Genome-wide Association Analysis of Maize Flowering Traits

Flowering regulation is important for maize to adapt to a variety of environments as well as associated with high yield.In this study,the genetic mechanism of three flowering traits of 310 maize inbred lines with rich genetic background was investigated in three years at three different environments such as days to tasseling(DTT),days to silking(DTS)and days to pollen shedding(DTP).Based on mean performance,the longest flowering time was observed in Zhanyi(2018),whereas the shortest in Shizong(2019).The coefficient of variance depicted the range from 3.62%to 9.06%for three flowering traits under all environments.Therefore,we have integrated these flowering traits corresponding to SNP molecular markers for genome-wide association study(GWAS).Results showed that 22 SNPs markers were significantly associated with DTT according to physical position and average linkage disequilibrium(LD)decay distance,and a total of 234 candidate genes were identified near these significantly associated SNP markers.Moreover,KEGG and GO analysis showed that these genes were enriched in the regulation of the physiological pathways for flowering.In more details,16 genes involved in development of floral organs are more worthy of our attention in future studies.

Genetic dissection of hexanol content in soybean seed through genome-wide association analysis

Hexanol is a major compound contributing to the off-flavors(the bean-like odor)of soybean derived soymilk.The most effective way to reduce the off-flavors of soymilk is the screening and utilization of soybean cultivars with improved hexanol content.However,no genome-wide genetic analysis for this particular trait has been conducted to date.The objective of the present study was to dissect the genetic basis of hexanol content in soybean seed through genome-wide association analysis(GWAS).A total of 105 soybean accessions were analyzed for hexanol content in a three-year experiments and genotyped by sequencing using the specific locus amplified fragment sequencing(SLAF-seq)approach.A total of 25 724 single nucleotide polymorphisms(SNPs)were obtained with minor allele frequencies(MAF)>5%.GWAS showed that 25 quantitative trait nucleotides(QTNs)were significantly associated with the hexanol concentration in soybean seed.These identified QTNs distributed on different genomic regions of the 15 chromosomes.A total of 91 genes were predicted as candidate genes underlying the seed hexanol level and six candidates were predicted possibly underlying the seed hexanol by gene based association.In this study,GWAS has been proven to be an effective way to dissect the genetic basis of the hexanol concentration in multiple genetic backgrounds.The identified beneficial alleles and candidate genes might be valuable for the improvement of marker-assisted breeding efficiency for low hexanol level and help to explore possible molecular mechanisms underlying hexanol content in soybean seed.

Genome-wide Association Analysis of Fast Chlorophyll Fluorescence Parameters in Maize

Fast chlorophyll fluorescence parameters are widely used to characterize the photosynthetic efficiency of plants. In this study, a genome-wide association analysis was used to detect key single-nueleotide polymorphisms （SNPs） associated with fast chlorophyll fluorescence parameters using more than 560 000 SNPs in a maize panel consisting of 404 inbred lines. In four fidd environments, 41 SNPs were detected to be associated with five fast chlorophyll fluorescence parameters, including ABS/CS0, ET0/CS0, TR0/ABS, ET0/TR0 and Pies. Among these identified SNPs, 8, 6, 18, 4 and 5 were significantly associated with ET0/TR0, ABS/ CS0, TR0/ABS, ET0/CS, and Plcs, respectively. These SNPs will help to discover genes for chlorophyll fluorescence parameters, better understand the genetic basis of photosynthesis, and assist in developing marker-assisted selection breeding programs in maize.

Genome-Wide Association Analysis and Allelic Mining of Grain Shape-Related Traits in Rice

