A SUPER Powerful Method for Genome Wide Association Study

Genome-Wide Association Studies shed light on the identification of genes underlying human diseases and agriculturally important traits. This potential has been shadowed by false positive findings. The Mixed Linear Model (MLM) method is flexible enough to simultaneously incorporate population structure and cryptic relationships to reduce false positives. However, its intensive computational burden is prohibitive in practice, especially for large samples. The newly developed algorithm, FaST-LMM, solved the computational problem, but requires that the number of SNPs be less than the number of individuals to derive a rank-reduced relationship. This restriction potentially leads to less statistical power when compared to using all SNPs. We developed a method to extract a small subset of SNPs and use them in FaST-LMM. This method not only retains the computational advantage of FaST-LMM, but also remarkably increases statistical power even when compared to using the entire set of SNPs. We named the method SUPER (Settlement of MLM Under Progressively Exclusive Relationship) and made it available within an implementation of the GAPIT software package.     

rMVP: A Memory-efficient,Visualization-enhanced,and Parallel-accelerated Tool for Genome-wide Association Study

Along with the development of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in genome-wide association studies (GWAS), and the associated computation is more challenging than ever. Here, we present a memory-efficient, visualization-enhanced, and parallel-accelerated R package called “rMVP” to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by Efficient Mixed-Model Association eXpedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS-related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.     

玉米出籽率全基因组关联分析

出籽率与玉米单穗产量密切相关,其遗传机制的解析对玉米高产育种具有重要意义.本研究利用309份玉米自交系为关联群体,利用固定和随机模型交替概率统一(FarmCPU)、压缩混合线性模型(CMLM)和多位点混合线性模型(MLMM)对2017年和2019年河南新乡原阳、周口郸城、海南三亚以及最佳线性无偏估计值(BLUE)的出籽...     

Recovering Power in Association Mapping Panels with Variable Levels of Linkage Disequilibrium

Association mapping has permitted the discovery of major QTL in many species. It can be applied to existing populations and, as a consequence, it is generally necessary to take into account structure and relatedness among individuals in the statistical model to control false positives. We analytically studied power in association studies by computing noncentrality parameter of the tests and its relationship with parameters characterizing diversity (genetic differentiation between groups and allele frequencies) and kinship between individuals. Investigation of three different maize diversity panels genotyped with the 50k SNPs array highlighted contrasted average power among panels and revealed gaps of power of classical mixed models in regions with high linkage disequilibrium (LD). These gaps could be related to the fact that markers are used for both testing association and estimating relatedness. We thus considered two alternative approaches to estimating the kinship matrix to recover power in regions of high LD. In the first one, we estimated the kinship with all the markers that are not located on the same chromosome than the tested SNP. In the second one, correlation between markers was taken into account to weight the contribution of each marker to the kinship. Simulations revealed that these two approaches were efficient to control false positives and were more powerful than classical models.     

GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction

Genome-wide association study (GWAS) and genomic prediction/selection (GP/GS) are the two essential enterprises in genomic research. Due to the great magnitude and complexity of genomic and phenotypic data, analytical methods and their associated software packages are frequently advanced. GAPIT is a widely-used genomic association and prediction integrated tool as an R package. The first version was released to the public in 2012 with the implementation of the general linear model (GLM), mixed linear model (MLM), compressed MLM (CMLM), and genomic best linear unbiased prediction (gBLUP). The second version was released in 2016 with several new implementations, including enriched CMLM (ECMLM) and settlement of MLMs under progressively exclusive relationship (SUPER). All the GWAS methods are based on the single-locus test. For the first time, in the current release of GAPIT, version 3 implemented three multi-locus test methods, including multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). Additionally, two GP/GS methods were implemented based on CMLM (named compressed BLUP; cBLUP) and SUPER (named SUPER BLUP; sBLUP). These new implementations not only boost statistical power for GWAS and prediction accuracy for GP/GS, but also improve computing speed and increase the capacity to analyze big genomic data. Here, we document the current upgrade of GAPIT by describing the selection of the recently developed methods, their implementations, and potential impact. All documents, including source code, user manual, demo data, and tutorials, are freely available at the GAPIT website (http://zzlab.net/GAPIT).     

