Academic literature on the topic 'Genetic correlation analysis'
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Journal articles on the topic "Genetic correlation analysis"
Beck, Jeffrey J., René Pool, Margot van de Weijer, Xu Chen, Eva Krapohl, Scott D. Gordon, Marianne Nygaard, et al. "Genetic meta-analysis of twin birth weight shows high genetic correlation with singleton birth weight." Human Molecular Genetics 30, no. 19 (May 6, 2021): 1894–905. http://dx.doi.org/10.1093/hmg/ddab121.
Full textWen, Yan, Feng Zhang, Xiancang Ma, Qianrui Fan, Wenyu Wang, Jiawen Xu, Feng Zhu, et al. "eQTLs Weighted Genetic Correlation Analysis Detected Brain Region Differences in Genetic Correlations for Complex Psychiatric Disorders." Schizophrenia Bulletin 45, no. 3 (June 15, 2018): 709–15. http://dx.doi.org/10.1093/schbul/sby080.
Full textOliveira, Taniele Carvalho de, Marco Antonio Aparecido Barelli, Valvenarg Pereira da Silva, Rafhael Felipin Azevedo, Danilo de Lima Gonçalves, Paulo Ricardo Junges dos Santos, Juliana Parisotto Poletine, Carla Galbiati, and Flávio Dessaune Tardin. "Correlations between characters and path analysis in sweet sorghum (Sorghum bicolor (L.) Moench) genotypes for juice production." Australian Journal of Crop Science, no. 15(02):2021 (February 3, 2021): 290–96. http://dx.doi.org/10.21475/ajcs.21.15.02.p3083.
Full textSkelly, Daniel A., Narayanan Raghupathy, Raymond F. Robledo, Joel H. Graber, and Elissa J. Chesler. "Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples." Genetics 212, no. 3 (May 21, 2019): 919–29. http://dx.doi.org/10.1534/genetics.118.301865.
Full textMotiar Roh, Md, A. S. M. Iqbal Hussain ., Md Saykhul Arifin ., Zerin Akhter ., and Mirza Hasanuzzaman . "Genetic Variability, Correlation and Path Analysis in Mungbean." Asian Journal of Plant Sciences 2, no. 17 (August 15, 2003): 1209–11. http://dx.doi.org/10.3923/ajps.2003.1209.1211.
Full textWerme, Josefin, Sophie van der Sluis, Danielle Posthuma, and Christiaan A. de Leeuw. "An integrated framework for local genetic correlation analysis." Nature Genetics 54, no. 3 (March 2022): 274–82. http://dx.doi.org/10.1038/s41588-022-01017-y.
Full textRuiz del Río, N., J. M. Abelairas Gómez, F. J. Alonso García de la Rosa, J. M. Peralta Calvo, and A. de las Heras Martín. "Genetic analysis in retinoblastoma and peripheral blood correlation." Archivos de la Sociedad Española de Oftalmología (English Edition) 90, no. 12 (December 2015): 562–65. http://dx.doi.org/10.1016/j.oftale.2015.09.001.
Full textMishra, Pragnyashree, A. K. Singh, and O. P. Singh. "Genetic variability, heritability, Genetic advance, correlation coefficient and path analysis in gladiolus." IOSR Journal of Agriculture and Veterinary Science 7, no. 7 (2014): 23–26. http://dx.doi.org/10.9790/2380-07722326.
Full textAli, A., K. Javed, I. Zahoor, and K. M. Anjum. "Analysis of non-genetic and genetic influences underlying the growth of Kajli lambs." South African Journal of Animal Science 50, no. 4 (October 29, 2020): 613–25. http://dx.doi.org/10.4314/sajas.v50i4.13.
Full textSharma, R. K., and K. Prasad. "Genetic divergence, correlation and path coefficient analysis in okra." Indian Journal of Agricultural Research 49, no. 1 (2015): 77. http://dx.doi.org/10.5958/0976-058x.2015.00011.6.
Full textDissertations / Theses on the topic "Genetic correlation analysis"
Habib, Farhat Abbas. "Genotype-phenotype correlation using phylogenetic trees." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187297400.
Full textAlbertsdóttir, Elsa. "Genetic analysis of competition traits in Icelandic horses /." Uppsala : Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/10360486.pdf.
Full textEl, Nagar Ayman Gamal Fawzy. "Genetic analysis of longevity in specialized lines of rabbits." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/52390.