Excavating single nucleotide polymorphisms (SNPs) significantly associated with rice grain shape and predicting candidate genes through genome-wide association study (GWAS) can provide a theoretical basis for discovery and utilization of excellent genetic resources in rice. Based on 16 352 SNPs, 161 natural indica rice varieties with various grain sizes in southern China were used for GWAS of grain shape-related traits, referring to grain length (GL), grain width (GW), 1000-grain weight (TGW), and grain length/width (GLW). Phenotypic statistics showed that coefficient of variation values for these four traits GL, GW, TGW and GLW were 9.92%, 9.09%, 20.20% and 16.38%, respectively. Each trait showed a normal distribution, and there was a certain correlation between these traits. Through general linear model correlation analysis, a total of 38 significant loci were identified, and a range of 100 kb upstream and downstream of the significant loci was identified as the candidate interval. On chromosome 3, GS3 and qGL3 were found to regulate GL. On chromosome 6, TGW6 and GW6a were found to regulate TGW. Also, some QTLs related to grain shape were found on chromosomes 5 and 9. Besides that, using sequenced 3K-germplasm resources, we found that there are 22 overlapped varieties between these two natural populations. Twenty-six SNPs and fourteen haplotypes were identified in five regions of GS3 genes. The detection of multiple candidate genes/QTLs within the candidate interval is beneficial for further excavation of superior rice genetic resources.

Genome-wide association mapping and genomic prediction of stalk rot in two mid-altitude tropical maize populations

Maize stalk rot reduces grain yield and quality.Information about the genetics of resistance to maize stalk rot could help breeders design effective breeding strategies for the trait.Genomic prediction may be a more effective breeding strategy for stalk-rot resistance than marker-assisted selection.We performed a genome-wide association study(GWAS)and genomic prediction of resistance in testcross hybrids of 677 inbred lines from the Tuxpe?o and non-Tuxpe?o heterotic pools grown in three environments and genotyped with 200,681 single-nucleotide polymorphisms(SNPs).Eighteen SNPs associated with stalk rot shared genomic regions with gene families previously associated with plant biotic and abiotic responses.More favorable SNP haplotypes traced to tropical than to temperate progenitors of the inbred lines.Incorporating genotype-by-environment(G×E)interaction increased genomic prediction accuracy.

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Marker-assisted selection(MAS)and genomic selection(GS)breeding have greatly improved the efficiency of rice breeding.Due to the influences of epistasis and gene pleiotropy,ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome.In this study,113 indica rice varieties(V)and their 565 testcross hybrids(TC)were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits.The original traits and general combining ability of the parents,as well as the original traits and midparent heterosis of TC,were subjected to genome-wide association analysis.In total,381 primary significantly associated loci(SAL)and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected.Among these loci,322 candidate genes located within or nearby the SALs were screened,204 of which were cloned genes.A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs.All the SALs were used to construct genetic networks,in which 91 pleiotropic loci were investigated.Additionally,we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs,primary SALs,secondary SALs or epistatic effect SALs as covariates.Although the prediction accuracies of the four models were generally not significantly different in the TC dataset,the incorporation of primary SALs,secondary SALs,and epistatic effect SALs significantly improved the prediction accuracies of 5(26%),3(16%),and 11(58%)traits in the V dataset,respectively.These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines.They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Leveraging the potential of big genomic and phenotypic data for genome-wide association mapping in wheat

Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.

Genome-wide association study of seedling nitrogen-use efficiency-associated traits in common wheat(Triticum aestivum L.)

Nitrogen(N)fertilizer application is essential for crop-plant growth and development.Identifying genetic loci associated with N-use efficiency(NUE)could increase wheat yields and reduce environmental pollution caused by overfertilization.We subjected a panel of 389 wheat accessions to N and chlorate(a nitrate analog)treatments to identify quantitative trait loci(QTL)controlling NUE-associated traits at the wheat seedling stage.Genotyping the panel with a 660K single-nucleotide polymorphism(SNP)array,we identified 397 SNPs associated with N-sensitivity index and chlorate inhibition rate.These SNPs were merged into 49 QTL,of which eight were multi-environment stable QTL and 27 were located near previously reported QTL.A set of 135 candidate genes near the 49 QTL included TaBOX(F-box family protein)and TaERF(ethylene-responsive transcription factor).A Tabox mutant was more sensitive to low-N stress than the wild-type plant.We developed two functional markers for Hap 1,the favorable allele of TaBOX.

