Quantitative Trait Nucleotide Detection Research Articles

Single nucleotide polymorphism profile for quantitative trait nucleotide in populations with small effective size and its impact on mapping and genomic predictions.

Increasing SNP density by incorporating sequence information only marginally increases prediction accuracies of breeding values in livestock. To find out why, we used statistical models and simulations to investigate the shape of distribution of estimated SNP effects (a profile) around quantitative trait nucleotides (QTNs) in populations with a small effective population size (Ne). A QTN profile created by averaging SNP effects around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles. However, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN. The QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN was contained in ± 1/Ne Morgan interval around the QTN, corresponding to 2 Mb in cattle and 5 Mb in pigs and chickens. With such large intervals, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely spaced QTN, and spurious signals. However, small Ne allows for accurate predictions with large data even without QTN identification because QTNs are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.

Genetic analysis of genotype-specific parameters in the DSSTA-CROPGRO-soybean phenology simulation model via a multi-GWAS method

ContextGenotype-specific parameters (GSP) are the fundamental elements for phenotypic prediction in the DSSAT-CROPGRO soybean phenology simulation model (DCS-PSM). Yet, it is unclear about the quantitative relationship between GSP and genomics, which limits the crop models used for breeding research. ObjectiveExploring the genetic composition of GSP related to four soybean phenological traits in DCS-PSM for providing a basis for whether GSP are genetic coefficient. Highlighting the breeding value of DCS-PSM from a genetic perspective and strengthening its genetic understanding, which could support a further crop model-assisted molecular breeding. MethodsWe conducted a multi-genome-wide association study (GWAS) method by intrgrating the single-locus with multi-locus GWAS models for GSP associated quantitative trait nucleotides (QTN) detection. And the heritability, functional annotation of QTN and genotype-environment interaction (GEI) effect were further analyzed. Results(i). All GSP had high broad-sense heritability, ranging form 88.20–98.80%. (ii). Multi-locus GWAS significantly increased the QTN detection efficiency, the GSP associated QTN detected by multi-GWAS method contributed 22.11–50.25% phenotypic variation explained (PVE) of GSP. Besides, the GSP associated QTN accounted for a high PVE of four phenological traits, ranging from 54.25% to 74.75%. (iii). Most QTN overlapped with the function-known genes which regulate soybean phenology through photoperiod-temperature pathways. (iv). GSP associated QTN could reveal the GEI effect and contributed 1.45–6.27% PVE of soybean phenology. ConclusionsThis study indicated that the genetic factor was the primary source of GSP’s variation, and the multi-GWAS method could be an alternative way for the genetic analysis of GSP. Phenology-related GSP had genetic potential and could be regarded as genetic coefficients and breeding targets.

Multi-omics analysis reveals the mechanism of seed coat color formation in Brassica rapa L.

Multi-omics analysis of the transcriptome, metabolome and genome identified major and minor loci and candidate genes for seed coat color and explored the mechanism of flavonoid metabolites biosynthesis in Brassica rapa. Yellow seed trait is considered an agronomically desirable trait with great potential for improving seed quality of Brassica crops. Mechanisms of the yellow seed trait are complex and not well understood. In this study, we performed an integrated metabolome, transcriptome and genome-wide association study (GWAS) on different B. rapa varieties to explore the mechanisms underlying the seed coat color formation. A total of 2,499 differentially expressed genes and 116 differential metabolites between yellow and black seeds with strong association with the flavonoid biosynthesis pathway was identified. In addition, 330 hub genes involved in the seed coat color formation, and the most significantly differential flavonoids biosynthesis were detected based on weighted gene co-expression network analysis. Metabolite GWAS analysis using the contents of 42 flavonoids in developing seeds of 159 B. rapa lines resulted in the identification of 1,626 quantitative trait nucleotides (QTNs) and 37 chromosomal intervals, including one major locus on chromosome A09. A combination of QTNs detection, transcriptome and functional analyses led to the identification of 241 candidate genes that were associated with different flavonoid metabolites. The flavonoid biosynthesis pathway in B. rapa was assembled based on the identified flavonoid metabolites and candidate genes. Furthermore, BrMYB111 members (BraA09g004490.3C and BraA06g034790.3C) involved in the biosynthesis of taxifolin were functionally analyzed in vitro. Our findings lay a foundation and provide a reference for systematically investigating the mechanism of seed coat color in B. rapa and in the other plants.

