The document describes using PLINK genetic analysis software to explore genetic risk factors for progressive supranuclear palsy (PSP). It analyzes 191 PSP tissue samples to test if SNPs associated with PSP risk influence expression of nearby genes. Linear regression identified 3 genes - MOBP, DUSP10, and SLCO1A2 - with significant expression differences based on minor allele. MOBP showed increased expression, while DUSP10 and SLCO1A2 showed decreased expression. This provides evidence that these genes may play a role in PSP and warrants further functional study.

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Exploring Genetic Risk-Factors for
Progressive Supranuclear Palsy
(PSP) Using PLINK Genetic Analysis
Software
Travis Ballard

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Acknowledgements
• Dr. Dru Roycik
• Ertekin-Taner Lab
• Nilufer Ertekin-Taner
• Mariet Allen
• Minerva Carrasquillo
• Sarah Lincoln
• Kim Malphrus
• Thuy Nguyen
• Jeremy Burgess
• Aurelie N’Songo

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Overview
• PSP: Background and Genetics
• Project Setup and Hypothesis
• Sample Collection
• PLINK Software: Background and Application
• Results
• Discussion

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PSP: Background
• ≈20,000 subjects in USA (1 in 100,000 people older
than 60)
• Symptoms: Inability to focus eyes, loss of balance,
slowed movements, etc.
• Pathology: neurofibrillary tangles (NFTs) of
hyperphosphorylated tau proteins in neurons.
• Average age at onset: ≈ 68 years
• Most PSP patients become dependent in 3-4 years and
die within 5-8 years of diagnosis
• Importance of PSP research
• Tauopathies: Alzheimer’s, Frontotemporal Dementia,
etc.

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PSP: Genetics
• MAPT gene (Chr 17): H1 Haplotype shown to strongly associate
with PSP
• Genome-Wide Association study (GWAS) identifies an additional 6
genetic loci: Hoglinger. G et al, Nature Genetics, 2011.
• Identified 3 significant loci (SNPs) (p<5x10-8): :) STX6 (Chr1),
EIF2AK3 (Chr2) and MOBP (Chr3).
• Identified 3 suggestive loci (SNPs) (5.7x10-7≥P>5x10-8): 1q41
intergenic locus, BMS1 (Chr10), SLCO1A2 (Chr12).
• GWAS - SNPs (Single Nucleotide Polymorphisms):
• A commonly occurring DNA sequence variation (1 nucleotide)
seen in human populations
• Used as genetic markers
• Knowledge gap: what is the function of the identified SNP(s) and
affected gene?

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Hypothesis
• Hypothesis: To see if common variants (SNPs
identified by Hoglinger et al) that associate with
PSP risk do so through influencing expression
of nearby gene(s).
• Previous studies provide evidence, but it has
been inconsistent
• Zou. F et al, PLoS Genetics, 2012.
• Ferrari et al, Neurobiology of Disease, 2014.
• Hoglinger et al, Nature Genetics, 2011.

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Sample Collection and Measuring
Expression
• 191 Temporal Cortex PSP Tissue Samples: RNA
extracted from tissue.
• Mayo Clinic Brain Bank (Dr. Dennis Dickson)
• WG-DASL microarray (Illumina Inc), >24,000 probes, >
18,000 unique genes.
• SNP genotypes from PSP GWAS using these samples:
Hoglinger et al.
serendip.brynmawr.edu

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Experiment Setup Chromosome SNP Gene Probe
1 rs1411478
STX6 ILMN_1777915
STX6 ILMN 2157951
MR1 ILMN_2167416
2 rs7571971
TEX37 ILMN_1774219
EIF2AK3 ILMN_1724984
BC046476
RPIA ILMN_1714809
3 rs1768208
SLC25A38 ILMN_1781231
RPSA ILMN_1664910
RPSA ILMN_2411723
SNORA6 ILMN_3245365
SNORA62 ILMN_1700074
MOBP ILMN_1750271
MOBP ILMN_1768947
MOBP ILMN_2298464
MOBP ILMN_2414962
1 rs6687758
DUSP10 ILMN_1759175
DUSP10 IlMN_1759488
DUSP10 ILMN_2401873
DUSP10 ILMN_2401878
LOC101929771
10 rs2142991 BMS1 ILMN_1772713
12 rs11568563
SLCO1A2 ILMN_1656097
SLCO1A2 ILMN_1720727
SLCO1A2 ILMN_1806979
SLCO1A2 ILMN_2381020
IAPP ILMN_1679527
• 6 SNPs: Identified from
Hoglinger et al. that
associate with PSP risk
• Chromosome: The
chromosome where the
SNPs are found
• 16 Genes: Genes found
to be “in cis” (+/- 100kb)
from the SNPs of
interest
• Found using UCSC Genome
Bioinformatics Website
• 25 Probes: 50 bp probes
found to anneal with
RNA sequences from
TC tissue samples
• Illumina (WG-DASL)

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PLINK Software: Background
• Open Source Software Package
• Genome Analysis Toolset
• Linear regression to test gene expression levels
• Requirements:
• Source Files (.map and .ped)
• Covar File (.txt)
• Pheno File (.txt)

