Relevant bibliographies by topics / Statistical and quantitative genetics

Academic literature on the topic 'Statistical and quantitative genetics'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Statistical and quantitative genetics.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Statistical and quantitative genetics"

1

Sen, Śaunak, and Gary A. Churchill. "A Statistical Framework for Quantitative Trait Mapping." Genetics 159, no. 1 (September 1, 2001): 371–87. http://dx.doi.org/10.1093/genetics/159.1.371.

Full text
Abstract:
AbstractWe describe a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses. Our main result is based on the observation that, by conditioning on the unobserved QTL genotypes, the problem can be split into two statistically independent and manageable parts. The first part involves only the relationship between the QTL and the phenotype. The second part involves only the location of the QTL in the genome. We developed a simple Monte Carlo algorithm to implement Bayesian QTL analysis. This algorithm simulates multiple versions of complete genotype information on a genomewide grid of locations using information in the marker genotype data. Weights are assigned to the simulated genotypes to capture information in the phenotype data. The weighted complete genotypes are used to approximate quantities needed for statistical inference of QTL locations and effect sizes. One advantage of this approach is that only the weights are recomputed as the analyst considers different candidate models. This device allows the analyst to focus on modeling and model comparisons. The proposed framework can accommodate multiple interacting QTL, nonnormal and multivariate phenotypes, covariates, missing genotype data, and genotyping errors in any type of inbred line cross. A software tool implementing this procedure is available. We demonstrate our approach to QTL analysis using data from a mouse backcross population that is segregating multiple interacting QTL associated with salt-induced hypertension.
APA, Harvard, Vancouver, ISO, and other styles
2

Neher, Richard A., and Boris I. Shraiman. "Statistical genetics and evolution of quantitative traits." Reviews of Modern Physics 83, no. 4 (November 10, 2011): 1283–300. http://dx.doi.org/10.1103/revmodphys.83.1283.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Sorensen, Daniel. "Developments in statistical analysis in quantitative genetics." Genetica 136, no. 2 (August 21, 2008): 319–32. http://dx.doi.org/10.1007/s10709-008-9303-5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zou, Fei, Brian S. Yandell, and Jason P. Fine. "Rank-Based Statistical Methodologies for Quantitative Trait Locus Mapping." Genetics 165, no. 3 (November 1, 2003): 1599–605. http://dx.doi.org/10.1093/genetics/165.3.1599.

Full text
Abstract:
Abstract This article addresses the identification of genetic loci (QTL and elsewhere) that influence nonnormal quantitative traits with focus on experimental crosses. QTL mapping is typically based on the assumption that the traits follow normal distributions, which may not be true in practice. Model-free tests have been proposed. However, nonparametric estimation of genetic effects has not been studied. We propose an estimation procedure based on the linear rank test statistics. The properties of the new procedure are compared with those of traditional likelihood-based interval mapping and regression interval mapping via simulations and a real data example. The results indicate that the nonparametric method is a competitive alternative to the existing parametric methodologies.
APA, Harvard, Vancouver, ISO, and other styles
5

Ye, Shuyun, Rhonda Bacher, Mark P. Keller, Alan D. Attie, and Christina Kendziorski. "Statistical Methods for Latent Class Quantitative Trait Loci Mapping." Genetics 206, no. 3 (May 26, 2017): 1309–17. http://dx.doi.org/10.1534/genetics.117.203885.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Chen, Meng, and Christina Kendziorski. "A Statistical Framework for Expression Quantitative Trait Loci Mapping." Genetics 177, no. 2 (July 29, 2007): 761–71. http://dx.doi.org/10.1534/genetics.107.071407.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Barton, N. H., and H. P. de Vladar. "Statistical Mechanics and the Evolution of Polygenic Quantitative Traits." Genetics 181, no. 3 (December 15, 2008): 997–1011. http://dx.doi.org/10.1534/genetics.108.099309.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zou, Fei, Brian S. Yandell, and Jason P. Fine. "Statistical Issues in the Analysis of Quantitative Traits in Combined Crosses." Genetics 158, no. 3 (July 1, 2001): 1339–46. http://dx.doi.org/10.1093/genetics/158.3.1339.

Full text
Abstract:
Abstract We consider some practical statistical issues in QTL analysis where several crosses originate in multiple inbred parents. Our results show that ignoring background polygenic variation in different crosses may lead to biased interval mapping estimates of QTL effects or loss of efficiency. Threshold and power approximations are derived by extending earlier results based on the Ornstein-Uhlenbeck diffusion process. The results are useful in the design and analysis of genome screen experiments. Several common designs are evaluated in terms of their power to detect QTL.
APA, Harvard, Vancouver, ISO, and other styles
9

Mitchell-Olds, T., and J. Bergelson. "Statistical genetics of an annual plant, Impatiens capensis. I. Genetic basis of quantitative variation." Genetics 124, no. 2 (February 1, 1990): 407–15. http://dx.doi.org/10.1093/genetics/124.2.407.

