Supplementary Note - Nature Research › original › nature-assets › ng › journal › v49...

Supplementary Note

GTEx Collaborators

Laboratory, Data Analysis & Coordinating Center (LDACC)—Analysis Working Group

François Aguet1, Kristin G. Ardlie1, Beryl B. Cummings1,2, Ellen T. Gelfand1, Gad Getz1,3, Kane Hadley1,

Robert E. Handsaker1,4, Katherine H. Huang1, Seva Kashin1,4, Konrad J. Karczewski1,2, Monkol Lek1,2,

Xiao Li1, Daniel G. MacArthur1,2, Jared L. Nedzel1, Duyen T. Nguyen1, Michael S. Noble1, Ayellet V.

Segrè1, Casandra A. Trowbridge1, Taru Tukiainen1,2

Statistical Methods groups—Analysis Working Group

Nathan S. Abell5,6, Brunilda Balliu6, Ruth Barshir7, Omer Basha7, Alexis Battle8, Gireesh K. Bogu9,10,

Andrew Brown11,12,13, Christopher D. Brown14, Stephane E. Castel15,16, Lin S. Chen17, Colby Chiang18,

Donald F. Conrad19,20, Nancy J. Cox21, Farhan N. Damani8, Joe R. Davis5,6, Olivier Delaneau11,12,13,

Emmanouil T. Dermitzakis11,12,13, Barbara E. Engelhardt22, Eleazar Eskin23,24, Pedro G. Ferreira25,26,

Laure Frésard5,6, Eric R. Gamazon21,27,28, Diego Garrido‐Martín9,10, Ariel D.H. Gewirtz29, Genna

Gliner30, Michael J. Gloudemans5,6,31, Roderic Guigo9,10,32, Ira M. Hall18,19,33, Buhm Han34, Yuan He35,

Farhad Hormozdiari23, Cedric Howald11,12,13, Hae Kyung Im36, Brian Jo29, Eun Yong Kang23, Yungil Kim8,

Sarah Kim‐Hellmuth15,16, Tuuli Lappalainen15,16, Gen Li37, Xin Li6, Boxiang Liu5,6,38, Serghei Mangul23,

Mark I. McCarthy39,40,41, Ian C. McDowell42, Pejman Mohammadi15,16, Jean Monlong9,10,43, Stephen B.

Montgomery5,6, Manuel Muñoz‐Aguirre9,10,44, Anne W. Ndungu39, Dan L. Nicolae36,45,46, Andrew B.

Nobel47,48, Meritxell Oliva36,49, Halit Ongen11,12,13, John J. Palowitch47, Nikolaos Panousis11,12,13,

Panagiotis Papasaikas9,10, YoSon Park14, Princy Parsana8, Anthony J. Payne39, Christine B. Peterson50,

Jie Quan51, Ferran Reverter9,10,52, Chiara Sabatti53,54, Ashis Saha8, Michael Sammeth55, Alexandra J.

Scott18, Andrey A. Shabalin56, Reza Sodaei9,10, Matthew Stephens45,46, Barbara E. Stranger36,49,57,

Benjamin J. Strober35, Jae Hoon Sul58, Emily K. Tsang6,31, Sarah Urbut46, Martijn van de Bunt39,40, Gao

Wang46, Xiaoquan Wen59, Fred A. Wright60, Hualin S. Xi51, Esti Yeger‐Lotem7,61, Zachary Zappala5,6,

Judith B. Zaugg62, Yi‐Hui Zhou60

Enhancing GTEx (eGTEx) groups

Joshua M. Akey29.63, Daniel Bates64, Joanne Chan5, Lin S. Chen17, Melina Claussnitzer1,65,66, Kathryn

Demanelis17, Morgan Diegel64, Jennifer A. Doherty67, Andrew P. Feinberg35,68,69,70, Marian S.

Fernando36,49, Jessica Halow64, Kasper D. Hansen68,71,72, Eric Haugen64, Peter F. Hickey72, Lei Hou1,73,

Farzana Jasmine17, Ruiqi Jian5, Lihua Jiang5, Audra Johnson64, Rajinder Kaul64, Manolis Kellis1,73,

Muhammad G. Kibriya17, Kristen Lee64, Jin Billy Li5, Qin Li5, Xiao Li5, Jessica Lin5,74, Shin Lin5,75, Sandra

Linder5,6, Caroline Linke36,49, Yaping Liu1,73, Matthew T. Maurano76, Benoit Molinie1, Stephen B.

