Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants

Loss-of-function mutations in the fruit softening gene POLYGALACTURONASE1 doubled fruit firmness in strawberry

Wildtype fruit of cultivated strawberry (Fragaria  [Formula: see text]  ananassa) are typically soft and highly perishable when fully ripe. The development of firm-fruited cultivars by phenotypic selection has greatly increased shelf-life, decreased postharvest perishability, and driven the expansion of strawberry production worldwide. Hypotheses for the firm-fruited phenotype include mutations affecting the expression of genes encoding polygalacturonases (PGs) that soften fruit by degrading cell wall pectins. Here we show that loss-of-function mutations in the fruit softening gene POLYGALACTURONASE1 (FaPG1; PG1-6A1) double fruit firmness in strawberry. PG1-6A1 was one of three tandemly duplicated PG genes found to be in linkage disequilibrium (LD) with a quantitative trait locus (QTL) affecting fruit firmness on chromosome 6A. PG1-6A1 was strongly expressed in soft-fruited (wildtype) homozygotes and weakly expressed in firm-fruited (mutant) homozygotes. Genome-wide association, quantitative trait transcript, DNA sequence, and expression-QTL analyses identified genetic variants in LD with PG1-6A1 that were positively correlated with fruit firmness and negatively correlated with PG1-6A1 expression. An Enhancer/Suppressor-mutator (En/Spm) transposable element insertion was discovered upstream of PG1-6A1 in mutant homozygotes that we hypothesize transcriptionally downegulates the expression of PG1-6A1. The PG1-6A1 locus was incompletely dominant and explained 26-76% of the genetic variance for fruit firmness among phenotypically diverse individuals. Additional loci are hypothesized to underlie the missing heritability. Highly accurate codominant genotyping assays were developed for modifying fruit firmness by marker-assisted selection of the En/Spm insertion and single nucleotide polymorphisms associated with the PG1-6A1 locus.

mmGEBLUP: an advanced genomic prediction scheme for genetic improvement of complex traits in crops through integrative analysis of major genes, polygenes, and genotype-environment interactions

Current genomic prediction (GP) models often fall short of fully capturing the genetic architecture of complex traits and providing practical breeding guidance, particularly under varying environments. Here, we propose the mmGEBLUP, an advanced GP scheme designed to tackle the current limitations in fully exploiting the genetic architecture of complex traits and to predict individual breeding value (BV) with multi-environment trial data. Our approach considers four genetic structural indicators to capture the genetic architectures stepwise across four models: the Genomic Best Linear Unbiased Prediction (GBLUP) model considers only main polygenic effects; the GEBLUP model includes both main and genotype-by-environment (GE) interaction polygenic effects; and the mmGBLUP and mmGEBLUP models further incorporate main and GE interaction effects of major genes. Through systematic simulations and applications to nine traits, three in rice and six in tobacco, we show stepwise increases in prediction accuracy from GBLUP to mmGEBLUP, providing evidence on the scale of heritability and polygenicity of traits. In practical terms, we predict four components of BV: major additive, minor additive, major interaction, and minor interaction. Interestingly, we discover that for traits like natural leaf number in tobacco, the major additive BVs for the top 20 individuals are substantially equal; it is the minor additive BV that causes the difference in the total BV. The relative size of major/minor additive BVs suggests performing either marker-assisted selection or genomic selection or both. Overall, mmGEBLUP is an advanced prediction scheme that enhances the understanding of genetic architectures and facilitate the genetic improvement of complex traits in crops under diverse environments.

Compressed variance component mixed model reveals epistasis associated with flowering in Arabidopsis

Introduction

Epistasis is currently a topic of great interest in molecular and quantitative genetics. Arabidopsis thaliana, as a model organism, plays a crucial role in studying the fundamental biology of diverse plant species. However, there have been limited reports about identification of epistasis related to flowering in genome-wide association studies (GWAS). Therefore, it is of utmost importance to conduct epistasis in Arabidopsis.

Method

In this study, we employed Levene's test and compressed variance component mixed model in GWAS to detect quantitative trait nucleotides (QTNs) and QTN-by-QTN interactions (QQIs) for 11 flowering-related traits of 199 Arabidopsis accessions with 216,130 markers.

