# Presentation: 016093

## Genomics and Population Studies

- Department of Genome Sciences
-        University of Washington
-     debnick@u.washington.edu

##           The Next Challenge

**          ****The Next Challenge**
**Understanding the link between -**
**DNA sequence                 Biology/Disease**
**   ****(Genotype)                    (Phenotype)**
**                       ****Environment**

**ATTCGCATGGACC**

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## Genomics - Lesson Learned

-  Large-scale projects - Drives technology development
-  	and feasibility
-  Collaborative projects - Many groups contributing
- 	to efforts
-  Data Sharing - Benefits to all - database mining of
- 	new information
-  New analysis tools and insights - Genes, Variation, 		Function

- Genome Sequences (basic code), HapMap and Structural Variation (differences),
- 	Encode (functional analysis)                Opportunities for all scientists  -
- 	Biology/Translation to Medicine

## Overview of Genomics and Population Studies

-  Genetic Analysis Strategies
-  What do we know about sequence variation in humans and status
- The HapMap and its impact on variation analysis
-  Implementation - Lots of new associations - The Big Wave is true!
-  How will identify valid associations? Replication, Replication, 		Replication - databases key
- Translational impact - diagnostics/prediction versus treatment
-  Identifying functional variation and new forms of variation
-  Whole genome sequencing coming

## Human Genetic Analysis

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**Multiple Genes with Small Contributions**
**       ****and Environmental Contexts **

**Variant(s) Common in the Population **

**Polymorphic Markers > 500,000 -1,000,000**
**Single Nucleotide Polymorphisms (SNPs)**

**Single Gene with Major Effect**

**Variant Rare in the Population**

**~600 Short Tandem Repeat Markers**

**Human Genetic Analysis**

- Families
- Linkage Studies

- Populations
- Association Studies

**Simple Inheritance (Segregate)**

**Complex Inheritance (Aggregate) **

## Total sequence variation in humans 

- Population size:	6x109  (diploid)
- Mutation rate:	2x10–8 per bp per generation
- Expected “hits”:	240 for each bp
- Every variant compatible with life exists in the population
- BUT: Most are vanishingly rare
- Compare 2 haploid genomes: 1 SNP per 1331 bp*

- *The International SNP Map Working Group, *Nature* **409**:928 - 933 (2001)

## SNPs in the Average Gene

- Average Gene Size -19 kb   ~ Compare 2 haploid - 1 in 1,000 bp
- ~100 SNPs  (200 bp) - 15,000,000 SNPs
- ~ 40 SNPs _>_ 0.05 MAF  (600 bp) - 6,000,000 SNPs
- ~ 5 coding SNPs (half change the amino acid sequence)

**Crawford et al  Ann Rev Genomics Hum Genet 2005;6:287-312**

## Finding SNPs:  Sequence-based SNP Mining

- RANDOM Sequence Overlap - SNP Discovery

- GTTACGCCAATACAG**G**ATCCAGGAGATTACC
- GTTACGCCAATACAG**C**ATCCAGGAGATTACC

- Genomic

- RRS
- Library

- Shotgun
- Overlap

- DNA
- SEQUENCING

- Random
- Shotgun

- Align to
- Reference

**> 11 Million SNPs**

**Notes:**
In order to answer this question we have to back up and revisit the approach used to find these SNPs.  HapMap Random Shotgun reads key point heer is that these are random process and the key is that by doing this these really only sample from a very small fraction inviduals or actually chromostomes and in actuality many of these SNPs are coming from the comparison obviously a minimum of two chromosoems.. - so what really does that mean for SNP disocvery.????  And

## {

- GTTACGCCAATACAG**G**ATCCAGGAGATTACC
- GTTACGCCAATACAG**C**ATCCAGGAGATTACC

- 2 chromosomes

**Notes:**
When we draw from two chromosomes, we can develop theoretical preductions of about what the fraction of SNPs you can discover using this method.  When using 2 chromosomes we actually can never discovery more than 50% of ao the most common SNPs - those occuring at 50% allele frequency.  But in truthfullness they were discovered in a few more chromosome than that but probably not much more than 8 chromosomes.  At that level you are able to ascertain nearly all of the very frequent polymorphisms (50% allele frequency), but a much lower percentage of the rare SNPs - in this case about 50% of the SNPs occurring at 10% frequency in the popualtion and this basically makes sense. In order to overcome the probability of seeing a rare SNps you would need to sample from more people.  BOTTOM LINE:  is that by using these large scale random approaches it isn’t possible to fully disover the entire spectrum of common and rare SNPs.

## HapMap Project:  Genotype validated SNPs in the dbSNP

**To produce a genome-wide map of common variation**
- Genotype 6 Million SNPs in Four populations in Two Phases:
-  CEPH (CEU) (Europe - n = 90, trios)
-  Yoruban (YRI) (Africa - n = 90, trios)
-  Japanese (JPT) (Asian - n = 45)
-  Chinese (HCB) (Asian - n =45)

**Nature 437: 1299-320, 2005**

**www.hapmap.org**

**Notes:**
The second objective of the hapMap project was to characterize SNP by genotyping in major representatative population from around the world.  The goal for this was to develop 600,000 SNPs genome-wide where they would have genotype each individual from four populations - Say something about trios - hapolotypes - you’ll be hearing more about that in the following talk from Dr. Crawford.

