Journal articles: 'Genetic correlation analysis'

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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.

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Abstract Birth weight (BW) is an important predictor of newborn survival and health and has associations with many adult health outcomes, including cardiometabolic disorders, autoimmune diseases and mental health. On average, twins have a lower BW than singletons as a result of a different pattern of fetal growth and shorter gestational duration. Therefore, investigations into the genetics of BW often exclude data from twins, leading to a reduction in sample size and remaining ambiguities concerning the genetic contribution to BW in twins. In this study, we carried out a genome-wide association meta-analysis of BW in 42 212 twin individuals and found a positive correlation of beta values (Pearson’s r = 0.66, 95% confidence interval [CI]: 0.47–0.77) with 150 previously reported genome-wide significant variants for singleton BW. We identified strong positive genetic correlations between BW in twins and numerous anthropometric traits, most notably with BW in singletons (genetic correlation [rg] = 0.92, 95% CI: 0.66–1.18). Genetic correlations of BW in twins with a series of health-related traits closely resembled those previously observed for BW in singletons. Polygenic scores constructed from a genome-wide association study on BW in the UK Biobank demonstrated strong predictive power in a target sample of Dutch twins and singletons. Together, our results indicate that a similar genetic architecture underlies BW in twins and singletons and that future genome-wide studies might benefit from including data from large twin registers.

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Wen, 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.

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Oliveira, 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.

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Sweet sorghum presents stems with juice similar to that of sugar cane, rich in fermentable sugars, that may be used in off-season for sugar and ethanol production optimizing the sugar-ethanol sector. The objective of this work was to determine genetic correlations between characters and perform path analysis between juice volume and its components. Twenty-five sweet sorghum genotypes were evaluated in randomized blocks design with three repetitions and studied variables were: number of days to flowering; plant height; number of stems; weight of green mass; weight of dry mass; number of leaves; diameter of stems; volume of extracted juice and percentage of total soluble solids. In order to verify the existence of variability among the genotypes, data were subjected to variance analysis by F-test. Subsequently, genetic parameters were determined, as soon as genetic correlation estimator’s method, performed by t-test, to determine phenotypic correlation and bootstrap method for determining environmental and genetics correlation coefficient. Before performing path analysis a multicollinearity diagnosis was also conducted. The results of genetic correlation and path analysis point weight of green mass as the main variable influencing the juice volume, allowing these characters in indirect selection for increasing juice volume

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Skelly, 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.

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Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

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Motiar 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.

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Werme, 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.

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Ruiz 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.

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Mishra, 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.

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Ali, 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.

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Data on 2931 Kajli lambs, born from 2007 to 2018, were used to quantify environmental and genetic effects on growth performance of Kajli sheep. Traits considered for evaluation were birth weight (BWT), 120-day adjusted weight (120DWT), 180-day adjusted weight (180DWT), 270-day adjusted weight (270DWT), and 365-day adjusted weight (365DWT). Fixed effects of year of birth, season of birth, sex, birth type, and dam age on these traits were evaluated using linear procedures of SAS, 9.1. Similarly, BWT, 120DWT, 180DWT, and 270DWT were used as fixed effects mixed model analyses. Variance components, heritability and breeding values were estimated by restricted maximum likelihood. The genetic trend for each trait was obtained by regression of the estimated breeding values (EBV) on year of birth. Analyses revealed substantial influence of birth year on all traits. Sex and birth type were the significant sources of variation for BWT and 120DWT. Season of birth did not influence birth weight meaningfully, but had a significant role in the expression of 120DWT, 180DWT, and 270DWT. Heritability estimates were generally low (0.003 ± 0.018 to 0.099 ± 0.067) for all traits. With the exception of the genetic correlation of 180DWT and 365DWT, the genetic correlations between trait were strong and positive. Only 365DWT had a positive genetic trend. Although the heritability estimates for almost all weight traits were low, high and positive genetic correlations between BWT and other weight traits suggest that selection based on BWT would result in the improvement of other weight traits as a correlated response.Keywords: bodyweight, breeding value, genetic correlation, sheep

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Sharma, 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.

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Wang, An, Yuan Li, Yaoling Ding, Liehuang Zhu, and Yongjuan Wang. "Efficient Framework for Genetic Algorithm-Based Correlation Power Analysis." IEEE Transactions on Information Forensics and Security 16 (2021): 4882–94. http://dx.doi.org/10.1109/tifs.2021.3117091.

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Konyak, WL, SP Kanaujia, A. Jha, HP Chaturvedi, and A. Ananda. "Genetic Variability, Correlation and Path Coefficient Analysis of Brinjal." SAARC Journal of Agriculture 18, no. 1 (July 25, 2020): 13–21. http://dx.doi.org/10.3329/sja.v18i1.48378.