Results

Our analysis detected 89 QTNs and 130 pairs of QQIs. Around these loci, 34 known genes previously reported in Arabidopsis were confirmed to be associated with flowering-related traits, such as SPA4, which is involved in regulating photoperiodic flowering, and interacts with PAP1 and PAP2, affecting growth of Arabidopsis under light conditions. Then, we observed significant and differential expression of 35 genes in response to variations in temperature, photoperiod, and vernalization treatments out of unreported genes. Functional enrichment analysis revealed that 26 of these genes were associated with various biological processes. Finally, the haplotype and phenotypic difference analysis revealed 20 candidate genes exhibiting significant phenotypic variations across gene haplotypes, of which the candidate genes AT1G12990 and AT1G09950 around QQIs might have interaction effect to flowering time regulation in Arabidopsis.

Discussion

These findings may offer valuable insights for the identification and exploration of genes and gene-by-gene interactions associated with flowering-related traits in Arabidopsis, that may even provide valuable reference and guidance for the research of epistasis in other species.

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

BACKGROUND

Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.

RESULTS

We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.

CONCLUSIONS

Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.

Genome-wide association studies revealed complex genetic architecture and breeding perspective of maize ear traits

BACKGROUND

Maize (Zea Mays) is one of the world's most important crops. Hybrid maize lines resulted a major improvement in corn production in the previous and current centuries. Understanding the genetic mechanisms of the corn production associated traits greatly facilitate the development of superior hybrid varieties.

RESULT

In this study, four ear traits associated with corn production of Nested Association Mapping (NAM) population were analyzed using a full genetic model, and further, optimal genotype combinations and total genetic effects of current best lines, superior lines, and superior hybrids were predicted for each of the traits at four different locations. The analysis identified 21-34 highly significant SNPs (-log₁₀P > 5), with an estimated total heritability of 37.31-62.34%, while large contributions to variations was due to dominance, dominance-related epistasis, and environmental interaction effects ([Formula: see text] 14.06% ~ 49.28%), indicating these factors contributed significantly to phenotypic variations of the ear traits. Environment-specific genetic effects were also discovered to be crucial for maize ear traits. There were four SNPs found for three ear traits: two for ear length and weight, and two for ear row number and length. Using the Enumeration method and the stepwise tuning technique, optimum multi-locus genotype combinations for superior lines were identified based on the information obtained from GWAS.

CONCLUSIONS

Predictions of genetic breeding values showed that different genotype combinations in different geographical regions may be better, and hybrid-line variety breeding with homozygote and heterozygote genotype combinations may have a greater potential to improve ear traits.

Identification of superior parents with high fiber quality using molecular markers and phenotypes based on a core collection of upland cotton (Gossypium hirsutum L.)

The combination of molecular markers and phenotypes to select superior parents has become the goal of modern breeders. In this study, 491 upland cotton (Gossypium hirsutum L.) accessions were genotyped using the CottonSNP80K array and then a core collection (CC) was constructed. Superior parents with high fiber quality were identified using molecular markers and phenotypes based on the CC. The Nei diversity index, Shannon's diversity index, and polymorphism information content among chromosomes for 491 accessions ranged from 0.307 to 0.402, 0.467 to 0.587, and 0.246 to 0.316, with mean values of 0.365, 0.542, and 0.291, respectively. A CC containing 122 accessions was established and was categorized into eight clusters based on the K2P genetic distances. From the CC, 36 superior parents (including duplicates) were selected, which contained the elite alleles of markers and ranked in the top 10% of phenotypic values for each fiber quality trait. Among the 36 materials, eight were for fiber length, four were for fiber strength, nine were for fiber micronaire, five were for fiber uniformity, and ten were for fiber elongation. In particular, the nine materials, 348 (Xinluzhong34), 319 (Xinluzhong3), 325 (Xinluzhong9), 397 (L1-14), 205 (XianIII9704), 258 (9D208), 464 (DP201), 467 (DP150), and 465 (DP208), possessed the elite alleles of markers for at least two traits and could be given priority in breeding applications for a more synchronous improvement of fiber quality. The work provides an efficient method for superior parent selection and will facilitate the application of molecular design breeding to cotton fiber quality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01300-0.