40 Factors Influencing Accuracy of Genomic Selection with Sequence Information

Abstract Incorporating the sequence information only marginally increases the accuracy of genomic selection. The purpose of this study was to find out why by examining profiles of Quantitative Trait Nucleotides (QTN). Multiple populations were simulated with different effective population sizes and number of animals. 100 equidistant QTN with identical substitution effects were included in 50k SNP genotypes. Analyses were by single-step GBLUP, with solutions converted to SNP values and subsequently to p-values for each SNP. Manhattan plots for standardized SNP solutions were noisy and were elevated only for few QTNs. Manhattan plots for p-values were similar to those for SNP solutions, indicating little impact of population structure. The number of significant QTN was lower with lower effective population size and increased with larger data; at most about 20% of QTNs were detected. A QTN profile was created by averaging SNP solutions ±100 SNP around each QTN. The profile showed a normal-like response but with a distinct peak for the QTN. While the peak was higher with more data and higher effective population size, the normal-like response was smaller with higher effective population size. QTNs explained little variance because of shrinkage. The accuracy of genomic selection would be 100% if all QTNs are identified and their variances known, to prevent shrinking or inflation. This study allows to see limits of application of QTN from sequence data for genomic selection. If all causative SNP are included in the data, only a fraction of them can be identified even under a very simplistic architecture. As variance of QTN are assumed constant or are crude approximations (like in BayesR), the estimated QTN effects are inaccurate. Additional complications in QTN detection are close-spaced QTN and false QTNs due to imputation. Small effective population size allows the genomic selection by GBLUP but complicates the use of QTNs.

A Fast Multi-Locus Ridge Regression Algorithm for High-Dimensional Genome-Wide Association Studies.

The mixed linear model (MLM) has been widely used in genome-wide association study (GWAS) to dissect quantitative traits in human, animal, and plant genetics. Most methodologies consider all single nucleotide polymorphism (SNP) effects as random effects under the MLM framework, which fail to detect the joint minor effect of multiple genetic markers on a trait. Therefore, polygenes with minor effects remain largely unexplored in today’s big data era. In this study, we developed a new algorithm under the MLM framework, which is called the fast multi-locus ridge regression (FastRR) algorithm. The FastRR algorithm first whitens the covariance matrix of the polygenic matrix K and environmental noise, then selects potentially related SNPs among large scale markers, which have a high correlation with the target trait, and finally analyzes the subset variables using a multi-locus deshrinking ridge regression for true quantitative trait nucleotide (QTN) detection. Results from the analyses of both simulated and real data show that the FastRR algorithm is more powerful for both large and small QTN detection, more accurate in QTN effect estimation, and has more stable results under various polygenic backgrounds. Moreover, compared with existing methods, the FastRR algorithm has the advantage of high computing speed. In conclusion, the FastRR algorithm provides an alternative algorithm for multi-locus GWAS in high dimensional genomic datasets.

How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?