Association mapping of iron deficiency chlorosis loci in soybean (Glycine max L. Merr.) advanced breeding lines

Association mapping is an alternative to mapping in a biparental population. A key to successful association mapping is to avoid spurious associations by controlling for population structure. Confirming the marker/trait association in an independent population is necessary for the implementation of the marker in other genetic studies. Two independent soybean populations consisting of advanced breeding lines representing the diversity within maturity groups 00, 0, and I were screened in multi-site, replicated field trials to discover molecular markers associated with iron deficiency chlorosis (IDC), a major yield-limiting factor in soybean. Lines with extreme phenotypes were initially screened to identify simple sequence repeat (SSR) markers putatively associated with the IDC. Marker data collected from all lines were used to control for population structure and kinship relationships. Single factor analysis of variance (SFA) and mixed linear model (MLM) analyses were used to discover marker/trait associations. The MLM analyses, which include population structure, kinship or both factors, reduced the number of markers significantly associated with IDC by 50% compared with SFA. With the MLM approach, three markers were found to be associated with IDC in the first population. Two of these markers, Satt114 and Satt239, were also found to be associated with IDC in the second confirmation population. For both populations, those lines with the tolerance allele at both these two marker loci had significantly lower IDC scores than lines with one or no tolerant alleles.     

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.     

Meta-Analysis Reveals the Association of Common Variants in the Uncoupling Protein (UCP) 1–3 Genes with Body Mass Index Variability

Background

The relationship between uncoupling protein (UCP) 1–3 polymorphisms and susceptibility to obesity has been investigated in several genetic studies. However, the impact of these polymorphisms on obesity is still under debate, with contradictory results being reported. Until this date, no meta-analysis evaluated the association of UCP polymorphisms with body mass index (BMI) variability. Thus, this paper describe a meta-analysis conducted to evaluate if the -3826A/G (UCP1); -866G/A, Ala55Val and Ins/Del (UCP2) and -55C/T (UCP3) polymorphisms are associated with BMI changes.

Methods

A literature search was run to identify all studies that investigated associations between UCP1-3 polymorphisms and BMI. Weighted mean differences (WMD) were calculated for different inheritance models.

Results

Fifty-six studies were eligible for inclusion in the meta-analysis. Meta-analysis results showed that UCP2 55Val/Val genotype was associated with increased BMI in Europeans [Random Effect Model (REM) WMD 0.81, 95% CI 0.20, 1.41]. Moreover, the UCP2 Ins allele and UCP3-55T/T genotype were associated with increased BMI in Asians [REM WMD 0.46, 95% CI 0.09, 0.83 and Fixed Effect Model (FEM) WMD 1.63, 95% CI 0.25, 3.01]. However, a decreased BMI mean was observed for the UCP2-866 A allele in Europeans under a dominant model of inheritance (REM WMD −0.18, 95% CI −0.35, −0.01). There was no significant association of the UCP1-3826A/G polymorphism with BMI mean differences.

Conclusions

The meta-analysis detected a significant association between the UCP2-866G/A, Ins/Del, Ala55Val and UCP3-55C/T polymorphisms and BMI mean differences.     

Characterization of Sugarcane Mosaic Virus Scmv1 and Scmv2 Resistance Regions by Regional Association Analysis in Maize

Sugarcane Mosaic Virus (SCMV) causes one of the most severe virus diseases in maize worldwide, resulting in reduced grain and forage yield in susceptible cultivars. In this study, two association panels consisting of 94 inbred lines each, from China and the U.S., were characterized for resistance to two isolates: SCMV-Seehausen and SCMV-BJ. The population structure of both association panels was analyzed using 3072 single nucleotide polymorphism (SNP) markers. The Chinese and the U.S. panel were both subdivided into two sub-populations, the latter comprised of Stiff Stalk Synthetic (SS) lines and Non Stiff Stalk Synthetic (NSS). The relative kinships were calculated using informative 2947 SNPs with minor allele frequency ≥ 5% and missing data ≤ 20% for the Chinese panel and 2841 SNPs with the same characteristics were used for the U.S. panel. The Scmv1 region was genotyped using 7 single sequence repeat (SSR) and sequence-tagged site (STS) markers, and 12 SSR markers were used for the Scmv2 region in the U.S. panel, while 5 of them were used for the Chinese panel. For all traits, a MLM (Mix Linear Model) controlling both population structure and relative kinship (Q + K) was used for association analysis. Three markers Trx-1, STS-11, and STS-12 located in the Scmv1 region were strongly associated (P = 0.001) with SCMV resistance, and explained more than 16.0%, 10.6%, and 19.7% of phenotypic variation, respectively. 207FG003 located in the Scmv2 region was significantly associated (P = 0.001) with SCMV resistance, and explained around 18.5% of phenotypic variation.     