Genomic integration to identify molecular biomarkers associated with indicator traits of gastrointestinal nematode resistance in sheep

This study aimed to integrate GWAS and structural variants to propose possible molecular biomarkers related to gastrointestinal nematode resistance traits in Santa Inês sheep. The phenotypic records FAMACHA, haematocrit, white blood cell count, red blood cell count, haemoglobin, platelets and egg counts per gram of faeces were collected from 700 naturally infected animals, belonging to four Brazilian flocks. A total of 576 animals were genotyped using the Ovine SNP12k BeadChip and were imputed using a reference population with Ovine SNP50 BeadChip. The GWAS approaches were based on SNPs, haplotypes, CNVs and ROH. The overlapping between the significant genomic regions detected from all approaches was investigated, and the results were integrated using a network analysis. Genes related to the immune system were found, such as ABCB1, IL6, WNT5A and IRF5. Genomic regions containing candidate genes and metabolic pathways involved in immune responses, inflammatory processes and immune cells affecting parasite resistance traits were identified. The genomic regions, biological processes and candidate genes uncovered could lead to biomarkers for selecting more resilient sheep and improving herd welfare and productivity. The results obtained are the start point to identify molecular biomarkers related to indicator traits of gastrointestinal nematode resistance in Santa Inês sheep.

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies

Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.

Multi-omics analysis dissects the genetic architecture of seed coat content in Brassica napus

Background

Brassica napus is an important vegetable oil source worldwide. Seed coat content is a complex quantitative trait that negatively correlates with the seed oil content in B. napus.

Results

Here we provide insights into the genetic basis of natural variation of seed coat content by transcriptome-wide association studies (TWAS) and genome-wide association studies (GWAS) using 382 B. napus accessions. By population transcriptomic analysis, we identify more than 700 genes and four gene modules that are significantly associated with seed coat content. We also characterize three reliable quantitative trait loci (QTLs) controlling seed coat content by GWAS. Combining TWAS and correlation networks of seed coat content-related gene modules, we find that BnaC07.CCR-LIKE (CCRL) and BnaTT8s play key roles in the determination of the trait by modulating lignin biosynthesis. By expression GWAS analysis, we identify a regulatory hotspot on chromosome A09, which is involved in controlling seed coat content through BnaC07.CCRL and BnaTT8s. We then predict the downstream genes regulated by BnaTT8s using multi-omics datasets. We further experimentally validate that BnaCCRL and BnaTT8 positively regulate seed coat content and lignin content. BnaCCRL represents a novel identified gene involved in seed coat development. Furthermore, we also predict the key genes regulating carbon allocation between phenylpropane compounds and oil during seed development in B. napus.

Conclusions

This study helps us to better understand the complex machinery of seed coat development and provides a genetic resource for genetic improvement of seed coat content in B. napus breeding.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02647-5.

Speeding up eQTL scans in the BXD population using GPUs

The BXD family of mouse strains are an important reference population for systems biology and genetics that have been fully sequenced and deeply phenotyped. To facilitate interactive use of genotype-phenotype relations using many massive omics data sets for this and other segregating populations, we have developed new algorithms and code that enable near-real-time whole-genome quantitative trait locus (QTL) scans for up to one million traits. By using easily parallelizable operations including matrix multiplication, vectorized operations, and element-wise operations, our method is more than 700 times faster than a R/qtl linear model genome scan using 16 threads. We used parallelization of different CPU threads as well as GPUs. We found that the speed advantage of GPUs is dependent on problem size and shape (the number of cases, number of genotypes, and number of traits). Our approach is ideal for interactive web services, such as GeneNetwork.org that need to display results in real-time. Our implementation is available as the Julia language package LiteQTL at https://github.com/senresearch/LiteQTL.jl.