Full text
Abstract:
Abstract Analysis of quantitative genetics in natural populations has been hindered by computational and methodological problems in statistical analysis. We developed and validated a jackknife procedure to test for existence of broad sense heritabilities and dominance or maternal effects influencing quantitative characters in Impatiens capensis. Early life cycle characters showed evidence of dominance and/or maternal effects, while later characters exhibited predominantly environmental variation. Monte Carlo simulations demonstrate that these jackknife tests of variance components are extremely robust to heterogeneous error variances. Statistical methods from human genetics provide evidence for either a major locus influencing germination date, or genes that affect phenotypic variability per se. We urge explicit consideration of statistical behavior of estimation and testing procedures for proper biological interpretation of statistical results.
APA, Harvard, Vancouver, ISO, and other styles
10

Hoeschele, I., P. Uimari, F. E. Grignola, Q. Zhang, and K. M. Gage. "Advances in Statistical Methods to Map Quantitative Trait Loci in Outbred Populations." Genetics 147, no. 3 (November 1, 1997): 1445–57. http://dx.doi.org/10.1093/genetics/147.3.1445.

Full text
Abstract:
Statistical methods to map quantitative trait loci (QTL) in outbred populations are reviewed, extensions and applications to human and plant genetic data are indicated, and areas for further research are identified. Simple and computationally inexpensive methods include (multiple) linear regression of phenotype on marker genotypes and regression of squared phenotypic differences among relative pairs on estimated proportions of identity-by-descent at a locus. These methods are less suited for genetic parameter estimation in outbred populations but allow the determination of test statistic distributions via simulation or data permutation; however, further inferences including confidence intervals of QTL location require the use of Monte Carlo or bootstrap sampling techniques. A method which is intermediate in computational requirements is residual maximum likelihood (REML) with a covariance matrix of random QTL effects conditional on information from multiple linked markers. Testing for the number of QTLs on a chromosome is difficult in a classical framework. The computationally most demanding methods are maximum likelihood and Bayesian analysis, which take account of the distribution of multilocus marker-QTL genotypes on a pedigree and permit investigators to fit different models of variation at the QTL. The Bayesian analysis includes the number of QTLS on a chromosome as an unknown.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Statistical and quantitative genetics"

1

Shen, Xia. "Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation." Doctoral thesis, Uppsala universitet, Beräknings- och systembiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170091.

Full text
Abstract:
This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).
APA, Harvard, Vancouver, ISO, and other styles
2

Silva, Heyder Diniz. "Aspectos biométricos da detecção de QTL'S ("Quantitative Trait Loci") em espécies cultivadas." Universidade de São Paulo, 2001. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-18102002-162652/.

Full text
Abstract:
O mapeamento de QTL's difere dos demais tipos de pesquisas conduzida em genética. Por se tratar basicamente de um procedimento de testes múltiplos, surge, neste contexto, um problema que se refere ao nível de significância conjunto da análise, e consequentemente, seu poder. Deste modo, avaliou-se, via simulação computacional de dados, o poder de detecção de QTL's da análise de marcas simples, realizada por meio de regressão linear múltipla, utilizando o procedimento stepwise" para seleção das marcas e procedimentos baseados em testes individuais, utilizando os critérios FDR e de Bonferroni para determinação nível de significância conjunto. Os resultados mostraram que o procedimento baseado em regressão múltipla, utilizando o procedimento stepwise" foi mais poderoso em identificar as marcas associadas a QTL's e, mesmo nos casos em que este procedimento apresentou poder ligeiramente inferior aos demais, verificou-se que o mesmo tem como grande vantagem selecionar apenas as marcas mais fortemente ligadas aos QTL's. Dentre os critérios FDR e de Bonferroni, o primeiro mostrou-se, em geral, mais poderoso, devendo ser adotado nos procedimentos de mapeamento por intervalo. Outro problema encontrado na análise de QTL's refere-se µa abordagem da interação QTL's x ambientes. Neste contexto, apresentou-se uma partição da variância da interação genótipos x ambientes em efeitos explicados pelos marcadores e desvios, a partir da qual obtiveram-se os estimadores da proporção da variância genética (pm), e da variância da interação genótipos x ambientes (pms), explicadas pelos marcadores moleculares. Estes estimadores independem de desvios das frequências alélicas dos marcadores em relação µ as esperadas (1:2:1 em uma geração F2, 1:1 em um retrocruzamento, etc.), porém, apresentam uma alta probabilidade de obtenção de estimativas fora do intervalo paramétrico, principalmente para valores elevados destas proporções. Contudo, estas probabilidades podem ser reduzidas com o aumento do número de repetições e/ou de ambientes nos quais as progênies são avaliadas. A partir de um conjunto de dados de produtividade de grãos, referentes µ a avaliação de 68 progênies de milho, genotipadas para 77 marcadores moleculares codominantes e avaliadas em quatro ambientes, verificou-se que as metodologias apresentadas permitiram estimar as proporções pm e pms, bem como classificar as marcas associadas a QTL's, conforme seu nível de interação. O procedimento permitiu ainda a identificação de regiões cromossômicas envolvidas no controle genético do caractere sob estudo conforme sua maior ou menor estabilidade ao longo dos ambientes.
In general terms, QTL mapping di®ers from other research ac-tivities in genetics. Being basically a multiple test procedure, problems arise which are related to the joint level of signi¯cance of the analysis, and consequently, to its power. Using computational simulation of data, the power of simple marker analysis, carried out through multiple linear regression, using stepwise procedures to select the markers was obtained. Procedures based on single tests, using both the FDR and the Bonferroni criteria to determinate the joint level of signi¯cance were also used. Results showed that the procedure based on multiple regression, using the stepwise technique, was the most powerful in identifying markers associated to QTL's. However, in cases where its power was smaller, its advantage was the ability to detect only markers strongly associates with QTL's. In comparision with the Bonferroni method, the FDR criterion was in general more powerful, and should be adopted in the interval mapping procedures. Additional problems found in the QTL analysis refer to the QTL x environment interaction. We consider this aspect by par-titioning the genotype x environment interaction variance in components explained by the molecular markers and deviations. This alowed estimating the proportion of the genetic variance (pm), and genotype x environment variance (pms), explained by the markers. These estimators are not a®ected by deviations of allelic frequencies of the markers in relation to the expected values (1:2:1 in a F2 generation, 1:1 in a backcross , etc). However, there is a high probability of obtaining estimates out of the parametric range, specially for high values of this proportion. Nevertheless, these probabilities can be reduced by increasing the number of replications and/or environments where the progenies are evaluated. Based on a set of grain yield data, obtained from the evaluation of 68 maize progenies genotyped for 77 codominant molecular markers, and evaluated as top crosses in four environments, the presented methodologies allowed estimating proportions pm and pms as well the classification of markers associated to QTL's, with respect to its level of genotype x environment interaction. The procedure also allowed the identification of chromosomic regions, involved in the genetical control of the considered trait, according to its stability, in relation to the observed environmental variation.
APA, Harvard, Vancouver, ISO, and other styles
3