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A field experiment was conducted during 2017-2018 for evaluating the performance of brinjal (Solanum melongena L.) genotypes under foothill condition of Nagaland following randomized block design with three replications at the experimental farm of Horticulture, SASRD, Nagaland University, Medziphema, Nagaland. Forty six brinjal genotypes viz. IIVR- 1, IIVR-2, IIVR- 3, IIVR- 4, IIVR-5, IIVR-6, IIVR-7, IIVR-8, IIVR-9, IIVR- 10, IIVR-11, IIVR-12, IIVR-13, IIVR-14, IIVR-15, IIVR-16, IIVR-17, IIVR- 18, IIVR-19, IIVR-20, IIVR-21, IIVR-22, IIVR-23, IIVR- 24, IIVR-25, IIVR- 26, IIVR-27, IIVR-28, IIVR-29, IIVR-30, IIVR-31, IIVR-32, IIVR-33, IIVR- 34, IIVR-35, IIVR-36, IIVR-37, IIVR-38, IIVR-39, IIVR-40, IIVR-41, IIVR- 42, IIVR-43, IIVR-44, IIVR-45 and Nagaland Local (check variety) were evaluated for their growth, yield, quality and genetic attributes. High phenotypic coefficient of variation, genotypic coefficient of variation and high heritability coupled with high genetic advance was found for anthocyanin content, fresh weight of fruit, number of leaves plant-1, yield plant-1, fruit length and fruit diameter. The study on path analysis revealed that maximum positive direct effect on yield is exerted by fresh weight of fruit (1.08) and number of fruits plant-1 (0.407). Based on the experimental results, it can be concluded that, genotype IIVR-31 and IIVR-7 was proved to be potential yielder under foothill condition of Nagaland. SAARC J. Agri., 18(1): 13-21 (2020)

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Naylor, Melissa G., Xihong Lin, Scott T. Weiss, Benjamin A. Raby, and Christoph Lange. "Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants." PLoS ONE 5, no. 5 (May 13, 2010): e10395. http://dx.doi.org/10.1371/journal.pone.0010395.

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Kumar, Dharminder, Rajeev Kumar, Sandeep Kumar, M. L. Bhardwaj, M. C. Thakur, Ramesh Kumar, Kuldeep Singh Thakur, et al. "Genetic Variability, Correlation and Path Coefficient Analysis in Tomato." International Journal of Vegetable Science 19, no. 4 (October 2, 2013): 313–23. http://dx.doi.org/10.1080/19315260.2012.726701.

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Lisboa, L. F., Y. Tong, D. Kumar, X. L. Pang, A. Åsberg, A. Hartmann, H. Rollag, A. G. Jardine, M. D. Pescovitz, and A. Humar. "Analysis and clinical correlation of genetic variation in cytomegalovirus." Transplant Infectious Disease 14, no. 2 (October 14, 2011): 132–40. http://dx.doi.org/10.1111/j.1399-3062.2011.00685.x.

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Xie, Lei, Yuan Yang, Yao Li, Shuifei Chen, Yueyao Feng, Ningjie Wang, Ting Lv, Hui Ding, Lu Wang, and Yanming Fang. "A Meta-Analysis Indicates Positive Correlation between Genetic Diversity and Species Diversity." Biology 10, no. 11 (October 23, 2021): 1089. http://dx.doi.org/10.3390/biology10111089.

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Species diversity (SD) and genetic diversity (GD) are the two basic levels of biodiversity. In general, according to the consensus view, the parallel effects of environmental heterogeneity, area, and connectivity on two levels, can drive a positive correlation between GD and SD. Conversely, a negative correlation or no correlation would be expected if these effects are not parallel. Our understanding of the relationships between SD and GD among different ecosystems, sampling methods, species, and under climate change remains incomplete. In the present study, we conducted a hierarchical meta-analysis based on 295 observations from 39 studies and found a positive correlation between genetic diversity and species diversity (95% confidence interval, 7.6–22.64%). However, significant relationships were not found in some ecosystems when we conducted species–genetic diversity correlation analysis based on a single ecosystem. Moreover, the magnitudes of the correlations generally decreased with the number of sampling units and the annual average the temperature of sampling units. Our results highlight the positive correlation between GD and SD, thereby indicating that protecting SD involves protecting GD in conservation practice. Furthermore, our results also suggest that global increases in temperature during the 21st century will have significant impacts on global biodiversity.

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Finkel, Deborah, and Matt McGue. "Age Differences in the Nature and Origin of Individual Differences in Memory: A Behavior Genetic Analysis." International Journal of Aging and Human Development 47, no. 3 (January 1, 1998): 217–39. http://dx.doi.org/10.2190/jepx-g60a-6qk3-9dr3.

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Measures of memory performance and cognitive and lifestyle variables were obtained from 147 younger twin pairs (27 to 60 years) and 194 older twin pairs (60 to 94 years) as part of the Minnesota Twin Study of Adult Development and Aging. Correlational and behavior genetic analyses were combined to investigate age differences in 1) the relationship between memory performance and cognitive and lifestyle variables, 2) genetic and environmental influences on memory performance, and 3) genetic and environmental mediation of the relationship between memory and predictor variables. No age differences in heritability were found. Qualitative age differences in memory processes were suggested by age group differences in the correlations between memory and cognitive factors. Even though age differences in phenotypic correlations had been demonstrated, no age differences in the genetic and environmental mediation of the correlations were found. In other words, the magnitude of the correlation between memory and cognitive factors varied across age groups but the nature of the relationship did not.

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Peter, Inga, Michael Vainder, and Gregory Livshits. "Genetic analysis of motor milestones attainment in early childhood." Twin Research 2, no. 1 (February 1, 1999): 1–9. http://dx.doi.org/10.1375/twin.2.1.1.