Genome-wide conditional association study reveals the influences of lifestyle cofactors on genetic regulation of body surface area in MESA population

Body surface area (BSA) is an important trait used for many clinical purposes. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). GWAS of BSA was conducted on 5,324 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from the Multi-Ethnic Study of Atherocloris (MESA) data using unconditional and conditional full genetic models. In this study, fifteen SNPs were identified (Experiment-wise PEW < 1×10-5) using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. By comparing association analysis results from unconditional and cofactor conditional models, we observed three different scenarios: (i) genetic effects of several SNPs did not affected by cofactors, e.g., additive effect of gene CREB5 (a≙ -0.013 for T/T and 0.013 for G/G, -Log10 PEW = 8.240) did not change in the cofactor models; (ii) genetic effects of several SNPs affected by cofactors, e.g., the genetic additive effect (a≙ 0.012 for A/A and -0.012 for G/G, -Log10 PEW = 7.185) of SNP of the gene GRIN2A was not significant in transportation cofactor model; and (iii) genetic effects of several SNPs suppressed by cofactors, e.g., additive (a≙ -0.018 for G/G and 0.018 for C/C, -Log10 PEW = 19.737) and dominance (d≙ -0.038 for G/C, -Log10 PEW = 27.734) effects of SNP of gene ERBB4 was identified using only transportation cofactor model. Gene ontology analysis showed that several genes are related to the metabolic pathway of calcium compounds, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer. This study revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes.

Conditional and unconditional genome-wide association study reveal complicate genetic architecture of human body weight and impacts of smoking

To reveal the impacts of smoking on genetic architecture of human body weight, we conducted a genome-wide association study on 5,336 subjects in four ethnic populations from MESA (The Multi-Ethnic Study of Atherosclerosis) data. A full genetic model was applied to association mapping for analyzing genetic effects of additive, dominance, epistasis, and their ethnicity-specific effects. Both the unconditional model (base) and conditional model including smoking as a cofactor were investigated. There were 10 SNPs involved in 96 significant genetic effects detected by the base model, which accounted for a high heritability (61.78%). Gene ontology analysis revealed that a number of genetic factors are related to the metabolic pathway of benzopyrene, a main compound in cigarettes. Smoking may play important roles in genetic effects of dominance, dominance-related epistasis, and gene-ethnicity interactions on human body weight. Gene effect prediction shows that the genetic effects of smoking cessation on body weight vary from different populations.

Genome-wide association study of maize plant architecture using F1 populations

Genome-wide association study of maize plant architecture using F₁ populations can better dissect various genetic effects that can provide precise guidance for genetic improvement in maize breeding. Maize grain yield has increased at least eightfold during the past decades. Plant architecture, including plant height, leaf angle, leaf length, and leaf width, has been changed significantly to adapt to higher planting density. Although the genetic architecture of these traits has been dissected using different populations, the genetic basis remains unclear in the F₁ population. In this work, we perform a genome-wide association study of the four traits using 573 F₁ hybrids with a mixed linear model approach and QTXNetwork mapping software. A total of 36 highly significant associated quantitative trait SNPs were identified for these traits, which explained 51.86-79.92% of the phenotypic variation and were contributed mainly by additive, dominance, and environment-specific effects. Heritability as a result of environmental interaction was more important for leaf angle and leaf length, while major effects (a, aa, and d) were more important for leaf width and plant height. The potential breeding values of the superior lines and superior hybrids were also predicted, and these values can be applied in maize breeding by direct selection of superior genotypes for the associated quantitative trait SNPs. A total of 108 candidate genes were identified for the four traits, and further analysis was performed to screen the potential genes involved in the development of maize plant architecture. Our results provide new insights into the genetic architecture of the four traits, and will be helpful in marker-assisted breeding for maize plant architecture.

Conditional GWAS revealing genetic impacts of lifestyle behaviors on low-density lipoprotein (LDL)

BACKGROUND

Accumulation of LDL cholesterol (LDL-c) within artery walls is strongly associated with the initiation and progression of atherosclerosis development. This complex trait is affected by multifactor involving polygenes, environments, and their interactions. Uncovering genetic architecture of LDL may help to increase the understanding of the genetic mechanism of cardiovascular diseases.