Ai, Ni, and 艾妮. "A novel framework for expression quantitative trait loci mapping." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4715214X.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bao, Haikun. "Bayesian hierarchical regression model to detect quantitative trait loci /." Electronic version (PDF), 2006. http://dl.uncw.edu/etd/2006/baoh/haikunbao.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Boddhireddy, Prashanth. "Development of highly recombinant inbred populations for quantitative-trait locus mapping." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1671.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Pearson, Caroline. "Analysis of a hierarchial Bayesian method for quantitative trait loci /." Electronic version (PDF), 2007. http://dl.uncw.edu/etd/2007-2/pearsonc/carolinepearson.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Baldoni, Pedro Luiz 1989. "Modelos lineares generalizados mistos multivariados para caracterização genética de doenças." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/307180.

Full text
Abstract:
Orientador: Hildete Prisco Pinheiro
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação
Made available in DSpace on 2018-08-24T09:34:36Z (GMT). No. of bitstreams: 1 Baldoni_PedroLuiz_M.pdf: 4328843 bytes, checksum: 0ab04f375988e62ac31097716ac0eaa5 (MD5) Previous issue date: 2014
Resumo: Os Modelos Lineares Generalizados Mistos (MLGM) são uma generalização natural dos Modelos Lineares Mistos (MLM) e dos Modelos Lineares Generalizados (MLG). A classe dos MLGM estende a suposição de normalidade dos dados permitindo o uso de várias outras distribuições bem como acomoda a superdispersão frequentemente observada e também a correlação existente entre observações em estudos longitudiais ou com medidas repetidas. Entretanto, a teoria de verossimilhança para MLGM não é imediata uma vez que a função de verossimilhança marginal não possui forma fechada e envolve integrais de alta dimensão. Para solucionar este problema, diversas metodologias foram propostas na literatura, desde técnicas clássicas como quadraturas numéricas, por exemplo, até métodos sofisticados envolvendo algoritmo EM, métodos MCMC e quase-verossimilhança penalizada. Tais metodologias possuem vantagens e desvantagens que devem ser avaliadas em cada tipo de problema. Neste trabalho, o método de quase-verossimilhança penalizada (\cite{breslow1993approximate}) foi utilizado para modelar dados de ocorrência de doença em uma população de vacas leiteiras pois demonstrou ser robusto aos problemas encontrados na teoria de verossimilhança deste conjunto de dados. Além disto, os demais métodos não se mostram calculáveis frente à complexidade dos problemas existentes em genética quantitativa. Adicionalmente, estudos de simulação são apresentados para verificar a robustez de tal metodologia. A estabilidade dos estimadores e a teoria de robustez para este problema não estão completamente desenvolvidos na literatura
Abstract: Generalized Linear Mixed Models (GLMM) are a generalization of Linear Mixed Models (LMM) and of Generalized Linear Models (GLM). The class of models GLMM extends the normality assumption of the data and allows the use of several other probability distributions, for example, accommodating the over dispersion often observed and also the correlation among observations in longitudinal or repeated measures studies. However, the likelihood theory of the GLMM class is not straightforward since its likelihood function has not closed form and involves a high order dimensional integral. In order to solve this problem, several methodologies were proposed in the literature, from classical techniques as numerical quadrature¿s, for example, up to sophisticated methods involving EM algorithm, MCMC methods and penalized quasi-likelihood. These methods have advantages and disadvantages that must be evaluated in each problem. In this work, the penalized quasi-likelihood method (\cite{breslow1993approximate}) was used to model infection data in a population of dairy cattle because demonstrated to be robust in the problems faced in the likelihood theory of this data. Moreover, the other methods do not show to be treatable faced to the complexity existing in quantitative genetics. Additionally, simulation studies are presented in order to verify the robustness of this methodology. The stability of these estimators and the robust theory of this problem are not completely studied in the literature
Mestrado
Estatistica
Mestre em Estatística
APA, Harvard, Vancouver, ISO, and other styles
8