Genome-Wide Association Study of Cooked Rice Textural Attributes and Starch Physicochemical Properties in indica Rice

Rice cooking and eating qualities(CEQ)are mainly determined by cooked rice textural parameters and starch physicochemical properties.However,the genetic bases of grain texture and starch properties in rice have not been fully understood.We conducted a genome-wide association study for apparent amylose content(AAC),starch pasting viscosities,and cooked rice textural parameters using 279 indica rice accessions from the 3000 Rice Genome Project.We identified 26 QTLs in the whole population and detected single nucleotide polymorphisms(SNPs)with the lowest P-value at the Waxy(Wx)locus for all traits except pasting temperature and cohesiveness.Additionally,we detected significant SNPs at the SUBSTANDARD STARCH GRAIN6(SSG6)locus for AAC,setback(SB),hardness,adhesiveness,chewiness(CHEW),gumminess(GUM),and resilience.We subsequently divided the population using a SNP adjacent to the Waxy locus,and identified 23 QTLs and 12 QTLs in two sub-panels,WxT and WxA,respectively.In these sub-panels,SSG6 was also identified to be associated with pasting parameters,including peak viscosity,hot paste viscosity,cold paste viscosity,and consistency viscosity.Furthermore,a candidate gene encoding monosaccharide transporter 5(OsMST5)was identified to be associated with AAC,breakdown,SB,CHEW,and GUM.In total,39 QTLs were co-localized with known genes or previously reported QTLs.These identified genes and QTLs provide valuable information for genetic manipulation to improve rice CEQ.

Genome-Wide Analysis for Yield-Related Agronomic and Biochemical Traits of Chinese and Bangladeshi Grass Pea Genotypes Using SSR Markers

Grass pea(Lathyrus sativus L.)is an imperative food crop cultured in dryland agricultural ecology.It is a vital source of dietary protein to millions of populaces living in low-income countries in South-East Asia and Africa.This study highlights the improvement of genomic properties and their application in marker-trait relationships for 17 yield-related characters in 400 grass pea genotypes from China and Bangladesh.These characters were assessed via 56 polymorphic markers using general linear model(GLM)(P+G+Q)and mixed linear model(MLM)(P+G+Q+K)in the tassel software based on the linkage disequilibrium and population structure analysis.Population structure analysis showed two major groups and one admixed group in the populace.Statistically significant loci pairs of linkage disequilibrium(LD)mean value(D′)was 0.479.A total of 99 and 61 marker-trait associations in GLM and MLM models allied to the 17 traits were accepted at a 5%level of significance.Among these markers,21 markers were associated with more than one trait;12 marker-trait associations passed the Bonferroni correction threshold.Both models found six markers C41936,C39067,C34100,C47146,C47638,and C43047 significantly associated with days to maturity,flower color,plant height,and seed per pod were detected in the Hebei and Liaoyang location(p≤0.01),and the interpretation rate(R^(2)value)11.2%to 43.6%.Conferring to the consequences,the association analysis methodology may operative system for quantitative,qualitative,and biochemical traits related to gene position mapping and support breeders in improving novel approaches for advancing the grass pea quality.

Genome-wide and candidate gene association studies identify BnPAP17 as conferring the utilization of organic phosphorus in oilseed rape

Phosphorus(P)is essential for living plants,and P deficiency is one of the key factors limiting the yield in rapeseed production worldwide.As the most important organ for plants,root morphology traits(RMTs)play a key role in P absorption.To investigate the genetic variability of RMT under low P availability,we dissected the genetic structure of RMTs by genome-wide association studies(GWAS),linkage mapping and candidate gene association studies(CGAS).A total of 52 suggestive loci were associated with RMTs under P stress conditions in 405 oilseed rape accessions.The purple acid phosphatase gene BnPAP17 was found to control the lateral root number(LRN)and root dry weight(RDW)under low P stress.The expression of BnPAP17 was increased in shoot tissue in P-efficient cultivars compared to root tissue and P-inefficient cultivars in response to low P stress.Moreover,the haplotype of BnPAP17^(Hap3)was detected for the selective breeding of P efficiency in oilseed rape.Over-expression of the BnPAP17^(Hap3)could promote the shoot and root growth with enhanced tolerance to low P stress and organic phosphorus(Po)utilization in oilseed rape.Collectively,these findings increase our understanding of the mechanisms underlying BnPAP17-mediated low P stress tolerance in oilseed rape.