Wild Bornean orangutan (Pongo pygmaeus wurmbii) feeding rates and the Marginal Value Theorem

The Marginal Value Theorem (MVT) is an integral supplement to Optimal Foraging Theory (OFT) as it seeks to explain an animal's decision of when to leave a patch when food is still available. MVT predicts that a forager capable of depleting a patch, in a habitat where food is patchily distributed, will leave the patch when the intake rate within it decreases to the average intake rate for the habitat. MVT relies on the critical assumption that the feeding rate in the patch will decrease over time. We tested this assumption using feeding data from a population of wild Bornean orangutans (Pongo pygmaeus wurmbii) from Gunung Palung National Park. We hypothesized that the feeding rate within orangutan food patches would decrease over time. Data included feeding bouts from continuous focal follows between 2014 and 2016. We recorded the average feeding rate over each tertile of the bout, as well as the first, midpoint, and last feeding rates collected. We did not find evidence of a decrease between first and last feeding rates (Linear Mixed Effects Model, n = 63), between a mid-point and last rate (Linear Mixed Effects Model, n = 63), between the tertiles (Linear Mixed Effects Model, n = 63), nor a decrease in feeding rate overall (Linear Mixed Effects Model, n = 146). These findings, thus, do not support the MVT assumption of decreased patch feeding rates over time in this large generalist frugivore.     

Genome-wide association mapping of three important traits using bread wheat elite breeding populations

The exponential development of molecular markers enables a more effective study of the genetic architecture of traits of economic importance, like test weight in wheat (Triticum aestivum L.), for which a high value is desired by most end-users. The association mapping (AM) method now allows more precise exploration of the entire genome. AM requires populations with substantial genetic variability of the traits of interest. The breeding lines at the end of a selection cycle, characterized for numerous traits, represent a potentially useful population for AM studies. Using three elite line populations, selected by several breeders and genotyped with about 2,500 Diversity Arrays Technology markers, several associations were identified between these markers and test weight, grain yield and heading date. To minimize spurious associations, we compared the general linear model and mixed linear model (MLM), which adjust for population structure and kinship differently. The MLM model with the kinship matrix was the most efficient. Finally, elite lines from several breeding programs had sufficient genetic variability to allow for the mapping of several chromosomal regions involved in the variation of three important traits.     

Tree age and bark thickness as traits linked to frost ring probability on Araucaria araucana trees in northern Patagonia

Frost events may damage the cambium and consequently the newly produced tracheids whose cell walls have not yet completed their lignifications, leading to the formation of frost rings. This study deals with the presence of frost rings in Araucaria araucana trees according to cambial age and bark thickness, under the assumption that these factors may be involved in physical or physiological mechanisms that increase resistance to freezing temperatures that impact the cambial tissue. The study was conducted in northern Patagonia at two sites of contrasting geomorphology, and therefore potentially associated with a differential degree of exposure to extreme cold. Wood plus bark cores were extracted from main stems at two heights from the ground and from each of the four cardinal point directions for 30 individuals per site. A Linear Mixed Model and a Generalized Linear Mixed Model were applied in order to relate the bark thickness and the frequency of frost rings in accordance with the different sampling points on the stem. It was observed that as bark becomes thicker with cambial age, the frequency of frost rings decreases, indicating a possible thermal-induced mechanism of bark protection. Consequently, there is an increase in the presence of frost rings at the younger stages of tree life. Although the mechanisms of cold hardiness in trees can be complex, including aspects of the tree physiology, our data indicated that as tree age increases, the thickness of the bark is higher, resulting in a potential effect of isolation and passive protection against the harmful effects of frosts. This mechanism may be relevant in the ecology, conservation and management of forests faced with extreme variability in future climate and changing scenarios.     

