Pedagogical Objectives QTL/Bioinformatics Unit - MDCUNE

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Oct 2, 2013 (3 years and 11 months ago)

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Pedagogical Objectives

Bioinformatics/Neuroinformatics
Unit


Review of genetics


Review/introduction of statistical analyses
and concepts


Introduce QTL analysis


Introduce bioinformatic tools


Review/introduction of molecular
techniques

So, now the gametes of


the F
1

have some of

the DNA from each

F
0

strain. So, the F
2

generation will

have a collage of the

F
0
DNA

Hey! We have at

Least three maps!

Chromosomal,

linkage, physical.

Linkage map is in centimorgans.


This is a PCR product for a marker


or Locus (remember Quantitative Trait Loci?)

it is a small stretch of DNA that is

different between the two different strains.

A marker can be in any aspect of the DNA:


gene, promoter, expressed sequence,

intron, “junk,” etc., etc..





Qualitative vs Quantitative Traits


Qualitative Traits



Influenced by a single gene


Typically follow simple
patterns of inheritance


Phenotypes fall into distinct
categories (nominal scale)


Trait expression is typically
unaffected by environment


Quantitative
Traits


Influenced by multiple genes,
perhaps interacting genes


Do not follow simple patterns
of inheritance


Phenotype is measured on
continuous scale (interval scale)


Trait expression may be
affected by environment

Pedagogical Objectives

Bioinformatics/Neuroinformatics
Unit


Review of genetics


Review/introduction of statistical analyses
and concepts


Introduce QTL analysis


Introduce bioinformatic tools


Review/introduction of molecular
techniques

What is key in using regression to control for

various extraneous variables is the additive

model of variance.

s
2
t
otal
=

s
2
sex
+

s
2
body

weight
+
s
2
brain

weight

+
s
2
age
+

s
2
error
+
s
2
olfactory

bulb genes

Thus, the total variance can be

partitioned into the variance

associated with each of these

extraneous variables such as sex,

body weight, brain weight, and

age. Then we can successively

remove the variance associated

with each of these variables

and hopefully just have

residual variance that only pertains

to olfactory bulbs.

Let us first consider the case

of simple linear regression

before we tackle the problem

of multiple regression.

^

Y

Y

_

Variance

predicted

by X

Residual

(error)

Body Weight (grams)

OB Volume

The variance left over after the

variance from the other

variable(s) has been removed

is the residual variance.

This residual variance is

precious to us because

it has the variance specific

to gene effects

on olfactory bulbs.

So the
SS
E


SS
yy

is our treasure,

yet another’s trash.

By using multiple regression, We can


remove the variance

associated with extraneous

variables and so statistically

control for these variables.

How to make mistakes with statistics


Type II (beta) errors

AKA false negatives


Small effect size


Small n


Greater variance in scores


Greater the error variance, the more Type II errors


Type I (alpha) errors

false positives


Stringency of the alpha error rate


Significant Individual point p = 1.5 x 10
-
5
for genome
-
wide


a

= .05


Suggested individual point p = 3 x 10
-
4
for genome wide
a

=
.63

Thus, lots of error variance

will give us false negatives

(Type II errors)

when we do QTL analyses!


Pedagogical Objectives

Bioinformatics/Neuroinformatics
Unit


Review of genetics


Review/introduction of statistical analyses
and concepts


Introduce QTL analysis


Introduce bioinformatic tools


Review/introduction of molecular
techniques

QTL is good for detecting the approximate locus of multiple genes

affecting a phenotype across all the chromosomes, except Y.

This is a graph that displays the likelihood ratio statistic as a function


of locus on the various chromosomes, which are numbered at top.


This is a PCR product for a marker


or Locus (remember Quantitative Trait Loci?)

it is a small stretch of DNA that is

different between the two different strains.

A marker can be in any aspect of the DNA:


gene, promoter, expressed sequence,

intron, “junk,” etc., etc..





B

D

B

B

B

B

B

B

B

B

B

B

B

B

D

D

D

D

D

D

D

D

D

D

D

D

Phenotypic Measurement


(Residual)

LRS is High

X

all

X

D

X

B

B

D

B

B

B

B

B

B

B

B

B

B

B

B

D

D

D

D

D

D

D

D

D

D

D

D

Phenotypic Measurement


(Residual)

LRS is LOW

X

all

X

D

X

B

Squiggly blue
line is the LRS.

This line is the criterion for
suggested level of
significance.

This line denotes the
criterion for significance p
< .05.

Likelihood ratio
statistic

Zooming in on Chromosome 6…

SNP
Density

Each Small colored box
represents a known
gene

Clickable track to
corresponding display in
UCSC Genome Browser.

QTL is good for detecting the approximate locus of multiple genes

affecting a phenotype across all the chromosomes, except Y.

This is a graph that displays the likelihood ratio statistic as a function


of locus on the various chromosomes, which are numbered at top.

Pedagogical Objectives

Bioinformatics/Neuroinformatics
Unit


Review of genetics


Review/introduction of statistical analyses
and concepts


Introduce QTL analysis


Introduce bioinformatic tools


Review/introduction of molecular
techniques

QTL is good for detecting the approximate locus of multiple genes

affecting a phenotype across all the chromosomes, except Y.

This is a graph that displays the likelihood ratio statistic as a function


of locus on the various chromosomes, which are numbered at top.

Clickable
genes!

For Gabarap, Dr.G’s gene.

Bioinformatics Tools


GeneNetwork
--
performs a QTL analysis and
reveals LRS as a function of markers on
chromosomes


UCSC Genome Browser
--

reveals known genes
over a given region of chromosome
--
also gives
--
also provides relative expression levels of genes
via “gene chips”


Allen Brain Atlas

provides in situ hybridization
data
--
cell layers & cells in which gene is
expressed.


Entrez gene
--
sequence of given genes including
coding sequence.


PubMed
--
articles about given genes (& other
stuff)


Pedagogical Objectives

Bioinformatics/Neuroinformatics
Unit


Review of genetics


Review/introduction of statistical analyses
and concepts


Introduce QTL analysis


Introduce bioinformatic tools


Review/introduction of molecular
techniques


This is a PCR product for a marker


or Locus (remember Quantitative Trait Loci?)

it is a small stretch of DNA that is

different between the two different strains.

A marker can be in any aspect of the DNA:


gene, promoter, expressed sequence,

intron, “junk,” etc., etc..





For Gabarap, Dr.G’s gene.

Gene Chips DNA microarrays


Can use fluorescent cDNAs (mRNAs or
RNAs) as probes.


Yield the
pattern

of gene expression across
2 different conditions.


Can examine many (thousands) of genes at
once.


Does not give cell
-
by
-
cell resolution.

In situ hybridization


Finding out which cell(s) express a gene by
probing for mRNA.


Probes to mRNA can be made of antisense DNA
or RNA (sense is control).


Probes are labeled.


Probes hybridize with specific mRNAs being
made in a cell.


Can only examine expression of one or two genes
at a time.