ORIGINAL PAPER
Major and minor QTL and epistasis contribute to fatty acid compositions and oil concentration in high-oil maize
Xiaohong Yang ?Yuqiu Guo ?Jianbing Yan ?
Jun Zhang ?Tongming Song ?Torbert Rocheford ?
Jian-Sheng Li
Received:31December 2008/Accepted:30September 2009/Published online:25October 2009óSpringer-Verlag 2009
Abstract High-oil maize is a useful genetic resource for genomic investigation in plants.To determine the genetic basis of oil concentration and composition in maize grain,a recombinant inbred population derived from a cross between normal line B73and high-oil line By804was phenotyped using gas chromatography,and genotyped with 228molecular markers.A total of 42individual QTL,associated with fatty acid compositions and oil concentra-tion,were detected in 21genomic regions.Five major QTL were identi?ed for measured traits,one each of which explained 42.0%of phenotypic variance for palmitic acid,15.0%for stearic acid,27.7%for oleic acid,48.3%for linoleic acid,and 15.7%for oil concentration in the RIL population.Thirty-six loci were involved in 24molecular marker pairs of epistatic interactions across all traits,which explained phenotypic variances ranging from 0.4to 6.1%.Seven of 18mapping candidate genes related to lipid
metabolism were localized within or were close to identi-?ed individual QTL,explaining 0.7–13.2%of the popula-tion variance.These results demonstrated that a few major QTL with large additive effects could play an important role in attending fatty acid compositions and increasing oil concentration in used germplasm.A larger number of minor QTL and a certain number of epistatic QTL,both with additive effects,also contributed to fatty acid com-positions and oil concentration.
Introduction
High-oil maize developed by arti?cial selection is a value-added crop since its kernels have advantages as sources of animal feed for higher caloric energy and better protein quality (Han et al.1987;Benitez et al.1999;Lambert et al.2004).Additionally,maize oil is considered as high quality oil for human health due to the high proportion of poly-unsaturated fatty acids (Lambert 2001).The long-term selection experiment for maize oil and protein was initiated with the open-pollinated variety Burr’s White in 1896(Dudley and Lambert 1992,2004).The oil concentration of Illinois High Oil (IHO)reached about 20%after 100generations of selection.In the early 1980s,Song et al.began to develop Chinese synthetic populations with increased oil concentration by arti?cial selection (Song et al.1999;Lambert et al.2004).Beijing High Oil (BHO)originated from a normal maize synthetic known as Zhongzong No.2,which was synthesized with 12inbred lines of Lancast heterotic group.Its oil concentration had increased from 4.71to 15.55%after 18selection cycles (Song and Chen 2004).These materials are unique resources providing an opportunity to examine the genetic basis of high oil concentration in maize.
Communicated by C.Scho
¨n.Electronic supplementary material The online version of this
article (doi:10.1007/s00122-009-1184-1)contains supplementary material,which is available to authorized users.
X.Yang áY.Guo áJ.Yan áJ.Zhang áT.Song áJ.-S.Li (&)Beijing Key Laboratory of Crop Genetic Improvement,National Maize Improvement Center of China,China Agricultural University,Yuanmingyuan West Road,Haidian,100193Beijing,China
e-mail:lijiansheng@https://www.doczj.com/doc/5718810909.html,;lijs@https://www.doczj.com/doc/5718810909.html,
J.Yan
International Maize and Wheat Improvement Center
(CIMMYT),Apdo.Postal 6-641,06600Mexico,D.F.,Mexico T.Rocheford
Department of Agronomy,Purdue University,West Lafayette,IN 47907,USA
Theor Appl Genet (2010)120:665–678DOI 10.1007/s00122-009-1184-1
A number of chromosomal regions and QTL controlling oil concentration were identi?ed using segregating mapping populations through molecular markers in maize(Goldman et al.1994;Berke and Rocheford1995;Mangolin et al.2004; Song et al.2004;Wassom et al.2008a;Zhang et al.2008). Recently,Laurie et al.(2004)estimated the number of QTL for oil concentration to be about50in a larger randomly mated population,IHO(70)9ILO(70),with SNP markers, which was consistent with the estimates obtained using traditional quantitative genetic methods(Dudley1977). Thus,a number of studies suggested that oil concentration was controlled by a large number of genes with individually small effects and mainly additive gene action.
Chemically,maize oil is a mixture,mainly(99%) comprising?ve fatty acids,viz.,palmitic(16:0),stearic (18:0),oleic(18:1),linoleic(18:2)and linolenic(18:3) acids(Lambert2001).Sixty-eight loci associated with the ?ve major fatty acids were detected by single-factor ANOVA using80RFLP markers in an IHO(90)9ILO (19)population of200F2S1lines(Alrefai et al.1995). Wassom et al.(2008b)identi?ed15QTL controlling these fatty acids using the same markers in the same populations described in Wassom et al.(2008a).
Epistasis,the interaction between alleles from two to more genetic loci(Fisher1918),may play an important role in evolutionary and quantitative variation in crops (Doebley et al.1995;Yu et al.1997;Lukens and Doebley 1999;Gadau et al.2002;Malmberg et al.2005;Mei et al. 2005;Xu and Jia2007).QTL mapping is one experimental approach to explore the role of epistasis in the genetic basis of complex traits(Carlborg and Haley2004).However, there are practical problems in ef?ciently measuring epi-static genetic variances.One common method is to use multiple interval mapping(MIM;Kao et al.1999)in the Windows QTL Cartographer software(Wang et al.2005), but this program only detects epistatic interactions among identi?ed QTL with main effects.Recently,several com-puter programs were developed that enable genome-wide scans for epistatic effects(Chase et al.1997;Holland1998; Manly et al.2001;Sen and Churchill2001;Borevitz et al. 2002;Yang et al.2007).Among these,the mixed linear model presented in QTLNetwork Version2.0,can integrate multiple QTL,epistasis,QTL-by-environment interactions, and epistasis-by-environment interactions into one map-ping system(Yang et al.2007).Using this software,epis-tasis contributing to genetic variation in oil concentration was identi?ed in oilseed crops(Zhao et al.2005,2006).In maize,a certain number of epistatic interactions affecting oil concentration and fatty acid compositions were identi-?ed in segregating populations(Dudley2008;Wassom et al.2008a,2008b).However,similar studies have not been undertaken in recombinant inbred line(RIL) populations.
Biosynthesis of storage oil in plant seeds is a complex metabolic process(Ohlrogge and Browse1995).Most of the biochemical steps are well known and many of the genes involved in these reactions have been characterized in Arabidopsis(Beisson et al.2003).However,much less is known in maize,and only a few genes related to lipid metabolism were cloned(Lee and Huang1994;Egli et al. 1995;Berberich et al.1998;Schreiber et al.2000).Some loci for the key enzymes in lipid metabolism associated with QTL for fatty acid compositions were identi?ed in Arabidopsis,sun?ower and mustard(Lionneton et al.2002; Pe′rez-Vich et al.2002;Hobbs et al.2004).In maize,the map positions of two fatty acid desaturases,fad2and fad6, were compared with the positions of QTL for the ratio of oleic:linoleic acids to determine if they map to the same genomic region(Mikkilineni and Rocheford2003).The results suggested that comparison of the positions of can-didate genes and QTL was a suitable strategy to investigate the molecular basis of quantitative traits.Additionally,the positioned candidate genes can be used to develop func-tional markers for increasing selection ef?ciency by mar-ker-assisted selection in plant breeding(Jeppe and Thomas 2003).
In the present study,a RIL population derived from a single cross with a high-oil line was used to:(1)map QTL for fatty acid compositions and oil concentration;(2) estimate the number and magnitude effects of QTL and epistatic interactions underlying fatty acid compositions and oil concentration;and(3)analyze the relationships between candidate genes involved in lipid metabolism and mapped QTL for fatty acid compositions and oil concentration.
Materials and methods
Genetic materials
A RIL population was developed from a cross between B73(normal inbred)and By804(high-oil inbred),which was selected from a high-oil population,BHO Cycle13. Two hundred and forty-?ve F7:8RILs along with both parents were planted in a randomized complete block design with three replications on the Agronomy Farm, China Agricultural University,Beijing,in2005and2006, respectively.Each family line was grown in a single5m row,rows were0.67m apart,and planting density was 45,000plants/ha.In order to avoid xenia effects,more than six plants in each row were pollinated by bulked pollen within the row and pollinated ears were harvested at maturity.Equal amounts of grain from each harvested ear in each row among RIL population were bulked for chemical analysis.Grain samples from two?eld
replications over2years were used for fatty acid compo-sitions and oil concentration analysis.
Genetic linkage and candidate gene mapping
A total of208markers,including202SSR markers and six IDP markers developed by our laboratory,were used to build a genetic framework map.SSR markers were selected from public maize database(https://www.doczj.com/doc/5718810909.html,). Genomic DNA was extracted from all245F7RILs and the parents using the modi?ed procedure of Murry and Thompson(1980).A SSR procedure was followed:tem-plate DNA50ng,primers0.67l M,109reaction buffer 19,MgCl22.5mM,dNTPs0.2mM,Taq DNA polymerase 0.5U,made the?nal volume of15l L with deionized double distilled water.The PCR reaction conditions were set as:denaturation at94°C for5min,followed by30cycles of denaturation at94°C for1min,annealing at58°C for 1min,elongation at72°C for1.5min,with?nal extension at72°C for5min and then stored in refrigerator at4°C.
In order to analyze the co-locations of QTL for fatty acid compositions and oil concentration with the genes of lipid metabolism,18candidate genes(supplementary Table1)were chosen to develop molecular markers for mapping according to the Arabidopsis lipid metabolism pathway(Beisson et al.2003;http://www.plantbiology. https://www.doczj.com/doc/5718810909.html,/lipids/genesurvey).Because the cDNA or DNA sequences in maize were unknown,known protein sequences of Arabidopsis were submitted to a tBLASTn (Altschul et al.1997)search for maize homologues of candidate genes in the NCBI database(http://www. https://www.doczj.com/doc/5718810909.html,/BLAST),and the highest matching hits were chosen for developing candidate gene markers.Gene-speci?c primers(supplementary Table1)for developing SNP and IDP markers were designed from gene or EST sequences,using the Primer5.0software(Clarke and Gorley 2001)to amplify and sequence parts of the genes.MAP-MAKER/EXP3.0(Lincoln et al.1993)was used to con-struct a genetic linkage map and to integrate the candidate genes into the map.The Kosambi mapping function was used for converting recombination values to map distances.