Full text[ES] El objetivo global de la presente tesis fue estudiar la longevidad funcional en cinco líneas españolas de conejos (A, V, H y LP), el carácter se definió como la longitud de la vida productiva. En el Capítulo 3, dirigido a comprobar la heterogeneidad genética de la longevidad entre las 5 líneas, se estimaron las varianzas aditivas y sus correspondientes heredabilidades efectivas. Y además se evaluó la importancia del orden de la palpación positiva (OPP), el estado fisiológico (PS) y el número de gazapos nacidos vivos (NBA) sobre el determinismo genético de la longevidad. Para ello se utilizaron 4 modelos de Cox de riesgos proporcionales; el primer modelo (Modelo 1) incluyó todos los factores anteriores, además del efecto del año-estación, el efecto de la consanguinidad y, finalmente, el valor aditivo de los animales como efecto aleatorio. Los otros tres modelos fueron igual que el Modelo 1 pero excluyendo OPP (Modelo 2), o PS (Modelo 3), o NBA (Modelo 4). Los datos de longevidad estaban referidos a 15,670 conejas y tuvieron una tasa de censura de 35.6%. La genealogía completa involucró a 19,405 animales. Las estimas de heredabilidad efectiva para la longevidad en las 5 líneas fueron bajas y variaron de 0.02±0.01 a 0.14±0.09. A pesar de la gran variación de las estimas puntuales de heredabilidad, los correspondientes intervalos HPD95% siempre se solaparon y por lo tanto la hipótesis de que todas las líneas tengan la misma heredabilidad no pudo descartase. Se observó que la exclusión de PS incrementó la varianza aditiva aproximadamente, en un 51, 39, 38, 83 y 75% en las líneas A, V, H, LP y R, respectivamente. El riesgo de muerte o eliminación disminuía a medida que avanzaba el OPP, observándose el riesgo más alto durante los primeros dos partos, partos en los que las conejas todavía están creciendo lo que sería un factor de riesgo importante. El nivel No-Gestante-No-Lactante de PS tuvo el mayor riesgo. Este nivel se interpreta como indicador de baja fertilidad y/o problemas de salud de la coneja. Las conejas que tenían cero NBA tuvieron el mayor riesgo de muerte o eliminación, aunque para el resto de niveles de NBA se apreció una disminución del riesgo a medida que aumenta la prolificidad. En el capítulo 4, se estimaron las correlaciones genéticas y ambientales entre la longevidad y dos caracteres de prolificidad [número de gazapos nacidos vivos (NBA) y el número de destetados (NW)]. El fichero de datos incluyó 58,329 partos y 57,927 destetes. También se estimaron las correlaciones entre longevidad y el porcentaje de días que la coneja pasó en los diferentes estados fisiológicos con respecto a la totalidad de su vida productiva. La única línea para la que se puede decir que la correlación genética entre NBA o NW y el riesgo fue significativamente diferente de cero fue la línea LP. Hubo evidencias de correlaciones genéticas no despreciables entre la longevidad y el porcentaje de días que la hembra pasó en cada estado fisiológico los dos caracteres. En el capítulo 5 se compararon las longevidades medias de las 5 líneas en su fundación y en períodos de tiempo determinados. La comparación de las líneas en el origen, utilizó todos los datos y un modelo genético (CM) que incluía los valores aditivos de los animales. Para la comparación en tiempos fijos se utilizó el mismo modelo, pero excluyendo los efectos aditivos del modelo de análisis (IM), utilizando sólo los datos correspondientes a cada período, por lo que las diferencias entre las líneas incluían los cambios debidos a la selección. Las líneas V, H y LP mostraron una superioridad sustancial sobre las líneas A y R. Los riesgos relativos máximos se observaron entre las líneas LP y R (0.239), y entre LP y A (0.317). Con respecto a las comparaciones en tiempos fijos, el patrón de las diferencias entre la línea de A y las otras líneas fue similar a los observados en la fundación.