Quantification of the simultaneous contributions of loci to multiple traits, a phenomenon called pleiotropy, is facilitated by the increased availability of high-throughput genotypic and phenotypic data. To understand the prevalence and nature of pleiotropy, the ability of multivariate and univariate genome-wide association study (GWAS) models to distinguish between pleiotropic and non-pleiotropic loci in linkage disequilibrium (LD) first needs to be evaluated. Therefore, we used publicly available maize and soybean genotypic data to simulate multiple pairs of traits that were either (i) controlled by quantitative trait nucleotides (QTNs) on separate chromosomes, (ii) controlled by QTNs in various degrees of LD with each other, or (iii) controlled by a single pleiotropic QTN. We showed that multivariate GWAS could not distinguish between QTNs in LD and a single pleiotropic QTN. In contrast, a unique QTN detection rate pattern was observed for univariate GWAS whenever the simulated QTNs were in high LD or pleiotropic. Collectively, these results suggest that multivariate and univariate GWAS should both be used to infer whether or not causal mutations underlying peak GWAS associations are pleiotropic. Therefore, we recommend that future studies use a combination of multivariate and univariate GWAS models, as both models could be useful for identifying and narrowing down candidate loci with potential pleiotropic effects for downstream biological experiments.

Choosing the optimal population for a genome-wide association study: A simulation of whole-genome sequences from rice.

A genome-wide association study (GWAS) needs to have a suitable population. The factors that affect a GWAS (e.g. population structure, sample size, and sequence analysis and field testing costs) need to be considered. Mixed populations containing subpopulations of different genetic backgrounds may be suitable populations. We conducted simulation experiments to see if a population with high genetic diversity, such as a diversity panel, should be added to a target population, especially when the target population harbors small genetic diversity. The target population was 112 accessions of Oryza sativa L. subsp. japonica, mainly developed in Japan. We combined the target population with three populations that had higher genetic diversity. These were 100 indica accessions, 100 japonica accessions, and 100 accessions with various genetic backgrounds. The results showed that the GWAS's power with a mixed population was generally higher than with a separate population. Also, the optimal GWAS populations varied depending on the fixation index (FST ) of the quantitative trait nucleotides (QTNs) and the polymorphism of QTNs in each population. When a QTN was polymorphic in a target population, a target population combined with a higher diversity population improved the QTN's detection power. By investigating FST and the expected heterozygosity (He ) as factors influencing the detection power, we showed that single nucleotide polymorphisms with high FST or low He are less likely to be detected by GWAS with mixed populations. Sequenced or genotyped germplasm collections can improve the GWAS's detection power by using a subset of the collections with a target population.

Association Mapping Analysis of Fatty Acid Content in Different Ecotypic Rapeseed Using mrMLM.

Brassica napus L. is a widely cultivated oil crop and provides important resources of edible vegetable oil, and its quality is determined by fatty acid composition and content. To explain the genetic basis and identify more minor loci for fatty acid content, the multi-locus random-SNP-effect mixed linear model (mrMLM) was used to identify genomic regions associated with fatty acid content in a genetically diverse population of 435 rapeseed accessions, including 77 winter-type, 55 spring-type, and 303 semi-winter-type accessions grown in different environments. A total of 149 quantitative trait nucleotides (QTNs) were found to be associated with fatty acid content and composition, including 34 QTNs that overlapped with the previously reported loci, and 115 novel QTNs. Of these, 35 novel QTNs, located on chromosome A01, A02, A03, A05, A06, A09, A10, and C02, respectively, were repeatedly detected across different environments. Subsequently, we annotated 95 putative candidate genes by BlastP analysis using sequences from Arabidopsis thaliana homologs of the identified regions. The candidate genes included 34 environmentally-insensitive genes (e.g., CER4, DGK2, KCS17, KCS18, MYB4, and TT16) and 61 environment-sensitive genes (e.g., FAB1, FAD6, FAD7, KCR1, KCS9, KCS12, and TT1) as well as genes invloved in the fatty acid biosynthesis. Among these, BnaA08g08280D and BnaC03g60080D differed in genomic sequence between the high- and low-oleic acid lines, and might thus be the novel alleles regulating oleic acid content. Furthermore, RT-qPCR analysis of these genes showed differential expression levels during seed development. Our results highlight the practical and scientific value of mrMLM or QTN detection and the accuracy of linking specific QTNs to fatty acid content, and suggest a useful strategy to improve the fatty acid content of B. napus seeds by molecular marker-assisted breeding.