To amplify the candidate gene fragments for sequenc-ing,DNA was ampli?ed with a PTC-200Peltier Ther-malcycler(Bio-Rad,USA)in a50-l l reaction volume containing100ng genomic DNA,5l l of109PCR reac-tion buffer, 1.5mM MgCl2,0.2mM of each dNTP, 0.2l M of each gene-speci?c primer,and1.25U of Taq polymerase(Tiangen).Thermal cycling conditions were 95°C for5min;35cycles of95°C for45s,optimal annealing temperature for45s,72°C for1.5min;followed by a?nal extension of10min at72°C.PCR products were analyzed on agarose gels,puri?ed using a multifunctional DNA puri?cation kit(Bioteke Corporation,China),and cloned into pGEM-T easy vector(Promega,USA).Three positive clones were picked and sequenced.The DNA-MAN software(Lynnon Biosoft,Canada)was used to search for nucleotide polymorphisms between sequences of the two parents.Subsequently,three types of genotyping, insertion–deletion polymorphisms(IDP,Bhattramakki et al.2002),cleaved ampli?ed polymorphic sequences (CAPS;Konieczny and Ausubel1993)and allele-compet-itive PCR(AC-PCR)SNP(Falque et al.2005)were employed to develop markers for mapping candidate genes. Measurements of fatty acid compositions
and oil concentration
Fatty acid compositions of223lines of the245RILs were measured because some RILs did not have enough grain or their kernels did not develop very well.Fifty kernels ran-domly chosen from the bulked grain of each plot were ground and a HP6890gas chromatogram(GC)(Agilent Technologies,USA)was employed for fatty acid analysis. All samples were measured with the two sub-samples at the same time.Lipids were extracted as described by Sukhija and Palmquist(1988)with some modi?cations:200–300mg of each sample was weighed and transferred to 15ml culture tubes.To each tube4ml methyl alco-hol:acetyl chloride(10:1)were added,followed by5ml of internal reference(methyl nonadecanoate,Sigma,USA), with1mg/ml in hexane,and after general vortexing, heated for2h in a water bath at80°C.After the contents were cooled to room temperature,5ml of7%K2CO3were added to neutralize extracted fatty acids,and the upper phase was transferred to sample bottles.Samples at250°C were automatically injected(1l l)and separated in the GC system equipped with a HP-INNOWAX polyethylene glycol capillary column(30m9320l m90.5l m, Agilent Technologies).The GC was operated at constant ?ow pressure of140.9kPa with an initial oven temperature of220°C,13min isothermal,then oven temperatures were increased by20°C/min to240°C,5min isothermal.The FID temperature was250°C and the split ratio of nitrogen was20:1.Fatty acids were identi?ed by comparison of their retention times with that of the internal reference.All data were acquired on ChemStation software(Agilent Technologies)and normalized to sample weight and to the internal reference.The oil concentration was calculated as the sum of all identi?ed fatty acid concentrations with percentage(%)of kernel weight.Individual fatty acids were expressed as percentage(%)of oil concentration. Phenotypic data analysis
The variance of fatty acid compositions and oil concen-tration were estimated using PROC GLM in SAS8.02
software(SAS Institute1999).The model for variance analysis was Y=l?a g?b y?(ab)gy?c yr?e gyr, where a g was the effect of the g th line,b y was the effect of the y th year,(ab)gy was the line9year interaction,c yr was the year9replicate interaction,and e gyr was the residual. All effects were considered to be random.
Broad-sense heritability(H2)was estimated as H2= r g2/(r g2?r gy2/r?r e2/yr)(Knapp et al.1985),where r g2is the genetic variance,r gy2is the interaction of genotype with year,r e2is the residual error,r is the number of replications, and y is the number of years.All the variances were acquired from variance analysis.To quantify the relation-ships among measured traits,correlation coef?cients were calculated for each trait pair using PROC CORR in SAS 8.02software(SAS Institute1999).
QTL and epistasis analysis
As described as Yang et al.2007,QTLNetwork version2.0 was employed for QTL and epistasis analysis.A mixed linear model was used to identify QTL at1-cM intervals with a window size of10cM.10-cM windows were de?ned to distinguish two adjacent test statistic peaks whether or not they represented two QTL.Two-dimen-sional(2D)genome scans were used to search for multiple interacting QTL.For each trait,a genome-wide threshold value of the F-statistic(a=0.01)for declaring the pres-ence of a QTL was estimated by10,000random permu-tations(Doerge and Churchill1996).A Bayesian method with Gibbs sampling was used to estimate QTL effects (Wang et al.1994).Some marker intervals coming directly from QTLNetwork version2.0were modi?ed if the posi-tion of adjacent QTL for different traits was less than 10cM.The sum of individual phenotypic variance explained by each QTL was calculated as the total phe-notypic variance explained by all QTL for each trait. Results
Variation in fatty acid compositions
and oil concentration
Eleven fatty acid compositions were determined using GC in RIL,and their sum was tabulated as oil concentration. Data for four major fatty acids,viz.palmitic(16:0),stearic (18:0),oleic(18:1),and linoleic(18:2)acids,and total oil concentration were used for subsequent analysis because the other seven fatty acids were present in only trace analyzed amounts.The means of the RIL population combined over years was close to the mid-parent value for all measured traits(Table1).A normal distribution with a wide range of variation was observed for each trait(data not shown).Oil concentration was positively correlated with the level of18:1,but was negatively correlated with levels of16:0and18:2(Table2).Oleic acid showed a highly negative correlation with18:2(r=-0.96)and low or moderate correlations were detected for other fatty acid pairwise comparisons(-0.43to0.38).The GLM indicated highly signi?cant effects due to genotype,year and geno-type9year interaction for each trait(Table1).For all traits,broad-sense heritability(H2)estimates were gener-ally high,ranging from86.8%for18:0–97.8%for18:2. The relatively high heritabilities indicated that much of the phenotypic variance in the RIL population was genetically controlled.
Table1Means,range,variances and broad-sense heritability(H2)for fatty acid compositions and oil concentration
Traits a16:018:018:118:2Oil
B73Mean13.45±0.17 2.30±0.1125.11±0.8255.85±0.72 4.08±0.20 By804Mean13.42±0.20 2.38±0.2135.21±0.3646.82±0.5011.18±0.58 RIL Mean13.47±0.06 2.09±0.0130.56±0.2251.29±0.21 6.64±0.04 Range9.17–18.42 1.10–3.4917.45–46.4737.49–63.41 3.71–10.41
F values year9replicate 4.39*20.64**0.89 4.190.09
Year214.34**210.69**26.66** 1.39** 2.52**
Genotype44.00**19.38**105.04**114.21**24.08**
Genotype9year 2.00** 2.56** 2.44** 2.57** 1.80**
Heritability(H2)b95.486.897.797.892.5
Con?dence interval c94.3–96.483.3–89.597.1–98.297.2–98.290.6–94.1
*,**Signi?cant at P\0.05and0.01,respectively
a16:0,Palmitic acid;18:0,stearic acid;18:1,oleic acid;18:2,linoleic acid
b Broad-sense heritability(H2)of fatty acid compositions and oil concentration
c90%Con?dence intervals of broad-sense heritability
Single-locus QTL for fatty acid compositions and oil concentration
Based on a linkage map of 1,675cM with an average interval of 7.3cM between adjacent markers,a total of 42QTL controlling fatty acid compositions and oil concen-tration were detected,corresponding to 21genomic regions because some QTL for different traits clustered in the same regions and shared the same molecular markers (Table 3,Supplementary Fig.1).These loci were distributed across all ten chromosomes.Individual loci for any given trait explained 0.7–48.3%of the phenotypic variance.Among the mapping population,all additive effects of mapped QTL for each trait accounted for a medium proportion of the total phenotypic variance,ranging from 53.4to 65.8%(Table 3).
For oil concentration,nine QTL distributed across all chromosomes,except chromosome 3and chromosome 7,were detected.The QTL,oil1-1,with the largest effect (15.7%of the phenotypic variance)was located on chro-mosome 1,?anked by umc2217and bnlg2086.The By804allele at this locus had an additive effect of 0.48%for increased oil concentration.Another important QTL for oil concentration,oil6,was located in the same region as ste6-1,ole6-1and lin6-1,and explained 8.4%of phenotypic variance with an additive effect of 0.35%on the
Table 2Pearson correlation coef?cients for trait pairs affecting fatty acid compositions and oil concentration in the RIL population Traits 16:018:0
18:1
18:2
Oil
16:0118:0-0.33**118:1-0.43**0.38**118:20.16**-0.40**-0.96**1Oil
-0.31**
0.07
0.43**
-0.33**
1
**Signi?cant at P =0.001
Table 3QTL for fatty acid compositions and oil concentration in RIL population Traits QTL a Chr Marker
interval P b A c
h 2(a)d (%)
16:0
pal11umc2232-umc1988134.5-0.338 2.7pal22phi96100-kt2
14.0-0.1850.8pal44bnlg2162-umc2286110.1-0.354 3.0pal55umc2373-umc122184.3-0.282 1.9pal66nc010-umc110563.4-0.6199.1pal88umc1130-umc156265.9-0.398 3.8pal99bnlg1401-umc221326.1 1.33242.0pal10
10umc1367-umc2016
40.2
-0.288 2.0
Subtotal e 65.318:0ste2
2phi083-u4113.60.051 1.3ste33phi053-sad7004109.1-0.16213.2ste55umc1491-umc12530.00.055 1.5ste6-16Q8-nc01054.30.17315.0ste6-26phi299852-umc2059152-0.1379.3ste88umc1075-umc130417.0-0.1369.2ste9
9bnlg1401-umc221327.7-0.088 3.9
Subtotal e 53.418:1ole2-1
2phi96100-kt216.20.4590.7ole2-22bnlg108-u4114.80.633 1.4ole45m1-bnlg229181.4 1.6289.0ole6-16Q8-nc010
41.5 2.85927.7ole6-26nc010-umc110567.4 1.394 6.6ole6-36umc1614-acda600189.8 1.4947.6ole77dupssr13-CA33103.4-0.763 2.0ole88umc1130-umc156265.90.904 2.8ole99umc1743-umc165449.5-0.873 2.6
Subtotal e
60.418:2lin1-1
1umc2217-bnlg208674.80.707 1.6lin1-21umc1725-phi064264.0-0.4610.7lin22bnlg108-u4114.8-0.564 1.0lin44m1-bnlg229181.4-1.115 4.0lin6-16Q8-nc010
42.5-3.88148.3lin6-26umc1614-acda600189.8-1.400 6.3lin6-36phi299852-umc2059147.00.786 2.0lin77dupssr13-CA33104.40.592 1.1lin99
bnlg1401-umc2213
26.7
-0.5120.8
Subtotal
e
65.8
Table 3continued Traits
QTL a
Chr Marker
interval
P b A c
h 2(a)d (%)
oil
oil1-11umc2217-bnlg208685.00.48015.7oil1-21ols1-phi308707215.20.200 2.7oil22phi96100-kt2
9.00.309 6.5oil44dupssr28-bnlg2162104.80.146 1.5oil55umc2373-umc122190.30.275 5.3oil66Q8-nc010
45.50.3518.4oil88umc1130-umc156265.90.279 5.3oil99bnlg1401-umc221322.10.233 3.7oil1010
umc1367-umc2016
40.2
0.303 6.2
Subtotal e
55.3
a
Number following the three letters represents the chromosome location of the QTL.Different lowercase numbers following the dash indicate putatively different QTL located on the same chromosome b
The QTL location on chromosome was estimated by QTLNetwork c
Additive effects estimated by QTLNetwork.Positive (?)indicates that the By804allele increases trait expression,and negative (-)indicates that the B73allele increases trait expression,respectively d
Percentage of phenotypic variance explained by individual additive effects of the mapped QTL e
Total percentage of phenotypic variance explained by all additive effects of the mapped QTL for each trait
chromosome6.The other minor QTL for oil concentration each explained 1.5–6.5%of the phenotypic variance. Alleles from By804,a high-oil parent,at all of mapped loci had increasing effects for oil concentration.