Galal, Ushma. "The statistical theory underlying human genetic linkage analysis based on quantitative data from extended families." Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2684_1361989724.

Full text
Abstract:

Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the 
development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do. 
There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections.

APA, Harvard, Vancouver, ISO, and other styles
9

Neto, Eduardo Leonardecz. "Competição intergenotípica na análise de testes de progênie em essências florestais." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/11/11137/tde-30102002-160556/.

Full text
Abstract:
No presente trabalho buscou-se introduzir o efeito da competição entre plantas nas análises dos testes de progênie/procedências em essências florestais, com o fim de identificar os seus efeitos e as distorções devidas à sua não observância. Para tanto, foram utilizados ensaios com níveis de precisão e mortalidades diferentes, de cinco espécies, a saber: Gallesia gorarema Vell. Moq., Eucaliptus grandis Hill ex Maider, Eucaliptus citridora Hook, Pinus elliottii Engl. var. elliottii e Araucaria angustifolia (Bert.) O. Ktze. Obtiveram-se as esperanças dos quadrados médios das fontes de variação da análise de variância nos delineamentos aqui utilizados. Com base nestas derivações, foi demonstrado explicitamente o viés nas estimativas de parâmetros genéticos quantitativos. Este viés está diretamente relacionado com a magnitude do coeficiente de regressão b e com a grandeza relativa das somas de quadrados de diferentes efeitos contidos na análise de variância da variável competição. Caso ignorado o efeito de competição, quando este influencia a variável resposta Y, os ponderadores b, que compõem o índice de seleção terão estimativas viesadas, gerando erro na seleção dos indivíduos superiores. Na análise de dados observou-se que a inclusão da competição, de maneira geral, reduziu as estimativas das componentes de variância, e por conseqüência, outras estimativas de parâmetros que são função destes, quando comparado com as estimativas feitas por via das análises sem o ajuste para a competição. A análise com a variável competição não mostrou diferenças significativas para o efeito de progênies. Isto demostra que a competição comportou-se de forma aleatória, o que corrobora para que seja colocada na análise como uma covariável; caso contrário esta teria que ser considerada uma componente da performance e introduzida numa análise multicaracterística. Utilizando as análises com e sem ajuste para a competição, para estimar os valores genéticos e o ganho com a seleção, observou-se que os indivíduos selecionados não são concordantes. Isto indica que os equívocos na seleção podem ser comuns, haja vista que o fato de se ajustar os dados faz com que o posto dos indivíduos tidos por superiores seja alterado. É recomendável considerar os efeitos da competição na análise de dados em que os indivíduos estão sujeitos a competir uns com os outros, no seu desenvolvimento.
The aim of this work was to introduce competition effects in the model underlying the analysis of forest tree experiments. Results were compared with analyses in which effects were neglected. Progeny trails with different levels of precision and mortality were used, including the following species: Gallesia gorarema Vell. Moq., Eucaliptus grandis Hill ex Maider, Eucaliptus citridora Hook, Pinus elliottii Engl. var. elliottii and Araucaria angustifolia (Bert.) O. Ktze. Mathematical expectation of mean squares values were derived and the bias of estimates was explicitly shown. Competition effects were found significant in all experiments, but were primarily of random nature. Bias was shown to be directly proportional to the magnitude of the regression parameter b and to the relative magnitude of sums of squares of the competition variable. Including the variable in general lead to a reduction of estimates of variance components and to smaller expected progress from selection. The b coefficients of multi-effect selection index are also biased if competition is ignored. Results indicated that different sets of genotypes could be selected if the analyses of data were carried out with or without the competition effects. Including a competition variable in the analysis of trials in which plants are exposed to competing with each other is recommendable.
APA, Harvard, Vancouver, ISO, and other styles
10

Randall, Joshua Charles. "Large-scale genetic analysis of quantitative traits." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:addfb69d-602c-43e3-ab18-6e6d3b269076.