[CAT] L'objectiu global de la present tesi va ser estudiar la longevitat funcional en cinc línies espanyoles de conills (A, V, H i LP), el caràcter es va definir com la longitud de la vida productiva. Al Capítol 3, dirigit a comprovar l'heterogeneïtat genètica de la longevitat entre les 5 línies, es van estimar les variàncies additives i les seues corresponents heretabilitats efectives. A més a més, es va avaluar la importància de factors dependents del temps, com l'orde de la palpació positiva (OPP) , l'estat fisiològic (PS) i el nombre de llorigons nascuts vius (NBA) sobre el determinisme genètic de la longevitat. Per a això es van utilitzar 4 models de Cox de riscos proporcionals; el primer model (Model 1) va incloure tots els factors anteriorment assenyalats, a més de l'efecte de l'any-estació, l'efecte de la consanguinitat i, finalment, el valor additiu dels animals com a efecte aleatori. Els altres tres models van ser igual que el Model 1 però excloent l'OPP (Model 2) , o PS (Model 3) , o NBA (Model 4) . Les dades de longevitat estaven referides a 15,670 conilles i van tindre una taxa de censura de 35.6%. La genealogia completa va involucrar a 19,405 animals. Les estimes d'heretabilitat efectiva (Model 1) per a la longevitat en les 5 línies van ser baixes i van variar de 0.02±0.01 a 0.14±0.09. A pesar de la gran variació de les estimes puntuals d'heretabilitat, els corresponents intervals HPD95% sempre es van solapar i per tant la hipòtesi que totes les línies tinguen la mateixa heretabilitat no va poder descartar-se. Es va observar que l'exclusió de PS va incrementar la variància additiva, aproximadament, en un 51, 39, 38, 83 i 75% en les línies A, V, H, LP i R, respectivament. El risc de mort o eliminació disminuïa a mesura que avançava l'OPP, observant-se el risc més alt durant els primers dos parts, en què les conilles encara estan creixent el que seria un factor de risc important. El nivell No-Gestant-No-Lactant de PS va tindre el major risc en comparació amb els altres nivells. Les conilles que tenien zero NBA van tindre el major risc de mort o eliminació, encara que per a la resta de nivells de NBA es va apreciar una disminució del risc a mesura que augmentà la prolificitat. Al Capítol 4, es van estimar les correlacions genètiques i ambientals entre la longevitat i dos caràcters de prolificitat [nombre de llorigons nascuts vius (NBA) i el nombre de deslletats (NW)]. El fitxer de dades va incloure 58,329 parts i 57,927 deslletaments. L'única línia per a la que es pot dir que la correlació genètica entre NBA o NW i el risc va ser significativament diferent de zero va ser la línia LP. Evidències de correlacions genètiques no menyspreables entre longevitat i els percentatge de dies que la femella va passar en cada estat fisiològic. Al Capítol 5 es compararen les longevitats mitges de les 5 línies en la seua fundació i en períodes de temps determinats. Per a la comparació de les línies a l'origen, es van utilitzar totes les dades i un model genètic (CM) que incloïa els valors additius dels animals, per la qual cosa es va considerar l'efecte de la selecció a partir de la fundació. En la comparació en temps fixos se va utilitzar el mateix model que en l'anterior, però excloent els efectes additius del model d'anàlisi (IM), utilitzant només les dades corresponents a cada període, per la qual cosa les diferències entre les línies incloïen els canvis deguts a la selecció. Les línies V, H i LP van mostrar una superioritat substancial sobre les línies A i R. Els riscos relatius màxims es van observar entre les línies LP i R (0.239), i entre LP i A (0.317). Respecte a les comparacions en temps fixos, el patró de les diferències entre la línia de A i les altres línies va ser semblant als observats en la fundació.
El Nagar, AGF. (2015). Genetic analysis of longevity in specialized lines of rabbits [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/52390
TESIS
Li, Fang Owens Kevin G. "Development of a genetic algorithm-correlation analysis (GA/CA) program for classification of chemical compounds using mass spectral data /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2803.
Full textBeyene, Yoseph Aydagn. "Genetic analysis of traditional Ethiopian Highland Maize (Zea Mays L.) using molecular markers and morphological traits : implication for breeding and conservation." Thesis, University of Pretoria, 2005. http://hdl.handle.net/2263/30529.
Full textThesis (PhD (Genetics))--University of Pretoria, 2005.
Genetics
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Wang, Liang. "Innovative damage assessment of steel truss bridges using modal strain energy correlation." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/53177/1/Liang_Wang_Thesis.pdf.
Full textAlarcón, Sergio Arciniegas. "Imputação de dados em experimentos com interação genótipo por ambiente: uma aplicação a dados de algodão." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-11032009-150202/.
Full textThe multienvironment trials are a special type of the two-factor experiments, widely used in genetic improvement of plants, where some cultivars are assessed in diferent locations. Generally, in these studies there is a di¤erential response of cultivars in each location that is called genotype environment interaction, or G x E, which is well explained by the additive main e¤ects and multiplicative interaction models (AMMI). Often the experiments GE may be unbalanced and one or several genotypes were not tested in some locations. Sometimes for the environments recommendations, the researcher may be interested in obtain estimates of those combinations G x E that were not tested and such estimates can be calcu- lated using the information of those combinations that were actually obtained. Additionally to the interest of the researchers in these estimates, the missing data may cause some pro- blems in the classical estimation of parameters in the AMMI models, because the parameter estimation need of a sequential process doing an analysis of variance followed by a singular value decomposition, which can not be calculated if there is a matrix of interaction with missing data. To solve these problems Bergamo (2007) and Bergamo et al. (2008) proposed a new technique using the distribution free multiple imputation (IMLD), and for this reason was decided to evaluate the recent development through the comparison with some methods of imputation that have been used successfully in experiments GE with missing data like the AMMI estimates based on alternating least squares ALS(0), ALS(1) (CALINSKI et al. 1992) and AMMI estimates with robust sub-model r-AMMI1 and r-AMMI2 (DENIS; BARIL, 1992). Thus, was developed a simulation study based on a matrix of true data genotypes (15) environments (27) of the upland cotton variety trials (ensaio estadual de algodoeiro her- báceo) 2000/01 (FARIAS, 2005), doing missed random (10%, 20%, 30%), imputing the data and comparing the methods through the root mean square predictive di¤erence (RMSPD) of the true value, the Procrustes statistic and the Spearman´s ranks correlation coe¢ cient. Also was made an analysis on the choice of the multiplicative components of an AMMI model after imputation on the complete data sets (observed + imputed). The results of the simulation study has shown that according to the distribution of RMSPD standardized, the r-AMMI1 method is better than the IMLD. However, using the Procrustes statistic was found that imputing data matrix with ALS(0), is obtained the greatest similarity related to the true data matrix. The ve methods considered show high correlation between the true and the imputed missing values. Finally, is recommended using the imputation data for the estimation of the parameters of an AMMI model under the presence of missing data, but for choosing the number of multiplicative terms is preferable take the decision only on the observed information.