Montgomery5,6, Jemma Nelson64, Fidencio J. Neri64, Meritxell Oliva36,49, Yongjin Park1,64, Brandon L.

Pierce17, Nicola J. Rinaldi1,73, Lindsay F. Rizzardi68, Richard Sandstrom64, Andrew Skol36,49,57, Kevin S.

Smith5,6, Michael P. Snyder5, John Stamatoyannopoulos64,74,77, Barbara E. Stranger36,49,57, Hua Tang5,

Emily K. Tsang6,31, Li Wang1, Meng Wang5, Nicholas Van Wittenberghe1, Fan Wu36,49, Rui Zhang5

NIH Common Fund

Concepcion R. Nierras78

Nature Genetics: doi:10.1038/ng.3981

NIH/NCI

Philip A. Branton79, Latarsha J. Carithers79,80, Ping Guan79, Helen M. Moore79, Abhi Rao79, Jimmie B.

Vaught79

NIH/NHGRI

Sarah E. Gould81, Nicole C. Lockart81, Casey Martin81, Jeffery P. Struewing81, Simona Volpi81

NIH/NIMH

Anjene M. Addington82, Susan E. Koester82

NIH/NIDA

A. Roger Little83

Biospecimen Collection Source Site—NDRI

Lori E. Brigham84, Richard Hasz85, Marcus Hunter86, Christopher Johns87, Mark Johnson88, Gene

Kopen89, William F. Leinweber89, John T. Lonsdale89, Alisa McDonald89, Bernadette Mestichelli89,

Kevin Myer86, Brian Roe86, Michael Salvatore89, Saboor Shad89, Jeffrey A. Thomas89, Gary Walters88,

Michael Washington88, Joseph Wheeler87

Biospecimen Collection Source Site—RPCI

Jason Bridge90, Barbara A. Foster91, Bryan M. Gillard91, Ellen Karasik91, Rachna Kumar91, Mark

Miklos90, Michael T. Moser91

Biospecimen Core Resource—VARI

Scott D. Jewell92, Robert G. Montroy92, Daniel C. Rohrer92, Dana R. Valley92

Brain Bank Repository—University of Miami Brain Endowment Bank

David A. Davis93, Deborah C. Mash93

Leidos Biomedical—Project Management

Anita H. Undale94, Anna M. Smith95, David E. Tabor95, Nancy V. Roche95, Jeffrey A. McLean95, Negin

Vatanian95, Karna L. Robinson95, Leslie Sobin95, Mary E. Barcus96, Kimberly M. Valentino95, Liqun Qi95,

Steven Hunter95, Pushpa Hariharan95, Shilpi Singh95, Ki Sung Um95, Takunda Matose95, Maria M.

Tomaszewski95

ELSI Study

Laura K. Barker97, Maghboeba Mosavel98, Laura A. Siminoff97, Heather M. Traino97

Genome Browser Data Integration & Visualization—EBI

Paul Flicek99, Thomas Juettemann99, Magali Ruffier99, Dan Sheppard99, Kieron Taylor99, Stephen J,

Trevanion99, Daniel R. Zerbino99


Genome Browser Data Integration & Visualization—UCSC Genomics Institute, University of

California Santa Cruz

Brian Craft100, Mary Goldman100, Maximilian Haeussler100, W. James Kent100, Christopher M. Lee100

Benedict Paten100, Kate R. Rosenbloom100, John Vivian100, Jingchun Zhu100

AFFILIATIONS

1The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge,

MA 02142, USA. 2Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston,

MA 02114, USA. 3Massachusetts General Hospital Cancer Center and Dept. of Pathology,

Massachusetts General Hospital, Boston, MA 02114, USA 4Department of Genetics, Harvard Medical

School, Boston, MA 02114, USA 5Department of Genetics, Stanford University, Stanford, CA 94305,

USA. 6Department of Pathology, Stanford University, Stanford, CA 94305, USA. 7Department of

Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben‐Gurion University of the

