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Inference of the Genetic Architecture Underlying BMI and Hei

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by Jamie Walters on 1 March 2014

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Transcript of Inference of the Genetic Architecture Underlying BMI and Hei

Materials and Methods
Collected 20, 240 QISP's from five separate cohorts that had height, age, weight and gender measurements. (Data compiled as 300, 000 SNP genotypes).
All over the age of 18 and of European descent.
BMI and height adjusted for number of co-variants, including between-sex variance.
*LOD scores were used to assess the significance of associated markers with BMI and height phenotypes.
Look How Far We've Come!
Genetic function of DNA: "32P goes in, 35S stays out."
Hershey & Chase (1952)

Semi-Conservative Replication of DNA
Meselson & Stahl (1958)

Transposable elements: "Jumping Genes"
McClintock (1953)

Molecular Evolution: "Apes & Humans are 99% similar"
King & Wilson (1975)

"Cracking the Code"
Nirenberg et al. (1966)

DNA Fingerprinting
Gill et al. (1994)


A little about the authors
To Help You Understand…
Important terms
:
Polygenicity
Narrow-sense Heritability
Broad-sense Heritability
SNP Chip
Identity by descent/state (IBD/S)

The Down Low
Two Phenotypic Traits Were Analyzed
:
BMI (Obesity is a growing epidemic)
Height

Performed
: Assessed 20, 240 sibling pairs; SNP chips; and COMPLICATED MATH…

Goal
:
"To
infer the genetic architecture
of BMI and height through
calculating heritability, the relative contribution of rare and common variance and the extent to which these traits are polygenic


(Hermani et al., 2013)

Inference of the Genetic Architecture Underlying BMI and Height with the Use of 20, 240 Sibling Pairs
Presented by:
Allison Parrill
Greg Penney
Mykala Pardy
Jamie Walters
Nicole Peddle


Gibran Hemani

Received PhD in from the Roslin Institute, University of Edinburg, Scotland

. Microarray
- An ordered array of DNA fragments fixed to a solid support which serves as a probe
- To detect the presence of complimentary sequences
- Often used to assess the expression of genes in various tissues and under various conditions
(Pierce, 2013)
*SNP chip

Hermani, G. et al. (2013) Inference of he Genetic Arcitecture Underlying BMI and Height with the use of 20, 240 Sibling Pairs. American Society of Human Genetics 93,
865-875



Reference
QISP - each sibling pair treated as independent
5 cohorts: Queensland Institute of Medical Research, Framingham Heart Study, Swedish Twin Registries TWINGENE Data Set, Netherland Twin Registry, and UK Twin Registries TWINSUK Data Set
Calculation of BMI used
self-reported measures of weight and height
by means of standard formula:
Raw Weight Value (kg) /
Square of Raw Height (m^2)
Material and Methods Continued...
Estimates
How far does the apple fall from the tree. How alike are the apples that fall!??
Full Siblings or Dizygotic twins
Chromosome-wide identity by descent (IBD)

calculation
:
approximate integration over the marker-wise IBD status for all markers across the chromosome on the basis of their genetic position:
distance between marker i and i + 1
estimate of IBD status at marker i
Genome wide IBD estimate

for sib-pairs

genetic length of chromosome j
*two alleles are
IBD
if inherited from the
same ancestral allele
Phenotype
-
modeled with linear mixed models of the form, y=
X : vector of phenotype
Y : design matrix of fixed effects
Beta : estimate of fixed effects
g : vector of genetic values
w : vector of common environmental effects
e : vector of residual values
Implications
Polygenicity, IBD and IBS
BMI -
Heritability coefficients
Chromosomal location of highly associated markers
Heritability (narrow sense)
also calculated
numerator: var(genetic)
denominator: var (phenotypic)
Partitioning of heritability estimates across chromosomes
*Arabidopsis thaliana - 5 chromsomes
construction of genetic relationship matrices
comparison of sibpairs by chromosome IBD regions
from chromosome wide IBD-estimates
joint analysis of form, y=
Meta-analysis
(within-locus - VH method)

linkage analysis - excluded ~250 QISPS for having squared difference of sibling phenotypes greater than 4 SD outside of the mean
Effect of particular genetic position =
SE =
Individual genetic score:
genetic score for marker associated with phenotype
Individual (i)
SNP (j)
number of minor alleles (Xij)
rare effect model:
* "assumes single SNP is responsible for entire genetic variance within a family without occurring in any other individuals in the population"
non-genetic variance

Within-Family Analysis
Microarray Hybridization
"If a trait is heritable, then sibling pairs who have higher than average genome wide IBD will be phenotypically more similar to each other".
Points to consider
http://www.complextraitgenomics.com/
Phenotypic variance (of BMI/Height)
additive genetic
common environment
specific environment
Expect heritability contribution of a chromosome to be directly proportional its size
Results
h^2 (BMI) =
0.42
, SE = 0.17

h^2 (Height) =
0.69
, SE = 0.14
positive relationship between chromosome size and genetic variance for height and BMI.
lower inflation of test stats, for BMI than for height is consistent with fact that BMI has lower heritability.
Evidence that the observation of seemingly large effects in linkage studies, is actually more likely to be a property of a highly polygenic underlying architecture.
Heritability estimates
Quantile-quantile Plots
genomic inflation factors increase as sample size, heritability, and number of causal variants increase.
higher inflation = higher heritability
genomic inflation factor
(BMI) = 1.65 (lower h^2, higher polygeneicty)
genomic inflation factor
(Height) = 2.18 (higher h^2, lower polygenicity)

Heat Maps

(representation of matrix)
r = correlation coefficient of phenotypes
c^2 = effect of shared environment
LOD scores
* spikes = associated markers
ONE Significant BMI Marker on Chromosome 20
Two Significant Height Markers on Chromosome 5 and 15
ONLY SIGNIFICANT BY TRADITIONAL GENOME-WIDE THRESHOLD (3.0), and NOT SIGNIFICANT BY MORE STRINGENT THRESHOLD (3.3)

Linkage peaks are indeed genome-wide significant when tested against null model (Null = no genetic variation anywhere in the genome).
Research Areas
Cod genome project (CGP)
Close to home..
Psychiatric genetics
Disease genetics
Tumor genetics
*individual differences
Applications
Current Research Goals
Why BMI???..$$$
The difference between "oh that's genetic" and "Trait heritability".
Generally, media does not know the difference!!!!
Identity by Descent & State:
consanguineous vs dissimilar X chromosomes
50% of time IBD = IBS
(DH040)
Trait assignment to RS region
Hamer et al., 1993
LOD SCORES
logarithm of the odds
LOD score analysis, simplified
LOD score assignments:
high scores in RS region of Xq28
Pierce, B.A. (2013) Genetics Essentials: Concepts and Connections. Second Edition. W.H. Freeman and Company: New York.
Compare the genome-wide IBD
to understand the actual genetic proportion shared by descent
Dr. Peter Visscher
BMI
Height
Hamer, H. (1993). A Linkage Between DNA Markers on the X Chromosome and Male Sexual Orientation. Science, 261(5119): 321-7.
(Hamer et al., 2013)
(Hamer et al., 2013)
(Hamer et al., 1993 adapted by Carr, 2014)
http://www.osc.mun.ca/dl/wkshop/Dr._Marlies_Rise-Genome_Atlantic-Ocean_Centre-NL.pdf
Dr. Rise (MUN)
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