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 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.

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 Diversity,Population Structure,and Genome-Wide Association Study of Seven Agronomic Traits in 273 Diverse Upload Cotton Accessions

Upland cotton(Gossypium hirsutum)is the most important plant producing natural fibers for the textile industry.In this study,we first investigated the phenotypic variation of seven agronomic traits of 273 diverse cotton accessions in the years 2017 and 2018,which were from 18 geographical regions.We found large variations among the traits in different geographical regions and only half of the traits in either years 2017 or 2018 followed a normal distribution.We then genotyped the collection with 81,612 high quality SNPs.Phylogenetic tree and population structure revealed a diverse genetic structure of the core collection,and geographical diversification was an important factor,but account for part of the variances of genetic diversification.We then performed genome-wide association study for the seven traits in the years 2017 and 2018,and the average values of each trait in the two years,respectively.We identified a total of 19 significant marker-trait associations and found that Pollen Ole e 1 allergen/extension could be the candidate gene associated with the fall-off cotton bolls from the last three branches.In addition,large variations were observed for the heritability of traits in the years 2017 and 2018.These results provide new potential candidate genes for further functional validation,which could be useful for genetic improvement and breeding of new cotton cultivars with better agronomic performances.

Genome-wide associations, polygenic risk, and Mendelian randomization reveal limited interactions between John Henryism and cynicism

BACKGROUND John Henryism(JH)is a strategy for dealing with chronic psychological stress characterized by high levels of physical effort and work.Cynicism is a belief that people are motivated primarily by self-interest.High scores on the JH scale and cynicism measures correlate with an increased risk of cardiovascular disease.High cynicism is also a hallmark of burnout syndrome,another known risk factor for heart disease.AIM To evaluate possible interactions between JH and cynicism hoping to clarify risk factors of burnout.METHODS We analyzed genetic and psychological data available from the Database of Genotypes and Phenotypes for genome-wide associations with these traits.We split the total available samples and used plink to perform the association studies on the discovery set(n=1852,80%)and tested for replication using the validation set(n=465).We used scikit-learn to perform supervised machine learning for developing genetic risk algorithms.RESULTS We identified 2,727,and 204 genetic associations for scores on the JH,cynicism and cynical distrust(CD)scales,respectively.We also found 173 associations with high cynicism,109 with high CD,but no associations with high JH.We also produced polygenic classifiers for high cynicism using machine learning with areas under the receiver operator characteristics curve greater than 0.7.CONCLUSION We found significant genetic components to these traits but no evidence of an interaction.Therefore,while there may be a genetic risk,JH is not likely a burnout risk factor.

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 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.

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.

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.

Statistical analysis for genome-wide association study

In the past few years, genome-wide association study （GWAS） has made great successes in identifying genetic susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, setbased association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.

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.

Casual associations between blood metabolites and colon cancer

BACKGROUND Limited knowledge exists regarding the casual associations linking blood metabolites and the risk of developing colorectal cancer.AIM To investigate causal associations between blood metabolites and colon cancer.METHODS The study utilized a two-sample Mendelian randomization(MR)analysis to investigate the causal impact of 486 blood metabolites on colorectal cancer.The primary method of analysis used was the inverse variance weighted model.To further validate the results several sensitivity analyses were performed,including Cochran's Q test,MR-Egger intercept test,and MR robust adjusted profile score.These additional analyses were conducted to ensure the reliability and robustness of the findings.RESULTS After rigorous selection for genetic variation,486 blood metabolites were included in the MR analysis.We found Mannose[odds ratio(OR)=2.09(1.10-3.97),P=0.024],N-acetylglycine[OR=3.14(1.78-5.53),P=7.54×10^(-8)],X-11593-O-methylascorbate[OR=1.68(1.04-2.72),P=0.034],1-arachidonoylglycerophosphocholine[OR=4.23(2.51-7.12),P=6.35×10^(-8)]and 1-arachidonoylglycerophosphoethanolamine 4[OR=3.99(1.17-13.54),P=0.027]were positively causally associated with colorectal cancer,and we also found a negative causal relationship between Tyrosine[OR=0.08(0.01-0.63),P=0.014],Urate[OR=0.25(0.10-0.62),P=0.003],N-acetylglycine[0.73(0.54-0.98),P=0.033],X-12092[OR=0.89(0.81-0.99),P=0.028],Succinylcarnitine[OR=0.48(0.27-0.84),P=0.09]with colorectal cancer.A series of sensitivity analyses were performed to confirm the rigidity of the results.CONCLUSION This study showed a causal relationship between 10 blood metabolites and colorectal cancer,of which 5 blood metabolites were found to be causal for the development of colorectal cancer and were confirmed as risk factors.The other five blood metabolites are protective factors.