Negev, Beer‐Sheva 84105, Israel. 8Department of Computer Science, Johns Hopkins University,

Baltimore, MD 21218, USA. 9Centre for Genomic Regulation (CRG), The Barcelona Institute for

Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain 10Universitat Pompeu Fabra (UPF),

Barcelona, Spain 11Department of Genetic Medicine and Development, University of Geneva Medical

School, 1211 Geneva, Switzerland. 12Institute for Genetics and Genomics in Geneva (iG3), University

of Geneva, 1211 Geneva, Switzerland. 13Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland. 14Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA

19104, USA. 15New York Genome Center, New York, NY 10013, USA. 16Department of Systems

Biology, Columbia University Medical Center, New York, NY 10032, USA. 17Department of Public

Health Sciences, The University of Chicago, Chicago, IL 60637, USA. 18McDonnell Genome Institute,

Washington University School of Medicine, St. Louis, MO 63108, USA. 19Department of Genetics,

Washington University School of Medicine, St. Louis, MO 63108, USA. 20Department of Pathology &

Immunology, Washington University School of Medicine, St. Louis, MO 63108, USA. 21Division of

Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN

37232, USA. 22Department of Computer Science, Center for Statistics and Machine Learning,

Princeton University, Princeton, NJ 08540, USA. 23Department of Computer Science, University of

California, Los Angeles, CA 90095, USA. 24Department of Human Genetics, University of California,

Los Angeles, CA 90095, USA. 25Instituto de Investigação e Inovação em Saúde (i3S), Universidade do

Porto, 4200‐135 Porto, Portugal. 26Institute of Molecular Pathology and Immunology (IPATIMUP),

University of Porto, 4200‐625 Porto, Portugal. 27Department of Clinical Epidemiology, Biostatistics

and Bioinformatics, Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The

Netherlands. 28Department of Psychiatry, Academic Medical Center, University of Amsterdam, 1105

AZ Amsterdam, The Netherlands. 29Lewis Sigler Institute, Princeton University, Princeton, NJ 08540,

USA. 30Department of Operations Research and Financial Engineering, Princeton University,

Princeton, NJ 08540, USA. 31Biomedical Informatics Program, Stanford University, Stanford, CA

94305, USA. 32Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), 08003 Barcelona, Spain. 33Department of Medicine, Washington University School of Medicine, St. Louis, MO 63108, USA. 34Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical

Center, Seoul 138‐736, South Korea. 35Department of Biomedical Engineering, Johns Hopkins

University, Baltimore, MD 21218, USA. 36Section of Genetic Medicine, Department of Medicine, The

University of Chicago, Chicago, IL 60637, USA. 37Department of Biostatistics, Mailman School of

Public Health, Columbia University, New York, NY 10032, USA. 38Department of Biology, Stanford

University, Stanford, CA 94305, USA. 38Wellcome Trust Centre for Human Genetics, Nuffield

Department of Medicine, University of Oxford, Oxford, OX3 7BN, UK. 40Oxford Centre for Diabetes,


Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, OX3 7LE, UK. 41Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LJ, UK. 42Computational

Biology & Bioinformatics Graduate Program, Duke University, Durham, NC 27708, USA. 43Human

Genetics Department, McGill University, Montreal, Quebec H3A 0G1, Canada. 44Departament

d'Estadística i Investigació Operativa, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain. 45Department of Statistics, The University of Chicago, Chicago, IL 60637, USA. 46Department of

Human Genetics, The University of Chicago, Chicago, IL 60637, USA. 47Department of Statistics and

Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA. 48Department of

Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. 49Institute for Genomics and

Systems Biology, The University of Chicago, Chicago, IL 60637, USA.50Department of Biostatistics, The

University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA. 51Computational Sciences, Pfizer Inc, Cambridge, MA 02139, USA. 52Universitat de Barcelona, 08028

Barcelona, Catalonia, Spain. 53Department of Biomedical Data Science, Stanford University, Stanford,

CA 94305, USA. 54Department of Statistics, Stanford University, Stanford, CA 94305, USA. 55Institute

of Biophysics Carlos Chagas Filho (IBCCF), Federal University of Rio de Janeiro (UFRJ), 21941902 Rio

de Janeiro, Brazil. 56Department of Psychiatry, University of Utah, Salt Lake City, UT 84108, USA 57Center for Data Intensive Science, The University of Chicago, Chicago, IL 60637, USA. 58Department

of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, USA. 59Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA. 60Bioinformatics