METHODS

We used a genetic model to analyze genetic effects including additive, dominance, epistasis, and ethnic interactions for data from the Multi-Ethnic Study of Atherosclerosis (MESA). Three lifestyle behaviors (reading, intentional exercising, smoking) were used as cofactor in conditional models.

RESULTS

We identified 156 genetic effects of 10 quantitative trait SNPs (QTSs) in base model and three conditional models. The total estimated heritability of these genetic effects was approximately 72.88% in the base model. Five genes (CELSR2, MARK2, ADAMTS12, PFDN4, and MAGI2) have biological functions related to LDL.

CONCLUSIONS

Compared with the based model LDL, the results in three conditional models revealed that intentional exercising and smoking could have impacts for causing and suppressing some of genetic effects and influence the levels of LDL. Furthermore, these two lifestyles could have different genetic effects for each ethnic group on a specific QTS. As most of the heritability in based model LDL and conditional model LDL|Smk was contributed from epistasis effects, our result indicated that epistasis effects played important roles in determining LDL levels. Our study provided useful insight into the biological mechanisms underlying regulation of LDL and might help in the discovery of novel therapeutic targets for cardiovascular disease.

Nux Vomica Exposure Triggered Liver Injury and Metabolic Disturbance in Zebrafish Larvae

Zebrafish larvae were used to further understand the liver toxicity of nux vomica. The histopathology, protein expression, and gene expression were assessed to confirm apoptosis in the liver, and then, profiles of the metabolites in zebrafish were investigated by untargeted metabolomic assessment to understand the potential toxicity mechanism of nux vomica. Histopathological observations showed that nux vomica caused damage to liver cells. Western blot results indicated increased expression of activated caspase3, and the result of real-time polymerase chain reaction showed a significant increase in the expression level of caspase-3, caspase-8, and caspase-9 genes (p < 0.05) compared with the control group. The liver injury from nux vomica was linked to the downregulation of amino acid (e.g., proline and alanine) and fatty acid (e.g., palmitoleic acid) metabolism and upregulation of some other fatty acid (e.g., arachidic acid) and purine (e.g., xanthine and uric acid) metabolism. The hepatotoxicity of nux vomica resulted from metabolic pathway disturbances, including small molecules involved in energy, purine, lipids, and amino acid metabolism.

Dissection of complicate genetic architecture and breeding perspective of cottonseed traits by genome-wide association study

BACKGROUND

Cottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement.

RESULTS

A genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids (palmitic, linoleic, oleic, myristic, stearic), respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines.

CONCLUSIONS

This study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy.

Full genetic analysis for genome-wide association study of Fangji: a powerful approach for effectively dissecting the molecular architecture of personalized traditional Chinese medicine

Elucidation of the systems biology foundation underlying the effect of Fangji, which are multi-herbal traditional Chinese medicine (TCM) formulas, is one of the major aims in the field. The numerous bioactive ingredients of a Fangji deal with the multiple targets of a complex disease, which is influenced by a number of genes and their interactions with the environment. Genome-wide association study (GWAS) is an unbiased approach for dissecting the genetic variants underlying complex diseases and individual response to a given treatment. GWAS has great potential for the study of systems biology from the point of view of genomics, but the capacity using current analysis models is largely handicapped, as evidenced by missing heritability. Recent development of a full genetic model, in which gene-gene interactions (dominance and epistasis) and gene-environment interactions are all considered, has addressed these problems. This approach has been demonstrated to substantially increase model power, remarkably improving the detection of association of GWAS and the construction of the molecular architecture. This analysis does not require a very large sample size, which is often difficult to meet for a GWAS of treatment response. Furthermore, this analysis can integrate other omic information and allow for variations of Fangji, which is very promising for Fangjiomic study and detection of the sophisticated molecular architecture of the function of Fangji, as well as for the delineation of the systems biology of personalized medicine in TCM in an unbiased and comprehensive manner.