Saborío, Montero Alejandro. "Study of the Host Genetic Control over the Ruminal Microbiota and their Relationships with Methane Emissions in Dairy Cattle." Doctoral thesis, Universitat Politècnica de València, 2022. http://hdl.handle.net/10251/172633.
Full text[ES] El análisis del control genético del hospedador sobre su microbiota ha sido señalado recientemente como un tema prometedor en diferentes campos de estudio. La relación entre el holobionte hospedador-microbioma y los fenotipos en el ganado lechero podría conducir a nuevos conocimientos en los programas de selección genética. Dentro de esta tesis doctoral, se realizó la estimación y análisis a través de diferentes enfoques estadísticos con el objetivo de desentrañar el control genético del hospedador sobre la microbiota en ganado lechero. Además, se analizó el rasgo de concentración de metano como un fenotipo potencial para ser incluido en el programa de mejora de ganado lechero español. Mayor abundancia relativa de la mayoría de los eucariotas (principalmente protozoos ciliados y hongos) y algunas arqueas (Methanobrevibacter spp. Methanothermus spp. y Methanosphaera spp.) fueron factores de riesgo para ser clasificadas en la categoría alta. Se propuso un conjunto de modelos de ecuaciones estructurales (SEM) de tipo recursivo dentro de un marco de Cadenas de Markov Monte Carlo (MCMC) para analizar conjuntamente la relación hospedador-metagenoma-fenotipo. Se estableció un modelo bivariado no-recursivo como punto de referencia. La heredabilidad de CH4 se estimó en 0,12 ± 0,01 en ambos modelos, recursivo y no recursivo. Asimismo, las estimaciones de heredabilidad para la abundancia relativa de los taxones se superpusieron entre los modelos y variaron entre 0.08 y 0.48. Las correlaciones genéticas entre la composición microbiana y el CH4 variaron de -0,76 a 0,65 en el modelo bivariado no recursivo y de -0,68 a 0,69 en el modelo recursivo. Doce matrices de relación de microbiota (K) fueron construidas a partir de diferentes métricas de distancia del microbioma, con el objetivo de comparar su desempeño dentro de un marco de estimación de componentes de varianza para CH4 y toda la microbiota. Análisis de simulación (n = 1000) y datos reales fueron desarrollados considerando cuatro modelos posibles: un modelo genómico aditivo (GBLUP), un modelo de microbioma (MBLUP), un modelo de efectos genéticos y microbioma (HBLUP) y un modelo de efectos de interacción genético, microbioma y genético × microbioma (HiBLUP). Un nuevo término "Holobiabilidad" fue definido para referirse a la proporción de la varianza atribuible a los efectos del holobionte hospedador-microbioma. Las estimaciones a partir de datos reales usando HiBLUP variaron dependiendo de la K utilizada y estuvieron entre 0.15-0.17, 0.15-0.21 y 0.42-0.59 para heredabilidad, microbiabilidad y holobiabilidad, respectivamente. El conjunto de datos de microbioma fue agregado a través de análisis de componentes principales (PCA), en pocos componentes principales (PCs) que fueron utilizados como aproximaciones del metagenoma central. Parte de la variabilidad condensada en estos PC está controlada por el genoma de la vaca, con estimaciones de heredabilidad para el primer PC (PC1) de ~ 0,30 en todos los niveles taxonómicos, con una gran probabilidad (> 83%) de que la distribución posterior sea > 0,20 y con un intervalo de mayor densidad posterior al 95% (95% HPD) no conteniendo cero. La mayoría de las estimaciones de correlación genética entre PC1 y metano fueron grandes (>0,70) en todos los niveles taxonómicos, con la mayor parte de la distribución posterior (> 82%) siendo > 0,50 y con su 95% HPD no conteniendo cero. Estos resultados sugieren que todo el metagenoma del rumen regula recursivamente las emisiones de metano en las vacas lecheras, y que tanto el CH4 como las composiciones de la microbiota están parcialmente controladas por el genotipo del hospedador. Las variables agregadas (PC) propuestas podrían ser usadas en programas de mejora de animales para reducir las emisiones de metano en las generaciones futuras.