Research Center and Departments of Statistics and Biological Sciences, North Carolina State

University, Raleigh, NC 27695, USA. 61National Institute for Biotechnology in the Negev, Beer‐Sheva,

84105 Israel. 62European Molecular Biology Laboratory, 69117 Heidelberg, Germany. 63Department

of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA 64Altius Institute

for Biomedical Sciences, Seattle, WA 98121, USA. 65Beth Israel Deaconess Medical Center Harvard

Medical School, Boston, MA 02215, USA. 66University of Hohenheim, 70599 Stuttgart, Germany. 67Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, Salt

Lake City, Utah 84112‐5550, USA. 68Center for Epigenetics, Johns Hopkins University School of

Medicine, Baltimore, MD 21205, USA. 69Department of Medicine, Johns Hopkins University School of

Medicine, Baltimore, MD 21205, USA. 70Department of Mental Health, Johns Hopkins University

School of Public Health, Baltimore, MD 21205, USA. 71McKusick‐Nathans Institute of Genetic

Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA. 72Department of

Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA. 73Computer Science and Artificial

Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. 74Department of Medicine, University of Washington, Seattle, WA 98195, USA. 75Division of

Cardiology, University of Washington, Seattle, WA 98195, USA. 76Institute for Systems Genetics, New

York University Langone Medical Center, New York, New York 10016, USA. 77Department of Genome

Sciences, University of Washington, Seattle, WA 98195, USA. 78Office of Strategic Coordination,

Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, NIH,

Rockville, MD 20852, USA. 79Biorepositories and Biospecimen Research Branch, Division of Cancer

Treatment and Diagnosis, National Cancer Institute, Bethesda, MD 20892, USA. 80National Institute

of Dental and Craniofacial Research, Bethesda, MD 20892, USA. 81Division of Genomic Medicine,

National Human Genome Research Institute, Rockville, MD 20852, USA. 82Division of Neuroscience

and Basic Behavioral Science, National Institute of Mental Health, NIH, Bethesda, MD 20892, USA. 83Division of Neuroscience and Behavior, National Institute on Drug Abuse, NIH, Bethesda, MD

20892, USA. 84Washington Regional Transplant Community, Falls Church, VA 22003, USA. 85Gift of

Life Donor Program, Philadelphia, PA 19103, USA. 86LifeGift, Houston, TX 77055, USA. 87Center for

Organ Recovery and Education, Pittsburgh, PA 15238, USA. 88LifeNet Health, Virginia Beach, VA

23453, USA. 89National Disease Research Interchange, Philadelphia, PA 19103, USA. 90Unyts, Buffalo,


NY 14203, USA. 91Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, NY 14263,

USA. 92Van Andel Research Institute, Grand Rapids, MI 49503, USA. 93Brain Endowment Bank, Miller

School of Medicine, University of Miami, Miami, FL 33136, USA. 94National Institute of Allergy and

Infectious Diseases, NIH, Rockville, MD 20852, USA. 95Biospecimen Research Group, Clinical Research

Directorate, Leidos Biomedical Research, Inc., Rockville, MD 20852, USA. 96Leidos Biomedical

Research, Inc., Frederick, MD 21701, USA. 97Temple University, Philadelphia, PA 19122, USA. 98Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University,

Richmond, VA 23298, USA. 99European Molecular Biology Laboratory, European Bioinformatics

Institute, Hinxton CB10 1SD, UK. 100UCSC Genomics Institute, University of California Santa Cruz,

Santa Cruz, CA 95064, USA.


Supplementary Figures

Supplementary Figure 1 – GTEx sample sizes in each of the tissues.

Adi

pose

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Supplementary Figure 2 – The RTC algorithm. Here x represents variants in the region, n is the number of variants in the region Q is the phenotype, and the orange peaks are recombination hotspots.