Genetic Parameters and Genome-Wide Association Studies for Body Size Traits of Shuxuan Cattle in China

In domestic cattle,the body size traits have important implications in terms of breed characteristics and production performance.Shuxuan cattle is a dual-purpose breed mainly raised in Sichuan province,China,for which we have known less about the genetic parameters and underlying candidate genes in relation to the body size traits.In this study,we obtained the genome-wide single nucleotide polymorphisms(SNPs)using the Illumina Bovine BeadChip in 275 Shuxuan cattle.These SNPs were first used for estimating genetic parameters for the withers height(WH)and diagonal body length(BL).Using the bivariate animal model,the estimates(±standard error)of heritabilities were 0.71±0.22 and 0.49±0.29 for BL,and their genetic correlation was 0.64±0.37.Second,the genome-wide association study(GWAS)was performed.However,these did not result into genome-wide significant SNPs for both WH and BL traits.According to a less stringent suggestive significance,some positional candidate genes were found,and some of them(such as FAM110B,TAS1R2,PAX3,and FHIT)were previously reported in literature to be associated with body size traits in cattle.In conclusion,we estimated the genetic parameters in Shuxuan cattle using genomic information for the first time,which are required for implementing the genomic selection programs in the future.

Causal association between 731 immunocyte phenotypes and liver cirrhosis: A bidirectional two-sample mendelian randomization analysis

BACKGROUND Liver cirrhosis is a progressive hepatic disease whose immunological basis has attracted increasing attention.However,it remains unclear whether a concrete causal association exists between immunocyte phenotypes and liver cirrhosis.AIM To explore the concrete causal relationships between immunocyte phenotypes and liver cirrhosis through a mendelian randomization(MR)study.METHODS Data on 731 immunocyte phenotypes were obtained from genome-wide assoc-iation studies.Liver cirrhosis data were derived from the Finn Gen dataset,which included 214403 individuals of European ancestry.We used inverse variable weighting as the primary analysis method to assess the causal relationship.Sensitivity analyses were conducted to evaluate heterogeneity and horizontal pleiotropy.RESULTS The MR analysis demonstrated that 11 immune cell phenotypes have a positive association with liver cirrhosis[P<0.05,odds ratio(OR)>1]and that 9 immu-nocyte phenotypes were negatively correlated with liver cirrhosis(P<0.05,OR<1).Liver cirrhosis was positively linked to 9 immune cell phenotypes(P<0.05,OR>1)and negatively linked to 10 immune cell phenotypes(P<0.05;OR<1).None of these associations showed heterogeneity or horizontally pleiotropy(P>0.05).CONCLUSION This bidirectional two-sample MR study demonstrated a concrete causal association between immunocyte phenotypes and liver cirrhosis.These findings offer new directions for the treatment of liver cirrhosis.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

fGWAS:An R package for genome-wide association analysis with longitudinal phenotypes

The past two decades have witnessed a revolution in identifying genetic risk factors underlying diseases and complex traits using genome-wide association studies （GWAS） （Risch and Merikangas, 1996; Hirschhom and Daly, 2005; Altshuler et al., 2008）. Together with advanced high-throughput technologies for genotyping and sequencing, GWAS have discovered thousands of susceptibility loci for various traits （Welter et al., 2014）.

Genome-wide association analysis reveals genetic loci and candidate genes associated with intramuscular fat in Duroc pigs

Intramuscular fat(IMF) is a major meat-quality trait in pigs. The content of IMF is directly associated with the taste and flavor of pork. As a complex trait, there could be multiple genes affecting IMF content in pork. Genomewide association study is a powerful tool to detect genomic regions associated with phenotypic variations. The objectives of the present study were to identify or refine the positions of genomic regions affecting IMF, and to characterize candidate genes and pathways that may influence this trait. Of note, we identified a significant region in longissium dorsi muscle in a Duroc pig population for IMF content with Porcine SNP60 v2 Bead Chip. This region spans 1.24 Mb on chromosome 8 and had been identified as a quantitative trait locus for IMF in Pietrain, Large White, Landrace, and Leicoma pigs. In this region, eight SNPs were significantly associated with IMF content. Three genes proximal to these significant SNPs were considered candidate genes, including ZDHHC16, LOC102162218 and PCDH7. Our results confirm several previous findings and highlight several genes that may contribute to IMF variation in Duroc pigs.