[CA] L'anàlisi del control genètic de l'hoste sobre la seva microbiota s'ha assenyalat recentment com un tema prometedor en diferents camps d'estudi. La relació entre el holobiont hoste-microbioma i els fenotips en bovins de llet podria conduir a nous coneixements en els programes de cria. Dins d'aquest doctorat es van realitzar tesis, estimacions i anàlisis mitjançant diferents enfocaments estadístics amb l'objectiu de desentranyar el control genètic de l'hoste sobre la microbiota en bestiar lleter. A més, es va analitzar el tret de concentració de metà com a fenotip potencial a incloure en el programa espanyol de cria de bestiar lleter. La major abundància relativa de la majoria dels eucariotes (principalment protozous i fongs ciliats) i algunes arquees (Methanobrevibacter spp. Methanothermus spp i Methanosphera spp.) Van ser factors de risc per classificar-se en les categories altes. Es va proposar un conjunt de models d'equacions estructurals (SEM) de tipus recursiu dins d'un marc de cadena Markov Monte Carlo (MCMC) per analitzar conjuntament la relació hoste-metagenoma-fenotip. Es van establir models no recursius com a referència. L'heretabilitat del CH4 es va estimar en 0,12 ± 0,01 en ambdós models, recursius i no recursius. De la mateixa manera, les estimacions d'heretabilitat de l'abundància relativa dels tàxons es van superposar entre models i van oscil·lar entre 0,08 i 0,48. Les correlacions genètiques entre la composició microbiana i el CH4 van oscil·lar entre -0,76 i 0,65 en els models bivariables no recursius i de -0,68 a 0,69 en els models recursius. Dotze matrius de relació de microbiota (K) de diferents mètriques de distància de microbiomes, amb l'objectiu de comparar el seu rendiment dins d'un marc d'estimació de components de variància per CH4 i anàlisi de microbiomes sencers en simulació (n = 1000, 25 rèpliques) i es van realitzar dades reals , considerant quatre possibles models: un model genòmic additiu (GBLUP), un model de microbioma (MBLUP), un model d'efectes genètics i microbiomes (HBLUP) i un model d'efectes d'interacció genètics, microbiomes i genètics × microbiomes (HiBLUP). Es va definir un nou terme "Holobiabilitat" per referir-se a la proporció de la variància fenotípica atribuïble als efectes holobiont del microbioma host. Les estimacions de dades reals mitjançant HiBLUP van variar en funció de la K utilitzada i van oscil·lar entre 0,15-0,17, 0,15-0,21 i 0,42-0,59 per heretabilitat, microbiabilitat i holobiabilitat, respectivament. El conjunt de dades de microbiomes es va agregar mitjançant l'anàlisi de components principals (PCA) en pocs components principals (PC) que es van utilitzar com a proxies del metagenoma principal. Part de la variabilitat condensada en aquestes PC està controlada pel genoma de la vaca, amb estimacions d'heretabilitat per a la primera PC (PC1) de ~ 0,30 a tots els nivells taxonòmics, amb una gran probabilitat (> 83%) de la distribució posterior> 0,20 i amb un 95% més alt interval de densitat posterior (95% HPD) que no conté zero. La majoria de les estimacions de correlació genètica entre PC1 i metà eren grans (>0,70) en tots els nivells taxonòmics, amb una gran part de la distribució posterior (> 82%)> 0,50 i amb un 95% de HPD que no contenia zero. Aquests resultats suggereixen que tot el metagenoma del rumen regula recursivament les emissions de metà en vaques lleteres i que tant el CH4 com les composicions de microbiota estan parcialment controlades pel genotip de l'hoste. Les variables agregades proposades (PC) es podrien utilitzar en programes de cria d'animals per reduir les emissions de metà en les generacions futures.