Regulatory Trait Concordance (RTC) Score

1. For xi .. xn:• Correct Q for Xi (linear regression) ‐> pseudo‐phenotype XiQ

(residuals)

2. For xi .. xn:• Calculate p‐value for eQTL‐XiQ association

3. Sort (decreasing) p‐values eQTL‐Xi .. nQ and find the rankof eQTL‐xGWASQ

4. RTC = (n ‐ RankGWAS)/ n

xi . . . . . . . . . . . . . . . . . . . . . . . . . . . xn

GWAS eQTL

Q


Supplementary Figure 3 – Simulation scheme when two variants are tagging different functional effects (H0). Here x represents variants in the region, n is the number of variants in the region Qp is the pseudo phenotype generated based on the true eQTL in the region, and the orange peaks are recombination hotspots.

Supplementary Figure 4 ‐ Simulation scheme when two variants are tagging the same functional effects (H1). Here x represents variants in the region, n is the number of variants in the region Qp is the pseudo phenotype generated based on the true eQTL in the region, and the orange peaks are recombination hotspots.

• Calculate RTC for T2‐T1Q and assume T2,causal as the causal eQTL variant andT1,causal as the causal GWAS variant.

• For each simulation iteration with N total iterat ions:

1. Select random T1,causal and T2,causal in the region with a true eQTL (T1,causal ≠T2,causal )

2. Find randomly selected T1 which is linked to T1,casual with r2 ≥ 0.5. Do the same

for T2 and T2,causal. (T1 ≠ T1,causal ≠ T2 ≠ T2,causal)

3. Generate a pseudo phenotype for T2,causal based on the β and intercept of thetrue eQTL and randomly distributed residuals of the true eQTL.

4. Rerun RTC with T1‐T2Qp

xi . . . . . . . . . . . . . . . . . . . . . . . . . . . xn

T1 T2

Qp

T1,causal T2, causal

RTC Simulation Scheme H0 (not‐shared)

r2 ≥ 0.5r2 ≥ 0.5

RTC Simulation Scheme H1(shared)

• Calculate RTC for T2‐T1Q and assume Tcausal as the casual eQTL variant.

• For each simulation iteration with N total iterat ions:

1. Select random and T2,causal random in the region with a true eQTL

2. Find randomly selected T1 which is linked to Tcausal with r2 ≥ 0.5. Do the samefor T2 and Tcausal. (T1 ≠ T2 ≠ Tcausal)

3. Generate a pseudo phenotype for Tcausal based on the β and intercept of thetrue eQTL and randomly distributed residuals of the true eQTL.

4. Rerun RTC with T1‐T2Qp

xi . . . . . . . . . . . . . . . . . . . . . . . . . . . xn

T1 T2

Qp

Tcausal

r2 ≥ 0.5

r2 ≥ 0.5


Supplementary Figure 5 – Conversion of RTC into a probability. We use the distributions generated under H0 and H1 with the RTC value (here as an example 0.9 to convert RTC into a probability. The π1 statistic is calculated from the p‐value distribution of eQTL effects of the GWAS variants. We take a range centred around the real RTC score, here 0.9, and find the proportion of values that overlap with this range under H0 and H1. Using these proportions, the π1 estimate, and Bayes’ theorem we calculate a probability of sharing.

| 0.9 =0.9 ∗ 1

0.9 ∗ 0 0.9 ∗ 1

H0 (Not‐shared) H1 (Shared)

RTC RTC


Supplementary Figure 6 – Number of independent signals per tissue (FDR = 5%). On the y‐axis total number independent eQTLs per tissue is plotted and the x‐axis represents the number of significant eQTL genes per tissue.

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Adipose − Subcutaneous

Adipose − Visceral (Omentum)

Adrenal Gland

Artery − Aorta

Artery − Coronary

Artery − Tibial

Brain − Anterior cingulate cortex (BA24)

Brain − Caudate (basal ganglia)

Brain − Cerebellar Hemisphere

Brain − Cerebellum

Brain − Cortex

Brain − Frontal Cortex (BA9)

Brain − Hippocampus

Brain − Hypothalamus

Brain − Nucleus accumbens (basal ganglia)

Brain − Putamen (basal ganglia)

