Genetic Variation, Heritability, Principal Component Analysis, Correlation and Path Coefficient Analysis in the Fruit Samples of Sechium edule (Jacq.) Sw. Genotypes

Genetic diversity, heritability, the genetic advance of yield, and associated traits are some important criteria to generate some basic information related to the genetic improvement of crops. Some characters of Sechium edule (Jacq.) Sw. genotype fruits have been evaluated for their improvement purpose. Genotypes and fruit samples of Sechium were randomly collected for fruit traits such as length (FL), width (FW), circumference/girth (FC), and the number of ridges (FR) from the various parts of village Kigwema of Kohima district, Nagaland at a mean value of latitude (25.60690 N), longitude (94.34250 E) and altitude (1538 masl) for the purpose. Genotypes and fruit samples collection for trait study normally distributed in histogram plot and normality test. Analysis of variance (ANOVA) estimated significant differences in fruit sample traits. The phenotypic coefficient of variation (PCV) was greater than the genotypic coefficient of variation (GCV) for all the traits. The phenotypic and genotypic coefficient of variation was recorded maximum for trait fruit length, while maximum heritability was recorded for trait fruit circumference. High heritability and high genetic advance estimates for fruit circumference suggest that it could be considered for further improvement through various breeding programs. Principle component analysis (PCA) showed that fruit length and fruit ridges numbers are responsible for most of the variations observed in the fruit morphology and could be considered for its improvement. Fruit width recorded maximum for correlation coefficient direct value indicated towards effect on the fruit circumference and direct selection of the trait for its improvement.

___

  • Akintunde A N. Path analysis step by step using Excel. Journal of technical science and technologies 2012; 1(1): 9-15.
  • Allard R W. Principles of plant breeding. New York: John Willey and Sons Inc.1960. p.
  • Bentler, P. M., Chih-Ping, C. (2016). Practical Issues in Structural Modeling. Sociological Methods and Research, 16(1), 78–117.
  • Burton C W, Devane E H. Estimating heritability in tall Festuca (Restucaarundinaceae) from donar material. Agron J. 1953; 45:1476-1481.
  • Chandran, K., & Padya, S. M. (2000). Morphological characterization of Arachis species of section Arachis. Plant Genetic Resources Newsletter, 121, 38-41.
  • Chatfield, C., & Collis. (1980). Introduction to multivariate analysis. Boca Raton: CRC Press.
  • Dag, O., Dolgun, A., & Konar, N. M. (2018). onewaytests: An R Package for One-Way Tests in Independent Groups Designs. The R Journal, 10 (1): 175–199.
  • Dimitrova, D. S., Kaishev, V. K., & Tan, S. (2020). Computing the Kolmogorov–Smirnov distribution when the underlying cdf is purely discrete, mixed or continuous. Journal of Statistical Software, 95 (10), 1–42.
  • Dyulgerova, B., & Valcheva, D. (2014). Heritability, variance components and genetic advance of yield and some yield related traits in Barley doubled haploid lines. Turkish Journal Agricultural Natural Science, 1 (special issue), 614-617.
  • Esposito, M. A., Martin, E. A., Cravero, V. P., & Cointry, E. (2007). Characterization of pea accessions by SRAPs markers. Scientia Horticulturae, 113, 329-335.
  • Everitt B, Landau S, Leese M, Stahl D. Cluster Analysis: Wiley series in probability and statistics. John wiley and sons. 2011. p.352.
  • Falconer, D. S., & Mackey, T. F. C. (1996). Introduction to quantitative genetics. New York: Longman.
  • Gelman, A. (2005). Analysis of variance? Why it is more important than ever. The Annals of Statistics, 33, 1–53.
  • Hartung, F., & Schiemann, J. (2014). Precise plant breeding using new genome editing techniques: opportunities, safety and regulation in the EU. The Plant Journal, 78 (5), 742–752.
  • Heckerman, D., Gurdasani, D., Kadie, C., Pomilla, C., Carstensen, T., Martin, H., Ekoru, K., Nsubuga R. N., Ssenyomo, G., Kamali, A., Kaleebu, P., Widmer, C., & Sandhu, M. S. (2016). Linear mixed model for heritability estimation that explicitly addresses environmental variation. Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7377–7382.
  • Johnson HW, Robinson HF, Comstock RE. Estimation of genetic and environmental variability in soybean. Journal of Agronomy1955; 47: 314-318.
  • Johnson, W., Penke, L., & Spinath, F. M. (2011). Understanding Heritability: What it is and What it is Not. European Journal of Personality, 25 (4), 287–294.
  • Jolliffe, I. (2002). Pricipal component analysis. Springer series in statistics (2nd edition), New York, USA: Springer. Karney, C. F. F. (2016). Sampling exactly from the normal distribution. ACM Transactions on Mathematical Software, 42, 1–14.
  • Kolmogorov, A. (1933). Sulla determinazione empirica di una legge di distribuzione. G. Ist. Ital. Attuari., 4, 83–91. Landau S, Everitt B S. A handbook of statistical analysis using SPSS. Boca Raton : Chapman and Hall/CRC- A CRC press company, London, New York.2004. p.300.
  • Luby, C. H., Kloppenburg, J., Michaels, T. E., & Goldman, I. L. (2015). Enhancing Freedom to Operate for Plant Breeders and Farmers through Open Source Plant Breeding. Crop Science, 55 (6), 2481–2488.
  • Mahdavi, D. B. (2013). The Non-Misleading Value of Inferred Correlation: An Introduction to the Cointelation Model. Wilmott Magazine, 2013 (67), 50–61.
  • Moore, D. S., & Shenk, D. (2017). The heritability fallacy. Wiley Interdisciplinary Reviews: Cognitive Science, 8(1–2), e1400.
  • Ning, Z., Pawitan, Y., & Shen, X. (2020). High-definition likelihood inference of genetic correlations across human complex traits. Nature Genetics, 52 (8), 859–864.
  • Okuyama, L. A., Federizzi, L. C., & Neto, J. F. B. (2004). Correlation and path analysis of yield and its components and plant traits in wheat. Ciencia Rural (Santa Maria), 34(6), 1701-1708.
  • Rosso, B., & Pagano, E. (2005). Evaluation of introduced and naturalized populations of red clover (Trifolium pretense L.) at Pergamino EEA-INTA, Argentina. Genetic Resources and Crop Evolution, 52, 507-511.
  • Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality. Biometrika, 52(3-4), 591-611.
  • Shore, H. (2011). Response Modeling Methodology. WIREs Comput Stat., 3 (4), 357–372.
  • Shore, H. (2012). Estimating Response Modeling Methodology Models. WIREs Comput Stat., 4 (3), 323–333.
  • Sivasubramanian S, Menon M. Heterosis and inbreeding depression in rice. Madras Agric J. 1973; 60:1139.
  • Smirnov, N. (1948). Table for estimating the goodness of fit of empirical distributions. Annals of Mathematical Statistics, 19 (2), 279–281.
  • Székely, G. J., Rizzo, M. L., & Bakirov, N. K. (2007). Measuring and testing independence by correlation of distances. Annals of Statistics, 35 (6), 2769–2794.
  • Tarka, P. (2017). An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52 (1), 313–354.
  • Tester, M., & Langridge, P. (2010). Breeding technologies to increase crop production in a changing world. Science, 327 (5967), 818–822.
  • Turkheimer, E. (2011). Still missing. Research in Human Development, 8 (3–4), 227–241.
  • Turkheimer, E. (2015). Genetic Prediction. The Hastings Center Report, 45 (5 Suppl.), 32–38.
  • Vrbik, J. (2018). Small-Sample Corrections to Kolmogorov–Smirnov Test Statistic. Pioneer Journal of Theoretical and Applied Statistics, 15 (1–2), 15–23.
  • Wray, N., & Visscher, P. (2008). Estimating Trait Heritability. Nature Education, 1 (1), 29.
  • Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. American Journal of Human Genetics, 88 (1), 76–82.
  • Yim, O., & Ramdeen, K. T. (2015). Hierarchical cluster analysis: comparison of three linkages measures and application to psychological data. The quantitative methods for Psychology, 11(1), 8-21.
Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi-Cover
  • ISSN: 1308-7576
  • Başlangıç: 1991
  • Yayıncı: Yüzüncü Yıl Üniversitesi Ziraat Fakültesi
Sayıdaki Diğer Makaleler

