INTRODUCTION Ever since human civilization took place, the bread wheat is an important cereal crop and a staple food cultivated globally to fulfil the requirement of human beings (FAOSTAT, 2015; Istipliler et al., 2015). Wheat consists of about 55% carbohydrates, 10-18 % of protein and 19% of calories which is required for humans and it is also used as a straw for feeding to animals (Shewry and Hey 2015). It is assumed that population will be 10 billion by 2050, so feed to this explosive population, the wheat yield has to be doubled by 2050, that’s why, the extensive research is required to enhance the grain yield of wheat, the improvement of wheat yield may increase by applying agronomic practices, and breeding programme (Mohammadi et al., 2021). The complex trait like grain yield is governed by minor genes and is the product of several contributing traits directly or indirectly. There are several morphological and biochemical traits are associated with economic productivity which is depending on genotypic and phenotypic correlation (Al-Ashkar et al., 2021). Knowledge about nature and magnitude of genetic association of components of economic importance can help to improve the efficiency of selection by making possible use of suitable combination of characters (Paux et al., 2022). It is considered that yield and their components are determined at different stage of the plant. These different characters are affected by various environmental factors. The correlation pattern of yield and yield components are necessary when compensation between yield components lead variation (Fernandes et al., 2021). The importance of genotypic and environmental interaction and their contribution to genetic slippage in the selection of complex characters improved the yield. The knowledge about correlation of yield and yield contribution traits reveals the indication in that selection pressure could be most profitable to be exercised in order to obtain plants having high yield ability (Poudel et al., 2021). Moreover, the path coefficients analysis is a method of calculation direct and indirect effects of traits on grain yield, and provides a way for examining specific forces responsible to produce a given correlation. The method of path analysis depends on the combination of knowledge of degree of correlation among the variables in a system and helps us to know the casual and effect relations, through partitioning of total correlation into direct and indirect effects to get actual information on the contribution of different components traits towards grain yield. In cases where the casual relations are uncertain, the method can be used to find out the logical consequences of any particular hypothesis in regard to them (Zhang et al., 2021). In the present study, the design line × tester is used to obtain the knowledge of the genotype, genetic mechanism that control the yield and yield contributing characters that have become of importance for the breeders. MATERIAL METHODS A. Parental genotype and crossing The basic material was 15 diverse wheat genotypes based on origin diversity that divided into two groups viz: 10 females (lines) and 5 males (testers). Ten females and five males were sown during rabi 2018-2019 for crossing purpose in L × T fashion at Section of Rabi cereals Nawabganj farm. All the females were crossed with each of five males to produce a sufficient amount of F1 seed of 50 crosses. The 50 F1 progenies were selfed to produce F2 seeds. The testers are selected based on wider adaptability, poor yielder, lower performance and broad genetic base. The details of genotypes are as follows (Table 1). Development of F1 seed: All fifteen genotypes, 10 lines (female) and 5 testers (males) were grown during Rabi season 2018-19 for making crosses in line × tester fashion and resultant seeds of 50 hybrids (F1) were harvested. Development of F2 seed: The half seed of each hybrid was sent for advancement at IIWBR-Regional Station, India in an off-season nursery to obtain seed for raising F2 generation. Rest half seed of each cross was procured to rase F1 generation in the final trial. B. Experimental sites and agronomic practices The parents and their crosses were evaluated under two different locations during the 2018-2019 and 2019-2020 growing seasons. The investigating material consisting of 115 genotypes (15 parents, 50 F1s and 50 F2) was sown on 27 November 2019. The whole experiment is conducted in a randomized completely block design (RCBD) with three replication at crop research farm Nawabganj of Chandra Shekhar Azad University of Agriculture and Technology, Kanpur. Each parent and F1 were planted in a single row while each F2 were planted in two rows of 3-meter-long plot and 22.5 cm apart, 10 cm Plant to plant distance was maintained. C. Morphological and biochemical characterization The biochemical and morphological characterizations were examined as Days to flowering (75%), Plant height (cm), Days to maturity, Number of productive tillers/plants, Flag leaf area (cm)2, Spike length (cm), Number of spikelets/spike, Number of grains/spike, biological yield/plant (gm), Grain yield /plant (gm), Test weight/1000 grain weight (gm), Harvest index (%), Seed hardness, Phenol color reaction and protein content (%). The following observation was recorded and summarized as given below. D. Data analysis The correlation and path coefficients among Parent + F1 + F2 had analyzed using the R statistical software package. The experiment results were analyzed to study parameters among treatments, and the significance association was calculated using least significant differences (LSD) P < 0.05. Statistical significance was marked at P<0.05 unless stated otherwise. Other factors such as temperature, climatic condition, topography, and soil characteristics affected the experiment. RESULTS AND DISCUSSION Phenotypic and genotypic correlation coefficients were estimated for fifteen characters in all possible pairs based on pooled calculation presented in Table 2. In this study, the pooled material parents+F1+F2 were studied. Days to flowering, days to maturity, number of productive tillers per plants, spike length, number of spikelets per spike, number of grains per spike, biological yield per plant, harvest index, seed hardness, seed hardness, and protein content showed high degree of positive correlation with grain yield per plant at phenotypic level, however, days to flowering, days to maturity, number of productive tillers per plant, spike length, number of spikelets per spike, number of grains per spike, biological yield per plant, harvest index, seed hardness, and protein content showed positive significant association with seed per plant at genotypic level (Table 2). Similar findings were reported for most of the characters by Aycicek and Yildirim (2006); Joshi et al. (2008); Anwar et al. (2009); Khan and Dar (2010); Mohsen et al. (2011); Khan et al. (2012); Kyosev and Desheva (2015). In general, the genotypic correlations were higher in magnitude than phenotypic correlation coefficients indicating strong positive inherent relationship between the characters (Table 2). The path coefficients analysis carried out at genotypic as well as phenotypic correlation coefficients, estimate based on pooled data were used to assess direct and indirect effects of various characters on the expression of grain yield per plant (Table 3 and 4 respectively). The path coefficients analysis of Days to flowering (0.0032), plant height (0.0073), number of productive tillers per plant (0.537), number of spikelets per spike (0.433), number of grains per spike (0.0073), biological yield per plant (0.8838), harvest index (0.3526) and seed hardness (0.0043) and protein content (0.0045) exerted positive direct effect on seed yield per plant, whereas days to maturity (-0.0113), flag leaf area (-0.0085), spike length (-0.0139), test weight/1000 grain weight (-0.0112), phenol color reaction (-0.0079) exerted negative direct effect on seed yield per plant at phenotypic level (Table-3).Plant height (0.0106), number of productive tillers per plant (0.0080), flag leaf area (0.0015), number of spikelets per spike (0.0220), number of grains per spike (0.0009), biological yield per plant (0.9261), harvest index (0.3625), seed hardness (0.0168), protein content (0.0022) exerted positive direct effect on grain yield per plant, whereas days to flowering (-0.0003), days to maturity (-0.0569), spike length (-0.0289), test weight/1000 grain weight (-0.0008), phenol color reaction (-0.0067) exerted negative direct effect on seed yield per plant at genotypic level (Table 4). These results are in agreement with the findings of Aycicek and Yildirim, (2006); Munir et al. (2007); Joshi et al. (2008); Anwar et al. (2009); Khan and Dar (2010); Mohsen et al. (2011); Khan et al. (2012); Kyosev and Desheva (2015). The residual effect determines how best the causal factors account for the variability of the dependent variable i.e. seed yield per plant. The low estimate of residual effect suggested that most of the important traits contributing to yield have been included in the study.