Breast − Mammary Tissue

Cells − EBV−transformed lymphocytes

Cells − Transformed fibroblasts

Colon − Sigmoid

Colon − Transverse

Esophagus − Gastroesophageal Junction

Esophagus − Mucosa

Esophagus − Muscularis

Heart − Atrial Appendage

Heart − Left Ventricle

Liver

Lung

Muscle − Skeletal

Nerve − Tibial

Ovary

Pancreas

Pituitary

Prostate

Skin − Not Sun Exposed (Sup rapubic)

Skin − Sun Exposed (Lower leg)

Small Intestine − Terminal Ileum

Spleen

Stomach

Testis

Thyroid

Uterus

Vagina

Whole Blood

5000

10000

2500 5000 7500 10000eGenes

Inde

pend

ent

sign

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

Number of tissues an eQTL is active inWhole_Blood FDR = 5%, common genes

Number of tissues an eQTL is active in

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Top 10 tissues with most sharing forWhole_Blood FDR = 5%, common genes

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Lung

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Nerve

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Esoph

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_Mus

cular

is

Artery

_Tibi

al

3 4 5 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

Number of tissues an eQTL is active inOvary FDR = 5%, common genes


0

50

100

150

Top 10 tissues with most sharing forOvary FDR = 5%, common genes

Pro

port

ion

of s

harin

g0.0

0.2

0.4

0.6

0.8

Uteru

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Vagina

Colon

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Tissue

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Gastro

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is

Stom

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Nerve

_Tibial

Adipo

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ous

3 4 5 6 7 8 9 10

11

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44

Number of tissues an eQTL is active inBrain_Cortex FDR = 5%, common genes


0

50

100

150

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Top 10 tissues with most sharing forBrain_Cortex FDR = 5%, common genes

Pro

port

ion

of s

harin

g

0.0

0.2

0.4

0.6

0.8

Brain_

Front

al_C

orte

x_BA9

rain_

Anter

ior_c

ingula

te_c

orte

x_BA24

Brain_

Cauda

te_b

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Nucleus

_acc

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ns_b

asal_g

angli

a

Brain_P

utam

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asal_g

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ia

Brain

_Hipp

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Brain_H

ypot

halam

us

Brain_C

ereb

ellum

Brain

_Cer

ebell

ar_H

emisp

here

Pituita

ry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

Number of tissues an eQTL is active inArtery_Tibial FDR = 5%, common genes


0

50

100

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200

250

Top 10 tissues with most sharing forArtery_Tibial FDR = 5%, common genes

Pro

port

ion

of s

hari

ng

0.0

0.2

0.4

0.6

Artery

_Aor

ta

Adipo

se_S

ubcu

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ous

Nerve

_Tibi

al

Esoph

agus

_Mus

cular

is

opha

gus_

Gastro

esop

hage

al_Ju

nctio

n

Artery

_Cor

onar

y

Breas

t_M

amm

ary_

Tissue

Lung

Thyro

id

Adipos

e_Visc

eral_O

men

tum

Supplementary Figure 7 – Further examples of tissue sharing. The left column displays the number of tissues eQTLs are active in for whole blood, ovary, brain cortex, and tibial artery, and the right column of histograms are the 10 ten tissues that these tissue are similar with.


Supplementary Figure 8 – The distribution of probability of sharing between the null GWAS variants and the eQTLs in all tissues merged. When compared to the distribution of the same metric in real GWAS hits in Figure 3b, we see that the enrichment of high probability of sharing is absent in this distribution.

Distrubution of null GWAS probabilities

P(shared)

Fre

qu

en

cy

0.0 0.2 0.4 0.6 0.8 1.0

01

00

00

20

00

03

00

00

40

00

0


Supplementary Figure 9 – ROC curve of r2 for causal links found by RTC. Here RTC score of >= 0.9 is taken as a true causal link between the two variants4, and the true positive and false positive rates of different r2 thresholds is plotted as the grey line where different r2 thresholds are labelled alongside the line.

Supplementary Figure 10 – The distribution of median LD in regions where RTC is high and r2 is low versus regions where both RTC and r2 are high.

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

R−squared ROC curve

False positive rate

Tru

e p

osi

tive

rate

0

0.1

0.2

0.4

0.5

0.7

0.8

0.9

1

RTC >= 0.9 when R2 < 0.1 RTC >= 0.9 when R2 > 0.9

0.0

0.2

0.4

0.6

0.8

1.0

Distributions of median LD in regions

Me

dia

n R

2 in

the

reg

ion

Mann−WhitneyP <2e−16


Supplementary Figure 11 – Correlation between RTC P(Shared) and the equivalent statistic calculated by COLOC. The red line represents the regression line.