PKWmEB: integration of Kruskal\u2013Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study

Although nonparametric methods in genome-wide association studies (GWAS) are robust in quantitative trait nucleotide (QTN) detection, the absence of polygenic background control in single-marker association in genome-wide scans results in a high false positive rate. To overcome this issue, we proposed an integrated nonparametric method for multi-locus GWAS. First, a new model transformation was used to whiten the covariance matrix of polygenic matrix K and environmental noise. Using the transferred model, Kruskal–Wallis test along with least angle regression was then used to select all the markers that were potentially associated with the trait. Finally, all the selected markers were placed into multi-locus model, these effects were estimated by empirical Bayes, and all the nonzero effects were further identified by a likelihood ratio test for true QTN detection. This method, named pKWmEB, was validated by a series of Monte Carlo simulation studies. As a result, pKWmEB effectively controlled false positive rate, although a less stringent significance criterion was adopted. More importantly, pKWmEB retained the high power of Kruskal–Wallis test, and provided QTN effect estimates. To further validate pKWmEB, we re-analyzed four flowering time related traits in Arabidopsis thaliana, and detected some previously reported genes that were not identified by the other methods.

PLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies

Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.

Methodological implementation of mixed linear models in multi-locus genome-wide association studies.

The mixed linear model has been widely used in genome-wide association studies (GWAS), but its application to multi-locus GWAS analysis has not been explored and assessed. Here, we implemented a fast multi-locus random-SNP-effect EMMA (FASTmrEMMA) model for GWAS. The model is built on random single nucleotide polymorphism (SNP) effects and a new algorithm. This algorithm whitens the covariance matrix of the polygenic matrix K and environmental noise, and specifies the number of nonzero eigenvalues as one. The model first chooses all putative quantitative trait nucleotides (QTNs) with ≤ 0.005 P-values and then includes them in a multi-locus model for true QTN detection. Owing to the multi-locus feature, the Bonferroni correction is replaced by a less stringent selection criterion. Results from analyses of both simulated and real data showed that FASTmrEMMA is more powerful in QTN detection and model fit, has less bias in QTN effect estimation and requires a less running time than existing single- and multi-locus methods, such as empirical Bayes, settlement of mixed linear model under progressively exclusive relationship (SUPER), efficient mixed model association (EMMA), compressed MLM (CMLM) and enriched CMLM (ECMLM). FASTmrEMMA provides an alternative for multi-locus GWAS.

Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies.

Genome-wide association study (GWAS) entails examining a large number of single nucleotide polymorphisms (SNPs) in a limited sample with hundreds of individuals, implying a variable selection problem in the high dimensional dataset. Although many single-locus GWAS approaches under polygenic background and population structure controls have been widely used, some significant loci fail to be detected. In this study, we used an iterative modified-sure independence screening (ISIS) approach in reducing the number of SNPs to a moderate size. Expectation-Maximization (EM)-Bayesian least absolute shrinkage and selection operator (BLASSO) was used to estimate all the selected SNP effects for true quantitative trait nucleotide (QTN) detection. This method is referred to as ISIS EM-BLASSO algorithm. Monte Carlo simulation studies validated the new method, which has the highest empirical power in QTN detection and the highest accuracy in QTN effect estimation, and it is the fastest, as compared with efficient mixed-model association (EMMA), smoothly clipped absolute deviation (SCAD), fixed and random model circulating probability unification (FarmCPU), and multi-locus random-SNP-effect mixed linear model (mrMLM). To further demonstrate the new method, six flowering time traits in Arabidopsis thaliana were re-analyzed by four methods (New method, EMMA, FarmCPU, and mrMLM). As a result, the new method identified most previously reported genes. Therefore, the new method is a good alternative for multi-locus GWAS.