Haplotype-based allele mining in the Japan-MAGIC rice population

Multi-parent advanced generation inter-cross (MAGIC) lines have broader genetic variation than bi-parental recombinant inbred lines. Genome-wide association study (GWAS) using high number of DNA polymorphisms such as single-nucleotide polymorphisms (SNPs) is a popular tool for allele mining in MAGIC populations, in which the associations of phenotypes with SNPs are investigated; however, the effects of haplotypes from multiple founders on phenotypes are not considered. Here, we describe an improved method of allele mining using the newly developed Japan-MAGIC (JAM) population, which is derived from eight high-yielding rice cultivars in Japan. To obtain information on the haplotypes in the JAM lines, we predicted the haplotype blocks in the whole chromosomes using 16,345 SNPs identified via genotyping-by-sequencing analysis. Using haplotype-based GWAS, we clearly detected the loci controlling the glutinous endosperm and culm length traits. Information on the alleles of the eight founders, which was based on the effects of mutations revealed by the analysis of next-generation sequencing data, was used to narrow down the candidate genes and reveal the associations between alleles and phenotypes. The haplotype-based allele mining (HAM) proposed in this study is a promising approach to the detection of allelic variation in genes controlling agronomic traits in MAGIC populations.

Quantitative Trait Transcripts Mapping Coupled with Expression Quantitative Trait Loci Mapping Reveal the Molecular Network Regulating the Apetalous Characteristic in Brassica napus L

The apetalous trait of rapeseed (Brassica napus, AACC, 2n = 38) is important for breeding an ideal high-yield rapeseed with superior klendusity to Sclerotinia sclerotiorum. Currently, the molecular mechanism underlying the apetalous trait of rapeseed is unclear. In this study, 14 petal regulators genes were chosen as target genes (TGs), and the expression patterns of the 14 TGs in the AH population, containing 189 recombinant inbred lines derived from a cross between apetalous "APL01" and normal "Holly," were analyzed in two environments using qRT-PCR. Phenotypic data of petalous degree (PDgr) in the AH population were obtained from the two environments. Both quantitative trait transcript (QTT)-association mapping and expression QTL (eQTL) analyses of TGs expression levels were performed to reveal regulatory relationships among TGs and PDgr. QTT mapping for PDgr determined that PLURIPETALA (PLP) was the major negative QTT associated with PDgr in both environments, suggesting that PLP negatively regulates the petal development of line "APL01." The QTT mapping of PLP expression levels showed that CHROMATIN-REMODELING PROTEIN 11 (CHR11) was positively associated with PLP expression, indicating that CHR11 acts as a positive regulator of PLP expression. Similarly, QTT mapping for the remaining TGs identified 38 QTTs, associated with 13 TGs, and 31 QTTs, associated with 10 TGs, respectively, in the first and second environments. Additionally, eQTL analyses of TG expression levels showed that 12 and 11 unconditional eQTLs were detected in the first and second environment, respectively. Based on the QTTs and unconditional eQTLs detected, we presented a hypothetical molecular regulatory network in which 14 petal regulators potentially regulated the apetalous trait in "APL01" through the CHR11-PLP pathway. PLP acts directly as the terminal signal integrator negatively regulating petal development in the CHR11-PLP pathway. These findings will aid in the understanding the molecular mechanism underlying the apetalous trait of rapeseed.

An association study revealed substantial effects of dominance, epistasis and substance dependence co-morbidity on alcohol dependence symptom count

Alcohol dependence is a complex disease involving polygenes, environment and their interactions. Inadequate consideration of these interactions may have hampered the progress on genome-wide association studies of alcohol dependence. By using the dataset of the Study of Addiction: Genetics and Environment with 3838 subjects, we conducted a genome-wide association studies of alcohol dependence symptom count (ADSC) with a full genetic model considering additive, dominance, epistasis and their interactions with ethnicity, as well as conditions of co-morbid substance dependence. Twenty quantitative trait single nucleotide polymorphisms (QTSs) showed highly significant associations with ADSC, including four previously reported genes (ADH1C, PKNOX2, CPE and KCNB2) and the reported intergenic rs1363605, supporting the overall validity of the analysis. Two QTSs within or near ADH1C showed very strong association in a dominance inheritance mode and increased the phenotype value of ADSC when the effect of co-morbid opiate or marijuana dependence was controlled. Highly significant association was also identified in variants within four novel genes (RGS6, FMN1, NRM and BPTF), two non-coding RNA and two epistasis loci. QTS rs7616413, located near PTPRG encoding a protein tyrosine phosphatase receptor, interacted with rs10090742 within ANGPT1 encoding a protein tyrosine phosphatase in an additive × additive or dominance × additive manner. The detected QTSs contributed to about 20 percent of total heritability, in which dominance and epistasis effects accounted for over 50 percent. These results demonstrated that perturbations arising from gene-gene interaction and conditions of co-morbidity substantially influence the genetic architecture of complex trait.

Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information

Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases. Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.

Genetic dissection of yield traits in super hybrid rice Xieyou9308 using both unconditional and conditional genome-wide association mapping

With the development and application of super rice breeding, elite rice hybrids with super high-yielding potential have been widely developed in last decades in China. Xieyou9308 is one of the most famous super hybrid rice varieties. To uncover the genetic mechanism of Xieyou9308’s high yield potential, a recombinant inbred line (RIL) population derived from cross of XieqingzaoB and Zhonghui9308 was re-sequenced and investigated on the grain yield (GYD) and its three component traits, number of panicles per plant (NP), number of filled grains per panicle (NFGP), and grain weight (GW). Unconditional and conditional genome-wide association analysis, based on a linear mixed model with epistasis and gene-environment interaction effects, were conducted, using ~0.7 million identified SNPs. There were six, four, seven, and seven QTSs identified for GYD, NP, NFGP, and GW, respectively, with accumulated explanatory heritability varying from 43.06% to 48.36%; additive by environment interactions were detected for GYD, some minor epistases were detected for NP and NFGP. Further, conditional genetic mapping analysis for GYD given its three components revealed several novel QTSs associated with yield than that were suppressed in our unconditional mapping analysis.

Metabolomic mechanisms of gypenoside against liver fibrosis in rats: An integrative analysis of proteomics and metabolomics data

Aims

To investigate mechanisms and altered pathways of gypenoside against carbon tetrachloride (CCl₄)-induced liver fibrosis based on integrative analysis of proteomics and metabolomics data.

Methods

CCl₄-induced liver fibrosis rats were administrated gypenoside. The anti-fibrosis effects were evaluated by histomorphology and liver hydroxyproline (Hyp) content. Protein profiling and metabolite profiling of rats liver tissues were examined by isobaric tags for relative and absolute quantitation (iTRAQ) approach and gas chromatography-mass spectrometer (GC-MS) technology. Altered pathways and pivotal proteins and metabolites were searched by integrative analysis of proteomics and metabolomics data. The levels of some key proteins in altered pathways were determined by western blot.

Results

Histopathological changes and Hyp content in gypenoside group had significant improvements (P<0.05). Compared to liver fibrosis model group, we found 301 up-regulated and 296 down-regulated proteins, and 9 up-regulated and 8 down-regulated metabolites in gypenoside group. According to integrative analysis, some important pathways were found, including glycolysis or gluconeogenesis, fructose and mannose metabolism, glycine, serine and threonine metabolism, lysine degradation, arginine and proline metabolism, glutathione metabolism, and sulfur metabolism. Furthermore, the levels of ALDH1B1, ALDH2 and ALDH7A1 were found increased and restored to normal levels after gypenoside treated (P<0.05).

Conclusions

Gypenoside inhibited CCl₄-induced liver fibrosis, which may be involved in the alteration of glycolysis metabolism and the protection against the damage of aldehydes and lipid peroxidation by up-regulating ALDH.

Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (Brassica napus L.)

Oilseed rape (Brassica napus) is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicted for each trait in two environments based on the predicted genotypic effects. As a result, 17 quantitative trait SNPs (QTSs) were identified significantly for target traits with total heritability varied from 58.47 to 87.98%, most of which were contributed by dominance, epistasis, and environment-specific effects. The results indicated that non-additive effects were large contributions to heritability and epistasis, and also noted that environment interactions were important variants for oilseed breeding. Our study facilitates the understanding of genetic basis of rapeseed yield trait, helps to accelerate rapeseed breading, and also offers a roadmap for precision plant breeding via marker-assisted selection.