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AbstractThe age of attainment for four motor developmental traits, such as turning over, sitting up without support, pulling up to a standing position and walking without support, was examined in 822 children, including 626 siblings from families with 2 to 6 children, 68 pairs of dizygotic twins and 30 pairs of monozygotic twins. Correlation analysis, carried out separately for each type of sibship, showed the highest pairwise correlations in monozygotic twins and the lowest correlation in non-twin siblings for all motor milestones. Variance component analysis was used to decompose the different independent components forming the variation of the studied trait, such as genetic effect, common twin environment, common sib environment and residual factors. The results revealed that the major proportion of the total variance after adjustment for gestation age for the attainment of each motor skill, except pulling up to standing position, is explained by the common twin environment (50.5 to 66.6%), whilst a moderate proportion is explained by additive genetic factors (22.2 to 33.5%). Gestational age was found to be an important predictor of appearance of all motor milestones, affecting delay of 4.5 to 8.6 days for the attainment of the motor abilities for each week of earlier gestation. The age of attainment of the standing position was affected only by shared sibs environment (33.3% of the total variance) and showed no influence of either genetic or common twin environment. Phenotypic between trait correlations were high and significant for all studied traits (range between 0.40 and 0.67, P < 0.01 in all instances). Genetic cross correlations, however, were not easily interpreted and did not show clear variance trends among the different groups of children.

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Ben-Chaim, Arnon, and Ilan Paran. "Genetic Analysis of Quantitative Traits in Pepper (Capsicum annuum)." Journal of the American Society for Horticultural Science 125, no. 1 (January 2000): 66–70. http://dx.doi.org/10.21273/jashs.125.1.66.

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Inheritance of 10 quantitative traits related to plant and fruit development was studied in an intraspecific cross between a bell-type `Maor' [Capsicum annuum L. var. annuum (Grossum Group) `Maor'] and a small-fruited pungent chilli line `Perennial' [C. annuum var. annuum (Longum Group) `Perennial']. Estimates of broad- and narrow-sense heritabilities, coefficients of genetic variance, and genotypic correlations were obtained from the segregation of 120 F3 families in 2 years. Three of the traits analyzed, days to first ripened fruit, plant height, and pedicel length, exhibited heterosis and transgressive segregation. Days to first ripened fruit and total soluble solids had low narrow-sense heritabilities. The other traits studied had moderate to high narrow-sense heritability estimates. Most of the genetic variation associated with traits that affect the size of the fruit and its shape was additive. The highest genetic correlation coefficients among pairs of traits were found between fruit weight and each of the three width characters: fruit diameter, pericarp thickness, and pedicel diameter. In contrast, fruit weight had a low correlation coefficient with fruit length, indicating that the size of the pepper fruit in this cross was determined primarily by its width.

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Eşiyok, Dursun, Kadri Bozokalfa, and Tansel Kaygisiz-Aşçioğul. "Variability, heritability and association analysis in plant traits of swiss chard (Beta vulgaris subsp. cicla)." Genetika 43, no. 2 (2011): 239–52. http://dx.doi.org/10.2298/gensr1102239e.

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Plant breeders mainly interested in improvement agronomic properties of desirable trait accompanied with genetic architecture of major characters and their interrelationship. Genetic variability and character association are prime importance and provide essential contribution for successful breeding programme. In the present research genetic variability, heritability, phenotypic and genotypic correlations of several agronomic characters were studied in fifty-four swiss chard accessions and cultivars. Analysis of variance observed highly significant variations among accessions, and phenotypic coefficient of variation (PCV) was higher magnitude of genotypic coefficient of variation (GCV) for all agronomic characters. The lamina length, lamina width, petiole thickness, petiole width, lightness, chroma and leaf dry matter observed high magnitude broad sense heritable characters accompanied with high genetic advance. Genotypic correlation coefficient showed higher than phenotypic correlation coefficient which could be explained by low environmental effect on investigated agronomic traits. Positive correlation was revealed between petiole length, lamina length, lamina width, petiole thickness and petiole width which comprise total yield in swiss chard.

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Ghavi Hossein-Zadeh, Navid. "A meta-analysis of genetic parameter estimates for milk and serum minerals in dairy cows." Journal of Dairy Research 89, no. 1 (February 2022): 19–28. http://dx.doi.org/10.1017/s0022029922000127.

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AbstractThis study aimed to conduct a meta-analysis based on a random-effects model to combine different published heritability estimates and genetic correlations for milk and serum minerals in dairy cows. In total, 59 heritability and 25 genetic correlation estimates from 12 articles published between 2009 and 2021 were used. The heritability estimates for milk macro-minerals were moderate to high and ranged from 0.311 (for Na) to 0.420 (for Ca). On the other hand, milk micro-minerals had lower heritabilities with a range from 0.013 (for Fe) to 0.373 (for Zn). The heritability estimates for serum macro-minerals were generally low and varied from 0.126 (for K) to 0.206 (for Mg). The estimates of genetic correlation between milk macro-minerals varied from −0.024 (between Na and K) to 0.625 (between Mg and P). The genetic correlations of milk Ca and P with milk yield were −0.171 and −0.211, respectively. The estimates of genetic parameters reported in this meta-analysis study are appropriate to utilize in breeding plans when valid estimates are not available for milk minerals in dairy cow populations.

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Rajan, Ivy Mary. "Drought Tolerance in Upland Rice: Genetic Variability and Correlation Analysis." International Journal of Pure & Applied Bioscience 7, no. 3 (June 15, 2019): 195–202. http://dx.doi.org/10.18782/2320-7051.7445.

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CUI, Jia-Cheng, Jia LIU, De-Sheng MEI, Yun-Chang LI, Li FU, Peng-Fei PENG, Jun WANG, and Qiong HU. "Genetic and Correlation Analysis on Pod Shattering Traits inBrassica napusL." Acta Agronomica Sinica 39, no. 10 (2013): 1791. http://dx.doi.org/10.3724/sp.j.1006.2013.01791.

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Saitoh, Fumihiko. "An Analysis of Normalized Correlation Image Matching by Genetic Algorithm." IEEJ Transactions on Electronics, Information and Systems 120, no. 2 (2000): 236–43. http://dx.doi.org/10.1541/ieejeiss1987.120.2_236.

