1.Genetic analysis of fruit shape traits at different maturation stages in sponge gourd.
Sheng ZHANG ; Jin HU ; Cai-fang ZHANG ; Ya-jing GUAN ; Ying ZHANG
Journal of Zhejiang University. Science. B 2007;8(5):338-344
The fruit shape is important quantitative trait closely related to the fruit quality. However, the genetic model of fruit shapes has not been proposed. Therefore, in the present study, analysis of genetic effects for fruit shape traits (fruit length and fruit perimeter) in sponge gourd was conducted by employing a developmental genetic model including fruit direct effects and maternal effects. Analysis approaches of unconditional and conditional variances were applied to evaluate the genetic behavior of fruit shape traits at economical and physiological maturation times. The results of variance analysis indicated that fruit length and fruit perimeter were simultaneously affected by fruit direct genetic effects and maternal effects. Fruit direct genetic effects were relatively more important for fruit shape traits at whole developmental period. The gene expression was most active at the economical maturation stage (1 approximately 12 d after flowering) for two shape traits, and the activation of gene was mostly due to direct dominance effects at physiological maturation stage (13 approximately 60 d after flowering). The coefficients due to different genetic effects, as well as the phenotypic correlation coefficients, varied significantly between fruit shape traits themselves at various maturation stages. The results showed that it was relatively easy to improve fruit shape traits for industrial purpose by carefully selecting the parents at economical maturation stage instead of that at physiological maturation stage.
Computer Simulation
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Fruit
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anatomy & histology
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physiology
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Luffa
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anatomy & histology
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physiology
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Models, Genetic
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Quantitative Trait, Heritable
2.A linkage analysis of quantitative trait loci for familial schizophrenia on chromosome 1.
Guiqing CAI ; Xinyao WU ; Tao LI ; David A COLLIER ; Xiehe LIU ; Bingjian FENG ; Hong DENG ; Dayue TONG ; Jianjin LI ; Jinghua OU
Chinese Journal of Medical Genetics 2002;19(4):281-284
OBJECTIVETo explore the molecular genetic relationship between chromosome 1 and quantitative trait loci for familial schizophrenia.
METHODSA series of assessment scales included positive and negative syndrome scale (PANSS), global assessment of functional scale (GAFS), premorbid schizoid and schizotypal traits scale (PSST), premorbid social adjustment scale (PSA) were applied to quantify the phenotypes of schizophrenia. Non-parametric linkage analysis of quantitative traits was conducted in 32 multiplex pedigrees with schizophrenia by using 29 microsatellite makers on chromosome 1.
RESULTSHaseman-Elston quantitative trait analysis detected a maximum Traditional H-E Lods of 1.73 and a maximum EH H-E Lods of 1.65 of negative symptoms (PANSS-N ) at 147.64 cM, which was overlapped to the positive region of 1q21-23 in qualitative linkage analysis.
CONCLUSIONThe results suggest there might be an independent quantitative trait locus of negative symptoms on 1q21-23 for familial schizophrenia.
Chromosomes, Human, Pair 1 ; genetics ; Family Health ; Genetic Linkage ; Humans ; Lod Score ; Microsatellite Repeats ; Quantitative Trait, Heritable ; Schizophrenia ; genetics
3.Estimating family correlation of quantitative traits using generalized estimating equation.
Chinese Journal of Epidemiology 2003;24(8):729-733
OBJECTIVETo study the method for measuring familial correlations of quantitative trait and analyzing family data set of body height.
METHODSGeneralized estimating equation 2 (GEE2) was employed to estimating both regression coefficients and the familial correlation. Analyses was carried out on software MAREG. A example from height pedigrees illustrated the method.
RESULTSGEE2 provided robust estimations of regression coefficients and familial correlations simultaneously. In body height the correlations between parents and offspring (r = 0.459) and between siblings (r = 0.671) were significantly higher than those between two parents (r = 0.184) after adjusting gender, residence and birth age. Of the same types of relative pairs, the correlation between pairs with individuals of the same gender (eg. father-son r = 0.603, mother-daughter r = 0.456, male sibling r = 0.947, female sibling r = 0.681) was higher than those individuals of different gender (eg father-daughter r = 0.431, mother-son r = 0.364, sibling with different gender r = 0.530).
