Fruit sugar linked to leaf complexity
 

So if i've understood correctly, from article :

[URL="http://www.plantcell.org/content/25/7/2465.long"]A Quantitative Genetic Basis for Leaf Morphology in a Set of Precisely Defined Tomato Introgression Lines[/URL]

Supplementary data : [URL]http://datadryad.org/resource/doi:10.5061/dryad.rm5v5[/URL]

From supp data "dataset 10", complexity of leaves (CompAll) is correlated with brix at >0.4 (so 40%)

From supp fig 48, one can infer how CompAll index is computed

Wouldn't that basically mean that potato leaf will be the least sweet possibility?

I was wondering that too

PL's can have those little leaflet bits as well.
I'm skeptical of this though... haven't time to read the paper.
But what does it mean.. more than 40% of the time, there was a correlation? That is still less than 50% of the time. :?!?: I mean it would leave you with 60% no correlation?
How many genotypes involved in the study?

Hi Bower,

The study is about 76 introgression lines. The correlation is Pearson correlation.

From the net : [url]https://statistics.laerd.com/stata-tutorials/pearsons-correlation-using-stata.php[/url]
[QUOTE]
A Pearson's correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, [I]r[/I], indicates how far away all these data points are to this line of best fit (i.e., how well the data points fit this new model/line of best fit). Its value can range from -1 for a perfect negative linear relationship to +1 for a perfect positive linear relationship. A value of 0 (zero) indicates no relationship between two variables. For example, you could use a Pearson's correlation to understand whether there is an association between exam performance and time spent revising (i.e., your two variables would be "exam performance", measured from 0-100 marks, and "revision time", measured in hours). If there was a moderate, positive association, we could say that more time spent revising was associated with better exam performance. Alternately, you could use a Pearson's correlation to understand whether there is an association between length of unemployment and happiness (i.e., your two variables would be "length of unemployment", measured in days, and "happiness", measured using a continuous scale). If there was a strong, negative association, we could say that the longer the length of unemployment, the greater the unhappiness.
[/QUOTE]

There is an example of a correlation of 0.4 representing a chart with the two parameters :
[IMG]https://statistics.laerd.com/statistical-guides/img/pearson-6.png[/IMG]

So it not means that there is a link 40% of the time (my mistake), but if you plot your plants by fruit sugar and leaf complexity, measures will tend to follow the linear curve x = 0.4y (if i've understood correctly this time).

Essentially what this means is that 40 percent of the sugar content can be predicted by the presence of complex leaves. Ie. You are likely to have higher brix fruit if the leaves are complex. There is still a strong possibility to have outliers. Ie. Potato leaves with high brix.
The linear curve does not need to follow y=0.4x, r squared is what they are talking about.

Thanks for the explanation. Very cool....
Will have to pay attention to the bibs and bobs on the leaves now. :dizzy::lol:

In the engineering world, a correlation of 40% is pretty worthless. In the biological world it is not a horrible correlation.

Even a 40% correlation would be useful, if there are noticeable differences in leaf type within the population being selected.
Sadly it was no use to me this year, since afaict all of the plants I grew this summer had a high leaf complexity and I couldn't identify any differences on this basis.
A couple of these lines have very sweet fruit but others are closer to average. And within the lines themselves, there were a few outliers which were missing the sweetness genetics, but there was no detectable difference in leaf complexity, that would have made me suspect it.

I don't think any of this data applies to potato leaf plants since they weren't included in the study, only varyingly more or less complex leaflets of RL plants are included.
To look at this small study and decide PL tomatoes cannot be as sweet as RL tomatoes would be inaccurate.


KarenO

Number of fruit on a plant is the biggest predictor of sugar content that I know of. I wonder if complex-leafed plants put out fewer fruit. That would explain the increased sugar.

One important thing to remember about this study is that the introgression lines typically have the true tomato parent in common, and are backcrossed or otherwise genetically manipulated to introduce one small definable segment of a wild parent genome while maintaining the majority of genes from that same tomato parent.

So the predictive value of leaflets for sweetness might not hold true at all in the general tomato population - unless they have wild tomato genes in the mix.

It is r squared that that defines the portion of the variation in brix that can be explained by leaf complexity in this data set. (0.4 x 0.4)=0.16 of 16%.

I wondered if Brandywine Sudduth's, Stupice, Matina, Tamina and Mikado have exactly the same potato leaves. I'll try to watch next year. For the records I want to use the style images from the first post in this thread.
It would be interesting connect with the measurement Brix, but I do not have refractometer. :(
Vladimír