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Å urlan-Momirovic, G., V. Bjelic, V. Rakonjac, T. Zivanovic, and G. Todorovic. "GENETIC, PHENOTYPIC VARIABILITY AND CORRELATION ANALYSIS IN SOME CABBAGE CULTIVARS." Acta Horticulturae, no. 462 (December 1997): 111–24. http://dx.doi.org/10.17660/actahortic.1997.462.14.

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Hardoon, David R., Ulrich Ettinger, Janaina Mourão-Miranda, Elena Antonova, David Collier, Veena Kumari, Steven C. R. Williams, and Michael Brammer. "Correlation-based multivariate analysis of genetic influence on brain volume." Neuroscience Letters 450, no. 3 (February 2009): 281–86. http://dx.doi.org/10.1016/j.neulet.2008.11.035.

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Najafov, Jamil, and Ayaz Najafov. "GECO: gene expression correlation analysis after genetic algorithm-driven deconvolution." Bioinformatics 35, no. 1 (July 13, 2018): 156–59. http://dx.doi.org/10.1093/bioinformatics/bty623.

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Liou, C. W., C. C. Huang, E. C. Y. Chee, Y. J. Jong, J. L. Tsai, C. Y. Pang, H. C. Lee, and Y. H. Wei. "MELAS syndrome: correlation between clinical features and molecular genetic analysis." Acta Neurologica Scandinavica 90, no. 5 (January 29, 2009): 354–59. http://dx.doi.org/10.1111/j.1600-0404.1994.tb02737.x.

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Li, Y., S. Y. Li, H. B. Wang, and H. J. Cheng. "The correlation analysis of children wheezing diseases and genetic gender." Paediatric Respiratory Reviews 14 (July 2013): S62. http://dx.doi.org/10.1016/s1526-0542(13)70081-2.

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Chai, Zenghao, Xu Yang, Zhilin Liu, Yunlin Lei, Wenhao Zheng, Mengyao Ji, and Jinfeng Zhao. "Correlation Analysis-Based Neural Network Self-Organizing Genetic Evolutionary Algorithm." IEEE Access 7 (2019): 135099–117. http://dx.doi.org/10.1109/access.2019.2942035.

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KAUSE, ANTTI. "Genetic analysis of tolerance to infections using random regressions: a simulation study." Genetics Research 93, no. 4 (July 18, 2011): 291–302. http://dx.doi.org/10.1017/s0016672311000176.

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SummaryTolerance to infections is the ability of a host to limit the impact of a given pathogen burden on host performance. This simulation study demonstrated the merit of using random regressions to estimate unbiased genetic variances for tolerance slope and its genetic correlations with other traits, which could not be obtained using the previously implemented statistical methods. Genetic variance in tolerance was estimated as genetic variance in regression slopes of host performance along an increasing pathogen burden level. Random regressions combined with covariance functions allowed genetic variance for host performance to be estimated at any point along the pathogen burden trajectory, providing a novel means to analyse infection-induced changes in genetic variation of host performance. Yet, the results implied that decreasing family size as well as a non-zero environmental or genetic correlation between initial host performance before infection and pathogen burden led to biased estimates for tolerance genetic variance. In both cases, genetic correlation between tolerance slope and host performance in a pathogen-free environment became artificially negative, implying a genetic trade-off when it did not exist. Moreover, recording a normally distributed pathogen burden as a threshold trait is not a realistic way of obtaining unbiased estimates for tolerance genetic variance. The results show that random regressions are suitable for the genetic analysis of tolerance, given suitable data structure collected either under field or experimental conditions.

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HASSAN, W. A., N. I. DIM, O. A. OSINOWO, and B. Y. ABUBAKAR. "GENETIC AND PHENOTYPIC CORRELATIONS FOR BODY WEIGHTS IN YANKASA SHEEP." Nigerian Journal of Animal Production 18 (January 12, 2021): 12–18. http://dx.doi.org/10.51791/njap.v18i.1931.

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Using paternal half-sib (PHS) analysis, coefficients of genetic and phenotypic correlations be- tween body weights of Yankasa lambs at birth, weaning (three months), six months, nine months and one year of age were estimate. The highest genetic correlation coefficient of 0.33 was obtained between birth and yearling weight. Six- month weight had very low and negative genetic correlation with yearling weight (-0.04). Phenotypic correlation coefficients for the various body weights pairs were positive and mostly of medium magnitude (0.12 -0.47).

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Li, Yanjie, Xianyin Ding, Jingmin Jiang, and Qifu Luan. "Inheritance and Correlation Analysis of Pulpwood Properties, Wood Density, and Growth Traits of Slash Pine." Forests 11, no. 5 (April 27, 2020): 493. http://dx.doi.org/10.3390/f11050493.

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Slash pine (Pinus elliottii) is the most important and a fast-growing material that is used for industrial timber and pulp production. A breeding program of slash pine that aims to improve wood properties has been employed for the past decade. This study analysed the genetics and correlation of growth traits and wood properties of a total of 1059 individual plants from 49 families of P. elliottii. Heritability, family ranking, genetic gain, and the relationship between these traits were estimated. The results showed that there was a significant negative genetic correlation between the holocellulose and lignin content. The heritabilities of these four traits were ranked from 0.18 to 0.32. The chemical wood traits did not show a strong correlation with diameter at breast height (DBH) and wood density. However, it is still possible to combine wood traits for selection. It was suggested that the genetic breeding selection could improve the growth and quality of P. elliottii.