Genome-wide association study for oat (Avena sativa L.) beta-glucan concentration using germplasm of worldwide origin

Detection of quantitative trait loci (QTL) controlling complex traits followed by selection has become a common approach for selection in crop plants. The QTL are most often identified by linkage mapping using experimental F₂, backcross, advanced inbred, or doubled haploid families. An alternative approach for QTL detection are genome-wide association studies (GWAS) that use pre-existing lines such as those found in breeding programs. We explored the implementation of GWAS in oat (Avena sativa L.) to identify QTL affecting β-glucan concentration, a soluble dietary fiber with several human health benefits when consumed as a whole grain. A total of 431 lines of worldwide origin were tested over 2?years and genotyped using Diversity Array Technology (DArT) markers. A mixed model approach was used where both population structure fixed effects and pair-wise kinship random effects were included. Various mixed models that differed with respect to population structure and kinship were tested for their ability to control for false positives. As expected, given the level of population structure previously described in oat, population structure did not play a large role in controlling for false positives. Three independent markers were significantly associated with β-glucan concentration. Significant marker sequences were compared with rice and one of the three showed sequence homology to genes localized on rice chromosome seven adjacent to the CslF gene family, known to have β-glucan synthase function. Results indicate that GWAS in oat can be a successful option for QTL detection, more so with future development of higher-density markers.     

No Evidence for Significant Effect of Body Size and Age on Male Mating Success in the Spot-legged Treefrog

In anurans, body size and age of individuals generally affect male mating success. To test whether body size and age have effects on male mating success in the foam-nesting treefrog Polypedates megacephalus, a species widely distributed in China, we analyzed differences in body size and age between mated and unmated males for three populations using a Generalized Linear Mixed Model(GLMM). The results showed that mated males did not exhibit larger body size and older age than unmated males, suggesting that large and/or old male individuals did not have greater mating success than small and/or young males. Moreover, we also found a non-significant size-assortative mating pattern for all populations. Our findings suggest that body size and age of the foam-nesting treefrog do not affect male mating success.     

Comparative methods for association studies: a case study on metabolite variation in a Brassica rapa core collection

Background

Association mapping is a statistical approach combining phenotypic traits and genetic diversity in natural populations with the goal of correlating the variation present at phenotypic and allelic levels. It is essential to separate the true effect of genetic variation from other confounding factors, such as adaptation to different uses and geographical locations. The rapid availability of large datasets makes it necessary to explore statistical methods that can be computationally less intensive and more flexible for data exploration.

Methodology/Principal Findings

A core collection of 168 Brassica rapa accessions of different morphotypes and origins was explored to find genetic association between markers and metabolites: tocopherols, carotenoids, chlorophylls and folate. A widely used linear model with modifications to account for population structure and kinship was followed for association mapping. In addition, a machine learning algorithm called Random Forest (RF) was used as a comparison. Comparison of results across methods resulted in the selection of a set of significant markers as promising candidates for further work. This set of markers associated to the metabolites can potentially be applied for the selection of genotypes with elevated levels of these metabolites.

Conclusions/Significance

The incorporation of the kinship correction into the association model did not reduce the number of significantly associated markers. However incorporation of the STRUCTURE correction (Q matrix) in the linear regression model greatly reduced the number of significantly associated markers. Additionally, our results demonstrate that RF is an interesting complementary method with added value in association studies in plants, which is illustrated by the overlap in markers identified using RF and a linear mixed model with correction for kinship and population structure. Several markers that were selected in RF and in the models with correction for kinship, but not for population structure, were also identified as QTLs in two bi-parental DH populations.     

Score test for linkage in generalized linear models

We derive a test for linkage in a Generalized Linear Mixed Model (GLMM) framework which provides a natural adjustment for marginal covariate effects. The method boils down to the score test of a quasi-likelihood derived from the GLMM, it is computationally inexpensive and can be applied to arbitrary pedigrees. In particular, for binary traits, relative pairs of different nature (affected and discordant) and individuals with different covariate values can be naturally combined in a single test. The model introduced could explain a number of situations usually described as gene by covariate interaction phenomena, and offers substantial gains in efficiency compared to methods classically used in those instances.     

