated individuals and often is more rapid and cost-effective than traditional linkage mapping. We emphasize here that linkage and association mapping are complementary approaches and are more similar than is often assumed. Unlike in vertebrates, where controlled crosses can be expensive or impossible (e.g., in humans), the plant scientific community can exploit the advantages of both controlled crosses and association mapping to increase statistical power and mapping resolution. While the time and money required for the collection of genotype data were critical considerations in the past, the increasing availability of inexpensive DNA sequencing and genotyping methods should prompt researchers to shift their attention to experimental design. This review provides thoughts on finding the optimal experimental mix of association mapping using unrelated individuals and controlled crosses to identify the genes underlying phenotypic variation. [ABSTRACT FROM AUTHOR]

Objective--To evaluate the quantitative inheritance of secondary hip joint osteoarthritis in a canine pedigree. Animals--137 Labrador Retrievers, Greyhounds, and mixed-breed dogs. Procedures--Necropsy scores ranging from 0 to 4 were obtained for each hip joint. Seven unaffected Greyhounds with normal hip joint conformation were also used for genetic modeling, but were not euthanized. Sixty-six male and 71 female dogs were allocated to 2 groups (≤ 12 months of age and > 12 months of age). Statistical models were developed to establish the inheritance pattern of hip joint osteoarthdtis that developed secondary to hip dysplasia. Results--62 dogs had evidence of osteoarthritis in a hip joint, and 75 had no evidence of osteoarthritis. After sex was adjusted for, the necropsy score was found to be inherited additively but without dominance, Each Labrador Retriever allele increased the necropsy score by 0.7 to 0.9 points, compared with the Greyhound allele, and male sex increased the necropsy score 0.74 over female sex. Approximately 10% of the variation in necropsy score was attributable to the litter of puppies' origin. Conclusions and Clinical Relevance--Because secondary hip joint osteoarthritis is inherited additively, selection pressure could be applied to reduce its incidence. Similar statistical models can be used in linkage and association mapping to detect the genes in the underlying quantitative trait loci that contribute to hip joint osteoarthritis. [ABSTRACT FROM AUTHOR]

Canine hip dysplasia is a common developmental inherited trait characterized by hip laxity, subluxation or incongruity of the femoral head and acetabulum in affected hips. The inheritance pattern is complex and the mutations contributing to trait expression are unknown. In the study reported here, 240 microsatellite markers distributed in 38 autosomes and the X chromosome were genotyped on 152 dogs from three generations of a crossbred pedigree based on trait-free Greyhound and dysplastic Labrador Retriever founders. Interval mapping was undertaken to map the QTL underlying the quantitative dysplastic traits of maximum passive hip laxity (the distraction index), the dorsolateral subluxation score, and the Norberg angle. Permutation testing was used to derive the chromosome-wide level of significance at p < 0.05 for each QTL. Chromosomes 4, 9, 10, 11 ( p < 0.01), 16, 20, 22, 25, 29 ( p < 0.01), 30, 35, and 37 harbor putative QTL for one or more traits. Successful detection of QTL was due to the crossbreed pedigree, multiple-trait measurements, control of environmental background, and marked advancement in canine mapping tools. [ABSTRACT FROM AUTHOR]

Summary: Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable, user manual, example data sets and tutorial document are freely available at http://www.maizegenetics.net/tassel. The source code for TASSEL can be found at http://sourceforge.net/projects/tassel. Contact: pjb39@cornell.edu [ABSTRACT FROM AUTHOR]

Objective--To determine whether a mutation in the fibrillin 2 gene (FBN2) is associated with canine hip dysplasia (CHD) and osteoarthritis in dogs. Animals--1,551 dogs. Procedures--Hip conformation was measured radiographically. The FBN2 was sequenced from genomic DNA of 21 Labrador Retrievers and 2 Greyhounds, and a haplotype in intron 30 of FBN2 was sequenced in 90 additional Labrador Retrievers and 143 dogs of 6 other breeds. Steady-state values of FBN2 mRNA and control genes were measured in hip joint tissues of fourteen 8-month-old Labrador Retriever-Greyhound crossbreeds. Results--The Labrador Retrievers homozygous for a 10-bp deletion haplotype in intron 30 of FBN2 had significantly worse CHD as measured via higher distraction index and extended-hip joint radiograph score and a lower Norberg angle and dorsolateral subluxation score. Among 143 dogs of 6 other breeds, those homozygous for the same deletion haplotype also had significantly worse radiographic CHD. Among the 14 crossbred dogs, as the dorsolateral subluxation score decreased, the capsular FBN2 mRNA increased significantly. Those dogs with incipient hip joint osteoarthritis had significantly increased capsular FBN2 mRNA, compared with those dogs without osteoarthritis. Dogs homozygous for the FBN2 deletion haplotype had significantly less FBN2 mRNA in their femoral head articular cartilage. Conclusion and Clinical Relevance--The FBN2 deletion haplotype was associated with CHD. Capsular gene expression of FBN2 was confounded by incipient secondary osteoarthritis in dysplastic hip joints. Genes influencing complex traits in dogs can be identified by genome-wide screening, fine mapping, and candidate gene screening. [ABSTRACT FROM AUTHOR]