Full text
Abstract:
Recent advances in genotyping technology coupled with an improved understanding of the architecture of linkage disequilibrium across the human genome have resulted in genome-wide association studies (GWAS) becoming a useful and widely applied tool for discovering common genetic variants associated with both quantitative traits and disease risk. After each GWAS was completed, it left behind a set of genotypes and phenotypes, often including anthropometric measures used as covariates. Genetic associations with anthropometric measures are not well characterized, perhaps due to lack of power to detect them in the sample sizes of individual studies. To improve power to detect variants associated with complex phenotypes such as anthropometric traits, data from multiple GWAS can be combined. This thesis describes the methods and results of several such analyses performed as part of the Genome-wide Investigation of ANThropemtric measures (GIANT) consortium, and compares various different methods that can be used to perform combined analyses of GWAS. In particular, the comparisons focus on comparing differences between meta-analysis methods, in which only summary statistics that result from within-study association testing are shared between studies, and mega-analysis methods in which individual-level genotype and phenotype data is analysed together. Finally, a brief discussion of technological means that have the potential to help overcome some of the challenges associated with performing mega-analyses is offered in order to suggest future work that could be undertaken in this area.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Statistical and quantitative genetics"

1

Prem, Narain. Statistical genetics. New York: Wiley, 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Statistical genetics. New York: Wiley, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Falconer, D. S. Introduction to quantitative genetics. London: Longman, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Falconer, D. S. Introduction to quantitative genetics. 3rd ed. Harlow: Longman, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Falconer, D. S. Introduction to quantitative genetics. 2nd ed. Burnt Mill, Harlow, Essex, England: Longman Scientific & Technical, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Falconer, D. S. Introduction to quantitative genetics. 3rd ed. Burnt Mill, Harlow, Essex, England: Longman, Scientific & Technical, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

J, Camp Nicola, and Cox Angela 1961-, eds. Quantitative trait loci: Methods and protocols. Totowa, N.J: Humana Press, 2002.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

1947-, Gianola Daniel, ed. Likelihood, Bayesian and MCMC methods in quantitative genetics. New York: Springer-Verlag, 2002.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Genetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Statistical and quantitative genetics"

1

Walsh, B. "Evolutionary Quantitative Genetics." In Handbook of Statistical Genetics, 533–86. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch17.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Xu, Shizhong. "Basic Concepts of Quantitative Genetics." In Principles of Statistical Genomics, 53–60. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-0-387-70807-2_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Xu, Shizhong. "Review of Elementary Statistics." In Quantitative Genetics, 39–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83940-6_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Jansen, R. C. "Quantitative Trait Loci in Inbred Lines." In Handbook of Statistical Genetics, 587–622. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Höschele, I. "Mapping Quantitative Trait Loci in Outbred Pedigrees." In Handbook of Statistical Genetics, 623–77. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gianola, D. "Inferences from Mixed Models in Quantitative Genetics." In Handbook of Statistical Genetics, 678–717. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Sieberts, S. K., and E. E. Schadt. "Inferring Causal Associations between Genes and Disease via the Mapping of Expression Quantitative Trait Loci." In Handbook of Statistical Genetics, 296–326. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Basford, Kaye E. "Statistical Interaction with Quantitative Geneticists to Enhance Impact from Plant Breeding Programs." In Statistics in Genetics and in the Environmental Sciences, 1–15. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8326-9_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Morota, Gota, Diego Jarquin, Malachy T. Campbell, and Hiroyoshi Iwata. "Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data." In Methods in Molecular Biology, 269–96. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2537-8_21.

Full text
Abstract:
AbstractThe advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
APA, Harvard, Vancouver, ISO, and other styles
10

Weller, Joel Ira. "Statistical methodologies for mapping and analysis of quantitative trait loci." In Plant Genomes: Methods for Genetic and Physical Mapping, 181–207. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2442-3_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Statistical and quantitative genetics"

1

Fan, QiaoChu, Zi jie Lu, and Yu chen Liu. "Statistical research methods for genetics." In 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), edited by Chi-Hua Chen, Xuexia Ye, and Hari Mohan Srivastava. SPIE, 2022. http://dx.doi.org/10.1117/12.2639273.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Robinson, John-Paul, Purushotham Bangalore, Jelai Wang, and Tapan Mehta. "Powering statistical genetics with the grid." In the 15th ACM Mardi Gras conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1341811.1341856.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Galas, David, James Kunert-Graf, and Nikita Sakhanenko. "Developing an information theory of quantitative genetics." In Entropy 2021: The Scientific Tool of the 21st Century. Basel, Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/entropy2021-09821.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Santana, Roberto, Hossein Karshenas, Concha Bielza, and Pedro Larrañaga. "Quantitative genetics in multi-objective optimization algorithms." In the 13th annual conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2001858.2001911.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Milkevych, V., E. Karaman, G. Sahana, L. Janss, Z. Cai, and M. S. Lund. "351. Quantitative trait simulation using MeSCoT software." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_351.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