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

P(Shared) RTC vs. H4 COLOCrho = 0.734 p = 3.9e−10

P(Shared) RTC

H4

CO

LO

C


Supplementary Figure 12 – Distribution of COLOC probabilities for cases where there is a high RTC sharing probability but a low COLOC sharing probability. COLOC requires summary statistics for both the eQTL and the GWAS effects for all the variants, so it follows that we need to intersect two lists of variants. In doing so, mainly due to different MAF thresholds used in eQTL (5%) and GWAS (1%) discovery, we lose some of the variants in a given region. COLOC calculates probabilities for five hypotheses, no eQTL effect, no GWAS effect, no eQTL and GWAS effect, shared effects, and independent effects, whereas RTC assumes the two effects are present in the region. In some regions, the most significant GWAS effect is lost (due to the intersection with eQTLs) which in turn results in COLOC calculating a high probability of no GWAS effect in the region hence decreasing the sharing probability.

No associationBoth

No associationGWAS

No associationeQTL Different causal Shared causal

0.0

0.2

0.4

0.6

0.8

Cases where RTC P(Shared) >= 0.9 and COLOC H4 <= 0.8

COLOC posterior probabilities

Pro

ba

bili

ty


Supplementary Figure 13 – The correlation between sharing probabilities calculated by RTC and COLOC in the simulation study. Correlation under both (a), null (b), and alternate (c) hypotheses. Null hypothesis refers to cases where the GWAS and the eQTLs are independent effects whereas the alternate is when it is a shared effect.

Supplementary Figure 14 – ROCs for RTC and COLOC based on the simulations. Calculated by varying the probability of sharing threshold at which two variants are called as tagging the same functional effect. False positive rates are calculated from the null simulation (what proportion of the null probabilities would have been called mistakenly as shared given a threshold) and the true positive rates from the alternate simulations (what proportion of the alternate probabilities would have been correctly called as shared given a threshold). Values next to the curves are the probability of sharing thresholds.

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Allrho = 0.801 p = 1.5e−115

RTC

CO

LOC

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Nullrho = 0.263 p = 2e−05

RTC

CO

LOC

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Alternaterho = 0.482 p = 2.5e−16

RTC

CO

LOC

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Receiver operating curve for RTC and COLOCSharing probability threshold >= 0.9

RTC sen = 0.91 spe = 0.95COLOC sen = 0.66 spe = 1

False positive rate

Tru

e p

osi

tive

rate

07.0 6.0 5.0 4.0 3.0 2.0 1.00.8

0.9

1

0

0.10.2

0.30.4

0.5

0.60.7

0.8

0.9

1

RTC

COLOC

a) b) c)


Supplementary Figure 15 – Effect of number of discoveries per tissue on the GWAS tissue enrichment score. (a) Plotted is the distribution of ‐log10 Bonferroni corrected p‐values of the correlation between enrichment score and numbers of eQTLs discovered per tissue, before and after normalization. The red line corresponds to the 0.05 significance threshold. The 11 diseases that still show a significant correlation after the normalization is listed. (b) Distributions of the r2 values giving rise to the p‐values in (a), before and after normalization.

Supplementary Figure 16 – Number of times a tissue is the most enriched for a GWAS trait.

Before After

05

10

15

20

P−value of correlation between eQTL count and probabilitiesBefore and after normalization (Min 5 GWAS in 50% of tissues)

−lo

g1

0(p

−va

lue

) B

onf

err

on

i co

rre

cte

d

Schizophrenia

Breast_cancer

Parkinson_s_disease

Urinary_metabolites__H_NM

Homocysteine_levels

Mann−Whitneyp = 1.86e−18

Before After

0.0

0.2

0.4

0.6

0.8

r^2 between eQTL count and probabilitiesBefore and after normalization (Min 5 GWAS in 50% of tissues)