[EN] The analysis of the host genetic control over its microbiota has recently been pointed out as a promising theme in different fields of study. The relationship between the host-microbiome holobiont and phenotypes in dairy cattle could lead to new insights in breeding programs. Within this Ph.D. thesis, estimation and analysis through different statistical approaches were performed aiming to unravel the host genetic control over the microbiota in dairy cattle. Besides, methane concentration trait was analyzed as a potential phenotype to be included in the Spanish dairy cattle breeding program. Higher relative abundance of most eukaryotes (mainly ciliate protozoa and fungi) and some archaea (Methanobrevibacter spp. Methanothermus spp and Methanosphera spp.) were risk factors for being classified in the high categories. a set of structural equation models (SEMs) of a recursive type within a Markov chain Monte Carlo (MCMC) framework was proposed to jointly analyze the host-metagenome-phenotype relationship. Non-recursive models were set as benchmark. Heritability of CH4 was estimated at 0.12 ± 0.01 in both, the recursive and non-recursive, models. Likewise, heritability estimates for the relative abundance of the taxa overlapped between models and ranged between 0.08 and 0.48. Genetic correlations between the microbial composition and CH4 ranged from -0.76 to 0.65 in the non-recursive bivariate models and from -0.68 to 0.69 in the recursive models. Regardless of the statistical model used, positive genetic correlations with methane were estimated consistently for the 7 genera pertaining to the Ciliophora phylum, as well as for those genera belonging to the Euryarchaeota (Methanobrevibacter sp.), Chytridiomycota (Neocallimastix sp.) and Fibrobacteres (Fibrobacter sp.) phyla. Twelve microbiota relationship matrices (K) from different microbiome distance metrics were built, aiming to compare its performance within a variance component estimation framework for CH4 and whole microbiome analysis on simulation (n = 1000, 25 replicates) and real data were performed, considering four possible models: an additive genomic model (GBLUP), a microbiome model (MBLUP), a genetic and microbiome effects model (HBLUP) and a genetic, microbiome and genetic × microbiome interaction effects model (HiBLUP). A new term "Holobiability" was defined to refer to the proportion of the phenotypic variance attributable to the host-microbiome holobiont effects. Estimates from real data using HiBLUP varied depending on the K used and ranged between 0.15-0.17, 0.15-0.21 and 0.42-0.59 for heritability, microbiability and holobiability, respectively. The microbiome dataset was aggregated through Principal Component Analysis (PCA) into few principal components (PCs) that were used as proxies of the core metagenome. Part of the variability condensed in these PCs is controlled by the cow genome, with heritability estimates for the first PC (PC1) of ~0.30 at all taxonomic levels, with a large probability (>83%) of the posterior distribution being > 0.20 and with the 95% highest posterior density interval (95%HPD) not containing zero. Most genetic correlation estimates between PC1 and methane were large (>0.70) at all taxonomic levels, with most of the posterior distribution (>82%) being >0.50 and with its 95%HPD not containing zero. These results suggest that rumen's whole metagenome recursively regulate methane emissions in dairy cows, and that both CH4 and the microbiota compositions are partially controlled by the host genotype. The purposed aggregated variables (PCs) could be used in animal breeding programs to reduce methane emissions in future generations.
This research was financed by RTA2015-00022-C03-02 (METALGEN) project from the national plan of research, development and innovation 2013-2020 and the Department of Economic Development and Competitiveness (Madrid, Spain). We thank the regional Holstein Associations and farmers collaborating in the project. Computational support from the High-Performance Computing Centre in Galicia (Spain) is acknowledged. Alejandro Saborío-Montero acknowledges the scholarship from Universidad de Costa Rica for his doctorate studies which partially conducted to the progress of this study.
Saborío Montero, A. (2021). Study of the Host Genetic Control over the Ruminal Microbiota and their Relationships with Methane Emissions in Dairy Cattle [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172633
TESIS
Compendio
Garcia, Marisol Peña. "Análise dos modelos AMMI bivariados." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-11022009-080418/.
Full textIs frequently nd in the studies the two way factor analysis, each factor with dierent number of levels, they conform a two way table of data, generally the analysis of the data is made with the analysis of variance - ANOVA, satisfying some assumptions, but there are some studies in which is very important the interaction, like the case of the improvement studies, where the objetive is select genotypes with optimum performance in dierents environments. The poor eciency in the genotypes and environment interaction (GE) analysis of the ANOVA can represents a problem for the researchers, that need to take advantage of the interaction. The additive main eects and multiplicative interactions model - AMMI, give advantages in the selection of genotypes when is compare with traditional methods, because give a better interaction (GE) analysis, also permit combine additive and multiplicative components in the same model, these models have demonstrated be ecient in the analysis with just one response variable but when there is more than one there is not a clear procedure to do the analysis. This work presents a analysis methodology for the bivariate AMMI models, doing individuals analysis in the response variables follow by the procrustes, which permit compare the results of the individuals analysis, and nally a conrmation of theses results with the multivariate analysis of variance - MANOVA. From the results can be concluded that the AMMI and the procrustes analysis give a good alternative for the bivariate AMMI models analysis.
Leite, Carla Daniela Suguimoto [UNESP]. "Efeitos genéticos e ambientais sobre o intervalo desmame-cio em fêmeas suínas." Universidade Estadual Paulista (UNESP), 2009. http://hdl.handle.net/11449/92600.