Dissecting Genetic Network of Fruit Branch Traits in Upland Cotton by Association Mapping Using SSR Markers

Genetic architecture of branch traits has large influences on the morphological structure, photosynthetic capacity, planting density, and yield of Upland cotton (Gossypium hirsutum L.). This research aims to reveal the genetic effects of six branch traits, including bottom fruit branch node number (BFBNN), bottom fruit branch length (BFBL), middle fruit branch node number (MFBNN), middle fruit branch length (MFBL), upper fruit branch node number (UFBNN), and upper fruit branch length (UFBL). Association mapping was conducted for these traits of 39 lines and their 178 F₁ hybrids in three environments. There were 20 highly significant Quantitative Trait SSRs (QTSs) detected by mixed linear model approach analyzing a full genetic model with genetic effects of additive, dominance, epistasis and their environment interaction. The phenotypic variation explained by genetic effects ranged from 32.64 ~ 91.61%, suggesting these branch traits largely influenced by genetic factors.

Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits.

Linkage Mapping of Stem Saccharification Digestibility in Rice

Rice is the staple food of almost half of the world population, and in excess 90% of it is grown and consumed in Asia, but the disposal of rice straw poses a problem for farmers, who often burn it in the fields, causing health and environmental problems. However, with increased focus on the development of sustainable biofuel production, rice straw has been recognized as a potential feedstock for non-food derived biofuel production. Currently, the commercial realization of rice as a biofuel feedstock is constrained by the high cost of industrial saccharification processes needed to release sugar for fermentation. This study is focused on the alteration of lignin content, and cell wall chemotypes and structures, and their effects on the saccharification potential of rice lignocellulosic biomass. A recombinant inbred lines (RILs) population derived from a cross between the lowland rice variety IR1552 and the upland rice variety Azucena with 271 molecular markers for quantitative trait SNP (QTS) analyses was used. After association analysis of 271 markers for saccharification potential, 1 locus and 4 pairs of epistatic loci were found to contribute to the enzymatic digestibility phenotype, and an inverse relationship between reducing sugar and lignin content in these recombinant inbred lines was identified. As a result of QTS analyses, several cell-wall associated candidate genes are proposed that may be useful for marker-assisted breeding and may aid breeders to produce potential high saccharification rice varieties.

Dissection of genetic architecture of rice plant height and heading date by multiple-strategy-based association studies

Xieyou9308 is a certified super hybrid rice cultivar with a high grain yield. To investigate its underlying genetic basis of high yield potential, a recombinant inbred line (RIL) population derived from the cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) was constructed for identification of quantitative trait SNPs (QTSs) associated with two important agronomic traits, plant height (PH) and heading date (HD). By re-sequencing of 138 recombinant inbred lines (RILs), a total of ~0.7 million SNPs were identified for the association studies on the PH and HD. Three association mapping strategies (including hypothesis-free genome-wide association and its two complementary hypothesis-engaged ones, QTL-based association and gene-based association) were adopted for data analysis. Using a saturated mixed linear model including epistasis and environmental interaction, we identified a total of 31 QTSs associated with either the PH or the HD. The total estimated heritability across three analyses ranged from 37.22% to 45.63% and from 37.53% to 55.96% for the PH and HD, respectively. In this study we examined the feasibility of association studies in an experimental population (RIL) and identified several common loci through multiple strategies which could be preferred candidates for further research.

Plant Metabolomics: An Indispensable System Biology Tool for Plant Science

As genomes of many plant species have been sequenced, demand for functional genomics has dramatically accelerated the improvement of other omics including metabolomics. Despite a large amount of metabolites still remaining to be identified, metabolomics has contributed significantly not only to the understanding of plant physiology and biology from the view of small chemical molecules that reflect the end point of biological activities, but also in past decades to the attempts to improve plant behavior under both normal and stressed conditions. Hereby, we summarize the current knowledge on the genetic and biochemical mechanisms underlying plant growth, development, and stress responses, focusing further on the contributions of metabolomics to practical applications in crop quality improvement and food safety assessment, as well as plant metabolic engineering. We also highlight the current challenges and future perspectives in this inspiring area, with the aim to stimulate further studies leading to better crop improvement of yield and quality.