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Baneh, Hasan, Javad Ahmadpanah, and Yahya Mohammadi. "Genetic analysis of reproductive characteristics in Iran-Black sheep." Acta Scientiarum. Animal Sciences 42 (January 30, 2020): e47380. http://dx.doi.org/10.4025/actascianimsci.v42i1.47380.

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This study was conducted to estimate genetic parameters and trends for reproduction traits using data collected at the breeding station of Iran-Black sheep during 1980 to 2004. The traits included in the analyses were litter size at birth (LSB) and weaning (LSW) and litter mean weight per lamb born (LMWLB) and weaned (LMWLW) as basic traits, and total litter weight at birth (TLWB) and weaning (TLWW) as composite traits. Direct heritability estimates for LSB, TLWB, LMWLB, LSW, TLWW and LMWLW were 0.11, 0.07, 0.33, 0.08, 0.09 and 0.11, respectively. The permanent environmental effects had significant impact on all traits and ranged from 0.05 to 0.16. Effect of service sire was highly significant (p < 0.01) for all traits except LMWLW. Estimates of genetic correlations ranged from -0.76 (LSB-LMWLB) to 0.98 (LSB-LSW). Phenotypic and environmental correlations were generally lower than those of genetic correlations. Environmental correlations ranged from -0.55 (LSW-LMWLW) to 0.99 (LSB-LSW). Also, the estimated correlation for the effect of service sire ranged from -0.77 (LMWLB-TLWW) to 0.96 (LSB-LSW and LSB-TLWW). The results suggest that selection based on TLWB could be more effective than the other traits to enhance reproductive performance in Iran-Black ewes.

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ECKMAN, RONAEL E., ROBERT WILLIAMS, and CRAIG NAGOSHI. "MARITAL ASSORTMENT FOR GENETIC SIMILARITY." Journal of Biosocial Science 34, no. 4 (September 24, 2002): 511–23. http://dx.doi.org/10.1017/s0021932002005114.

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The present study involved analyses of a Caucasian American sample (n=949) and a Japanese American sample (n=400) for factors supporting Genetic Similarity Theory (GST). The analyses found no evidence for the presence of genetic similarity between spouses in either sample for the blood group analyses of nine loci. All results indicated random mating for blood group genes. The results did not provide consistent substantial support to show that spousal similarity is correlated with the degree of genetic component of a trait for a set of seventeen individual differences variables, with only the Caucasian sample yielding significant correlations for this analysis. A third analysis examining the correlation between presence of spousal genetic similarity and spousal similarity on observable traits was not performed because spousal genetic similarity was not observed in either sample. The overall implication of the study is that GST is not supported as an explanation for spousal similarity in humans.

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Kurniawan, Fariz Am, Jakaria, and Rudy Priyanto. "ANALISIS KORELASI GENETIK ANTARA BOBOT LAHIR, BOBOT SAPIH, DAN BOBOT YEARLING PADA SAPI BRAHMAN CROSS (BX)." Jurnal Sains Terapan 12, Khusus (December 12, 2022): 148–53. http://dx.doi.org/10.29244/jstsv.12.khusus.148-153.

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Genetic correlations between productive traits have important role in beef cattle selection. This study aimed to estimate genetic correlation between birth, weaning, and yearling weight of Brahman Cross (BX) cattle. The experiment was carried out at PT Lembu Jantan Perkasa (PT LJP) Serang, Banten. There were 31 heads of BX offspring, which were obtained from 3 sire and 31 dams. The average birth weight was 23.1 ± 2.21 Kg, weaning weight 73.5 ± 13.02 Kg, and yearling weight was 257.9 ± 61.15 Kg. Genetic correlation was analyzed by covariance analysis. The genetic correlation between birth weight and weaning weight was 0.12, birth weight and yearling weight 0.31, and weaning weight and yearling weight was 0.47. The results indicated that the selection based on birth and weaning weight could be conducted in order to increase yearling weight in BX cattle, since these variables have high and positive genetic correlation to yearling weight. ABSTRAKKorelasi genetik diantara sifat-sifat produksi memiliki peranan penting dalam kegiatan seleksi sapi pedaging. Penelitian ini bertujuan untuk menduga nilai korelasi genetik antara bobot lahir, sapih, dan bobot yearling pada Sapi Brahman Cross (BX). Penelitian dilaksanakan di PT Lembu Jantan Perkasa, Serang Banten. Materi penelitian meliputi 31 ekor sapi BX yang berasal dari 3 pejantan dan 31 ekor induk betina. Rataan bobot lahir yang didapatkan sebesar 23,1 ± 2,21 Kg, bobot sapih sebesar 73,5 ± 13,02 Kg, dan bobot yearling sebesar 257,9 ± 61,15 Kg. Korelasi genetik dianalisis dengan analisis peragam antar saudara tiri. Nilai korelasi genetik antara bobot lahir dengan sapih sebesar 0,12, bobot lahir dengan bobot yearling sebesar 0,31, sedangkan bobot sapih dan bobot yearling sebesar 0,47. Hasil menunjukan bahwa pelaksanaan seleksi dapat dilakukan berdasarkan bobot lahir dan bobot sapih dalam rangka peningkatan peforma sapi BX, dikarenakan kedua peubah memiliki korelasi genetik yang positif dan tinggi terhadap bobot yearling.

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Ohn, Jung Hun. "The landscape of genetic susceptibility correlations among diseases and traits." Journal of the American Medical Informatics Association 24, no. 5 (March 21, 2017): 921–26. http://dx.doi.org/10.1093/jamia/ocx026.