Full textCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A seleção baseada em características reprodutivas tem sido muito empregada em programas de melhoramento genético de suíno. Assim, objetivaram-se avaliar os efeitos ambientais e genéticos que influenciam o intervalo desmame-cio (IDC) e verificar sua influência no número de nascidos total (TL), nascidos vivos (NV) e mortos (NM) em fêmeas suínas. Para análise dos efeitos ambientais, utilizaram-se 8.104 dados da 1ª a 6ª ordem de parição, e, para as estimativas dos parâmetros genéticos, apenas as informações do 1º ao 3º IDC, o que resultou em 6.548 observações, que foram analisadas pelo método REML, utilizando-se modelos uni e multicaracterística. Para este último, considerou-se cada IDC (1º, 2º e 3º) como uma característica distinta. Avaliaram-se, também, as correlações genéticas entre o IDC, TL, NM e idade ao primeiro parto (IPP). Para os fatores ambientais, o modelo incluiu como efeitos fixos rebanho, linhagem, ano (AP) e estação (EP) de parto, e as covariáveis idade da porca ao parto (IDPP), TL e duração da lactação (DL). A DL, na forma linear, e a IDPP, na forma quadrática, influenciaram o IDC. Rebanho, AP e EP foram fontes de variação significativas, enquanto TL e linhagem não o foram. Não foi observada influência do IDC sobre TL, NV, nem sobre NM. A herdabilidade estimada para o IDC pelo modelo de repetibilidade foi baixa. As correlações genéticas entre os IDC (1º, 2º e 3º) foram de moderada a baixa magnitude, evidenciando que o modelo multicaracterística é mais indicado para as estimativas de parâmetro genético nessa população. As correlações genéticas entre IDC, TL e NM, assim como IDC e IPP foram favoráveis à seleção.
Selection for reproductive traits has been largely used in swine breeding programs. The aims of this study were to evaluate environmental and genetic effects that affect the weaning-to-estrus interval (WEI) in sows and to assess their influence on litter size (LS), number of live born (LP) and dead born piglets (DP). Data consisting of 8,104 WEI from the 1st to 6th farrowing recorded in two herds were used for environmental analysis, but for estimating the genetic parameters only data from the 1st to 3rd farrowing were used, totalling 6,548 records. Genetic analysis was performed using the REML method with single and multitrait models, where each WEI was considered as a different trait. Genetic correlations among WEI, LS, DP and age at first farrowing (AFF) were also estimated using a multitrait model. For the environmental analysis, the model included as fixed effects the herd, line, and year (YF) and season (SF) of farrowing, and as covariates the sow’s age at farrowing (SAF), LS, and lactation length (LL). The effects were linear for LL and quadratic for SAF. The herd, YF and SF were important sources of variation, whereas LS and line were not significant. There were no effects of WEI on the litter traits (LS, LP and DP). The heritability estimated for WEI was low, and genetic correlations among its different intervals were of moderate to low magnitude, evidencing that a multitrait model was more indicated for estimating the genetic parameters for this trait in this population. The genetic correlations between WEI and LS, DP and AFF would be favourable in a selection.
Books on the topic "Genetic correlation analysis"
Coimbra, João. Torre de Babel e Monte Sinai: modelos de exegese do Antigo Testamento. Brazil Publishing, 2022. http://dx.doi.org/10.31012/978-65-5861-755-6.
Full textDel Giudice, Marco. Evolutionary Psychopathology. Oxford University Press, 2018. http://dx.doi.org/10.1093/med-psych/9780190246846.001.0001.
Full textZucker, Robert A., and Sandra A. Brown, eds. The Oxford Handbook of Adolescent Substance Abuse. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199735662.001.0001.
Full textBook chapters on the topic "Genetic correlation analysis"
Ahmad Dar, Mayasar, and Deepmala Sharma. "Revisiting the Genomics and Genetic Codes Using Walsh-Hadamard Spectrum Analysis." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 106–13. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_11.
Full textDeych, Elena, Robert Culverhouse, and William D. Shannon. "Microarray Dimension Reduction Based on Maximizing Mantel Correlation Coefficients Using a Genetic Algorithm Search Strategy." In Studies in Classification, Data Analysis, and Knowledge Organization, 49–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_5.
Full textBhagya Lakshmi, Nandipati, Nagaraju Devarakonda, Zdzislaw Polkowski, and Anusha Papasani. "A Quick Dynamic Attribute Subset Method for High Dimensional Data Using Correlation-Guided Cluster Analysis and Genetic Algorithm." In Advances in Intelligent Systems and Computing, 395–408. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0475-2_35.
Full textSimm, Geoff, Geoff Pollott, Raphael Mrode, Ross Houston, and Karen Marshall. "Analysing genetic variation in farm animals." In Genetic improvement of farmed animals, 178–92. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789241723.0178.
Full textDu, Lei, Jingwen Yan, Sungeun Kim, Shannon L. Risacher, Heng Huang, Mark Inlow, Jason H. Moore, Andrew J. Saykin, Li Shen, and [Authorinst]for the Alzheimer’s Dis Initiative. "GN-SCCA: GraphNet Based Sparse Canonical Correlation Analysis for Brain Imaging Genetics." In Brain Informatics and Health, 275–84. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23344-4_27.