Eight QTL were associated with16:0and explained 65.3%of the total phenotypic variance.One major QTL for 16:0,pal9,terminally located on chromosome9S,and bordered by markers bnlg1401and umc2213,contributed 42.0%of the explained phenotypic variance.The additive effect of the By804alleles increased16:0by 1.332% absolutely.The remaining seven QTL were located on chromosomes1,2,4–6,8and10with explained variances ranging from0.8to9.1%;B73alleles increased16:0at these loci.
Seven QTL were involved in18:0and accounted for 53.4%of the total phenotypic variance.Two QTL with large effects,ste6-1,?anked by the markers Q8and nc010 on chromosome6,and ste3,?anked by the marker phi053 and sad7004on chromosome3,accounted for15.0and 13.2%,respectively,of the phenotypic variance.The other QTL explained only1.3–9.3%of the phenotypic variance. By804alleles contributed to increased18:0at three loci, ste2,ste5and ste6-1,and B73alleles contributed increases at the other loci.
Nine QTL were signi?cantly associated with18:1.The strongest QTL,ole6-1,located in the same position as st6-1,explained27.7%of the phenotypic variance,and By804alleles had an additive effect of 2.85%for increasing18:1.The phenotypic variance explained by the other eight QTL ranged from0.7to9.0%.Alleles of these eight QTL with increasing effects came from By804with the exception of ole7and ole9.Collectively,the nine QTL explained60.4%of the total phenotypic variance.
A total of nine QTL controlling18:2explained65.8% of the total phenotypic variance.One major QTL,lin6-1, with a very large effect localized to the same region as ste6-1,accounted for48.3%of the phenotypic variance. B73alleles at this locus had an additive effect of3.88% for increased18:2.Another eight QTL with small effects explained0.7–6.3%of the phenotypic variance. Among these eight loci,alleles with increasing effects at three loci were contributed by By804and?ve came from B73.
Epistatic QTL for fatty acid compositions
and oil concentration
Twenty-four pairs of epistatic QTL involving36loci were identi?ed for all measured traits(Table4).The number of epistatic QTL pairs for each trait ranged from two to seven. The proportion of total phenotypic variance explained by all epistatic QTL ranged from5.2to12.6%for each trait. Based on genetic effects,mapped epistatic QTL comprised three types:interactions between two QTL with additive effects(AA),interactions between a QTL with additive effect and a locus without signi?cant additive effect(AN or NA),and interactions between two loci with only epistatic effects(NN).There were both positive and negative epi-static interactions,indicating the combination of alleles at interacting loci with increasing contributions from the same and different parents,respectively.
For16:0,six pairs of epistatic QTL were detected, explaining0.4–6.1%of the phenotypic variance in the RIL population.Of these,increased16:0from three pairs of loci came from the parental digenic combination,and three came from combinations of alleles from different parents.These QTL were partitioned into four NA inter-actions and two NN interactions.Five pairs of epistatic QTL(two AN,and three NN interactions)for18:0were detected,accounting for 1.5–2.0%of the phenotypic variance.All those with increasing effects came from the parental digenic combination,except for an interaction between one locus?anked by umc2408-bnlg197on chromosome3and another?anked by umc1241-umc1695 on chromosome7.For18:1,four pairs of epistatic QTL with explained variance from0.5to2.5%were partitioned into one AA interaction,two NA interactions and one NN interaction.One of four pairs with increasing effect came from combinations of alleles from different parents, whereas the remaining ones were from parental digenic combinations.Seven pairs of epistatic QTL for18:2(two AN or NA)interactions and?ve NN interactions) explained0.5to1.6%of the phenotypic variance.The epistatic effects of these interactions came from different parents except one interaction between a locus?anked by umc1725-phi064on chromosome1and another?anked by umc1367-bnlg1655on chromosome10.Two pairs of epistatic QTL were detected for oil concentration.One accounted for1.3%of the phenotypic variance with an increased epistatic effect from different parents,and another explained3.8%of phenotypic variance with an increased epistatic effect coming from the parental digenic combination.
Co-location of mapped candidate genes
with single-loci QTL
A total of18candidate genes related to lipid metabolism, having either InDel or SNP polymorphisms between the parents,were mapped using20PCR-based markers cor-responding to the candidate genes(Table5;Fig.1). Among these markers,11were IDP,seven were CAPS and two were AC-PCR SNP.Four markers corresponding to two putative functional genes were located at different loci due to multiple copies.They were ketoacyl-ACP synthase II (KAS II)on chromosome bins 2.07–2.09and7.04,and
Stearoyl-ACP desaturase(SAD)on chromosome bins 3.05–3.06and8.05–8.06.
Comparing map positions of candidate genes and detected QTL for fatty acid compositions and oil concen-tration(Fig.1),seven candidate genes were located in regions containing18mapped QTL with explained phe-notypic variance ranging from0.7%(ole2-1)to13.2% (ste3)(Table6).They were Acetyl-CoA carboxylase(AC-Case),KAS II,Ketoacyl-ACP synthase III(KAS III)and SAD for fatty acid synthesis,and Monoacylglycerol acyltrans-ferase(MAGAT),Acyl-CoA:diacylglycerol acyltransferase (DGAT)and Oil-body Oleosin17KD(OLE17)for oil synthesis and storage.ACCase(u4)fell within QTL,ste2, ole2-2and l in2,explaining1.3%of the phenotypic vari-ance for18:0,1.4%for18:1and1.0%for18:2,respec-tively.KAS II(kass5001B),located close to QTL ole7and lin7,contributed2.0%of the explained genetic variance for 18:1and1.1%for18:2.KAS III(kt2)fell within a cluster of QTL for16:0,18:1and oil concentration with explained variances ranging from0.7to6.5%.SAD(sad7004)co-located with the strong QTL,st3,accounting for up to 13.2%of the phenotypic variance for18:0.MAGAT(m1)
Table4Epistatic QTL for fatty acid compositions and oil concentration in RIL population
Trait Chr i Marker interval i P i a Chr j Marker interval j P j a Interaction b AA c h2(aa)d(%)
16:02phi083-phi092121.18phi119-bnlg208640.2N*N0.1260.4 2C9_3-mmc027138.28umc1562-umc114180.7NA-0.1600.6
2mmc027-kass2172.38umc1130-umc156265.9NA0.292 2.0
2mmc027-kass2172.38phi121-bnlg204660.9NA-0.507 6.1
6umc1614-acda6001100.37phi034-umc140944.9N*A-0.303 2.2
6phi299852-umc2059146.07phi091-atf264.5N*N0.236 1.3 Subtotal e12.6
18:06Q8-nc01054.39bnlg1401-umc221327.7AA0.054 1.5 1bnlg400-umc1955169.19Q2-umc1170 4.6NN0.057 1.6
2phi083-u4113.63bnlg1144-umc101234.0AN0.057 1.6
3phi036-bnlg163861.98umc2082-bnlg186354.2NN0.056 1.6
3umc2408-bnlg197127.37umc1241-umc1695 1.0NN-0.064 2.0 Subtotal e8.3
18:11umc2217-bnlg208680.03bnlg1144-umc101245.0N*N0.750 1.9 1umc1988-umc1122152.46umc1614-acda600189.8N*A0.862 2.5
1bnlg400-umc1955162.26nc010-umc110567.4NA-0.722 1.8
2phi96100-kt216.29umc1743-umc165449.5AA0.3820.5 Subtotal e 6.7
18:21umc2232-umc1988138.55umc2143-aca184.0N*N-0.704 1.6 1bnlg400-umc1955164.39umc1794-bnlg152572.7NN-0.3980.5
1umc1725-phi064264.010umc1367-bnlg165540.9AN*0.638 1.3
2apat5-phi101049216.39bnlg1401-umc221326.7NA-0.5250.9
3phi053-sad7004103.15bnlg1237-f22a129.7N*N-0.543 1.0
6phi126-umc175310.910umc1337-phi05023.9NN*-0.3940.5
8bnlg2082-bnlg186353.89umc1078-umc147166.5NN-0.77 1.9 Subtotal e7.7
oil1umc2217-bnlg208685.01ols1-ph308707215.2AA-0.14 1.3 6Q8-nc01045.59bnlg1525-bnlg1904118.2AN0.238 3.9 Subtotal e 5.2
*Without signi?cant additive QTL for this trait but with signi?cant additive QTL for other trait in this study
a QTL location on chromosome was estimated by QTLNetwork
b Types of epistati
c interaction.AA interactions between two QTL with additive effects,NA(AN)interactions between a QTL with additive effects an
d a locus without signi?cant additiv
e effects,NN interaction between two loci with epistatic effects only
c AA means the epistatic effects estimate
d by QTLNetwork.Positive(?)means that parental digenic genotypes increas
e trait expression,and negative(-)means that recombinant alleles from two parents increase trait expression,respectively
d Percentag
e o
f phenotypic variance explained by individual epistatic effects of the mapped QTL
e Total percentage o
f phenotypic variance explained by all epistatic effects of the mapped QTL for each trait
was located within two QTL,ole4and lin4,and accounted for9.0and4.0%of the explained variance,respectively. DGAT(acda6001)fell within a locus containing two QTL for18:1and18:2,explaining7.6and6.3%of the variance, respectively.OLE17(ols1)was associated with oil1-2, accounting for2.7%of the variance in oil concentration. Discussion
Comparison of mapped QTL between the BHO
and IHO populations
Combined previously mapped QTL for fatty acid compo-sitions and oil concentration(Alrefai et al.1995;Berke and Rocheford1995;Laurie et al.2004;Song et al.2004;Clark et al.2006;Wassom et al.2008a,2008b;Zhang et al.2008) with present results,28and56genomic regions related to oil synthesis and accumulations were identi?ed in the BHO and IHO populations,respectively(Fig.1).Comparing locations within chromosome bins,23loci were located to common chromosome regions,such as the loci on chro-mosome bins2.02,3.05,5.04and6.04representing37.7% of all mapped loci in both populations.However,the strongest QTL for oil concentration differed between the populations.oil1-1in bin1.04,making the largest contri-bution to oil concentration in the present studies as well as in the earlier F2and F3studies(Song et al.2004;Zhang et al.2008),was not identi?ed in the IHO population.Only a small proportion of loci controlling each fatty acid(two loci for16:0,four for18:0,two for18:1and two for18:2) were shared between the populations.A notable exception was a locus in bin6.04that shared the largest magnitude of genetic effects for18:0,18:1and18:2(Alrefai et al.1995; Wassom et al.2008b).The largest effective QTL for16:0 was closely linked to bnlg4.07(bin7.04)in the IHO population,but was located in bin9.02in the BHO pop-ulation.The different locations of mapped QTL between the two populations have two explanations.First,different genetic loci for fatty acid compositions and oil concen-tration are involved in the different original populations. IHO was developed from an open-pollinated variety Burr’s White in US;while BHO was developed from Zhongzong No.2synthetic which was synthesized by several inbred lines in China(Dudley and Lambert1992,2004;Song and Chen2004).These two basic populations with different background may contain a few different favorable alleles with different allele frequency.Second,different loci for high oil concentration were selected during the production of the different populations.The changes per generation for oil concentration were different in two populations,0.15% for IHO and0.60%for BHO(Dudley and Lambert2004; Song and Chen2004).Both explanations suggest it should be possible to accumulate more favorable alleles for enhancing oil concentration in both populations.