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Abstract Objective: The aim of the study was to comprehensively explore the genetic susceptibility correlations among diseases and traits from large-scale individual genotype data. Materials and Methods: Based on a knowledge base of genetic variants significantly (P < 5 × 10−8) linked with human phenotypes, genetic risk scores (GRSs) of diseases or traits were calculated for 2504 individuals with whole-genome sequencing data from the 1000 Genomes Project. Associations between diseases/traits were statistically evaluated by pairwise correlation analysis of GRSs. Overlaps between the genetic susceptibility correlations and disease comorbidity associations from hospital claims data in more than 30 million patients in United States were assessed. Results: Correlation analysis of GRSs revealed 823 significant correlations among 78 diseases and 89 traits (false discovery rate adjusted P-value or Q-value < 0.01). It is noticeable that GRSs were correlated in 464 associations (56.4%) even if they were combinations of distinct sets of risk variants without chromosomal linkage, suggesting the presence of genetic interactions beyond chromosome position. When 312 significant genetic susceptibility correlations between diseases were compared to nationwide disease comorbidity correlations obtained from data from 32 million Medicare claims in the United States, 108 overlaps (34.6%) were found that had both genetic susceptibility and epidemiologic comorbid correlations. Conclusion: The study suggests that common genetic background exists between diseases and traits with epidemiologic associations. The GRS correlation approach provides a rich source of candidate associations among diseases and traits from the genetic perspective, warranting further epidemiologic studies.

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Ortiz Grisales, Sanín, Magda Piedad Valdés Restrepo, Franco Alirio Vallejo Cabrera, and Diosdado Baena García. "Genetic Correlations and Path Analysis in Butternut Squash Cucurbita moschata Duch." Revista Facultad Nacional de Agronomía Medellín 68, no. 1 (January 1, 2015): 7399–409. http://dx.doi.org/10.15446/rfnam.v68n1.47827.

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A genetic correlation and path analysis was carried out for yield and fruit quality components of diallel crosses of butternut squash Cucurbita moschata Duch, using open-pollinated introductions, S0 and inbred lines S1 and S2 at two sites: Candelaria and Buga, located in the Department of Valle del Cauca, Colombia. A split-plot experimental design with arranged, random blocks and four replicates was used. The average fruit weight presented positive genetic correlations with the total production per plant in all three generations and at both test sites. The total plant dry matter and total production per plant presented a high genetic correlation, and the fruit dry matter was inversely correlated with the total carotene. The total carotene and fruit color were independent. The path analysis confirmed high genetic correlations between the number of fruits per plant, average fruit weight, and total plant dry matter, with direct, positive effects, that were significantly greater than zero for the variables of number of fruits per plant and average fruit weight in regards to the total plant dry matter. The total production per plant expressed a direct, negative effect on the total plant dry matter. The fruit dry matter presented an indirect, negative effect on the total plant dry matter through the average fruit weight and an indirect, positive effect through the total production per plant.

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Lin, Bochao D., Gonneke Willemsen, Abdel Abdellaoui, Meike Bartels, Erik A. Ehli, Gareth E. Davies, Dorret I. Boomsma, and Jouke J. Hottenga. "The Genetic Overlap Between Hair and Eye Color." Twin Research and Human Genetics 19, no. 6 (November 17, 2016): 595–99. http://dx.doi.org/10.1017/thg.2016.85.

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We identified the genetic variants for eye color by Genome-Wide Association Study (GWAS) in a Dutch Caucasian family-based population sample and examined the genetic correlation between hair and eye color using data from unrelated participants from the Netherlands Twin Register. With the Genome-wide Complex Trait Analysis software package, we found strong genetic correlations between various combinations of hair and eye colors. The strongest positive correlations were found for blue eyes with blond hair (0.87) and brown eyes with dark hair (0.71), whereas blue eyes with dark hair and brown eyes with blond hair showed the strongest negative correlations (-0.64 and -0.94, respectively). Red hair with green/hazel eyes showed the weakest correlation (-0.14). All analyses were corrected for age and sex, and we explored the effects of correcting for principal components (PCs) that represent ancestry and describe the genetic stratification of the Netherlands. When including the first three PCs as covariates, the genetic correlations between the phenotypes disappeared. This is not unexpected since hair and eye colors strongly indicate the ancestry of an individual. This makes it difficult to separate the effects of population stratification and the true genetic effects of variants on these particular phenotypes.

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Wang, Meiling, Wei Shao, Xiaoke Hao, Shuo Huang, and Daoqiang Zhang. "Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis." Bioinformatics 38, no. 8 (February 10, 2022): 2323–32. http://dx.doi.org/10.1093/bioinformatics/btac074.

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Abstract Motivation As a rising research topic, brain imaging genetics aims to investigate the potential genetic architecture of both brain structure and function. It should be noted that in the brain, not all variations are deservedly caused by genetic effect, and it is generally unknown which imaging phenotypes are promising for genetic analysis. Results In this work, genetic variants (i.e. the single nucleotide polymorphism, SNP) can be correlated with brain networks (i.e. quantitative trait, QT), so that the connectome (including the brain regions and connectivity features) of functional brain networks from the functional magnetic resonance imaging data is identified. Specifically, a connection matrix is firstly constructed, whose upper triangle elements are selected to be connectivity features. Then, the PageRank algorithm is exploited for estimating the importance of different brain regions as the brain region features. Finally, a deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) method is developed for the identification of genetic associations with functional connectivity phenotypic markers. This approach is a regularized, deep extension, scalable multi-SNP-multi-QT method, which is well-suited for applying imaging genetic association analysis to the Alzheimer’s Disease Neuroimaging Initiative datasets. It is further optimized by adopting a parametric approach, augmented Lagrange and stochastic gradient descent. Extensive experiments are provided to validate that the DS-SCCA approach realizes strong associations and discovers functional connectivity and brain region phenotypic biomarkers to guide disease interpretation. Availability and implementation The Matlab code is available at https://github.com/meimeiling/DS-SCCA/tree/main. Supplementary information Supplementary data are available at Bioinformatics online.

