You cannot give a direct answer to your question without knowing more information, for example, what type of population are you considering, an outbred population, or a backcross or f-2, etc.If you are dealing with an outbred population, the gene could even be closer to the marker with the smaller regression value.  I hope this helps.   

Dear Sir thanks for this question it varies from your findings.Few of the time if we take

a example of microalbumin profile for kidney diseases rather then creatine and GFR test. Both equally important ok.So dont mis interpret you directly say its linkage disruption or  invovement.I am telling you few of the times you may not get significant association but invovement is there.So it wiser to modify the term linkage disruption.

You cannot give a direct answer to your question without knowing more information, for example, what type of population are you considering, an outbred population, or a backcross or f-2, etc.If you are dealing with an outbred population, the gene could even be closer to the marker with the smaller regression value.  I hope this helps.   

Sir,

This is F2 population

Dear Krishna,

nice question. The association of the phenotype to the markers as you mentioned based on regression (61 and 76%), has nothing to do with the gene. Why? because simply the phenotype could be controlled by many genes. Besides, genetic distances based on genetic maps (cM) is different from actual distances (recombination fraction). This is in part can give you a picture why one of the previous comments was asking about the type of population/cross you were using, where your answer cam as (F2). This means that recombination frequency, that reflects your true distance between your assessed markers, is statistically depend on some factors, which one of them is the type of cross. So, what is this relate to your question and what is the answer!!! you can not force the data to tell you what it can not have. This means that if your data is correlate the markers at a specific value, this means that this marker is correlated to the phenotype at this level of confidence by a value of (61 and 76%) than any other markers in your data. This actually gives importance to your data as an explanation to (or) how much the amount of variation in your phenotype data can be explained better through these two markers at this level of confidence by the specified values you mentioned. So, this means that one marker can explain 61% of your phenotypic data despite the variation that could be existed between the individuals you used in your population. However, if you want to expand your conclusions to include a specific gene(s), you need to allocate this/these gene(s) to the markers by using what is called validation assessment. This can be done by comparative genetic maps and consensus maps from previous studies and by then you can tell if your marker is close to the proposed genes related to your phenotype or not. This means that, you have a good data, however, it is not enough to jump to such conclusion.

wish you all the best,

 Thank you Dr.Ahmed ElFatih_Eldoliefy