ﺑﺮرﺳﯽ ارﺗﺒﺎط ﺑﯿﻦ ﻧﺸﺎﻧﮕﺮﻫﺎي ISSR وﺻﻔﺎت ﻣﻬﻢ در ارﻗﺎم ﻣﺨﺘﻠﻒ ﺳﻮﯾﺎ ﺑﺎ روش رﮔﺮﺳﯿﻮن

analysis of Association among ISSR Markers and Important Traits in Different Soybean Cultivars by Regression Method

ﯾﮑﯽ از ﭘﯿﺸﺮﻓﺖ ﻫﺎي ﺑﺰرگ در زﻣﯿﻨﻪ ي ﺗﺸﺨﯿﺺ ﻧﺸﺎﻧﮕﺮ ﻫﺎ، ﺷﻨﺎﺳﺎﯾﯽ ﻧﺸﺎﻧﮕﺮ ﻫﺎي ﭘﯿﻮﺳﺘﻪ ﺑﺎ ﺻﻔﺎت زراﻋﯽ ﻣﻔﯿﺪ، اﺳﺖ و ﻫﻤﭽﻨﯿﻦ ارزش ﻧﺸﺎﻧﮕﺮ ﻫﺎ در آﻧﺎﻟﯿﺰ وراﺛﺖ ﺻﻔﺎت در ﮔﯿﺎﻫﺎن زراﻋﯽ، ﻣﺸﺨﺺ ﺷﺪه اﺳﺖ. در اﯾﻦ ﺗﺤﻘﯿﻖ ارﺗﺒﺎط ﺑﯿﻦ ﺻﻔﺎت زراﻋﯽ و ﻧﺸﺎﻧﮕﺮ ﻣﻮﻟﮑﻮﻟﯽ ISSR در ژﻧﻮﺗﯿﭗ ﻫﺎي ﻣﺨﺘﻠﻒ ﺳﻮﯾﺎ ﻣﻮرد ﺑﺮرﺳﯽ ﻗﺮار ﮔﺮﻓﺖ. ﺗﻨﻮع ژﻧﺘﯿﮑﯽ ارﻗﺎم ﻣﺨﺘﻠﻒ ﺳﻮﯾﺎ ﺑﺎ اﺳﺘﻔﺎده از10آﻏﺎزﮔﺮ ISSR ﻧﺸﺎن داد ﮐﻪ از ﺗﻌﺪاد 68 ﻗﻄﻌﻪ اي ﮐﻪ در ﮐﻞ ارﻗﺎم ﺗﻮﻟﯿﺪ ﺷﺪ 34 ﻗﻄﻌﻪ ﭼﻨﺪ ﺷﮑﻞ ﺑﻮدﻧﺪ .ﻣﻘﺪارPIC ﺑﯿﻦ 0/147 ﺗﺎ 0/058 در آﻏﺎزﮔﺮﻫﺎ ﻣﺘﻐﯿﺮ ﺑﻮد. ﺑﯿﺸﺘﺮﯾﻦ ﻣﯿﺰان ﺷﺎﺧﺺ ﻧﺸﺎﻧﮕﺮي ﻣﺮﺑﻮط ﺑﻪ آﻏﺎزﮔﺮ 6-PRl ﺑﺎ ﻣﻘﺪار 8/39 ﺑﻪ دﺳﺖ آﻣﺪ ﮐﻪ ﻧﺸﺎن دﻫﻨﺪه ﻗﺪرت ﺗﻔﮑﯿﮏ ﺑﺎﻻﺗﺮ اﯾﻦ آﻏﺎزﮔﺮ ﻧﺴﺒﺖ ﺑﻪ ﺳﺎﯾﺮ آﻏﺎزﮔﺮ ﻫﺎ اﺳﺖ. ﺗﺠﺰﯾﻪ ارﺗﺒﺎط ﺑﺎ اﺳﺘﻔﺎده از ﻣﺎﺗﺮﯾﺲ ﺳﺎﺧﺘﺎر ﺟﻤﻌﯿﺖ و ﺑﺎ ﻣﺪل ﻫﺎي آﻣﺎري MLM،GLM وﺑﺎ اﺳﺘﻔﺎده از ﻧﺮم اﻓﺰار TASSEL ﺑﺮاي 21 ﻣﺘﻐﯿﺮ اﻧﺠﺎم ﺷﺪ. ﻣﺪل MLM در ﺳﻄﺢ اﺣﺘﻤﺎل 5 درﺻﺪ 6 ﻧﺸﺎﻧﮕﺮ ﻣﺮﺗﺒﻂ ﺑﺎ ﺻﻔﺎت ارزﯾﺎﺑﯽ ﺷﺪه را ﺷﻨﺎﺳﺎﯾﯽ ﻧﻤﻮد. ﻣﻄﺎﺑﻖ ﻧﺘﺎﯾﺞ ﺗﺠﺰﯾﻪ ارﺗﺒﺎط ﺑﺎﻻﺗﺮﯾﻦ ﺿﺮﯾﺐ ﺗﺒﯿﯿﻦ ﺑﻪ ﻧﺸﺎﻧﮕﺮISSR9-2 ﺑﺎ ﺗﻮﺟﯿﻪ 0/21 درﺻﺪ از ﺗﻐﯿﯿﺮات ﺗﻌﺪاد ﺷﺎﺧﻪ اﺻﻠﯽ اﺧﺘﺼﺎص داﺷﺖ. در ﻣﺠﻤﻮع ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺑﺎﻻ ﺑﻮدن آﻣﺎره ﻫﺎي ﺗﻨﻮع ژﻧﺘﯿﮑﯽ ﺑﺮاي ﻧﺸﺎﻧﮕﺮ ﻫﺎيISSR2-،ISSR10-1 3وISSR2-4 ﻣﯽ ﺗﻮان از آن ﻫﺎ در ﺑﺮﻧﺎﻣﻪ ﻫﺎي اﺻﻼﺣﯽ دﯾﮕﺮ اﺳﺘﻔﺎده ﮐﺮد.

.One of the big advances in identifying markers is the identification of continuous markers with agronomic traits, as well as the value of markers in the analysis of heritability of traits in crops is specified. In this study was assessed, the relationship between agronomic traits and molecular marker ISSR in different genotypes of soybean. The genetic diversity different genotypes of soybean by using the 10 ISSR primer showed that Among 68 amplified fragment of cultivars were generated, 34 fragments were ploy morphic. PIC values varied between 0.147 to 0.058 for primers. Maximum level of marker index obtained for primer PRI-6 with value of 8.39 which indicating a higher resolution of this primer than the other. Association analysis was performed using structure matrix and statistical models of GLM and MLM by using TASSEL software for 21 variables. The MLM model in 5 percent probability level identified 6 markers related to evaluated traits. According to the results of Association analysis the highest of coefficient determination was for ISSR9-2 with 0.21 percent explanation of variation of number main branch. A total of, due to the high genetic diversity statistics, the ISSR10-1, ISSR2-3 and ISSR2-4 markers can be used to other breeding programs