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GWAS and prior knowledge to uncover genegene interac7ons Marylyn D. Ritchie, PhD Director, Center for Systems Genomics The Pennsylvania State University Biochemistry and Molecular Biology July 18, 2013

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Page 1: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

GWAS  and  prior  knowledge  to  uncover  gene-­‐gene  interac7ons  

Marylyn  D.  Ritchie,  PhD  Director,  Center  for  Systems  Genomics  

The  Pennsylvania  State  University  Biochemistry  and  Molecular  Biology  

July  18,  2013  

Page 2: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

As  of  7/9/2013,  the  catalog  includes  1,654  publica7ons  and  10,976  SNPs.  

Page 3: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Distribu7on  of  Effects  

1.2          1.4        1.6        1.8        2.0        2.2        2.4    

Median  =  1.28  

Courtesy  of  Teri  Manolio  

Mostly  1ny  effects  

Page 4: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Moore  and  Williams.  Am  J  Hum  Genet.  2009;  85(3):  309–320  

Distribu7on  of  Effects  

Page 5: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Missing  Heritability  •  Under  our  nose  •  Out  of  sight  •  In  the  architecture  •  Underground  networks  •  Lost  in  diagnosis  •  The  great  beyond  

Maher,  B.  Nature  2008;  456:18-­‐21.  

Page 6: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Biology  is  complex  

Page 7: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

 Moore  and  Williams,  BioEssays  27:637–646,  2005  

Sta7s7cal  vs.  biological  epistasis  

Page 8: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

If  interac7ons  with  minimal  main  effects  are  the  norm  rather  than  the  excep7on,  can  we  analyze  all  possible  combina7ons  of  loci  with  tradi7onal  approaches  to  detect  purely  interac7on  effects  ?  

NO  

Page 9: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

SNP’s in each subset

Num

ber o

f Pos

sibl

e C

ombi

natio

ns

n  ~500,000  SNPs  to  span  the  genome  (HapMap)  

1 2 3 4 5

5 x 105

2 x 1016

1 x 1011

3 x 1021

2 x 1026

2 x 1026 combinations

* 1 combination per second

* 86400 seconds per day

---------

2.979536 x 1021 days to complete

(8.163113 x 1018 years)

How  many  combina7ons  are  there?  

Page 10: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

SNP’s in each subset

Num

ber o

f Pos

sibl

e C

ombi

natio

ns

n  ~500,000  SNPs  to  span  the  genome  (HapMap)  

1 2 3 4 5

5 x 105

2 x 1016

1 x 1011

3 x 1021

2 x 1026

2 x 1026 combinations

* 1 combination per second

* 86400 seconds per day

---------

2.979536 x 1021 days to complete

(8.163113 x 1018 years)

5 Million SNPs in current technology

# SNPs # models time** 1 SNP 5.00x106 5 sec 2 SNPs 1.25x1013 144 days 3 SNPs 2.08x1019 2.4x108 days

4 SNPs 2.60x1025 3.01x1014 days

5 SNPs 2.60x1031 3.01x1020 days

**assuming 1 CPU that performs 1 million tests per second

How  many  combina7ons  are  there?  

Page 11: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

5.47x1012 days

5 Million SNPs in current technology

# SNPs # models time** 1 SNP 5.00x106 5 sec 2 SNPs 1.25x1013 144 days 3 SNPs 2.08x1019 2.4x108 days

4 SNPs 2.60x1025 3.01x1014 days

5 SNPs 2.60x1031 3.01x1020 days

**assuming 1 CPU that performs 1 million tests per second

Page 12: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Epistasis  Analysis  in  GWAS  data  

•  Exhaus7ve  evalua7on  

•  Evaluate  interac7ons  in  top  hits  from  single-­‐SNP  analysis  

•  Use  prior  biological  knowledge  to  evaluate  specific  combina7ons  –  “Candidate  Epistasis”  

Bush  WS,  Dudek  SM,  Ritchie  MD.    Biofilter:  a  knowledge-­‐integra7on  system  for  the  mul7-­‐locus  analysis  of  genome-­‐wide  associa7on  studies.    Pacific  Symposium  on  Biocompu4ng,  368-­‐79  (2009).  

Page 13: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

•  Use publicly available databases to establish relationships between gene-products

•  Suggestions of biological epistasis between genes

•  Integrating information from the genome, transcriptome, and proteome into analysis

Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pacific Symposium on Biocomputing, 368-79 (2009).

The  Biofilter  

Page 14: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Bush WS, Dudek SM, Ritchie MD. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pacific Symposium on Biocomputing, 368-79 (2009).