r^2

Number of times a tissue is the top tissue for all GWAS traits

Top

tiss

ue

co

un

tfo

r a

ll G

WA

S t

raits

0

10

20

30

40

50

60

70

Liver

Smal

l_In

testi

ne_T

erm

inal

_Ile

um

Pituita

ry

Brain_A

nter

ior_

cingu

late

_cor

tex_

BA24

Skin_N

ot_S

un_E

xpos

ed_S

upra

pubi

c

Splee

n

Brain

_Hyp

otha

lamus

Who

le_B

lood

Adipo

se_S

ubcu

tane

ous

Adren

al_G

land

Thyro

id

Esoph

agus

_Muc

osa

Esoph

agus

_Mus

cular

is

Testi

s

Artery

_Cor

onar

y

Artery

_Tib

ial

Cells_

EBV.tra

nsfo

rmed

_lym

phoc

ytes

Cells_

Tran

sform

ed_f

ibrob

lasts

Colon

_Sig

moi

d

Brain

_Cer

ebel

lar_

Hemisp

here

Brain

_Hip

poca

mpu

s

Heart_

Left_

Vent

ricle

Lung

Vagin

a

Brain

_Fro

ntal

_Cor

tex_

BA9

Mus

cle_S

kele

tal

Uteru

s

Pancr

eas

Prosta

te

Brain

_Cau

date

_bas

al_g

angl

ia

Brain

_Put

amen

_bas

al_g

angl

ia

Esoph

agus

_Gas

troes

opha

geal

_Jun

ction

Skin_S

un_E

xpos

ed_L

ower

_leg

Artery

_Aor

ta

Brain

_Cer

ebellu

m

Adipo

se_V

iscer

al_O

men

tum

Brain

_Cor

tex

Nerve

_Tibi

al

Breas

t_M

amm

ary_

Tissue

Colon

_Tra

nsve

rse

Heart_

Atrial_A

ppen

dage

Ovary

Brain

_Nuc

leus

_acc

umbe

ns_b

asal

_gan

glia

Stom

ach


Supplementary Figure 17 – Distribution of the tissue causality score for CAD and lipid measurements, in tissues that identify SORT1 as the causal gene for the 1p23 locus vs. the tissues that do not. Tissues that correctly identify SORT1 contribute significantly (p = 0.0007) higher to the genetic causality of these complex phenotypes than the ones that do not.

Tissues that identify SORT1 Tissues that identify another gene

24

68

Tissue causality enrichment distributions for CAD and lipid measurements

Fo

ld e

nric

hm

en

tove

r th

enu

ll

Mann−Whitneyp = 0.000397


Supplementary Figure 19 – Tissue sharing of eQTLs with RTC. Here tissue sharing results from skeletal muscle eQTLs with coronary artery eQTLs is shown as an example. (a) The distribution of tissue sharing RTC scores, showing an enrichment for high RTC values indicating high degree of sharing between these tissues. (b) LD between the two variants (r2) vs. the RTC score. (c) RTC score vs. the calculated probability of sharing. The red points represent significant eQTLs in either skeletal muscle or coronary artery, whereas the grey points are for eQTLs significant in other tissues.

Distribution of RTCMuscle_Skeletal_in_Artery_Coronary

RTC

Fre

quen

cy

0.0 0.2 0.4 0.6 0.8 1.0

010

0020

0030

0040

0050

00

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

r^2 vs. RTC

r^2

RT

C

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

RTC vs. P(Shared|RTC)Muscle_Skeletal_in_Artery_Coronary

RTC

P(S

hare

d|R

TC

)

a) b) c)

Supplementary Figure 18 – Distribution of simulated RTC values. Under the null H0 (a) and under the alternative H1 hypotheses (b). As expected under H0 simulated RTC values are uniform and under H1 there is an enrichment of high RTC values.

Distribution of H0 RTC values (All regions)All tissues merged

RTC

0e+

001e

+06

2e+

063e

+06

4e+

06

0.0 0.2 0.4 0.6 0.8 1.0

Distribution of H1 RTC values (All regions)All tissues merged

RTC0e

+00

1e+

072e

+07

3e+

074e

+07

5e+

07

0.0 0.2 0.4 0.6 0.8 1.0

b)a)


Supplementary Note - Nature Research › original › nature-assets › ng › journal › v49...

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Transcript of Supplementary Note - Nature Research › original › nature-assets › ng › journal › v49...