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Microchimerism: a blessing in disguise?

This question was answered on Jun 26, 2011. View the Answer
DEAR Tutor,
I appreciate you can answer only three questiion at a time. but these are questions that have already be answered, but completely. I asked for the graph of the case and control sample, instead you sent me a notepad with variables and NOT in a TABLE. this is making things difficult for the both of us, because you are not adressing the problem. therefore I keep sending it back to you.. I have copied the variables in the notepad into a table and I need you to confirm if i have arranged it the way it should look. I could not copy the 7252700 JPG GRAPH you sent. so could you please copy and paste it on slide 12. The work is overdue for submission already. But I would rather submit a complete piece of work LATE. Than submit an INCOMPLETE PIECE OF WORK EARLY.

7252700_Statistics_and_Probability_revised_assignment[1] correction2.ppt

Microchimerism: a blessing in
• • Cell transfer happens through the umbilical cord during gestation
period in both ways. Some of the transferred cells survive in the
host body, and proliferate. This is called Microchimerism. When fetal
cells survive in mother’s body, it’s fetal microchimereism.
Microchimerism has been shown to provide protective effect in
Breast Cancer in a previous paper: Gadi VK, Malone KE, Guthrie KA, Porter PL, Nelson JL (2008) Case-Control Study of
Fetal Microchimerism and Breast Cancer. PLoS ONE 3(3): e1706.
doi:10.1371/journal.pone.0001706. • • Since this most probably to happen through some secondary
immunological mechanism, and Breast Cancer susceptibility and
surface antigens vary with genotype, we can do this study for a
specific ethnic group, for example Asians.
This study is of course practicable but will involve a lot of effort for
data collection. The scientific hypothesis
• The null hypothesis we have here is that level of fetal
microchimerism (FMc) has no effect on being affected
with Breast Cancer, i.e. FMc does not provide a
protective role in Breast Cancer.
• When a woman bears a male child, some cells with XYchromosome survive in FMc in the mother’s body. We
can quantify the amount of FMc by amount of fetal cells,
which is measured by the relative quantity of Ychromosome specific marker in the sample, and will be
measured by Quantitiative PCR. Experimental design - 1
• Analysis will be done on blood samples obtained from
parous Asian women who have borne at least one
male child.
• Samples can be collected from Breast Cancer patients
in the corresponding ward of a public health center, as
well as maternity wards.
• Ethical permission has to be obtained from the
corresponding authority, each patient has to be filled
up with a pre-tested questionnaire and their identity
must be kept confidential.
• Sterilized environment and instruments, and
competent work-force has to be used for safety
reasons. Data measurement and
• Breast cancer affected status is a 0/1 variable.
Level of FMc can be obtained from checking a Ychromosome specific marker, say DYS14 in the
blood sample.
• Data has to be collected for some confounders
that include age, contraceptive use, age of onset
of menarche, male children borne, smoking status,
history of breastfeeding, miscarriage and abortion.
• Since the level of FMc is very less in the host
body, we need to amplify the amount of Y
chromosomes to detect it in the sample. For
simultaneous multiplication and detection,
quantitative PCR is used. Experimental Design - 2
• Case samples have to be collected from Asian
women who come to the Cancer ward of a public
hospital / health establishment.
• Control samples are women with no history of
malignancy. An easy way to get these samples is
from general section of maternity ward.
• For case and control samples, 5 and 3
quantifications in the quantitative PCR will be
• Since Breast cancer patients are not that frequent
to come by, the data collection phase will take
considerable time. It is best to continue analyzing
samples simultaneously with data collection. Summarization of results
• We take presence of FMc defined as whether
the amount of FMc in sample is above/below
a certain threshold.
• A 2-by-2 contingency table can be obtained
by considering Breast cancer affected/not
affected status and presence/absence of
• Different regressions have to be performed
and the p-values for significance of
regressions have to be found out for other
variables, taking case/control status as
dependent variable to ensure no bias is there
among the two groups. Graphical representation
• A plot of levels of FMc in the samples, with the index of a data
point in x-axis and case/control status highlighted in different
colors will give an idea about the effect, if any, of FMc across
the two groups. The plot for the original paper is given below. Statistical tests used
• Odds ratio can be obtained from the
contingency table and then it will be
checked that if it is significant. A
significantly more/less value will suggest
that Breast cancer happens more with
presence/absence of FMc.
• For multiple regression of other
explanatory variables on case/control
status, overall F-tests and individual t-tests
have to be done. Overall F test
• In multiple linear regression this is used to test whether
the regression is significant or not as a whole.
• The null hypothesis is that the vector of regression
coefficients has all elements equal to zero.
• When we have n samples, each of dimension p, and
we assume that samples are i.i.d. multivariate normal,
then the test statistic F = MSreg/MSres follows an F
distribution with parameters p and n – p. So we reject
the null hypothesis at 95% confidence level if the value
of the test statistic exceeds the 0.05-tail of the
corresponding distribution. Individual t test
• In multiple linear regression this test is used to determine
whether a certain independent variable is significant in the
• The null hypothesis is that the corresponding regression
coefficient is zero.
• When we have n samples, each of dimension p, and we assume
that samples are i.i.d. multivariate normal, then the test statistic t
= sqrt(n – p)* bi/s where bi is the estimate for the i-th regression
coefficient (we’re testing significance of the i-the variable), and s
is the estimate of error variance. Under the assumption of
normality of samples as before, this follows a t distribution with
degrees of freedom n – p.
• So we reject the null hypothesis at 95% confidence level if the
absolute value of t exceeds the 0.025 tail of the corresponding t
distribution. Table
case control FMc level (/10^6
maternal cells 1
4 0
0 0
12.6 5 0 0 6 0 3.11 7 0 0 8 0 0 9 0 9.21 10 0 2 11 1 2.45 12
13 1
1 0
32.54 14 1 21.22 15
16 1
1 3.43
0 17 1 9.76 18 1 3.47 19 1 65.3 20 1 234.56

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This question was asked on Jun 25, 2011 and answered on Jun 26, 2011.

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