Aslıhan ESRİNGÜ, Melek EKİNCİ, Metin TURAN

Geleneksel Olmayan Bir Agro-Ekolojik Bölgede Yetiştirilen Lavanta Çeşitlerinin Uçucu Yağ Kimyasal Bileşimi

Rumyana GEORGİEVA, Hristofor KIRCHEV, Vanya DELIBALTOVA, Aleksandar MATEV, Petar CHAVDAROV, Tzvetanka RAYCHEVA

Tritikale Islah Çalışmalarında Biplot Analiz Tekniğinin Seleksiyonda Kullanılması

Enver KENDAL

Su Stres Koşulları Altında Tuzluluğun Siyah Havuç Bitkisinin (Daucus Carota L.) Verim, Verim Bileşenleri ve Bitki Su Tüketimi Üzerine Etkileri

Mehmet ALTUN, Hakan ARSLAN

Atmosferik ve Basınçlı Kızartma İşlemlerinin Köftenin Kalite Özelliklerine Etkileri

Şeyma ŞİŞİK OĞRAŞ

Farklı Organik Atık Uygulamasının Toprak Kalitesi Üzerine Etkisinin SMAF Modeli ile Belirlenmesi

Pelin ALABOZ, Orhan DENGİZ, Sena PACCİ, Sinan DEMİR, Cengiz TÜRKAY

Nemrut Krater (Turkey) Gölü’nde Yaşayan Barbus ercisianus (Cypriniformes: Cyprinidae) Balık Türünde Ligula intestinalis (Cestoda: Pseudophyllidea)‘in İlk Bildirimi

Şükrü ÖNALAN, Ataman Altuğ ATICI, Ahmet SEPİL, Fazıl ŞEN

Bazı Patates Klonlarının Alternaria solani’ye Karşı Dayanıklılıklarının Belirlenmesi

Özge KOYUTURK, İlker POLAT, Aslı YILMAZ, Başak ÖZYILMAZ, Rahime KARATAŞ, Levent YAZİCİ, Güngör YILMAZ, Yusuf YANAR, Nejdet KANDEMİR

Düzeltme: Farklı Ekolojilerde Yetişen Fındık (Corylus avellana L.) Çeşit ve Genotiplerinin Stoma Yoğunluk ve Dağılımlarının Belirlenmesi

Haydar KURT, Adnan DOĞAN

Bademde Kanser ve Geriye Doğru Ölüm Etmeni Neoscytalidium novaehollandiae’nın Tanılanması, Patojenisitesi ve Bazı Fungisitlerin In Vitro Etkinlikleri

Nedim SAKÇI, Şener KURT, Aysun UYSAL, Emine Mine SOYLU, Merve KARA, Soner SOYLU