Among nine loci controlling QTL for oil concentration, eight were associated with QTL for one to three individual fatty acids(Table2,Fig.1).According to standards
Table5Polymorphisms of candidate genes between two mapping parents and their map positions Marker type Enzyme Marker InDel/enzyme/SNP Flanking marker Bin
IDP FAD8fad8349bp umc1403-phi001 1.03 MCAT mcat81310bp umc1403-phi001 1.03
FAD6f6a6bp umc1556-umc1955 1.07–1.08
KASIII kt221bp umc1265-umc1518 2.02
PAPase pap428bp nc003-umc2373 2.06
KASII kass214bp mmc0271-umc1551 2.07–2.09
GPAT gat56bp dupssr23-umc2408 3.06
MAGAT m13bp umc1299-bnlg2291 4.06
ACA aca62bp umc2143 5.08
TE atf224bp phi091-umc11127.03
KASI kb13bp phi0289.00–9.01 CAPS OLE17ols1Xsp I umc2047-phi308707 1.09–1.1 LPAAT apat5Eco RI bnlg1520-phi101049 2.09–2.1
KASII kass5001B Taq I CA33-umc11257.04
SAD sad7004Stu I phi053-dupssr23 3.05–3.06
sad2702Bgl II umc1149-umc17248.06 DGAT acda6001Pst I umc1614-nc013 6.05
FAE fae2Mun I phi065-umc12589.03
AC-PCR SNP ACCase u4T/G,G/A Bnlg108-umc1635 2.04–2.05 FAD2f22a C/G bnlg1237-phi085 5.05–5.06
proposed by Groh et al.(1998),the number of common loci controlling oil concentration and fatty acid compositions ranged from two to six(oil and16:0shared six;oil and 18:0two;oil and18:1three;oil and18:2three).18:1was highly correlated with18:2(r=-0.96),and?ve loci controlled QTL for both18:1and18:2simultaneously.For other pairs of fatty acids,one to four loci had joint effects (18:2and18:0shared four QTL;18:2and16:0one;18:1 and18:0two;18:1and16:0three;18:0and16:0one). These results were similar to those reports by Alrefai et al. (1995)for the IHO population.Generally,common geno-mic regions with QTL for different traits could be explained by pleiotropic effects of single genes,or by close physical linkage of genes controlling different traits. Pleiotropic effects of single genes are probably the more likely reason.Among26common loci for two traits,the directions of favorable alleles of21loci were the same as those of the phenotypic correlations between them.This suggests that centralized common QTL for target traits could be exploited for molecular breeding by marker-assisted selection(Champoux et al.1995;Redonˇa and Mackill1998).
Genetic basis of fatty acid compositions
and oil concentration in high-oil maize
So far,a large number of QTL associated with different agronomic and economic traits have been identi?ed using molecular markers in crops.For each trait,a single QTL may explain large or small amounts of variation.According to Salvi and Tuberosa(2005),a QTL explaining more than 15%of the phenotypic variance in a primary genetic analysis can be de?ned as a major QTL.According to this criterion,?ve major QTL were identi?ed in the present study(Table7).The phenotypic variance explained by the major QTL for each trait accounted for24.3–65.7%of the total variance.Several recent studies demonstrated that important polygenic agronomic and domestication-related traits were controlled by a few major QTL with large effects.For example,two major QTL,fw2.2and GS3,were responsible for fruit size in tomato and grain size in rice, respectively(Grandillo et al.1999;Fan et al.2006). Konishi et al.(2006)and Li et al.(2006)identi?ed two major QTL,qSH1and sh4,associated with seed shattering in rice and accounting for about70%of the phenotypic variance.Two major QTL,tb1and tga1,governing mor-phological differences between maize and teosinte, explained30–50%of the phenotypic variance(Doebley et al.1990;Doebley and Stec1993).It can be concluded that major QTL also play a signi?cant role in oil synthesis and accumulation in high-oil maize,although multiple minor QTL are also present.
Barton and Keightley(2002)pointed out that short-term (\20generations)selection responses tend to be based on alleles of larger effect whereas sustained responses to long-term selection must be based on many extremely minor effects.In this study,the high-oil parent of the mapping population,By804,was developed from BHO after a rel-atively low number of generations of selection(cycle-13). The oil concentration of the BHO population reached 15.53%after18cycles,nearly the same as the IHO pop-ulation with15.29%oil concentration after65cycles of
Table6Co-locations between candidate genes in lipid metabolism and QTL for fatty acid compositions and oil concentration
*Candidate gene position mapped by Mikkilineni and Rocheford(2003)
a Additive effects estimated by QTLNetwork.Means of positive(?)and negative(-) signs refer to Table3
b Percentage of phenotypi
c variance explaine
d by individual additiv
e effects o
f the mapped QTL Enzyme Marker Bin Flankin
g marker QTL A a h2(a)b
OLE17ols1 1.09–1.1umc2047-phi308707oil1-20.200 2.7 KASIII kt2 2.02umc1265-umc1518pal2-0.1850.8
ole2-10.4590.7
oil20.309 6.5 ACCase u4 2.04bnlg108-umc1635ste20.051 1.3
ole2-20.633 1.4
lin2-0.564 1.0 SAD sad7004 3.05–3.06phi053-dupssr23ste3-0.16213.2 MAGAT m1 4.06umc1299-bnlg2291ole4 1.6289.0
lin4-1.115 4.0 DGAT acda6001 6.05umc1614-nc013ole6-3 1.4947.6
lin6-2-1.400 6.3 KASII kass5001B7.04CA33-umc1125ole7-0.763 2.0
lin70.592 1.1 FAD2* 4.06ole4 1.6289.0
lin4-1.115 4.0 FAD2*10.03pal10-0.288 2.0
oil100.303 6.2
selection(Dudley and Lambert2004;Song and Chen 2004).Therefore,the accumulation of major QTL for oil concentration and fatty acid compositions during popula-tion improvement in BHO can explain the magnitude of response to selection after just18cycles.
Thirty-seven of the42individual QTL mapped in this study belonged to the minor QTL category,and most were mapped in previous studies with different mapping popu-lations(Goldman et al.1994;Alrefai et al.1995;Berke and Rocheford1995;Laurie et al.2004;Mangolin et al.2004; Song et al.2004;Clark et al.2006;Wassom et al.2008a,b; Zhang et al.2008).The total phenotypic variances explained by all minor QTL for each trait varied from23.8% (16:0)to65.4%(oil)(Table7).These results were similar to those from studies in the RM10:S1population with IHO germplasm(Laurie et al.2004).Six of19loci containing minor QTL for oil concentration identi?ed in the earlier F2 or F3populations of B739By804(Song et al.2004)were not detected in the RIL population.Such minor QTL may be unstable and easily in?uenced by environment.
Another notable?nding in the present study is that24 pairs of epistatic QTL were associated with fatty acid compositions and oil concentration.However,they made only limited contributions to increased fatty acid compo-sitions and oil concentration,as the sum of phenotypic variance explained by all epistatic QTL for each trait ranged from8.6%for oil concentration to16.1%for16:0 (Table7).These results can be contrasted with those obtained in rapeseed using similar statistical methods (Zhao et al.2005,2006),where epistatic interactions explained nearly the same proportion of phenotypic vari-ance(30.3%)as additive effects(40–45%).This demon-strated that epistasis could make a substantial contribution to variation in oil concentration in different crops.Among epistatic effects,one type of epistasis occurred between loci with additive effects,the remaining two types occurred between pairs of loci in which at least one QTL had no detectable additive effects,indicating that loci without signi?cant additive effects played important roles in epi-static interactions that indirectly in?uence regulation of oil synthesis and accumulation.
Oil concentration comprising a mixture of different fatty acids in maize grain was the most complex among the?ve measured traits,but only two pairs of epistatic interactions were detected.Nine loci contributed to epistatic effects for one trait with non-additive effects but had individual effects for other traits(Table4).As expected,some map-ped candidate genes,such as TE(atf2),LPAAT(apat5),and ACA(aca),were located within QTL without signi?cant additive effects,but they interacted with other loci to in?uence oil synthesis and accumulation.These results provide additional evidence to support the existence of the epistatic interactions in?uencing fatty acid compositions and oil concentration.
The magnitudes of individual QTL with additive effects were greater than those of epistatic QTL for all measured traits.The average percentages of total phenotypic variance explained by individual QTL and epistatic QTL for all traits were60.0and8.1%,respectively,indicating that additive effects contributing to variation in fatty acid compositions and oil concentration are predominated in our population.The results of this study lead to a conclusion about the genetic basis of fatty acid compositions and oil concentration in high-oil maize.Whereas a few major QTL with large additive effects may play an important role in increasing fatty acid compositions and oil concentration, many minor and a certain number of epistatic QTL,also with additive effects,additionally contribute to both fatty acid compositions and oil concentration.