"Quantitative real-time PCR as a supplementary tool for molecular cytogenetics." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-044.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Bijma, P., A. D. Hulst, and M. C. M. de Jong. "163. A quantitative genetic theory for infectious diseases." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_163.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Davoodi, P., A. Ehsani, R. Vaez Torshizi, and A. A. Masoudi. "596. Chicken quantitative traits follow the omnigenic model." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_596.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

"Methods of computer vision to extract the quantitative characteristics of the wheat spike." In Plant Genetics, Genomics, Bioinformatics, and Biotechnology. Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 2019. http://dx.doi.org/10.18699/plantgen2019-060.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Chiang, Chih-Hung. "Statistical analysis of ultrasonic measurements in concrete." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2002. http://dx.doi.org/10.1063/1.1472938.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Statistical and quantitative genetics"

1

Ott, Jurg. Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1996. http://dx.doi.org/10.21236/ada326461.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ott, Jurg. Statistical Genetics Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada337861.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600017.bard.

Full text
Abstract:
The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) determine causative polymorphisms based on concordance between the bulls’ genotypes for specific polymorphisms and their status for a QTL; (4) validate putative quantitative trait variants by genotyping a sample of Israeli Holstein cows; and (5) perform gene expression analysis using statistical methodologies, including determination of signatures of selection, based on somatic cells of cows that are homozygous for contrasting quantitative trait variants; and (6) analyze genes with putative quantitative trait variants using data mining techniques. Current methods for genomic evaluation are based on population-wide linkage disequilibrium between markers and actual alleles that affect traits of interest. Those methods have approximately doubled the rate of genetic gain for most traits in the U.S. Holstein population. With determination of causative polymorphisms, increasing the accuracy of genomic evaluations should be possible by including those genotypes as fixed effects in the analysis models. Determination of causative polymorphisms should also yield useful information on gene function and genetic architecture of complex traits. Concordance between QTL genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects that are segregating in the U.S. Holstein population; a probability of <10²⁰ was used to accept the null hypothesis that no segregating gene within the chromosomal segment was affecting the trait. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome. Variant sites were identified from previous studies (such as the 1000 Bull Genomes Project) and from DNA sequencing of bulls unique to this project, which is one of the largest marker variant surveys conducted for the Holstein breed of cattle. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: (1) complete or nearly complete concordance, (2) nominal significance of the polymorphism effect after correction for all other polymorphisms, and (3) marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. The missense polymorphism Phe279Tyr in GHR at 31,909,478 base pairs on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage, 12 additional missensepolymorphisms on chromosome 14 were found that had nearly complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The markers used in routine U.S. genomic evaluations were increased from 60,000 to 80,000 by adding markers for known QTLs and markers detected in BARD and other research projects. Objectives 1 and 2 were completely accomplished, and objective 3 was partially accomplished. Because no new clear-cut causative polymorphisms were discovered, objectives 4 through 6 were not completed.
APA, Harvard, Vancouver, ISO, and other styles
4

Blower, D. J. Some General Quantitative Considerations for the Statistical Analysis of Isoperformance Curves. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada531669.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Peter Striupaitis and R.E. Gaensslen. Quantitative/Statistical Approach to Bullet-to-Firearm Identification with Consecutively Manufactured Barrels. Office of Scientific and Technical Information (OSTI), January 2005. http://dx.doi.org/10.2172/892804.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Suen, Guozhen, Yanhua Chua, and Jincan Xian. Solvability Results for Convex, Quasi-n-Dimensional Curves in Quantitative Statistical Systems In D-Dimensional Space. Web of Open Science, February 2020. http://dx.doi.org/10.37686/qrl.v1i1.4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Paran, Ilan, and Molly Jahn. Genetics and comparative molecular mapping of biochemical and morphological fruit characters in Capsicum. United States Department of Agriculture, March 2005. http://dx.doi.org/10.32747/2005.7586545.bard.