Full textForseth, Ry R., and Frank C. Schroeder. "Correlating Secondary Metabolite Production with Genetic Changes Using Differential Analysis of 2D NMR Spectra." In Fungal Secondary Metabolism, 207–19. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-122-6_15.
Full textLoosen, Simon, Matthias Meinke, and Wolfgang Schröder. "Numerical Analysis of the Turbulent Wake for a Generic Space Launcher with a Dual-Bell Nozzle." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 163–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53847-7_10.
Full textHötte, Felix, Oliver Günther, Christoph von Sethe, Matthias Haupt, Peter Scholz, and Michael Rohdenburg. "Lifetime Experiments of Regeneratively Cooled Rocket Combustion Chambers and PIV Measurements in a High Aspect Ratio Cooling Duct." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 279–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53847-7_18.
Full textPandey, H. K., S. K. Srivastava, and Prashant Pandey. "Petro-Geochemical Analysis and Their Correlation for Genesis of Fluoride Contamination in Groundwater of District Sonbhadra, U.P., India." In Springer Hydrogeology, 61–79. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4552-3_5.
Full textNordberg, Ana. "Biobank and Biomedical Research: Responsibilities of Controllers and Processors Under the EU General Data Protection Regulation." In GDPR and Biobanking, 61–89. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49388-2_5.
Full textConference papers on the topic "Genetic correlation analysis"
Liefooghe, Arnaud, and Bilel Derbel. "A Correlation Analysis of Set Quality Indicator Values in Multiobjective Optimization." In GECCO '16: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908812.2908906.
Full textLiu, Yang, Xiaojun Bai, and Xiaofeng Kang. "Analysis and Study of the Correlation of Genetic Loci." In 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/mcei-16.2016.237.
Full textSun, Shaofei, Hongxin Zhang, Weijun Cheng, Liang Dong, Yang Wang, and Xiaotong Cui. "An Improved Correlation Electromagnetic Analysis Based On Genetic Algorithm Optimization." In 2019 International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM). IEEE, 2019. http://dx.doi.org/10.1109/iwem.2019.8887920.
Full textBhuva, Meha, Richard Sandford, and William Griffiths. "O01 Genetic analysis and phenotypic correlation in ductal plate malformation." In Abstracts of the British Association for the Study of the Liver Annual Meeting, 22–24 November 2021. BMJ Publishing Group Ltd and British Society of Gastroenterology, 2021. http://dx.doi.org/10.1136/gutjnl-2021-basl.1.
Full textBoutte, David, and Jingyu Liu. "Sparse canonical correlation analysis applied to fMRI and genetic data fusion." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706603.
Full textChing-Roa, Vincent Ralph D., Songfeng Han, Jingxuan Ren, Gabriel A. Ramirez, Seung Hyun Kim, and Regine Choe. "Diffuse Correlation Tomography Geometry Optimization with Genetic Algorithm Using Singular Value Analysis." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jw3a.22.
Full textLin, Dongdong, Jigang Zhang, Jingyao Li, Vince Calhoun, and Yu-Ping Wang. "Identifying genetic connections with brain functions in schizophrenia using group sparse canonical correlation analysis." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556466.
Full textMingon Kang, Baoju Zhang, Xiaoyong Wu, Chunyu Liu, and Jean Gao. "Sparse generalized canonical correlation analysis for biological model integration: A genetic study of psychiatric disorders." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6609794.
Full textPacheco-Vega, Arturo, Mihir Sen, K. T. Yang, and Rodney L. McClain. "Correlations of Fin-Tube Heat Exchanger Performance Data Using Genetic Algorithms, Simulated Annealing and Interval Methods." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/htd-24296.
Full textSha, Jiahang, Jingxuan Bao, Kefei Liu, Shu Yang, Zixuan Wen, Yuhan Cui, Junhao Wen, et al. "Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer’s Disease." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995342.
Full textReports on the topic "Genetic correlation analysis"
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 textLers, Amnon, Majid R. Foolad, and Haya Friedman. genetic basis for postharvest chilling tolerance in tomato fruit. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7600014.bard.
Full textAharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.
Full textSela, Hanan, Eduard Akhunov, and Brian J. Steffenson. Population genomics, linkage disequilibrium and association mapping of stripe rust resistance genes in wild emmer wheat, Triticum turgidum ssp. dicoccoides. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598170.bard.
Full textFallik, Elazar, Robert Joly, Ilan Paran, and Matthew A. Jenks. Study of the Physiological, Molecular and Genetic Factors Associated with Postharvest Water Loss in Pepper Fruit. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7593392.bard.
Full textAbbo, Shahal, Hongbin Zhang, Clarice Coyne, Amir Sherman, Dan Shtienberg, and George J. Vandemark. Winter chickpea; towards a new winter pulse for the semiarid Pacific Northwest and wider adaptation in the Mediterranean basin. United States Department of Agriculture, January 2011. http://dx.doi.org/10.32747/2011.7597909.bard.
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