Association of candidate genes with QTL for fatty acid compositions and oil concentration
Mapping gene orthologs of known lipid metabolic enzymes provided an opportunity to investigate the co-location of
Table7Summary of QTL types for fatty acid compositions and oil concentration in RIL population
Trait QTL
action Number h2(%)
Range Subtotal a%of each trait
16:0Major14242.053.9
Minor70.8–9.123.330.0
Epistatic60.4–6.112.616.1
Total b77.9
18:0Major215.01524.3
Minor5 1.3–9.338.462.2
Epistatic5 1.5–2.08.313.5
Total b61.7
18:1Major127.727.741.3
Minor80.7–9.032.748.7
Epistatic40.5–2.5 6.710.0
Total b67.1
18:2Major148.348.365.7
Minor80.7–6.317.523.8
Epistatic70.5–1.67.710.5
Total b73.5
Oil Major115.715.726.0
Minor8 1.5–8.439.665.4
Epistatic2 1.3–3.9 5.28.6
Total b60.5
a Total percentage of phenotypic variance explained by major,minor, or epistatic QTL
b Total percentage of phenotypi
c variance explaine
d by all mapped QTL for each trait
candidate genes and mapped QTL for fatty acid composi-tions and oil concentration.In present study,one ACCase isoform,accA,a rate-limited enzyme in the fatty acid biosynthetic pathway(Ohlrogge and Browse1995),was located within the chromosome2locus controlling QTL ste2,ole2-2and lin2.Two genes encoding two condensing enzymes,3-ketoacyl-ACP synthase II(KASII)and3-ke-toacyl-ACP synthase III(KAS III),were mapped close to clusters with three QTL(pal2,ole2-1,oil2;chromosome2) and two QTL(ole7,lin7;chromosome7),respectively. These locations imply that genes upstream in the lipid metabolic pathway may contribute indirectly to fatty acid compositions and oil concentration in high-oil maize. Stearoyl-ACP desaturase(SAD),a key enzyme to convert stearic acid to oleic acid(Ohlrogge and Browse1995),fell within QTL ste3accounting for13.2%of the variance in 18:0.Two acyltransferase genes,coding monoacylglycerol acyltransferase(MAGAT)and diacylglycerol acyltrans-ferase(DGAT),co-located with loci on chromosomes4 (4.06)and6(6.05)where QTL for18:1and for18:2were located,respectively.Similarly,a previously mapped locus,FAD2,was also located in bin4.06(Mikkilineni and Rocheford2003),the same genomic region as MAGAT. The gene encoding an oil body-embedded protein,OLE17, was mapped to chromosome1,in the same region as QTL oil1-2.Even though many factors may be involved in the formation of a co-location between a candidate gene and a QTL for measured traits,21genomic regions(the total length was190cM)were detected to associate with fatty acid compositions and oil concentration in this study, which accounted for11.34%of the whole genome (1,675cM);and only18candidate genes(20markers) from oil metabolism pathway were mapped.The proba-bility of co-location caused by chance should be very low. However,the actual functions of such genes should be con?rmed by association mapping or gene complementa-tion in future investigations.If con?rmed,they can be used to develop functional markers or as means to clone the QTL.
According to the mapping results,only seven(39%)of 18mapped candidate genes were co-located with mapped QTL with minor effects.One explanation for such a low level is that molecular markers based on candidate genes may not cover all isoforms of the genes since most of the genes involved in lipid metabolism belong to multiple gene families(Ohlrogge et al.2004).Most of the candidate genes,except KAS II and SAD,mapped only to one locus, suggesting that other isoforms might also affect oil syn-thesis and accumulation.One isoform of DGAT,DGAT1-2,recently cloned by Zheng et al.(2008),was identi?ed to control major QTL in Bin6.04.Another?nding supported the viewpoint that two copies of SAD mapped to bins3.05–3.06and8.05–8.06,but only the locus on bin3.05–3.06was associated with stearic acid.Interestingly,no candidate gene co-located with major QTL in bin1.04and9.02 (Fig.1).It suggests other genes may also regulate oil synthesis and accumulation,such as genes controlling embryo size,transcription factors,protein kinases and other regulatory factors.
Major QTL application in molecular breeding
and genomic research
A notable discovery in the present study was that?ve major QTL for fatty acid compositions and oil concentra-tion in three genomic regions were detected.This should allow introgression favorable alleles from high-oil maize into high-yielding normal lines using marker-assisted backcrossing.The strongest QTL for oil concentration, oil1-1,located on chromosome1,was stable over different generations(Song et al.2004;Zhang et al.2008).The additive effects of the By804allele at this locus increased oil concentration by0.52,0.59and0.48%in F2,F3and the RIL population,respectively.The next strongest QTL,oil6, with additive effects of0.35%for oil concentration was located in the same chromosome region as three major QTL,st6-1,ole6-1and lin6-1.Two important chromosome regions,bin1.04and bin6.04,will be available for intro-gression of the favorable alleles from high-oil lines using markers identi?ed in molecular breeding program.
The major QTL identi?ed in this study also provide opportunities to clone these QTL using genomic tech-niques.As a vegetable oil,the quality of maize oil was determined by the ratio of saturated and unsaturated fatty acids(Lambert2001).Palmitic acid is a key intermediate in lipid metabolism,and directly determines the ratio of saturated and unsaturated fatty acid.The major QTL pal9 with 1.33%of additive effects identi?ed in this study (Table3)thus has positive effects in oil concentration by regulating the amount of16:0.Inspection of the maize sequence database in the region of pal9revealed that more than895genes occurred within this region.Among them,several might encode functional genes,such as transcription factors,protein kinases and other regulatory factors.Thus,data from the maize genome sequencing initiative(https://www.doczj.com/doc/5718810909.html,)will greatly facilitate elucidation of the genomic factors that deter-mine oil synthesis and accumulation in maize grain.Such continuing investigation will be helpful in unraveling the mysteries of oil regulation and accumulation in maize grain.
Acknowledgments Financial support was provided by the National Natural Science Foundation of China and Chinese High Technology Project.We gratefully acknowledge Prof.Jun Zhu’s guidance for using QTLNetwork Version2.0software and Professor R.A.McIn-tosh for language editing.