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PLINK Software: Covariate (Covar File)
and Phenotype (Pheno File)
• Covariate: a separate independent variable that
can affect the outcome, and therefore must be
controlled for in the analysis
• Covariates in Analysis: Age (at death), Sex,
Plate Number, RIN, RINsqadj
• Phenotype: observable characteristics
• Phenotypes in Analysis: RNA expression levels

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Covar and Pheno Files
ID ID Age Sex PLATE1 PLATE2 PLATE3 RIN RINsqAdj
69 0 0 1 0 7.7 0.1296
70 0 0 1 0 6.7 0.4096
74 0 0 0 1 7.8 0.2116
82 1 0 0 1 8.2 0.7396
73 0 0 1 0 9.2 3.4596
69 0 0 0 1 7.1 0.0576
75 1 0 0 1 7.6 0.0676
83 1 0 1 0 8.3 0.9216
74 0 0 0 1 7.1 0.0576
78 1 0 0 1 6.7 0.4096
NPID IID ILMN_2157951 ILMN_2167416 ILMN_1664910 ILMN_1750271 ILMN_1768947 ILMN_1781231 ILMN_2298464
15.7397934 13.2321708 11.7584582 15.9580168 15.2491192 14.5503862 14.4902412
15.5289177 13.204756 11.6217994 15.9758974 14.9968709 14.4209033 14.5123251
15.629838 12.3850761 11.2715722 15.9069542 15.7140688 14.9485737 14.3859536
15.76936 12.9322205 11.5469332 16.0113726 15.5636609 14.9310719 15.2289481
15.4934428 13.3845384 12.3805212 15.3161377 13.9524526 14.2260739 10.0128804
15.4777566 12.5442412 9.657677 15.8857631 14.7365809 15.0998402 13.9225059
15.536933 12.711772 10.4334592 16.0390272 15.2043414 14.9364464 15.1720413
15.4872322 12.5512534 11.2909865 15.9714389 15.3352613 14.6407763 15.5255146
15.0215872 14.0594668 11.5670533 15.817371 14.9313739 14.4859635 8.82559472
15.5338693 12.6217641 10.3772529 15.8428141 14.6125317 14.6898065 14.0931556
Covar File Example
Pheno File Example

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Linear Regression Code
• --plink
• --file PSP_stage1_hg19_496_Clean_13115
• --pheno Master Pheno File__020515.txt
• --covar Covar_ForPSP200Array_020415.txt
• --hide-covar
• --all-pheno
• --linear
• --ci 0.95
• --out PSPeQTL_020515

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Results Chromosome SNP Minor Allele Gene Probe P Beta
1 rs1411478 A
STX6 ILMN_1777915 0.2122 -0.01381
STX6 ILMN 2157951 0.6351 -0.00537
MR1 ILMN_2167416 0.2454 -0.04438
2 rs7571971 T
TEX37 ILMN_1774219 0.3048 -0.04496
EIF2AK3 ILMN_1724984 0.09547 0.05047
BC046476
RPIA ILMN_1714809 0.9177 0.004761
3 rs1768208 T
SLC25A38 ILMN_1781231 0.4769 -0.01439
RPSA ILMN_1664910 0.2178 0.08442
RPSA ILMN_2411723 0.1058 -0.03033
SNORA6 ILMN_3245365 0.5924 0.02499
SNORA62 ILMN_1700074 0.5792 0.01356
MOBP ILMN_1750271 0.5937 -0.01062
MOBP ILMN_1768947 0.9868 0.000823
MOBP ILMN_2298464 0.002852 0.3734
MOBP ILMN_2414962 0.3841 0.05417
1 rs6687758 G
DUSP10 ILMN_1759175 0.08173 -0.1077
DUSP10 IlMN_1759488 0.1003 -0.1182
DUSP10 ILMN_2401873 0.09112 -0.09944
DUSP10 ILMN_2401878 0.01264 -0.1718
LOC101929771
10 rs2142991 C BMS1 ILMN_1772713 0.7285 0.007044
12 rs11568563 G
SLCO1A2 ILMN_1656097 0.7055 -0.02321
SLCO1A2 ILMN_1720727 0.4149 0.1271
SLCO1A2 ILMN_1806979 0.7414 0.03812
SLCO1A2 ILMN_2381020 6.32E-13 -0.8932
IAPP ILMN_1679527 0.1552 0.0859
• 3 Genes show
statistically significant
expression values
• MOBP,
DUSP10, and
SLCO1A2
• MOBP exhibits
increased expression
in subjects with the
minor allele
• DUSP10 and
SLCO1A2 exhibit
decreased expression
in subjects with the
minor allele

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Results contd.
MOBP SLCO1A2 DUSP10
T=Minor Allele
GG: N=26
GT: N=86
TT: N=75
G=Minor Allele
TT: N=158
TG: N=25
GG: N=4
G=Minor Allele
AA: N=117
AG: N=61
GG: N=9

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Discussion

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Discussion and Conclusions
• MOBP and SLCO1A2 may play role in PSP
• DUSP10’s role is more ambiguous
• Dr. Taner’s lab is pursuing functional studies of
MOBP and SLCO1A2
• Answering hypothesis

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Questions?

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