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Ansari, K. I., N. Palacios, C. Araya, T. Langin, D. Egan, and F. M. Doohan. "Genetic variation between Colletotrichum lindemuthianum isolates." Plant Protection Science 38, SI 2 - 6th Conf EFPP 2002 (December 31, 2017): 378–80. http://dx.doi.org/10.17221/10496-pps.

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We characterized the genetic diversity of seventy-three C. lindemuthianum isolates collected from 10 different countries by Amplified Fragment Length Polymorphism (AFLP) analysis. The results of this research highlighted the fact that there is huge variation in the genetic diversity between isolates from different countries. The molecular profile of the isolates showed correlation with geographic origin of the isolates.

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Makore, Fortunate, Cosmos Magorokosho, Shorai Dari, Edmore Gasura, Upenyu Mazarura, Casper N. Kamutando, and Xavier Mhike. "Genetic Evaluation and Correlation Analysis Among Various Quantitative Traits in Maize Single-Cross Hybrids Under Diverse Environments." Journal of Agricultural Science 13, no. 5 (April 15, 2021): 104. http://dx.doi.org/10.5539/jas.v13n5p104.

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Genetic variation abundance, high genetic advance coupled with high heritability estimates presents the most suitable condition for selection. Ninety-five hybrids generated from elite and new inbred lines crossed using half diallel mating design were evaluated under diverse environments. The objectives were to estimate genetic variances, heritability of traits and genetic advance and to determine correlations of grain yield and its component characters in maize hybrids. Analysis of variance revealed significant differences among genotypes for all traits studied except for ear rots. Estimates of phenotypic coefficient of variation were slightly higher than genotypic coefficient of variation for all traits suggesting low influence of environment in the expression of these traits. High heritability and genetic estimates were recorded for grain yield (79%; 30.27%), plant height (85%; 102.42%) and ear height (86%; 117.15%) whilst high heritability and low genetic advance were observed for anthesis date (87%; 5.8%), texture (75%; 8%) and ear position (71%; 0.23%). Correlation between environments using grain yield data revealed existence of a very strong positive correlation between CIMMYT2 and RARS2 suggesting that the sites have the same discriminating effect. Correlation among traits revealed that grain yield had significant (P < 0.05) positive correlation with plant height and ear height. Similarly, plant height had significant and positive correlation with ear height while ear position was positively correlated to ear height. Path analysis showed that plant height, ears per plant and ear position had positive direct effects on grain, while anthesis date, ear height, ear position, grain moisture content at harvest and texture indirectly influenced grain yield. These characters’ contribution to grain yield is important and the strong association with grain yield implied that these can be used as secondary traits to indirectly select for grain yield performance in this set of germplasm across all the environments.

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Mrdakovic, Marija, Vesna Peric-Mataruga, Larisa Ilijin, Milena Vlahovic, D. Mircic, Vera Nenadovic, and Jelica Lazarevic. "Analysis of correlation structure in Lymantria dispar L. larvae from locally adapted populations." Archives of Biological Sciences 65, no. 2 (2013): 525–31. http://dx.doi.org/10.2298/abs1302525m.

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The influence of allelochemical stress and population origin on the patterns of phenotypic and genetic correlations among life history traits and digestive enzyme activities were investigated in larvae of the gypsy moth (Lymantria dispar L.; Lepidoptera: Lymantriidae). Thirty-two full-sib families from oak (suitable host plant, Quercus population), and twenty-six full-sib families from locust-tree (unsuitable host plant, Robinia population) forests were reared on an artificial diet, with or without a 5% tannic acid supplement. Comparison of correlation matrices revealed significant similarity between the two populations in the structure of phenotypic and genetic correlations of life history traits and of digestive enzyme activities. The patterns of correlations of the examined traits, within each of the two locally adapted populations and in the presence of allelochemical stress, remained stabile despite the different selection pressures that mold these traits.

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Mratinic, Evica, Vera Rakonjac, and Dragan Milatovic. "Genetic parameters of yield and morphological fruit and stone properties in apricot." Genetika 39, no. 3 (2007): 315–24. http://dx.doi.org/10.2298/gensr0703315m.

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Yield, fruit and stone weight and dimensions were studied in 24 apricot cultivars over a four-year period, to determine components of variability, heritability and genetic and phenotypic correlations. The analysis of the components of total variance evidenced that genetic differences between cultivars determined to the highest percentage the variability of fruit height and stone weight and dimensions, whereas the variation of yield, and fruit weight, breadth and thickness was predominantly determined by ecological factors. The values of heritability coefficients, in a broader sense, were found to be relatively high for fruit and stone weight and dimensions (88-93%), and medium for yield (48%). It was established by correlation analysis that all genetic and phenotypic coefficients of correlation between fruit and stone weight and dimensions were positive and statistically very significant. Yield was in a very significant, positive genetic correlation with a majority of studied characters except for fruit breadth. However, the phenotypic coefficients of correlation between those properties were not statistically significant.

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Lodhi, P. S., P. P. Singh, I. S. Naruka, S. S. Kushwah, and Awani K. Singh. "Genetic variability, correlation and path analysis in fenugreek (Trigonella foenum-graecumL.)." Indian Journal of Horticulture 72, no. 3 (2015): 429. http://dx.doi.org/10.5958/0974-0112.2015.00085.7.