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大学文科专业介绍及就业介绍 汉语言文字学 专业概况：分为现代汉语和古代汉语两个大方向，主要研究从上古到现代的汉语的口语系统与文字系统的演变规律、结构特征和现实状况。 研究方向：汉语方言、汉语史、汉语词汇学、现代汉语语法修辞、汉字信息处理、对外汉语教学等。 就业前景：一般就职于高等院校、国家文化出版部门、中学、对外汉语培训机构等。 经济学 培养目标：毕业生能在综合经济管理部门、政策研究部门、金融机构和企业单位从事经济分析、预测、规划和经济管理实际工作。 主干课程：政治经济学（资、社）、西方经济学（微、宏）、会计学、统计学、货币银行学 就业方向：就业方向为政府经济管理部门，政策研究部门高校及金融、税务、证券、投资公司等单位。 法学 培养目标：培养能在律师、行政机关、检察机关、企事业单位和教学科研机构从事法律工作的复合型高级专门人才。
主干课程：法理学、中国法制史、宪法、行政法和行政诉讼法、民法、经济法、刑法、民事诉讼法、刑事诉讼法、国际法、国际私法。另设实践性教学环节，包括法律咨询、观摩审判、模拟审判，专业实习等。 就业方向：在法院、检察院等从事政法工作；在工商、税务、房地产、海关以及各类公司律师工作、在高等院校从事法律教育及法律研究工作。 国际政治 培养目标：本专业培养能在党政机关、企事业单位、高校和科研等部门从事外交和研究等方面的国际政治学高级专门人才。 主干课程：国际关系学、国际政治学概论、国际法、国际经济学、美国政治与外交、当代政府与公务员制度。 就业方向：毕业后主要在党政机关、企事业单位、高校和科研等部门从事外交、外事、对外宣传、教学和研究等方面的工作。 对外汉语 主干课程：现代汉语、古代汉语、文学概论、中国古代文学、中国文化概要、外国文化概要、英语听说、英语精读、英语写作 就业方向：在高等院校从事对外汉语教学和研究工作；在国家机关外事部门从事对外宣传等工作；在新闻、出版等部门从事创作等工作。 历史学
大学专业介绍与就业方向（理科）。 1.数学与应用数学 专业介绍：本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力，并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。 就业去向：毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 推荐院校：大学、南开大学、大学、理工大学、复旦大学、大学、中国科学技术大学大学。 2.信息与计算科学 专业介绍：本专业培养能在科技、教育和经济部门从事研究、教学、应用软件开发和管理工作等方面的高级专门人才。 就业去向：主要到科技、教育和经济部门从事研究、教学和应用开发及管理工作。 推荐院校：大学、清华大学、大学、南开大学、大学、大学、交通大学、理工大学。 3.应用物理学 专业介绍：本专业培养具有坚实的数理基础，熟悉物理学基本理论和发展趋势，熟悉计算机语言，掌握实验物理基本技能和数据处理的方法，获得技术开发以及工程技术方面的基本训练，具有良好的科学素养和创新意识。 就业去向：毕业生能在应用物理、电子信息技术、材料科学与工程、计
算机技术等相关科学领域从事应用研究、技术开发以及教学和管理工作。 推荐院校：大学、清华大学、大学、大学、南开大学、大学、中国科学技术大学、大学。 4.应用化学 专业介绍：本专业以高分子材料、精细化工和计算机在化学化工中的应用技术为专业方向，培养具有可从事相关领域的科学研究，工业开发和管理知识的高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5.环境科学 专业介绍：本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的高级专业人才。 就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。 6.环境工程专业 专业介绍：本专业培养具备城市和城镇水、气、声、固体废物等污染防治和给排水工程，水污染控制规划和水资源保护等方面知识的环境工程学科高级工程技术人才。 就业去向：主要至政府部门、规划部门、经济管理部门、环保部门、设计单位、工矿企业、科研单位、学校等从事规划、设计、施工、管理、教育和研究开发方面的工作。
大学生专业选择与就业情况的调查报告 文件排版存档编号：[UYTR-OUPT28-KBNTL98-UYNN208]
关于大学生专业选择与就业情况的调查报告前言：本次活动的参与者周小小；实践主题为大学生专业与就业情况研究；现将此次实践活动活动的有关情况报告如下： 这是一个阳光充足的日子，我们在一所学校附近开展的有关大学生专业选择与就业情况研究的调查报告。此卷主要意在了解就业市场对我们大学生专业选择与就业状况的影响，以及对就业问题的一些看法和感受。 随着中国高等教育规模的扩展，高等教育毕业生所面临的就业市场的供求关系正在发生变化，这种变化对于高等教育与经济发展和劳动力市场的影响将是深刻的。了解扩招后第一批毕业生的工作找寻、工作落实情况，以及他们对高等教育和劳动力市场的期望，不仅对于高等教育本身的发展至关重要，而且也会对国家的劳动、人事和整个教育制度等方方面面产生深远的影响，为此“大学生专业选择与就业情况研究”在今年12月对高校毕业生的就业情况进行了一次规模较大的认真调查。 根据有关调查表明对于工作与专业的对口程度的估计，本科和研究生层次的对口率明显高于专科生的层次。另外对就业信息来源渠道的统计分析可以看出，目前高校毕业生从学校获得的就业信息依然是占主导地位的，期间我们发出问卷60份，收回有效答卷也是60份。在经过整理和分析数据后，我们发现了很多问题，其中存在的主要问题有： （一）大学生普遍的职业目标是获取生活与工作的平衡，或成为领导者或管理者，当然有部分认的目标是满足个人兴趣，有发展的空间，可是到目前为止，85%的受访者还没有找到自己满意的工作。 （二）大学生普遍感到就业形势不容乐观，基本原因是近些年来高校扩招的
大学理科主要专业与就业方 向 介绍 1.数学与应用数学 专业介绍：本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力，并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。 就业去向：毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 重点院校：北京大学、南开大学、天津大学、 天津理工大学、复旦大 学、四川大学、中国科 学技术大学、武汉大学。 2.信息与计算科学 专业介绍：本专业培养 能在科技、教育和经济 部门从事研究、教学、 应用软件开发和管理工 作等方面的高级专门人 才。 就业去向：主要到科技、 教育和经济部门从事研 究、教学和应用开发及 管理工作。 重点院校：北京大学、 清华大学、浙江大学、 南开大学、南京大学、 吉林大学、西安交通大 学、天津理工大学。 3.应用物理学 专业介绍：本专业培养 具有坚实的数理基础， 熟悉物理学基本理论和 发展趋势，熟悉计算机 语言，掌握实验物理基 本技能和数据处理的方 法，获得技术开发以及 工程技术方面的基本训 练，具有良好的科学素 养和创新意识。 就业去向：毕业生能在 应用物理、电子信息技 术、材料科学与工程、 计算机技术等相关科学 领域从事应用研究、技 术开发以及教学和管理 工作。 重点院校：北大、清华、 南京大学、浙大、南开 大学、天津大学、中国 科学技术大学、武汉大 学。 4.应用化学 专业介绍：本专业以高 分子材料、精细化工和
计算机在化学化工中的应用技术为专业方向，培养具有可从事相关领域的科学研究，工业开发和管理知识的高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5.环境科学 专业介绍：本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的专业人才。 就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。 6.环境工程专业 专业介绍：本专业培养 具备城市和城镇水、气、 声、固体废物等污染防 治和给排水工程，水污 染控制规划和水资源保 护等方面知识的环境工 程学科高级工程技术人 才。 就业去向：主要至政府 部门、规划部门、经济 管理部门、环保部门、 设计单位、工矿企业、 科研单位、学校等从事 规划、设计、施工、管 理、教育和研究开发方 面的工作。 重点院校：华中科技大 学、南开大学、天津大 学、天津理工大学 7.计算机科学与技术 专业介绍：培养能在科 研部门、教育单位、企 业、事业、技术和行政 管理部门等单位从事计 算机教学、科学研究和 应用的计算机科学与技 术学科的高级专门科学 技术人才。 就业去向：计算机科学 与技术类专业的毕业生 适合到各系统或行业的 相关部门从事软件开 发、经营和维护，也可 从事教学、科研和技术 工作。 重点院校：北京大学、 北京理工大学、吉林大 学、南京大学、南开大 学、天津大学、天津理 工大学 8.生物工程(生物科学) 专业介绍：本专业培养 能在生物技术与工程领 域从事设计、生产、管
大学专业介绍及就业方向-土木建筑类大学专业解读
大学专业介绍及就业方向土木建筑类大学专业解读 一、大学专业介绍及就业方向： 大学中的土木建筑类专业包括：建筑学、城市规划、建筑环境与能源应用、土木工程、给排水工程、城市地下空间工程、历史建筑保护工程、景观建筑设计、水务工程、建筑设施智能技术、道路桥梁与渡河工程等专业。其中建筑学、城市规划、建筑环境与能源应用、土木工程四大专业为土木建筑类专业中的主要专业，现在我们就介绍建筑学、城市规划、建筑环境与能源应用、土木工程四个主要专业课程设置、培养目标以及就业与深造方向。 建筑学 学科：工学 门类：土建类 专业名称：建筑学 业务培养目标：本专业培养具备建筑设计、城市设计、室内设计等方面的知识，能在设计部门从事设计工作，并具有多种职业适应能力的通用型、复合型高级工程技术人才。 业务培养要求：本专业学生主要学习建筑设计、城市规划原理、建筑工程技术等方面的基本理论与基本知识，受到建筑设计等方面的基本训练，具有项目策划、建筑设计方案和建筑施工图绘制等方面的基本能力。 毕业生应获得以下几方面的知识和能力： l．具有较扎实的自然科学基础、较好的人文社会科学基础和外语语言综合能力； 2．掌握建筑设计的基本原理和方法，具有独立进行建筑设计和用多种方式表达设计意图的能力以及具有初步的计算机文字、图形、数据的处理能力； 3．了解中外建筑历史的发展规律，掌握人的生理、心理、行为与建筑环境的关系，与建筑有关的经济知识、社会文化习俗、法律与法规的基本知识，以
及建筑边缘学科与交叉学科的相关知识； 4．初步掌握建筑结构及建筑设备体系与建筑的安全、经济、适用、美观的关系的基本知识，建筑构造的原理与方法，常用建筑材料及新材料的性能。具有合理选用和一定的综合应用能力，并具有一定的多工种间组织协调能力； 5．具有项目前期策划、建筑设计方案和建筑施工图绘制的能力，具有建筑美学的修养。 主干学科：建筑学 主要课程：建筑设计基础、建筑设计及原理、中外建筑历史、建筑结构与建筑力学、建筑构造。 主要实践性教学环节：包括美术实习、工地实习、建筑测绘实习、建筑认识实习、设计院生产实习，一般安排40周。 修业年限：四年或五年 授予学位：建筑学学士 就业深造 适合在建筑设计院、城市规划设计院、建筑设计事务所、房地产开发公司、各企事业单位的基建部门、高等学校、国家政府部门工作。 城市规划 学科：工学 门类：土建类 专业名称：城市规划 业务培养目标：本专业培养具备城市规划、城市设计等方面的知识，能在城市规划设计、城市规划管理、决策咨询、房地产开发等部门从事城市规划设计与管理，开展城市道路交通规划、城市市政工程规划、城市生态规划、园林游憩系统规划，并能参与城市社会与经济发展规划、区域规划、城市开发、房地产筹划以及相关政策法规研究等方面工作的城市规划学科高级工程技术人才。
大学专业介绍与就业方向（理科）。 １.数学与应用数学 专业介绍:本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力,并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。?就业去向:毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 推荐院校:北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2.信息与计算科学 专业介绍:本专业培养能在科技、教育和经济部门从事研究、教学、应用软件开发和管理工作等方面的高级专门人才。 就业去向：主要到科技、教育和经济部门从事研究、教学和应用开发及管理工作。 推荐院校:北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学。?3.应用物理学?专业介绍:本专业培养具有坚实的数理基础,熟悉物理学基本理论和发展趋势，熟悉计算机语言,掌握实验物理基本技能和数据处理的方法，获得技术开发以及工程技术方面的基本训练，具有良好的科学素养和创新意识。?就业去向:毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、技术开发以及教学和管理工作。?推荐
院校：北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学。 4．应用化学 专业介绍:本专业以高分子材料、精细化工和计算机在化学化工中的应用技术为专业方向，培养具有可从事相关领域的科学研究,工业开发和管理知识的高级专门人才。?就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。?5.环境科学?专业介绍:本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的高级专业人才。?就业去向:主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。?6.环境工程专业 专业介绍:本专业培养具备城市和城镇水、气、声、固体废物等污染防治和给排水工程，水污染控制规划和水资源保护等方面知识的环境工程学科高级工程技术人才。 就业去向：主要至政府部门、规划部门、经济管理部门、环保部门、设计单位、工矿企业、科研单位、学校等从事规划、设计、施工、管理、教育和研究开发方面的工作。?推荐院校:华中科技大学、南开大学、天津大学、天津理工大学?7.计算机科学与技术 专业介绍:培养能在科研部门、教育单位、企业、事业、技术和行政管理部门等单位从事计算机教学、科学研究和应用的计算机科学与技术学科的高级专门科学技术人才。?就业去向：计算机科学与技术类专业的毕业生适合到各系统或行业的相关部门从事软件开发、经营和维护，也可从事教学、科研和技术工作。
中国大学理科专业介绍大全 经济学 主要课程：宏观经济学、微观经济学、政治经济学、金融学、财政学、会计学、统计学、中国转型经济学、国际经济学、发展经济学、国别（地区）经济、当代经济学流派、经济学说史、博 弈论与信息经济学、计算机技术和软件、计量经济学、数据库及其应用。 就业方向：学生毕业后，能成为在国家机关、企事业单位和社会团体，特别是在与经济密切相关的行政机关、教研机构、公司从事经济工作。 金融学 主要课程：政治经济学、微观经济学、宏观经济学、计量经济学、货币银行学、商业银行经营管理、国际金融、金融市场学、投资银行学、保险学、证券投资学、中央银行学、金融会计、公 司金融、金融工程等。 就业方向：本专业能胜任银行、证券、保险、信托或其他金融领域工作的高级专门人才。 国际政治专业 主要课程：政治学原理、国际政治学概论、国际法、国际政治经济学、世界经济、近现代国际关系、当代国际关系、当代中国外交概论、国际组织概论、当代西方国际关系理论、外交学概论、 欧洲地区政治与经济、亚太地区经济与政治、当代西方政治思潮、当代美国外交、中西文 化比较、中外政治制度比较、涉外礼仪、涉外事务模拟教程。 就业方向：本专业主要为党政机关、外事部门、新闻媒体、涉外企事业单位、教学科研等部门培养从事外事外交、政策研究、涉外宣传、国际情报分析处理，以及教学和科研工作的专门人才。广播电视新闻学专业 主干课程：广播电视概论、广播电视编辑与制作、电视摄像、电视专题与电视栏目、广播电视采访写作、电视作品分析、媒介经营管理、中外新闻史、新闻职业道德与法规、大众传播学、经 济新闻概论、广告学等。 就业方向：本专业能在电台、电视台、报社、杂志社、企事业单位和政府部门从事编辑、采访、策划、节目主持、宣传和经济管理等方面工作的高级专门人才。 工商管理专业 就业方向：本专业培养能在国家机关、企事业单位、科研机构、金融单位、各类国有和非国有企业从事管理、教学及科研工作的高级管理人才。 主要课程：管理学、管理经济学、西方经济学、战略管理、生产运作、物流管理、财务管理、质量管理、人力资源开发与管理、管理信息系统、管理系统工程、组织行为学、会计学、统计学、 财政学、货币银行学、国际企业管理、国际贸易、国际金融、标准化与质量认证、产业经 济学、计算机操作系统及应用、商务英语等。 人力资源管理专业 就业方向：本专业旨在培养掌握现代人力资源管理基本理论与基本操作技能，熟悉人力资源管理相关法规和政策，具有良好的个人可信度、人际沟通能力和组织协调能力，既能为组织做出战 略贡献又能很好地完成人力资源管理各项具体职能的高素质复合型专门人才。 主要课程：管理学、经济学、管理学、心理学、法学\组织行为学、战略管理、人力资源管理、人力资源培训与开发、职业生涯管理、人力资源管理软件运用、统计与社会调查方法、组织设 计与工作分析、人员素质测评、绩效管理、薪酬管理、劳动法与员工关系管理、福利管理、 领导学、团队管理、ERP、生产管理、物流管理、市场营销、财务管理、金融管理、税收 管理等 市场营销专业
大学理科工科专业介绍及就业去向 1.电气工程及其自动化专业 : 具备电机及其控制、电器及其控制、电力系统及其 自动化、建筑电气等工程技术领域基础理论和基本知识，能够从事设计制造、研 制开发、试验分析、系统运行、自动控制、电力电子技术、生产管理以及电子与 计算机技术应用的应用型高级工程技术人才。想做好电气工程师要具备以 下几点： 1、熟悉供配电系统 2、电力拖动系统 3、自动化控制系统 4、具备点机械的知识。主干学科：电气工程、控制科学与工程、计算机科学与技术 主要课程：电路理论、电子技术、电力电子技术、自动控制原理、微机原理与 应用、电气工程基础、电机学、电器学、电力系统分析、电机设计、高低压电 器、电机控制、智能化电器原理与应用、电力系统继电保护、电力系统综合自 动化、建筑供配电等。 就业方向 : 电气工程及其自动化专业培养的毕业生就业面宽、适应性强。该专业的 毕业生主要面向电力行业就业，可从事电力设计、建设、调试、生产、运行、市场 运营、科技开发和技术培训等工作，也可从事其他行业中的电气技术工作。主要就 业单位有电力公司、电力设计院、电力规划院、电力建设部门、电力生产单位、电 气工程研究开发公司和研究院以及具有电气相关专业的院校。自动控制、电力电子 技术、信息处理、试验分析、研制开发、经济管理以及电子与 计算机技术应用等领域的工作, 电气自动化专业就业前景怎样： 1. 电业局 2. 设计院3. 工程局 最好的是电业局。福利好，待遇高。然后是设计院，工作相对比较轻松。最艰 苦的是工程局。因为要随着工程地点到处跑。但是工资也不低。总的来说是很 不错的。而且还可以向自动化、电子等方向转行。这个专业，强电，弱电都有 的。因为，主要学习系统的东西。专业科目，主要是，电路、数学电子、模拟 电子、控制系统、经典控制理论、现代控制理论、微机控制理论、还有电机拖 动、 PLC、电厂供电、单片机等。 2013-2014 年电气工程及其自动化专业排名[3] 排序学校名称水平开此专业学校数 1西安交通大学5★367 2清华大学5★367 3浙江大学5★367 4华中科技大学5★367 5重庆大学5★367 6西南交通大学5★367 7哈尔滨工业大学5★367 8天津大学5★367 9西北工业大学5★367 10沈阳工业大学5★367 11哈尔滨理工大学5★367 12合肥工业大学5★367 13河北工业大学5★367 14中国矿业大学5★367 15华北电力大学5★367
大学理科专业介绍与就业方向 1. 数学与应用数学 专业介绍：本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力，并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。 就业去向：毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 推荐院校：北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2. 信息与计算科学 专业介绍：本专业培养能在科技、教育和经济部门从事研究、教学、应用软件开发和管理工作等方面的高级专门人才。 就业去向：主要到科技、教育和经济部门从事研究、教学和应用开发及管理工作。推荐院校：北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学。 3. 应用物理学 专业介绍：本专业培养具有坚实的数理基础，熟悉物理学基本理论和发展趋势，熟悉计算机语言，掌握实验物理基本技能和数据处理的方法，获得技术开发以及工程技术方面的基本训练，具有良好的科学素养和创新意识。 就业去向：毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、技术开发以及教学和管理工作。 推荐院校：北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学。 4. 应用化学 专业介绍：本专业以高分子材料、精细化工和计算机在化学化工中的应用技术为专业方向，培养具有可从事相关领域的科学研究，工业开发和管理知识的高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5. 环境科学专业介绍：本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的高级专业人才。 就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。 6. 环境工程专业 专业介绍：本专业培养具备城市和城镇水、气、声、固体废物等污染防治和给排水工程，水污染控制规划和水资源保护等方面知识的环境工程学科高级工程技术人才。 就业去向：主要至政府部门、规划部门、经济管理部门、环保部门、设计单位、工矿企业、科研单位、学校等从事规划、设计、施工、管理、教育和研究开发方面的工作。 推荐院校：华中科技大学、南开大学、天津大学、天津理工大学
理工科专业介绍与就业方向 1.数学与应用数学?专业介绍:本专业特点就是理工结合，培养具有宽厚得数学基础，熟练得计算机应用与开发技能，较强得外语能力，并掌握一定得应用科学知识，能运用数学得理论与方法解决实际问题得高级科技人才。?就业去向：毕业生适合到科研、工程、经济、金融、管理等部门与高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析与预测等多方面得研究与管理工作。?推荐院校:北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2、信息与计算科学 专业介绍:本专业培养能在科技、教育与经济部门从事研究、教学、应用软件开发与管理工作等方面得高级专门人才。?就业去向：主要到科技、教育与经济部门从事研究、教学与应用开发及管理工作. 推荐院校:北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学.?3、应用物理学?专业介绍：本专业培养具有坚实得数理基础，熟悉物理学基本理论与发展趋势,熟悉计算机语言,掌握实验物理基本技能与数据处理得方法，获得技术开发以及工程技术方面得基本训练,具有良好得科学素养与创新意识。?就业去向：毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、技术开发以及教学与管理工作。?推荐院校：北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学.?4、应用化学?专业介绍：本专业以高分子材料、精细化工与计算机在化学化工中得应用技术为专业方向,培养具有可从事相关领域得科学研究,工业开发与管理知识得高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5、环境科学?专业介绍:本专业培养能在科研机构、高等院校、行政部门与企事业等单位从事科研、教学、规划与管理、环境评价与环境监测等工作得高级专业人才。 就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护与环境管理等工作. 6、环境工程专业
大学专业介绍与就业方向集录 （理科） 1.数学与应用数学 专业介绍：本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力，并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。 就业去向：毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 推荐院校：北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2.信息与计算科学专业介绍：本专业培养能在科技、教育和经济部门从事研究、教学、应用软件开发和管理工作等方面的高级专门人才。 就业去向：主要到科技、教育和经济部门从事研究、教学和应用开发及管理工作。推荐院校：北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学。 3.应用物理学专业介绍：本专业培养具有坚实的数理基础，熟悉物理学基本理论和发展趋势，熟悉计算机语言，掌握实验物理基本技能和数据处理的方法，获得技术开发以及工程技术方面的基本训练，具有良好的科学素养和
创新意识。 就业去向：毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、技术开发以及教学和管理工作。 推荐院校：北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学。 4.应用化学 专业介绍：本专业以高分子材料、精细化工和计算机在化学化工中的应用技术为 专业方向，培养具有可从事相关领域的科学研究，工业开发和管理知识的高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5.环境科学 专业介绍：本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的高级专业人才。就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。 6.环境工程专业专业介绍：本专业培养具备城市和城镇水、气、声、固体废物等污染防治和给排水工程，水污染控制规划和水资源保护等方面知识的环境工程学科高级工程技术人才。 就业去向：主要至政府部门、规划部门、经济管理部门、环保部门、设计单位、工矿企业、科研单位、学校等从事规划、设计、施工、管理、教育和研
大学理科专业介绍与就业方向 1、数学与应用数学 专业介绍:本专业特点就是理工结合,培养具有宽厚的数学基础,熟练的计算机应用与开发技能,较强的外语能力,并掌握一定的应用科学知识,能运用数学的理论与方法解决实际问题的高级科技人才。 就业去向:毕业生适合到科研、工程、经济、金融、管理等部门与高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析与预测等多方面的研究与管理工作。 推荐院校:北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2、信息与计算科学 专业介绍:本专业培养能在科技、教育与经济部门从事研究、教学、应用软件开发与管理工作等方面的高级专门人才。 就业去向:主要到科技、教育与经济部门从事研究、教学与应用开发及管理工作。 推荐院校:北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学。 3、应用物理学 专业介绍:本专业培养具有坚实的数理基础,熟悉物理学基本理论与发展趋势,熟悉计算机语言,掌握实验物理基本技能与数据处理的方法,获得技术开发以及工程技术方面的基本训练,具有良好的科学素养与创新意识。 就业去向:毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、技术开发以及教学与管理工作。 推荐院校:北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学。 4、应用化学 专业介绍:本专业以高分子材料、精细化工与计算机在化学化工中的应用技术为专业方向,培养具有可从事相关领域的科学研究,工业开发与管理知识的高级专门人才。 就业去向:主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5、环境科学 专业介绍:本专业培养能在科研机构、高等院校、行政部门与企事业等单位从事科研、教学、规划与管理、环境评价与环境监测等工作的高级专业人才。 就业去向:主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护与环境管理等工作。 6、环境工程专业 专业介绍:本专业培养具备城市与城镇水、气、声、固体废物等污染防治与给排水工程,水污染控制规划与水资源保护等方面知识的环境工程学科高级工程技术人才。 就业去向:主要至政府部门、规划部门、经济管理部门、环保部门、设计单位、工矿企业、科研单位、学校等从事规划、设计、施工、管理、教育与研究开发方面的工作。 推荐院校:华中科技大学、南开大学、天津大学、天津理工大学 7、计算机科学与技术 专业介绍:培养能在科研部门、教育单位、企业、事业、技术与行政管理部门等单位从事计算机教学、科学研究与应用的计算机科学与技术学科的高级专门科学技术人才。 就业去向:计算机科学与技术类专业的毕业生适合到各系统或行业的相关部门从事软件开发、经营与维护,也可从事教学、科研与技术工作。