## Correlations among SNP 

- genotypes
- can simplify site selection
- for genotyping

## Variation in the Human IL1A Gene

-  18.5 kb
-  50 SNPs

**Carlson et al. (2004) **
**Am J Hum Genet. 74: 106-120.**

## Threshold LD: r2 
Bin 1: 22 sites
Bin 2: 18 sites
Bin 3: 5 sites
Genotype 1 SNP from each bin 
- TagSNP, chosen for biological intuition or ease of assay design 

**Bin 1: 22 sites**
**Bin 2: 18 sites**
**Bin 3: 5 sites**
- Genotype 1 SNP from each bin
- - TagSNP, chosen for biological intuition or ease of assay design

- New approaches for site selection -
- LDSelect

## Common Variants - LD (Association) Patterns

- All  SNPs

- SNPs > 10% MAF

**African-**
**American**

**European-**
**American**

## Genotyping Systems

**100,000 or 500,000 Quasi-Random SNPs**

**100,000, 317,000, 550,000, 650,000Y SNPs**

**Affymetrix **

**Illumina **

- A significant proportion of common SNPs can be captured

**1 Million Products are here and on the way!**

## Applying Genome Variation - Will it work? YES!!

**Hits:**
**Macular Degeneration, Obesity, Cardiac Repolarization,**
**Inflammatory Bowel Disease, Diabetes T1 and T2, Coronary Artery Disease.Rheumatoid Arthritis, Breast Cancer, Colon Cancer, ......**
**There are misses as well unclear why - Phenotype, Coverage,****	****Environmental Contexts?**
**Example of a miss - Hypertension **
**-There are lots more hits in these data sets - sample size, low ****	****proxy coverage with other SNPs .....**
**Analysis of associations between phenotype(s) and even individual sites is daunting and this will just be the first stage,**
**and this does even consider multi-site interactions.**

## Replication A Must

**Replication**
**Replication**
**Replication**

**Hirschhorn & Daly Nat. Genet. Rev. 6: 95, 2005**
**NCI-NHGRI Working Group on Replication Nature 447: 655, 2007**

## Genetic Studies

**Families**
**LINKAGE **

**Controls       Cases**** **
**ASSOCIATION **

** ****MODEL ORGANISMS **

## New Target Protein for Warfarin

****** ****-Carboxylase**
**(*****GGCX*****)**

**Clotting Factors**
**(FII, FVII, FIX, FX, Protein C/S/Z)**

-  Rost et al. & Li, et al., *Nature* (2004)

**Notes:**
And encoded for the gene now named VKORC1.   In this work by Rost et al that should the overt warfarin resistance was due to non-synonymous mutation in VKORC1 - that is patients needing doses at 25-50 mg/d had clear predisposing mutations.  Interestingly, NO nonsynomymous mutations were found in control chromosomes.

## Low

**VKORC1 SNPs and haplotypes show a strong association with warfarin dose**

**Low**

**High**

**Rieder et al N Engl J Med 352: 2285-93, 2005**

**SNP Function:  VKORC1 Expression**

- mechanism

- All SNPs non-coding but are present in evolutionarily conserved non-coding regions - mRNA expression is associated with warfarin dosing

## Associated SNPs can be diagnostic/predictive but finding functional SNPs to understand mechanism will take time but offers the promise of new therapies 

- ENCODE PROJECT - Identify the functional elements in the Human Genome - 1% now and soon all
- Nature 447: 799, 2007
- Transcriptional Regulatory Elements
- Expressed Sequences
- Chromatin Structure
- Replication
- Multi-species Conservation
- .......

## Structural Variation Project

- Types of Structural Variants
- Insertions/Deletions
- Inversions
- Duplications
- Translocations
- Size:
- Large-scale (>100 kb)
- intermediate-scale (500 bp–100 kb)
- Fine-scale (1–500 bp)

- Nature 447: 161-165, 2007

## ??

**allele frequency**

**HIGH**

**LOW**

**effect**
**size**

**WEAK**

**STRONG**

** ****LINKAGE**** **

** ****ASSOCIATION **

**??**

**Ardlie, Kruglyak &  Seielstad (2002) Nat. Genet. Rev. 3: 299-309**
**Zondervan & Cardon (2004) Nat. Genet. Rev. 5: 89-100 **

**Common Disease**
**Many Rare Variants**

## High Density Lipoprotein (HDL)

**High Density Lipoprotein (HDL)**

**Sequencing Known Candidate Genes for Functional Variation**
**From Individuals at the Tails of the Trait Distribution **

**Low HDL**

**High HDL**

**Individuals**

## ABCA1 and HDL-C

**Observed excess of rare, nonsynonymous variants in low HDL-C samples at ABCA1**
**Demonstrated functional relevance in cell culture**

**Cohen et al, Science **
** ****305, 869-872, 2004 **
**Many examples emerging**
**Common Disease**
**  ****Rare Variants**

## Personalized Human Genome Sequencing

- Solexa - an example

## Genomics - Summary

- New Insights in Variation - Types and Patterns
-        Structural Variation and Regions under Selection
- 	- Environmental Response and Immune Genes
- New Insights into function - ENCODE
- New Technologies - Genotyping and Sequencing
- 	Common and Rare Variation
- Common Interactive Projects that Share Data, Analysis 	Teams and Findings before Publication 		Worldwide