LOKI:  Library  of  Knowledge  Integra7on  

Page 15: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

The  Biofilter  •  Method  described:  Bush  et  al.  2009  Pacific  Symposium  on  Biocompu4ng  

•  Applica7ons  –  Mul7ple  Sclerosis  

•  Bush  et  al.  2009  ASHG  talk,  2011  Genes  &  Immunity  –  HDL  

•  Turner  et  al.  2010  ASHG  Talk,  2011  PLoS  ONE  –  HIV  Pharmacogenomics  

•  Grady  et  al.  2010  ASHG  poster,  2011  Pacific  Symposium  on  Biocompu4ng  –  Lipid  traits  

•  Holzinger  et  al.  in  prepara7on  –  BMI  

•  Verma  et  al.,  in  prepara7on  –  Cataracts  

•  Hall  et  al.,  in  prepara7on  

Page 16: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Using  Biofilter:  GWAS  Annota7on  Are  there  biological  rela7onships  between  significant  results?  

Page 17: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Using  Biofilter:  Priori7zing  Analysis  Is  there  epistasis  in  genes  whose  products  interact  either  directly  

or  through  a  metabolic  intermediate?      

Page 18: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Using  Biofilter:  Priori7zing  Analysis  Is  there  epistasis  between  genes  of  two  related  pathways?    

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Candidate  Approaches  Pros  

•  Smaller  set  of  genes  to  explore  •  Fewer  sta7s7cal  tests  •  Results  will  have  solid  

interpreta7ons  

Cons  •  Limited  by  current  state  of  

knowledge  •  Limita7ons  of  learning  completely  

novel  biology  

Page 20: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

§ 930  trio  families  from  US  and  UK  (IMSGC)  § Genotyped  on  Affymetrix  500K  array  

§ Post  QC  ~300,000  SNPs  Figure 1

Page 21: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

§  eMERGE  Genome-­‐wide  associa7on  study  (Illumina  660)  §  Phenotype:  median  HDL  for  anyone  having  2+  HDL  

measurements  in  their  EMR  §  Marshfield  PMRP  n=3903  §  Vanderbilt  BioVU  n=1858  

Page 22: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Peripheral Cell Lipid

Source

ABCA1

FC

CE

FC CELCAT

Peripheral Cell Lipid

Destination

LIPCTGàFFA

LIPGPLàFFA

LPLTGàFFA

TG

CE

CETP

HepatobiliaryElimination

Page 23: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

1)  SNPs  from  GWAS  catalog  for  a  par7cular  disease-­‐trait  associa7on  

3)  SNPs  from  KEGG,  Reactome,  or  Netpath  linked  to  SNPs  from  GWAS  Catalog  in  LOKI  

4)  Exhaus7ve  SNP-­‐SNP  models  

Future  Direc7ons  

2)  Map  SNPs  –>  gene        -­‐>  pathway  using  Biofilter  

SNP1  –  SNP2  SNP1  –  SNP3  SNP1  –  SNP4  SNP1  –  SNP5  

 .  .  .      

Page 24: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Summary  

•  Biofilter  is  a  bioinforma7cs  applica7on  to  annotate,  filter,  and  construct  gene-­‐gene  models  for  evalua7on  

•  We  have  successfully  used  Biofilter  in  a  number  of  genome-­‐wide  interac7on  analyses  to  iden7fy  replica7ng/confirmatory  gene-­‐gene  models  

•  The  GWAS  catalog  is  an  important  and  useful  public  database  incorporated  into  LOKI  –  the  knowledge  base  from  which  Biofilter  draws  its  informa7on  

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Future  Direc7ons  

•  Integrate  more  public  databases  into  LOKI  – Regulatory  regions  – Non-­‐coding  regions  

•  Develop  addi7onal  filtering  and  model  construc7on  strategies  based  on  specific  hypotheses  

•  Develop  a  user-­‐interface  for  ease  of  use  

Page 26: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

Acknowledgements  HDL  project  -­‐  eMERGE    MS  project  -­‐  IMSGC    

Ritchie  Lab  Greoa  Armstrong,  project  manager  Carrie  Buchanan  Moore,  MD/PhD  student*  Scoo  Dudek,  sorware  developer  Alex  Frase,  sorware  developer*  Molly  Hall,  PhD  student  Neerja  Ka7yar,  PhD  student*  Dokyoon  Kim  PhD,  Postdoctoral  fellow  Ruowang  Li,  PhD  student  Sarah  Pendergrass  PhD,  Research  Associate*  Anurag  Verma,  Bioinforma7cs  Programmer  Shefali  Verma,  Bioinforma7cs  Analyst  John  Wallace,  sorware  developer*  Dan  Wolfe,  bioinforma7cs  research  assistant*      

                 *  -­‐  working  on  Biofilter    

Page 27: GWAS and prior knowledge to uncover gene-gene interactionsSNP’s in each subset ! ~500,000%SNPs%to%span%the%genome%(HapMap) 1 2 3 4 5 5 x 105 2 x 1016 1 x 1011 3 x 1021 2 x 1026 2

www.gene7c-­‐programming.org  

• [email protected]  •  hop://ritchielab.com  

Just  because  we  have  not  found  it  yet,  doesn’t  mean  it’s  not  there…..