一、理科生学什么专业好 理科专业基本属于技术类型专业，各个专业优势存在区分差异，理科考生只有以职业兴趣为导向，兼顾大学和专业选择，细致分析未来行业发展状况，才能选择出一个好的专业，以下是详细分析： 1、行业分析：6大技术领域八大人才需求 教育研究院的一项研究表明，随着我国经济与社会的发展，在今后10年内，我国对人才的需要将有较大的变化。 从技术和产业发展的角度来说，今后几年我国将大力发展6大技术领域：生物技术、信息技术、新材料技术、新能源技术、空间技术、海洋技术。6大技术可形成9大高科技产业：生物工程、生物医药、光电子信息、智能机械、软件、超导体、太阳能、空间产业和海洋产业。 按照国家人事部的有关统计预测，我国今后几年内急需人才主要有以下8大类： 以电子技术、生物工程、航天技术、海洋利用、新能源新材料为代表的高新技术人才；信息技术人才；机电一体化专业技术人才；农业科技人才；环境保护技术人才；生物工程研究与开发人才；国际贸易人才；律师人才. 2 、报考指南：兴趣为先专业各有优势 2.1 专业优势有区分职业兴趣和未来就业是选择标准 首先要看你的出发点是什么，你必须先明白，现在的热门专业，也就是就业好的专业不一定就是高薪专业。还有，很多高考的热门专业，毕业之后并不很好找工作（这方面的专业比如说生物工程，和材料工程） 你选专业之前，一定先要选一个好的行业。不一定专业好才能赚钱。比如说现在的电子信息工程，软件工程。这些事ＩＴ行业的。还有机械行业也非常不错，比如这方面的机械制造及自动化，车辆工程。还有热动方面的，能源与动力自动化，热能工程，飞行器动力工程。 同时，你还应该在好学校和好专业之间做出一个选择。 3 、大学VS专业—两者完美结合 情况1：如果你要上一个名校选大学优先， 国内名校比如说西安交通大学，南京大学，哈工大、武汉大学等等，如果你的高考分数允许上这些大学的强势专业，你就有必要去报考他们的强势专业，从而选择选择一个好的行业。但是如果你的高考分数不够，
大学专业介绍与就业方向（理科） 1.数学与应用数学 专业介绍：本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力，并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。 就业去向：毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 推荐院校：北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2.信息与计算科学 专业介绍：本专业培养能在科技、教育和经济部门从事研究、教学、应用软件开发和管理工作等方面的高级专门人才。 就业去向：主要到科技、教育和经济部门从事研究、教学和应用开发及管理工作。 推荐院校：北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学。 3.应用物理学 专业介绍：本专业培养具有坚实的数理基础，熟悉物理学基本理论和发展趋势，熟悉计算机语言，掌握实验物理基本技能和数据处理的方法，获得技术开发以及工程技术方面的基本训练，具有良好的科学素养和创新意识。就业去向：毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、
技术开发以及教学和管理工作。 推荐院校：北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学。 4.应用化学 专业介绍：本专业以高分子材料、精细化工和计算机在化学化工中的应用技术为专业方向，培养具有可从事相关领域的科学研究，工业开发和管理知识的高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5.环境科学 专业介绍：本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的高级专业人才。 就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。 6.环境工程专业 专业介绍：本专业培养具备城市和城镇水、气、声、固体废物等污染防治和给排水工程，水污染控制规划和水资源保护等方面知识的环境工程学科高级工程技术人才。 就业去向：主要至政府部门、规划部门、经济管理部门、环保部门、设计单位、工矿企业、科研单位、学校等从事规划、设计、施工、管理、教育和研究开发方面的工作。 推荐院校：华中科技大学、南开大学、天津大学、天津理工大学
大学专业介绍与就业方向集录(文理科） 大学专业介绍与就业方向集录(文理科） 一：大学专业介绍与就业方向（理科） 1.数学与应用数学 专业介绍：本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力，并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。 就业去向：毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 推荐院校：北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2.信息与计算科学 专业介绍：本专业培养能在科技、教育和经济部门从事研究、教学、应用软件开发和管理工作等方面的高级专门人才。 就业去向：主要到科技、教育和经济部门从事研究、教学和应用开发及管理工作。 推荐院校：北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学。
3.应用物理学 专业介绍：本专业培养具有坚实的数理基础，熟悉物理学基本理论和发展趋势，熟悉计算机语言，掌握实验物理基本技能和数据处理的方法，获得技术开发以及工程技术方面的基本训练，具有良好的科学素养和创新意识。 就业去向：毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、技术开发以及教学和管理工作。 推荐院校：北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学。 4.应用化学 专业介绍：本专业以高分子材料、精细化工和计算机在化学化工中的应用技术为专业方向，培养具有可从事相关领域的科学研究，工业开发和管理知识的高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5.环境科学 专业介绍：本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的高级专业人才。 就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。
大学专业介绍及就业方向土木建筑类大学专业 解读 标准化管理部编码-[99968T-6889628-J68568-1689N]
大学专业介绍及就业方向土木建筑类大学专业解读 一、大学专业介绍及就业方向： 大学中的土木建筑类专业包括：建筑学、城市规划、建筑环境与能源应用、土木工程、给排水工程、城市地下空间工程、历史建筑保护工程、景观建筑设计、水务工程、建筑设施智能技术、道路桥梁与渡河工程等专业。其中建筑学、城市规划、建筑环境与能源应用、土木工程四大专业为土木建筑类专业中的主要专业，现在我们就介绍建筑学、城市规划、建筑环境与能源应用、土木工程四个主要专业课程设置、培养目标以及就业与深造方向。 建筑学 学科：工学 门类：土建类 专业名称：建筑学 业务培养目标：本专业培养具备建筑设计、城市设计、室内设计等方面的知识，能在设计部门从事设计工作，并具有多种职业适应能力的通用型、复合型高级工程技术人才。 业务培养要求：本专业学生主要学习建筑设计、城市规划原理、建筑工程技术等方面的基本理论与基本知识，受到建筑设计等方面的基本训练，具有项目策划、建筑设计方案和建筑施工图绘制等方面的基本能力。 毕业生应获得以下几方面的知识和能力： l．具有较扎实的自然科学基础、较好的人文社会科学基础和外语语言综合能力； 2．掌握建筑设计的基本原理和方法，具有独立进行建筑设计和用多种方式表达设计意图的能力以及具有初步的计算机文字、图形、数据的处理能力； 3．了解中外建筑历史的发展规律，掌握人的生理、心理、行为与建筑环境的关系，与建筑有关的经济知识、社会文化习俗、法律与法规的基本知识，以及建筑边缘学科与交叉学科的相关知识； 4．初步掌握建筑结构及建筑设备体系与建筑的安全、经济、适用、美观的关系的基本知识，建筑构造的原理与方法，常用建筑材料及新材料的性能。具有合理选用和一定的综合应用能力，并具有一定的多工种间组织协调能力；
大学理科专业介绍与就业方向
大学专业介绍与就业方向（理科）。 电子科学与技术 专业介绍：该专业培养能从事科学研究、技术开发和应用以及教学工作的高级专业人才。 就业前景：该专业毕业生主要分配到科研院所、高等院校、大型企事业单位及高新技术公司从事光电子、光通讯、激光技术及其应用等方面的研究、设计、开发和管理工作。 推荐学校：南开大学、天津大学、天津工业大学、天津理工大学、北京交通大学 水利水电工程 专业介绍：培养具有水利水电工程的规划、勘测、设计、施工、管理和科学研究等方面的专门知识，有创新精神和实际动手能力的水利水电工程建设实用性和通用性宽口径的高级技术人才。 就业去向：学生毕业后可在水利水电、电力行业的管理、设计、科学研究机构与工程施工单位和高等院校从事相关的规划、设计、施工、管理、科学研究和教学等工作。也可在土木建筑及其他行业从事相关的工作。 10.通信工程 专业介绍：本专业培养掌握光波、无线、多媒体通讯技术、通讯系统和通讯网等方面知识，能在通信领域从事研究、设计、制造、运营及从事通讯技术开发与应用、管理与决策的高级工程技术人才。 就业去向：适合邮电部所属各邮电管理局及公司从事科研、技术开发、经营及管理
工作，也可到军队、铁路、电力等部门从事相应的工作。 推荐院校：北京邮电大学、天津大学、天津理工大学、华中科技大学、西安电子科技大学 11.机械工程及自动化专业 专业介绍：本专业培养能在工业生产第一线从事机械工程及自动化领域的设计制造、科技开发、应用研究、运行管理和市场营销等方面工作的宽口径、通识型高级工程技术人才。 就业前景：可在机械、电子、冶金、石油化工、航空航天、兵工等领域的企事业单位从事生产技术、研究开发、工程建设、行政管理及教育工作等。机械工程及其自动化专业是集机械、电子、信息技术为一体的综合性专业，知识结构先进，适应面宽广，发展潜力大，是一个发展迅速、需求巨大的热门专业。 推荐院校：西安交通大学、浙江大学、天津理工大学、天津工业大学 12.过程装备与控制工程 专业介绍：本专业培养具备化学工程、机械工程、控制工程和管理工程等方面知识的高级专门人才，以适应现代化工业发展的需要。 就业前景：主要到化工、石油、能源、轻工、环保、医药、食品、机械及劳动安全等部门从事工程设计、技术开发、生产技术、经营管理以及工程科学研究等方面的工作。本专业毕业生就业几率高，且多能与专业对口。 13.热能与动力工程专业介绍：本专业培养能够从事制冷、空气调节、供热等能源工程的高级工程技术和管理人才。专业内容包括汽车发动机、涡轮机械、锅炉与换热设备、制冷与空调、新能源开发与利用。 就业前景：在国民经济和部门，从事动力机械(如热力发动机、流体机械、水力机械)的动力工程(如水电动力工程、制冷及低温工程、空调工程)的设计、制造、运行、管理、实验研究和安装、开发、营销等工作。该专业属能源动力类，是国家重点发
大学理科专业介绍与就业方向 1.数学与应用数学 专业介绍：本专业特点是理工结合，培养具有宽厚的数学基础，熟练的计算机应用和开发技能，较强的外语能力，并掌握一定的应用科学知识，能运用数学的理论和方法解决实际问题的高级科技人才。 就业去向：毕业生适合到科研、工程、经济、金融、管理等部门和高等院校从事教学、计算机应用、科学计算、软件设计、信息管理、经济动态分析和预测等多方面的研究和管理工作。 推荐院校：北京大学、南开大学、天津大学、天津理工大学、复旦大学、四川大学、中国科学技术大学武汉大学。 2.信息与计算科学 专业介绍：本专业培养能在科技、教育和经济部门从事研究、教学、应用软件开发和管理工作等方面的高级专门人才。 就业去向：主要到科技、教育和经济部门从事研究、教学和应用开发及管理工作。 推荐院校：北京大学、清华大学、浙江大学、南开大学、南京大学、吉林大学、西安交通大学、天津理工大学。 3.应用物理学 专业介绍：本专业培养具有坚实的数理基础，熟悉物理学基本理论和发展趋势，熟悉计算机语言，掌握实验物理基本技能和数据处理的方法，获得技术开发以及工程技术方面的基本训练，具有良好的科学素养和创新意识。 就业去向：毕业生能在应用物理、电子信息技术、材料科学与工程、计算机技术等相关科学领域从事应用研究、技术开发以及教学和管理工作。 推荐院校：北京大学、清华大学、南京大学、浙江大学、南开大学、天津大学、中国科学技术大学、武汉大学。 4.应用化学 专业介绍：本专业以高分子材料、精细化工和计算机在化学化工中的应用技术为专业方向，培养具有可从事相关领域的科学研究，工业开发和管理知识的高级专门人才。 就业去向：主要到科研机构、高等学校及企事业单位等从事科学研究、教学及管理。 5.环境科学 专业介绍：本专业培养能在科研机构、高等院校、行政部门和企事业等单位从事科研、教学、规划与管理、环境评价和环境监测等工作的高级专业人才。 就业去向：主要到科研机构、高等学校、企业事业单位及行政部门等从事科研、教学、环境保护和环境管理等工作。 6.环境工程专业 专业介绍：本专业培养具备城市和城镇水、气、声、固体废物等污染防治和给排水工程，水污染控制规划和水资源保护等方面知识的环境工程学科高级工程技术人才。 就业去向：主要至政府部门、规划部门、经济管理部门、环保部门、设计单位、工矿企业、科研单位、学校等从事规划、设计、施工、管理、教育和研究开发方面的工作。