Full text
Abstract:
Original objectives: The overall goal of our work was to gain information regarding the genetic and molecular control of pathways leading to the production of secondary metabolites determining major fruit quality traits in pepper and to develop tools based on this information to assist in crop improvement. The specific objectives were to: (1) Generate a molecular map of pepper based on simple sequence repeat (SSR) markers. (2) Map QTL for capsaicinoid (pungency) content (3) Determine possible association between capsaicinoid and carotenoid content and structural genes for capsaicinoid and carotenoid biosynthesis. (4) Map QTL for quantitative traits controlling additional fruit traits. (5) Map fruit-specific ESTs and determine possible association with fruit QTL (6) Map the C locus that determines the presence and absence of capsaicinoid in pepper fruit and identify candidate genes for C.locus. Background: Pungency, color, fruit shape and fruit size are among the most important fruit quality characteristics of pepper. Despite the importance of the pepper crop both in the USA and Israel, the genetic basis of these traits was poorly understood prior to the studies conducted in the present proposal. In addition, molecular tools for use in pepper improvement were lacking. Major conclusions and achievements: Our studies enabled the development of a saturated genetic map of pepper that includes numerous SSR markers. This map has been integrated with a number of other independent maps resulting in the publication of a single resource map consisting of more than 2000 markers. Unlike previous maps based primarily on tomato-originated RFLP markers, the new maps are based on PCR markers that originate in Capsicum providing a comprehensive and versatile resource for marker-assisted selection in pepper. We determined the genetic and molecular bases of qualitative and quantitative variation of pungency, a character unique to pepper fruit. We mapped and subsequently cloned the Pun1 gene that serves as a master regulatoar for capsaicinoid accumulation and showed that it is an acyltransferase. By sequencing the Pun1 gene in pungent and non-pungent cultivars we identified a deletion that abolishes the expression of the gene in the latter cultivars. We also identified QTL that control capsaicinoid content and therefore pungency level. These genes will allow pepper breeders to manipulate the level of pungency for specific agricultural and industrial purposes. In addition to pungency we identified genes and QTL that control other key developmental processes of fruit development such as color, texture and fruit shape. The A gene controlling anthocyanin accumulation in the immature fruit was found as the ortholog of the petunia transcription factor Anthocyanin2. The S gene required for the soft flesh and deciduous fruit nature typical of wild peppers was identified as the ortholog of tomato polygalacturonase. We identified two major QTL controlling fruit shape, fs3.1 and fs10.1, that differentiate elongated and blocky and round fruit shapes, respectively. Scientific and agricultural implications: Our studies allowed significant advances in our understanding of important processes of pepper fruit development including the isolation and characterization of several well known genes. These results also provided the basis for the development of molecular tools that can be implemented for pepper improvement. A total of eleven refereed publications have resulted from this work, and several more are in preparation.
APA, Harvard, Vancouver, ISO, and other styles
8

Krommes, J. A., and Chang-Bae Kim. A new'' approach to the quantitative statistical dynamics of plasma turbulence: The optimum theory of rigorous bounds on steady-state transport. Office of Scientific and Technical Information (OSTI), June 1990. http://dx.doi.org/10.2172/6765264.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Blum, Abraham, Henry T. Nguyen, and N. Y. Klueva. The Genetics of Heat Shock Proteins in Wheat in Relation to Heat Tolerance and Yield. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568105.bard.

Full text
Abstract:
Fifty six diverse spring wheat cultivars were evaluated for genetic variation and heritability for thermotolerance in terms of cell-membrane stability (CMS) and triphenyl tetrazolium chloride (TTC) reduction. The most divergent cultivars for thermotolerance (Danbata-tolerant and Nacozari-susceptible) were crossed to develop an F8 random onbred line (RIL) population. This population was evaluated for co-segragation in CMS, yield under heat stress and HSP accumulation. Further studies of thermotolerance in relations to HSP and the expression of heterosis for growth under heat stress were performed with F1 hybrids of wheat and their parental cultivars. CMS in 95 RILs ranged from 76.5% to 22.4% with 71.5% and 31.3% in Danbata and Nacozari, respectively. The population segregated with a normal distribution across the full range of the parental values. Yield and biomass under non-stress conditions during the normal winter season at Bet Dagan dit not differ between the two parental cultivar, but the range of segregation for these traits in 138 RILs was very high and distinctly transgressive with a CV of 35.3% and 42.4% among lines for biomass and yield, respectively. Mean biomass and yield of the population was reduced about twofold when grown under the hot summer conditions (irrigated) at Bet Dagan. Segregation for biomass and yield was decreased relative to the normal winter conditions with CV of 20.2% and 23.3% among lines for biomass and yield, respectively. However, contrary to non-stress conditions, the parental cultivars differed about twofold in biomass and yield under heat stress and the population segregated with normal distribution across the full range of this difference. CMS was highly and positively correlated across 79 RILs with biomass (r=0.62**) and yield (r=0.58**) under heat stress. No such correlation was obtained under the normal winter conditions. All RILs expressed a set of HSPs under heat shock (37oC for 2 h). No variation was detected among RILs in high molecular weight HSP isoforms and they were similar to the patterns of the parental cultivars. There was a surprisingly low variability in low molecular weight HSP isoforms. Only one low molecular weight and Nacozari-specific HSP isoform (belonging to HSP 16.9 family) appeared to segregate among all RILs, but it was not quantitatively correlated with any parameter of plant production under heat stress or with CMS in this population. It is concluded that this Danbata/Nacozari F8 RIL population co-segregated well for thermotolerance and yield under heat stress and that CMS could predict the relative productivity of lines under chronic heat stress. Regretfully this population did not express meaningful variability for HSP accumulation under heat shock and therefore no role could be seen for HSP in the heat tolerance of this population. In the study of seven F1 hybrids and their parent cultivars it was found that heterosis (superiority of the F1 over the best parent) for CMs was generally lower than that for growth under heat stress. Hybrids varied in the rate of heterosis for growth at normal (15o/25o) and at high (25o/35o) temperatures. In certain hybrids heterosis for growth significantly increased at high temperature as compared with normal temperature, suggesting temperature-dependent heterosis. Generally, under normal temperature, only limited qualitative variation was detected in the patterns of protein synthesis in four wheat hybrids and their parents. However, a singular protein (C47/5.88) was specifically expressed only in the most heterotic hybrid at normal temperature but not in its parent cultivars. Parental cultivars were significantly different in the sets of synthesized HSP at 37o. No qualitative changes in the patterns of protein expression under heat stress were correlated with heterosis. However, a quantitative increase in certain low molecular weight HSP (mainly H14/5.5 and H14.5.6, belonging to the HSP16.9 family) was positively associated with greater heterosis for growth at high temperature. None of these proteins were correlated with CMS across hybrids. These results support the concept of temperature-dependent heterosis for growth and a possible role for HSP 16.9 family in this respect. Finally, when all experiments are viewed together, it is encouraging to find that genetic variation in wheat yield under chronic heat stress is associated with and well predicted by CMS as an assay of thermotolerance. On the other hand the results for HSP are elusive. While very low genetic variation was expressed for HSP in the RIL population, a unique low molecular weight HSP (of the HSP 16.9 family) could be associated with temperature dependant heterosis for growth.
APA, Harvard, Vancouver, ISO, and other styles
10