Backfat thickness is a good predictor of carcass lean content, an economically important trait, and a main breeding target in pig improvement. In this study, the candidate genes and genomic regions associated with the tenth rib backfat thickness trait were identified in two independent pig populations, using a genome-wide association study of porcine 60K SNP genotype data applying the compressed mixed linear model (CMLM) statistical method. For each population, 30 most significant single-nucleotide polymorphisms (SNPs) were selected and SNP annotation implemented using Sus scrofa Build 10.2. In the first population, 25 significant SNPs were distributed on seven chromosomes, and SNPs on SSC1 and SSC7 showed great significance for fat deposition. The most significant SNP (ALGA0006623) was located on SSC1, upstream of the MC4R gene. In the second population, 27 significant SNPs were recognized by annotation, and 12 SNPs on SSC12 were related to fat deposition. Two haplotype blocks, M1GA0016251-MARC0075799 and ALGA0065251-MARC0014203-M1GA0016298-ALGA0065308, were detected in significant regions where the PIPNC1 and GH1 genes were identified as contributing to fat metabolism. The results indicated that genetic mechanism regulating backfat thickness is complex, and that genome-wide associations can be affected by populations with different genetic backgrounds.

Height is one of the most heritable and easily measured traits in maize (Zea mays L.). Given a pedigree or estimates of the genomic identity-by-state among related plants, height is also accurately predictable. But, mapping alleles explaining natural variation in maize height remains a formidable challenge. To address this challenge, we measured the plant height, ear height, flowering time, and node counts of plants grown in .64,500 plots across 13 environments. These plots contained .7300 inbreds representing most publically available maize inbreds in the United States and families of the maize Nested Association Mapping (NAM) panel. Jointlinkage mapping of quantitative trait loci (QTL), fine mapping in near isogenic lines (NILs), genome-wide association studies (GWAS), and genomic best linear unbiased prediction (GBLUP) were performed. The heritability of maize height was estimated to be .90%. Mapping NAM family-nested QTL revealed the largest explained 2.1 ± 0.9% of height variation. The effects of two tropical alleles at this QTL were independently validated by fine mapping in NIL families. Several significant associations found by GWAS colocalized with established height loci, including brassinosteroid-deficient dwarf1, dwarf plant1, and semi-dwarf2. GBLUP explained .80% of height variation in the panels and outperformed bootstrap aggregation of family-nested QTL models in evaluations of prediction accuracy. These results revealed maize height was under strong genetic control and had a highly polygenic genetic architecture. They also showed that multiple models of genetic architecture differing in polygenicity and effect sizes can plausibly explain a population's variation in maize height, but they may vary in predictive efficacy. [ABSTRACT FROM AUTHOR]

Objective--To identify quantitative trait loci (QTL) associated with osteoarthritis (CA) of hip joints of dogs by use of a whole-genome microsatellite scan. Animals--116 founder, backcross, F1, and F2 dogs from a crossbred pedigree. Procedures--Necropsy scores and an optimized set of 342 microsatellite markers were used for interval mapping by means of a combined backcross and F, design module from an online statistical program. Breed and sex were included in the model as fixed effects. Age of dog at necropsy and body weight at 8 months of age were also included in the model as covariates. The chromosomal location at which the highest F score was obtained was considered the best estimate of a QTL position. Chromosome-wide significance thresholds were determined empirically from 10,000 permutations of marker genotypes. Results--4 chromosomes contained putative QTL for CA of hip joints in dogs at the 5% chromosome-wide significance threshold: chromosomes 5, 18, 23, and 31. Conclusions and Clinical Relevance--Osteoarthritis of. canine hip joints is a complex disease to which many genes and environmental factors contribute. Identification of contributing QTL is a strategy to elucidate the genetic mechanisms that underlie this disease. Refinement of the putative QTL and subsequent candidate gene studies are needed to identify the genes involved in the disease process. [ABSTRACT FROM AUTHOR]