CONCLUSIONGEE2 should be considered a standard method for the investigation of familial aggregation due to its flexibility and robustness.
Adult ; Body Height ; genetics ; Female ; Humans ; Male ; Middle Aged ; Models, Genetic ; Models, Statistical ; Multifactorial Inheritance ; Nuclear Family ; Pedigree ; Quantitative Trait Loci ; Quantitative Trait, Heritable ; Regression Analysis ; Risk Factors ; Sex Factors
4.Genetic variability and interrelationships of mainly quantitative traits in Glycyrrhiza uralensis cultivated population.
Fulai YU ; Yuqiang FANG ; Wenquan WANG ; Qiuling WANG ; Fengbo LIU
China Journal of Chinese Materia Medica 2011;36(18):2457-2461
OBJECTIVEThe main aim of the research was to evaluate genetic variability and interrelationships of mainly quantitative traits in 2-year population, and provide a basis for high-yield breeding of Glycyrrhiza uralensis.
METHODFour genotype G. uralensis population were transplanting in four different environment using complete randomized block design with three replication, and the 10 quantitative traits, including plant height (PH), stem diameter (SD), tiller number (TN), taproot length (TRL), root length (RL), root diameter (RD), diameter of 20 cm below the root head (D20), taperingness (TR), lateral root number (LRN) and root fresh weight (RFW) were measured in field.
RESULTThe difference among population for all evaluated traits were significant (P<0.05) through Duncan's multiple range tests, and the coefficient of variation of RFW and LRN were above 25%. The analysis of variance was used to evaluate the traits of four populations across to four different environment Genotype, environment and their interaction effect were significant (P<0.05) or highly significant (P<0.01) for mainly evaluated traits. Simple correlation between traits showed that PH, SD, LRN, RL, RD and D20 had highly significant (P<0.01) and positive correlation with RFW. Results of the path coefficient analyses showed that D20 had the greatest positive direct effect on RFW, followed by the traits of PH and RL.
CONCLUSIONSelection for increased D20, RL and PH would be the best indirect selection traits for increasing root yield. Meanwhile, ample genetic variability exists in the G. uralensis 2-year population, it could be used for breeding improvement of root yield.
Breeding ; methods ; Environment ; Genes, Modifier ; Glycyrrhiza uralensis ; genetics ; growth & development ; metabolism ; Phenotype ; Plant Roots ; genetics ; metabolism ; Plant Stems ; genetics ; metabolism ; Plants, Medicinal ; chemistry ; Quantitative Trait Loci ; genetics ; Quantitative Trait, Heritable
5.Tagging and mapping of QTLs controlling lint yield and yield components in upland cotton (Gossypium hirsutum L.) using SSR and RAPD markers.
Jian-Mei YIN ; Yao-Ting WU ; Jun ZHANG ; Tian-Zhen ZHANG ; Wang-Zhen GUO ; Xie-Fei ZHU
Chinese Journal of Biotechnology 2002;18(2):162-166
Using interval mapping and marker simple regression methods, the QTLs of yield and its components in (Simian 3 x TM-1) F2 and F2:3, were tagged and Mapped with 39 SSR and 10 RAPD markers having polymorphism between parents screened from 301 pair SSR primers and 1040 RAPD primers. Simian 3 is being grown extensively in Yangtze River cotton-growing valley characterized as high productivity with more bolls and higher lint percent, whereas TM-1, Genetic standard in Upland cotton with more heavy boll weight. In the present report, two QTLs controlling boll size with 18.2% and 21.0% phenotype variance explained in F2:3 generation, one QTL controlling lint percent with 24.9% phenotype variance explained in F2 generation and 5.9% in F2:3 generation and one QTL controlling 100-seed weight with 15.6% phenotype variance explained in F2:3 generation were mapped in Chromosome 9. Additionally, another QTL responsible for 100-seed weight was identified and mapped at the same position in Chromosome 9 in F2:3 generation. It is worth for further to be studied whether it is one QTL for pleiotrophism or two closely linked QTLs. The molecular markers mapped and tagged closely with main QTLs of yield traits in this paper can be used for MAS in cotton high-yield breeding program.