Zhang, Hongbin B., David J. Bonfil, and Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis: Chickpea as a Model. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7586464.bard.

Full text
Abstract:
The goals of this project were to develop essential genomic tools for modern chickpea genetics and genomics research, map the genes and quantitative traits of importance to chickpea production and generate DNA markers that are well-suited for enhanced chickpea germplasm analysis and breeding. To achieve these research goals, we proposed the following research objectives in this period of the project: 1) Develop an ordered BAC library with an average insert size of 150 - 200 kb (USA); 2) Develop 300 simple sequence repeat (SSR) markers with an aid of the BAC library (USA); 3) Develop SSR marker tags for Ascochyta response, flowering date and grain weight (USA); 4) Develop a molecular genetic map consisting of at least 200 SSR markers (Israel and USA); 5) Map genes and QTLs most important to chickpea production in the U.S. and Israel: Ascochyta response, flowering and seed set date, grain weight, and grain yield under extreme dryland conditions (Israel); and 6) Determine the genetic correlation between the above four traits (Israel). Chickpea is the third most important pulse crop in the world and ranks the first in the Middle East. Chickpea seeds are a good source of plant protein (12.4-31.5%) and carbohydrates (52.4-70.9%). Although it has been demonstrated in other major crops that the modern genetics and genomics research is essential to enhance our capacity for crop genetic improvement and breeding, little work was pursued in these research areas for chickpea. It was absent in resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. For instance, there were no large-insert BAC and BIBAC libraries, no sufficient and user- friendly DNA markers, and no intraspecific genetic map. Grain sizes, flowering time and Ascochyta response are three main constraints to chickpea production in drylands. Combination of large seeds, early flowering time and Ascochyta blight resistance is desirable and of significance for further genetic improvement of chickpea. However, it was unknown how many genes and/or loci contribute to each of the traits and what correlations occur among them, making breeders difficult to combine these desirable traits. In this period of the project, we developed the resources, tools and infrastructure that are essential for chickpea genomics and modern genetics research. In particular, we constructed the proposed large-insert BAC library and an additional plant-transformation-competent BIBAC library from an Israeli advanced chickpea cultivar, Hadas. The BAC library contains 30,720 clones and has an average insert size of 151 kb, equivalent to 6.3 x chickpea haploid genomes. The BIBAC library contains 18,432 clones and has an average insert size of 135 kb, equivalent to 3.4 x chickpea haploid genomes. The combined libraries contain 49,152 clones, equivalent to 10.7 x chickpea haploid genomes. We identified all SSR loci-containing clones from the chickpea BAC library, generated sequences for 536 SSR loci from a part of the SSR-containing BACs and developed 310 new SSR markers. From the new SSR markers and selected existing SSR markers, we developed a SSR marker-based molecular genetic map of the chickpea genome. The BAC and BIBAC libraries, SSR markers and the molecular genetic map have provided essential resources and tools for modern genetic and genomic analyses of the chickpea genome. Using the SSR markers and genetic map, we mapped the genes and loci for flowering time and Ascochyta responses; one major QTL and a few minor QTLs have been identified for Ascochyta response and one major QTL has been identified for flowering time. The genetic correlations between flowering time, grain weight and Ascochyta response have been established. These results have provided essential tools and knowledge for effective manipulation and enhanced breeding of the traits in chickpea.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Statistical and quantitative genetics' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography