# Presentation: 372844

## Selecting a Subset of Markers to Carry Forward 

**Nancy J. Cox, Ph.D.**
**The University of Chicago**

- Follow-up to GWAs

## Overview

**Nitty-gritty decisions on thresholds of all types**
**Primary analyses beyond the SNPs genotyped on your platform**
**Additional information to consider in choosing SNPs**
**External (linkage signals, other validation)**
**Internal (bioinformatics)**

## Nitty-Gritty Decisions on Thresholds of All Types

**Set thresholds prior to analysis, or use quality flags in prioritizing SNPs for follow up?**
**Minor allele frequencies?**
**HWE controls? HWE cases (for departures not consistent with a genetic model)?**

## Thresholds Before or Flags After Analysis?

**Using quality flags has the advantage of preserving FLS (funny-looking signals) that may be real, and/or CNVs**
**Under-appreciated challenge of this approach is confusion in sharing data (what the “top” 100, 1000, etc signals are depends on how you weight the flags)**

## Follow Up Limited by Cost? Time? Samples? Variant type?

**Testing UNtyped Alleles (TUNA) or imputation approaches may eliminate need for follow up of “additional” SNPs within original samples, but CNV follow up may be different**
**Number of SNPs for follow up samples may be predetermined, or significance or FDR threshold may be predetermined, or decided afterward**

## Overview

**Nitty-gritty decisions on thresholds of all types**
**Primary analyses beyond the SNPs genotyped on your platform**
**Additional information to consider in choosing SNPs**
**External (linkage signals, other validation)**
**Internal (bioinformatics)**

## TUNA Nicolae

** ****Key ingredients are multi-locus measure of LD and reference sample (HapMap)**

## Slide 8

| Haplotype | T-A1-A2-A3 |  Frequency |
| --- | --- | --- |
| H1 |  0 - 0 - 0 - 0 | 0.058 |
| 
H2 | 
 1 - 0 - 1 - 0 | 
0.350 |
|  |  |  |
| H3 |  1 - 1 - 0 - 1 | 0.575 |
|  |  |  |
| H4 |  1 - 0 - 0 - 1 | 0.017 |

## Advantages    Disadvantages

**Can utilize existing information on LD and haplotypes in HapMap data**
**No arbitrary block definitions**
**1 df test for each known variant with specified “uniqueness”**

**In silico comparisons require biological validation**
**Cannot capture all of the information from variation not yet known**

## TUNA

**For high-density screens, can be used for in silico follow-up**
**Set low threshold and TUNA “type” every SNP in the vicinity of a signal to decide which to actually type**
**Can convert lower density screens to higher density**
**Really useful in enabling comparisons of studies across disparate platforms**

## TUNA

**For each untyped SNP, determine if that SNP provides sufficiently unique information to interrogate (r****2****<0.7 to a typed SNP);**
**For each of those SNPs identify, using all combinations of 2-, 3- and 4-locus haplotypes comprised of platform SNPs within 100Kb on each side, the smallest subset able to interrogate genotypes with sufficient accuracy; multi-locus R****2**** value > threshold (0.7 to 1.0)**

## TUNA

**Primary template need be derived only once for each high-throughput SNP set and each HapMap release (but may choose to optimize for the set of SNPs passing QC for any given study)**
**Deriving template takes a few hours on our local cluster; conducting full analyses, given template takes less than 1 hour**

##  Affymetrix 100K Set 

| HapMap
Sample | SNPs Typed | r2>.7 | r2<.7 Md>.7 | Residual |
| --- | --- | --- | --- | --- |
| CEU | 95482 | 813533 | 208697 | 1405338 |
| CHB+JPT | 90814 | 717922 | 161625 | 1413278 |
| YRI | 94682 | 428106 | 171014 | 2123772 |

** ****Affymetrix 100K Set **

## Affymetrix 500K Platform

| HapMap
Sample | SNPs Typed | r2>.7 | r2<.7 Md>.7 | Residual |
| --- | --- | --- | --- | --- |
| CEU | 419780 | 1393622 | 189643 |  553877 |
| CHB+JPT | 400882 | 1289371 | 162067 |  567398 |
| YRI | 452247 |  968909 | 335162 | 1099764 |

**Affymetrix 500K Platform**

## Illumina 317K Platform

| HapMap
Sample | SNPs Typed | r2>.7 | r2<.7 Md>.7 | Residual |
| --- | --- | --- | --- | --- |
| CEU | 306784 | 1549401 | 256098 |  443847 |
| CHB+JPT | 278170 | 1313317 | 248813 |  578423 |
| YRI | 276142 |  784412 | 418378 | 1375858 |

**Illumina 317K Platform**

## TUNA

**With TUNA, biased or inaccurate estimators of reference (HapMap) frequencies affects only the power, not the type I error**
**May be of particular value in samples, such as the Mexican Americans, which may not be well-represented in reference samples**

## Slide 17

## Slide 18

## Slide 19

## Overview

**Nitty-gritty decisions on thresholds of all types**
**Primary analyses beyond the SNPs genotyped on your platform**
**Additional information to consider in choosing SNPs**
**External (linkage signals, other validation)**
**Internal (bioinformatics)**

## “External” Information for Prioritizing SNPs

**Cases from some of the studies come from families used in prior linkage mapping studies; or linkage mapping information available**
**Other GWAs on the same or related phenotypes can add substantially to strategies for prioritizing**

## MODY Genes and Pancreatic Beta-cell Function - A Transcriptional Regulatory Network

**HNF-6**

**HNF-3******

**HNF-4******

**MODY1**

**HNF-1******

**HNF-1******

**IPF-1**

**MODY5**

**MODY3**
**T2DM**

**MODY4**
**PNDM1**
**T2DM**

**Glucokinase**
**MODY2**
**PNDM2**

**Insulin**
**Familial hyperinsulinemia/**
**hyperproinsulinemia**

**Islet-1**
**MODY?**

**NeuroD1**
**MODY6**

**Target genes**
**Glycolysis (Aldolase B, L-**
**     ****pyruvate kinase)**
**Mitochondrial function (UCP2)**
**Genes that control ********-cell**
**    ****replication and apoptosis**

**GLUT2**
**Fanconi-Bickel**
**syndrome**

**Insulin receptor**
**Diabetes**

**Islet-brain-1**
**Diabetes**

**Pax4**
**MODY?**

**HNF-4******

**Nkx2.2**
**Nkx6.1**

**Amylin**
**T2DM**

**Neurogenin3**
**NeuroD4**

## Slide 23

## “Internal” Information for Prioritizing SNPs

**Downstream bioinformatics analysis (pathway, network theory, etc.) requires input of GENEs**
**Signals come as SNPs or CNVs**
**NEED BETTER ANNOTATIONS FOR SNP => GENE CONVERSION**

## Better SNP and CNV Annotation

**Physical annotations (coding synonymous, nonsynonymous, intronic, within 2 kb of gene, between genes)**
**LD relationships to local genes**
**Expression phenotype information**
**Need measurement of how well each gene is interrogated directly or indirectly by your platform for downstream bioinformatics**

## Functional Patterns

**genes associated at ****p≤10****-3**

| Biological Processes (N=18,484) | T2D GWA | 100K platform | P-value |
| --- | --- | --- | --- |
| Cell adhesion | 3.7% | 1.7% | .0010 |
| Neuronal Activities | 4.1% | 2.0% | .0014 |
| Developmental Processes | 10.7% | 7.7% | .0177 |
| Immunity and defense | 1.2% | 4.8% | .0002 |
|  |  |  |  |
| Molecular Functions (N=12,454) | T2D GWA | 100K platform | P-value |
| Cell adhesion molecule | 4.1% | 1.6% | .001 |
| Nucleic acid binding | 11.7% | 8.3% | .033 |
| Proteases | 2.5% | 1.2% | .039 |
| Defense/immunity protein | 0.3% | 1.8% | .041 |
|  |  |  |  |
| Pathways (N=3,730) | T2D GWA | 100K platform | P-value |
| VEGF signaling | 9.3% | 1.3% | 1.7x10-15 |
| Endothelin signaling | 5.6% | 1.7% | 4.2x10-4 |
| p53 pathway feedback loops 2 | 3.7% | 1.6% | 0.04 |

## EACC

- NJC1

- TCW4

## EACC

- NJC1

- TCW4

## EACC

- NJC1

- TCW4

| SNP | Physical | Class |  |  |  |
| --- | --- | --- | --- | --- | --- |
| rs2311458 | NJC1 | intron |  |  |  |

## EACC

- NJC1

- TCW4

| SNP | Physical | Class | LD R2>.9 |  |  |
| --- | --- | --- | --- | --- | --- |
| rs2311458 | NJC1 | intron | NJC1
EACC |  |  |

## EACC

- NJC1

- TCW4

| SNP | Physical | Class | LD | Global
 | Local |
| --- | --- | --- | --- | --- | --- |
| rs2311458 | NJC1 | intron | NJC1
EACC | NAH1
10-9
MAH2
10-6
EACC
10-6 | 5/22  .04
NJC1
1/35  10-6
EACC
18/18 
TCW4 |

## rs2311458

**Obvious Gene Annotation**
**NJC1**

**Alternative/Additional Gene Annotations**
**EACC**
**NAH1**
**MAH2**

**SNP**

## SCAN

**SNP CNV ANnotation**

## Colleagues and Collaborators

**University of Chicago**
**	****Nancy Cox Lab – Geoffrey Hayes, Anna Pluzhnikov, Cheri Roe, Piper Below, Anuar Konkashbaev, Ying Sun**
**	****Dept. of Medicine – Graeme Bell**
**	****Dept. of Human Genetics – Mark Abney, Anna Di Rienzo, Carole Ober, Jonathan Pritchard**
**	****Dept. of Statistics – Dan Nicolae****,**** Mary Sara McPeek, Matthew Stephens**
**University of Texas Health Sciences Center**
**	****Craig Hanis****,**** Eric Boerwinkle**

## Slide 35