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Sattar, M. A., N. Sultana, M. M. Hossain, M. H. Rashid, and A. K. M. Aminul Islam. "GENETIC VARIABILITY, CORRELATION AND PATH ANALYSIS IN POTATO (Solanum tuberosum L.)." Bangladesh Journal of Plant Breeding and Genetics 20, no. 1 (June 30, 2007): 33–38. http://dx.doi.org/10.3329/bjpbg.v20i1.17023.

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Genotypic and phenotypic variability, heritability, genetic advance, correlation coefficients and path coefficients analysis were done for yield and its contributing characters in 28 genotypes of potato. High heritability coupled with high genetic advance as percent of mean and high genotypic coefficients of variation were observed for number of tubers per plant, yield per plant and average weight of a tuber suggesting selection for these traits would give good response. Genotypic and phenotypic correlation of the number of tubers per plant and weight of tubers per plant were highly significant. Plant vigour, number of compound leaves per plant and number of tubers per plant, average weight of a tuber and dry matter content of tuber had high degree of positive association with tuber yield per plant. As per path analysis, average weight of tuber and total number of tubers per plant contributed maximum direct effect to tuber yield indicating their importance as selection index for yield improvement.DOI: http://dx.doi.org/10.3329/bjpbg.v20i1.17023

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Khomphet, Thanet, Warin Intana, Athakorn Promwee, and Shams Shaila Islam. "Genetic Variability, Correlation, and Path Analysis of Thai Commercial Melon Varieties." International Journal of Agronomy 2022 (March 10, 2022): 1–6. http://dx.doi.org/10.1155/2022/7877239.

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In the selection phase of melon breeding programs, genetic variability is a critical component for yield improvement. The goals of this study were to discover the variables that affect melon fruit weight and examine genetic variability, correlation, and path analysis of eight melon varieties. The experiment was arranged as a completely randomized block design with 4 blocks. It was conducted between July and September 2021 at the School of Agricultural Technology, Walailak University, Nakhon Si Thammarat, Thailand. The result found that stem diameter and length, leaf length, width, number, and chlorophyll, day to 50% male and female flowering, and fruit perimeter, height, and weight were highly significant across the varieties. The genotypic coefficients of variation (GCV) of observed variables were all lower than phenotypic coefficients of variation (PCV). Fruit weight (15.462 and 19.865%) had the highest GCV and PCV. High broad-sense heritability was linked to high (H) or moderate (M) genetic advance as a percentage of the mean from stem length (67.606%: H and 21.992%: H), fruit weight (60.586%: H and 24.793%: H), fruit perimeter (76.395%: H and 12.258%: M), and fruit height (69.828%: H and 12.122%: M). The maximum and significant genotypic correlation value was obtained between leaf length and leaf width (r = 1.000). Fruit weight is positively correlated with fruit perimeter (r = 0.940) and fruit height (r = 0.831). According to correlation and path analyses, stem diameter and length, leaf chlorophyll, and fruit perimeter and height were considered variables for fruit weight improvement in the breeding programs. It suggests that the increase in traits with a favorable direct influence on fruit weight may directly contribute to fruit weight.

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Yang, Lei. "Shot Performance Influence Factors Correlation Degree Analysis Based on Genetic Algorithms." Open Electrical & Electronic Engineering Journal 9, no. 1 (September 30, 2015): 479–82. http://dx.doi.org/10.2174/1874129001509010479.

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The paper first applies Newton mechanical and physical knowledge to establish shot throwing process mathematical model, calculates throwing performance is related to α, h, t0, v0 these five factors; the next, it adopts genetic algorithms to solve optimal throwing performance and these five factors best parameters, from which optimal throwing performance is 21.78m; finally it adopts grey relational degree algorithms and analyzes five influence factorsf primary and secondary relation is α, h, t0, v0 , which provides scientific evidence for making scientific training plans, during training, coaches and athletes should pay attention to foster strengths and circumvent weakness, give their own advantages into full play so that can get more ideal results.

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PATIDAR, PAWAN KUMAR, RAMESH CHOUDHARY, and BHANWAR LAL JAT. "Genetic variability, divergence, correlation and path analysis in Foeniculum vulgareMill. germplasm." ASIAN JOURNAL OF BIO SCIENCE 12, no. 2 (October 15, 2017): 209–22. http://dx.doi.org/10.15740/has/ajbs/12.2/209-222.

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Alam, MK. "Genetic correlation and path coefficient analysis in groundnut (Arachis hypogaea L.)." SAARC Journal of Agriculture 12, no. 1 (December 3, 2014): 96–105. http://dx.doi.org/10.3329/sja.v12i1.21116.

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Correlation and path co-efficient analysis were carried out for pod yield and its yield components characters in 45 genotypes of groundnut. The genotypic correlation co-efficient were found to be of relatively higher magnitude than the corresponding phenotypic correlation co-efficient, indicating strong inherent association between the characters. Pod yield showed significant positive association with secondary branches/plant, harvest index, 100-pod weight, 100-kernel weight, pod size, diseases incidence and canopy temperature. Path co-efficient analysis revealed high direct effects of primary branches/plant, secondary branches/plant and harvest index. Hence, it would be rewarding to give due importance on the selection of these characters for rapid improvement in pod yield of groundnut. DOI: http://dx.doi.org/10.3329/sja.v12i1.21116 SAARC J. Agri., 12(1): 96-105 (2014)

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Journal articles on the topic 'Genetic correlation analysis'

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