Genome-wide association study of vitamin E in sweet corn kernels

Vitamin E,consisting of tocopherols and tocotrienols,serves as a lipid-soluble antioxidant in sweet corn kernels,providing nutrients to both plants and humans.Though the key genes involved in the vitamin E biosynthesis pathway have been identified in plants,the genetic architecture of vitamin E content in sweet corn kernels remains largely unclear.In the present study,an association panel of 204 inbred lines of sweet corn was constructed.Seven compounds of vitamin E were quantified in sweet corn kernels at 28 days after pollination.A total of 119 loci for vitamin E were identified using a genome-wide association study based on genotyping by sequencing,and a genetic network of vitamin E was constructed.Candidate genes identified were involved mainly in RNA regulation and protein metabolism.The known gene ZmVTE4,encodingγ-tocopherol methyltransferase,was significantly associated with four traits(α-tocopherol,α-tocotrienol,theα/γ-tocopherol ratio,and theα/γ-tocotrienol ratio).The effects of two causative markers on ZmVTE4 were validated by haplotype analysis.Finally,two elite cultivars(Yuetian 9 and Yuetian 22)with a 4.5-fold increase in the sum ofα-andγ-tocopherols were developed by marker-assisted selection,demonstrating the successful biofortification of sweet corn.Three genes(DAHPS,ADT2,and cmu2)involved in chorismate and tyrosine synthesis were significantly associated with theα/γ-tocotrienol ratio.These results shed light on the genetic architecture of vitamin E and may accelerate the nutritional improvement of sweet corn.

Genome-wide association study for starch content and constitution in sorghum(Sorghum bicolor(L.) Moench)

Starch is the most important component in endosperm of sorghum grain.Usually,two types of starch are present:amylose(AM)and amylopectin(AP).The levels of AM and AP contents play a significant role in the appearance,structure,and quality of sorghum grains and in marketing applications.In the present study,a panel of 634 sorghum(Sorghum bicolor(L.)Moench)accessions were evaluated for starch,AM,and AP contents of grain,which included a mini core collection of 242 accessions from the International Crops Research Institute for the Semi-Arid Tropics(ICRISAT)in India,and 252 landraces and 140 cultivars from China.The average starch content was 67.64%and the average AM and AP contents were 20.19 and 79.81%,respectively.We developed a total of 260000 high-confidence single nucleotide polymorphism(SNP)markers in the panel of 634 accessions of S.bicolor using specific locus amplified fragment sequencing(SLAF-seq).We performed genome-wide association studies(GWAS)of starch,AM,and AM/AP of grain and SNP markers based on a mixed linear model(MLM).In total,70 significant association signals were detected for starch,AM,and AM/AP ratio of grain with P<4.452×10^-7,of which 10 SNPs were identified with significant starch,51 SNPs were associated with AM,and nine SNPs were associated with the AM/AP ratio.The Gene Ontology(GO)analysis identified 12 candidate genes at five QTLs associated with starch metabolism within the 200-kb intervals,located on chromosomes 1,5,6,and 9.Of these genes,Sobic.006G036500.1 encodes peptidyl-prolyl cis-trans-isomerase CYP38 responsible for hexose monophosphate shunt(HMS)and Sobic.009G071800 encodes 6-phospho-fructokinase(PFK),which is involved in the embden-meyerhof pathway(EMP).Kompetitive allele specific PCR(KASP)markers were developed to validate the GWAS results.The C allele is correlated with a high starch content,while the T allele is linked with a low level of starch content,and provides reliable haplotypes for MAS in sorghum quality improvement.

Implications of discoveries from genome-wide association studies in current cardiovascular practice

Genome-wide association studies(GWAS)have identified several genetic variants associated with coronary heart disease(CHD),and variations in plasma lipoproteins and blood pressure(BP).Loci corresponding to CDKN2A/CDKN2B/ANRIL,MTHFD1L,CELSR2,PSRC1 and SORT1 genes have been associated with CHD,and TMEM57,DOCK7,CELSR2,APOB,ABCG5,HMGCR,TRIB1,FADS2/S3,LDLR,NCAN and TOMM40-APOE with total cholesterol.Similarly,CELSR2-PSRC1-SORT1,PCSK9,APOB,HMGCR,NCAN-CILP2-PBX4,LDLR,TOMM40-APOE,and APOC1-APOE are associated with variations in low-density lipoprotein cholesterol levels.Altogether,forty,forty three and twenty loci have been associated with high-density lipoprotein cholesterol,triglycerides and BP phenotypes,respectively.Some of these identified loci are common for all the traits,some do not map to functional genes,and some are located in genes that encode for proteins not previously known to be involved in the biological pathway of the trait.GWAS have been successful at identifying new and unexpected genetic loci common to diseases and traits,thus rapidly providing key novel insights into disease biology.Since genotype information is fixed,with minimum biological variability,it is useful in early life risk prediction.However,these variants explain only a small proportion of the observed variance of these traits.Therefore,the utility of genetic determinants in assessing risk at later stages of life has limited immediate clinical impact.The future application of genetic screening will be in identifying risk groups early in life to direct targeted preventive measures.