Genetic polymorphisms and gastric cancer risk: a comprehensive review synopsis from meta-analysis and genome-wide association studies

Objective: In the past few decades, more than 500 reports have been published on the relationship between single nucleotide polymorphisms(SNPs) on candidate genes and gastric cancer(GC) risk. Previous findings have been disputed and are controversial. Therefore, we performed this article to summarize and assess the credibility and strength of genetic polymorphisms on the risk of GC.Methods: We used Web of Science, PubMed, and Medline to identify meta-analyses published before July 30 th, 2018 that assessed associations between variants on candidate genes and the risk of GC. Cumulative epidemiological evidence of statistical associations was assessed combining Venice criteria and a false-positive report probability(FPRP) test.Results: Sixty-one variants demonstrated a significant association with GC risk, whereas 29 demonstrated no association. Nine variants on nine genes were rated as presenting strong cumulative epidemiological evidence for a nominally significant association with GC risk, including APE1(rs1760944), DNMT1(rs16999593), ERCC5(rs751402), GSTT1(null/presence), MDM2(rs2278744), PPARG(rs1801282), TLR4(rs4986790), IL-17 F(rs763780), and CASP8(rs3834129). Eleven SNPs were rated as moderate, and 33 SNPs were rated as weak. We also used the FPRP test to identify 13 noteworthy SNPs in five genome-wide association studies.Conclusions: Sixty-one variants are significantly associated with GC risk, and 29 variants are not associated with GC risk;however, five variants on five genes presented strong evidence for an association upgraded from moderate. Further study of these variants may be needed in the future. Our study also provides referenced information for the genetic predisposition to GC.

Genetic dissection of the grain-filling rate and related traits through linkage analysis and genome-wide association study in bread wheat

Wheat grain yield is generally sink-limited during grain filling.The grain-filling rate(GFR)plays a vital role but is poorly studied due to the difficulty of phenotype surveys.This study explored the grain-filling traits in a recombinant inbred population and wheat collection using two highly saturated genetic maps for linkage analysis and genome-wide association study(GWAS).Seventeen stable additive quantitative trait loci(QTLs)were identified on chromosomes 1B,4B,and 5A.The linkage interval between IWB19555 and IWB56078 showed pleiotropic effects on GFR_(1),GFR_(max),kernel length(KL),kernel width(KW),kernel thickness(KT),and thousand kernel weight(TKW),with the phenotypic variation explained(PVE)ranging from 13.38%(KW)to 33.69%(TKW).198 significant marker-trait associations(MTAs)were distributed across most chromosomes except for 3D and 4D.The major associated sites for GFR included IWB44469(11.27%),IWB8156(12.56%)and IWB24812(14.46%).Linkage analysis suggested that IWB35850,identified through GWAS,was located in approximately the same region as QGFR_(max)2B.3-11,where two high-confidence candidate genes were present.Two important grain weight(GW)-related QTLs colocalized with grain-filling QTLs.The findings contribute to understanding the genetic architecture of the GFR and provide a basic approach to predict candidate genes for grain yield trait QTLs.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

Pathway-based analysis of genome-wide association study of circadian phenotypes

Sleepiness affects normal social life, which attracts more and more attention. Circadian phenotypes contribute to obvious individual differences in susceptibility to sleepiness. We aimed to identify candidate single nucleotide polymorphisms（SNPs） which may cause circadian phenotypes, elucidate the potential mechanisms, and generate corresponding SNP-gene-pathways. A genome-wide association studies（GWAS） dataset of circadian phenotypes was utilized in the study. Then, the Identify Candidate Causal SNPs and Pathways analysis was employed to the GWAS dataset after quality control filters. Furthermore, genotype-phenotype association analysis was performed with HapMap database. Four SNPs in three different genes were determined to correlate with usual weekday bedtime,totally providing seven hypothetical mechanisms. Eleven SNPs in six genes were identified to correlate with usual weekday sleep duration, which provided six hypothetical pathways. Our results demonstrated that fifteen candidate SNPs in eight genes played vital roles in six hypothetical pathways implicated in usual weekday bedtime and six potential pathways involved in usual weekday sleep duration.