Genome Wide Association Studies Using a New Nonparametric Model Reveal the Genetic Architecture of 17 Agronomic Traits in an Enlarged Maize Association Panel

Association mapping is a powerful approach for dissecting the genetic architecture of complex quantitative traits using high-density SNP markers in maize. Here, we expanded our association panel size from 368 to 513 inbred lines with 0.5 million high quality SNPs using a two-step data-imputation method which combines identity by descent (IBD) based projection and k-nearest neighbor (KNN) algorithm. Genome-wide association studies (GWAS) were carried out for 17 agronomic traits with a panel of 513 inbred lines applying both mixed linear model (MLM) and a new method, the Anderson-Darling (A-D) test. Ten loci for five traits were identified using the MLM method at the Bonferroni-corrected threshold −log₁₀ (P) >5.74 (α = 1). Many loci ranging from one to 34 loci (107 loci for plant height) were identified for 17 traits using the A-D test at the Bonferroni-corrected threshold −log₁₀ (P) >7.05 (α = 0.05) using 556809 SNPs. Many known loci and new candidate loci were only observed by the A-D test, a few of which were also detected in independent linkage analysis. This study indicates that combining IBD based projection and KNN algorithm is an efficient imputation method for inferring large missing genotype segments. In addition, we showed that the A-D test is a useful complement for GWAS analysis of complex quantitative traits. Especially for traits with abnormal phenotype distribution, controlled by moderate effect loci or rare variations, the A-D test balances false positives and statistical power. The candidate SNPs and associated genes also provide a rich resource for maize genetics and breeding.     

A genome-wide screen for hyposmia susceptibility Loci

Olfactory dysfunction is an important public health problem in the United States, with approximately 14 million elderly Americans having chronic olfactory impairment. We performed a genome-wide linkage scan for loci influencing susceptibility to hyposmia in the Hutterites, a founder population of European ancestry. Using interviews regarding the olfactory medical history and psychophysical smell testing, we identified 25 individuals with severe hyposmia. Elimination of subjects with confounding conditions yielded 7 hyposmics for analysis. A 52-member pedigree including all affected individuals was constructed from the larger, >1623-member pedigree, and a genome-wide screen for loci influencing the trait of hyposmia using 1123 markers was performed. The most significant evidence for linkage with hyposmia extended over a 45 cM region on chromosome 4q (P = 0.0013). Although this signal meets the criteria for suggestive linkage only and will require replication, these results offer the strongest data to date on the effects of genetic variation on olfactory dysfunction.     

Linkage mapping of melanoma (MLM) using 172 microsatellite markers

The incidence of malignant melanoma is currently increasing faster than any other cancer and in 5-12% of cases occurs in a familial context in which the disease cosegregates as an autosomal dominant trait. To identify the location of genes that predipose individuals to familial melanoma (MLM), we have carried out linkage analysis in three large Australian melanoma pedigrees using 172 microsatellite markers spread across all autosomes. Three additional smaller families were typed for 70 of the same markers. In five of the six families we found lod scores between 1.0 and 2.3, which may provide evidence for the location of melanoma genes in proximity to some of these markers. If this turns out to be the case, these data potentially demonstrate that MLM is genetically heterogeneous since there was no marker for which all families gave significantly high LODs. These data provide the foundation for an exclusion map for melanoma and, more importantly, high-light areas of the genome for others to substantiate the potential positions of some of the genes that may be responsible for susceptibility to MLM.     

Association analysis and population structure of flowering-related traits in super sweet corn inbred lines with simple sequence repeat markers

This study assessed the genetic and phenotypic variation of 90 super sweet corn inbred lines and performed association analyses of six agronomical traits using 100 simple sequence repeats (SSR), ultimately detecting 590 alleles, with an average of 5.90 alleles per locus. The average genetic diversity and Polymorphism information content values were 0.54 and 0.50, respectively. Using population structure analysis, inbred lines were divided into three major groups and one admixed group. Association analysis was performed with a general linear model using a Q-matrix (Q GLM) and a mixed linear model using Q and K-matrices (Q + K MLM). Q GLM found 33 marker-trait associations involving 20 SSR markers that were associated with six agronomic traits. Q + K MLM identified four marker-trait associations involving three markers that were associated with traits of days of tasseling (DT) and days of silking (DS). Q GLM and Q + K MLM detected four significant marker-trait associations (SMTAs), with a level of significance of P < 0.01. In overlapping SMTAs, phi051 was associated with DT, umc1708 was associated with DS, and umc2341 was associated with two traits: DT and DS. The detection of loci associated with traits in this study may provide greater opportunities to improve quality by marker-assisted selection (MAS). Finally, these results will be helpful for breeders in choosing parental lines for crossing combinations as well as markers for using MAS in super sweet corn breeding programs in Korea.