China
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Chromosome Mapping
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Crops, Agricultural
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Crosses, Genetic
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Genetic Linkage
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Genetic Markers
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Gossypium
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genetics
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Polymorphism, Single-Stranded Conformational
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Quantitative Trait, Heritable
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Random Amplified Polymorphic DNA Technique
6.Sample size requirements for association studies on gene-gene interaction in case-control study.
Chinese Journal of Epidemiology 2004;25(7):623-626
OBJECTIVESample size requirements for association studies on gene-gene interaction in case-control study.
METHODSSelecting different parameters (such as inheritance mode, susceptibility frequency, frequency of allele for disease, OR of gene main effect) and infilling them into QUANTO software based on conditional logistic regression mode.
RESULTS(1) The main parameters influencing the sample size requirements were the levels of interaction between genes and the susceptibility frequency. The numbers of sample were the same between recessive and dominant when susceptibility frequency were the same. (2) Sample size for testing of gene-gene interaction was different from that for testing of genetic effects.
CONCLUSIONIt was convenient to use the numbers of sample size from the results for gene-gene interaction in case-control study.
Case-Control Studies ; Gene Frequency ; Genetic Markers ; genetics ; Genetic Predisposition to Disease ; genetics ; Genotype ; Humans ; Logistic Models ; Models, Genetic ; Quantitative Trait, Heritable ; Research Design ; Risk Factors ; Sample Size ; Sensitivity and Specificity
7.Quantitative Analysis of SMN1 Gene and Estimation of SMN1 Deletion Carrier Frequency in Korean Population based on Real-Time PCR.
Tae Mi LEE ; Sang Wun KIM ; Kwang Soo LEE ; Hyun Seok JIN ; Soo Kyung KOO ; Inho JO ; Seongman KANG ; Sung Chul JUNG
Journal of Korean Medical Science 2004;19(6):870-873
Spinal muscular atrophy (SMA) is an autosomal recessive disorder, caused by homozygous absence of the survival motor neuron gene (SMN1) in approximately 94% of patients. Since most carriers have only one SMN1 gene copy, several SMN1 quantitative analyses have been used for the SMA carrier detection. We developed a reliable quantitative real-time PCR with SYBR Green I dye and studied 13 patients with SMA and their 24 parents, as well as 326 healthy normal individuals. The copy number of the SMN1 gene was determined by the comparative threshold cycle (Ct) method and albumin was used as a reference gene. The homozygous SMN1 deletion ratio of patients was 0.00 and the hemizygous SMN1 deletion ratio of parents ranged from 0.39 to 0.59. The delta delta Ct ratios of 7 persons among 326 normal individuals were within the carrier range, 0.41-0.57. According to these data, we estimated the carrier and disease prevalence of SMA at 1/47 and 1/8,496 in Korean population, respectively. These data indicated that there would be no much difference in disease prevalence of SMA compared with western countries. Since the prevalence of SMA is higher than other autosomal recessive disorders, the carrier detection method using real-time PCR could be a useful tool for genetic counseling.
Adult
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Aged
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Aged, 80 and over
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DNA Mutational Analysis/*methods
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Female
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Genetic Predisposition to Disease/epidemiology
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Genetic Screening/*methods
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Heterozygote
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Heterozygote Detection/methods
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Humans
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Korea/epidemiology
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Male
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Middle Aged
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Muscular Atrophy, Spinal/*epidemiology/genetics/*metabolism
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Nerve Tissue Proteins/*genetics
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Polymorphism, Genetic
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*Quantitative Trait, Heritable
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Reverse Transcriptase Polymerase Chain Reaction/*methods
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Risk Assessment/*methods
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Risk Factors