1. Genomic Analysis: GWAS

2. Genetic Markers
Genetic marker – a locus used to identify a chromosome or locate other genes on a genetic map
Many different types including SNPs, VNTRs (microsatellites), RFLPs, etc.

3. Genetic Markers: SNPs Single Nucleotide Polymorphisms
Polymorphic single bases
Four possible states at any single base in a genome
Usually only two are observed, ancestral and variant
Advantages –
Low mutation rate (stable)
High abundance (every bases)
Easy to type
Disadvantages –
Rate heterogeneity
Ascertainment bias
Low information content

4. Genetic Markers: VNTRs
Short tandem repeats, microsatellites, STRs
Mononucleotide, dinucleotide, trinucleotide, etc.
Allele lengths are variable ((TA)3, (TTAA)12, (AGT)33, etc.)
Many possible variants in a population
Most often occur in non-coding regions
Advantages –
Low ascertainment bias
Easy to identify
Highly informative
Disadvantages –
High mutation frequency
Complex mutation behavior
Difficult to automate genotyping

5. SNPs: Single nucleotide polymorphisms
Responsible for 90% of all human genetic variation
~12,000,000 documented SNPs in the NCBI database
Categorized as coding (in an exon) or noncoding (the majority)
Coding SNPs can be synonymous or nonsynonymous
Most SNPs are completely neutral
Often used as markers for pinpointing disease causing polymorphisms

6. Finding ‘phenotypic’ SNPs
Many genes
~25,000 genes, many can be candidates, many may contribute to particular phenotypes
Many SNPs
~12,000,000 SNPs, ability to predict functional SNPs is limited
Methods to select candidate SNPs (narrow  broad):
Only functional SNPs in a candidate gene
Systematic screen of SNPs in a candidate gene
Systematic screen of SNPs in an entire metabolic pathway
Systematic screen for all coding changes (exome screening)

7. Genomic Medicine Exome sequencing
Exome – the coding sequences of all annotated protein coding genes; ~1% of the genome
Accomplished via target-capture methods
What’s the major potential drawback?

8. Genomic Medicine
First application of exome sequencing to syndrome with unknown cause
Miller syndrome – thought to be recessive
Suggests that effected individuals require two variants (one on each chromosome)
Exomes of four individuals sequenced including a pair of siblings
Narrowed to a single gene, DHODH, dihydroorotate dehydrogenase, biosynthesis of pyrimidines
All individuals harbored compound heterozygous mutations for missense mutations
All parents were carriers
Ng et al. 2010, Nature Genetics 42, 30-35

9. Finding ‘responsible’ SNPs in an ocean of variation
Many genes
~25,000 genes, many can be candidates, many may contribute to particular phenotypes
Many SNPs
~12,000,000 SNPs, ability to predict functional SNPs is limited
Methods to select candidate SNPs (narrow  broad):
Only functional SNPs in a candidate gene
Systematic screen of SNPs in a candidate gene
Systematic screen of SNPs in an entire pathway
Systematic screen for all coding changes
Genome-wide screen (GWAS)

10. Introduction to genomic analysis
A genome-wide association study (GWAS) is an approach that involves rapidly scanning markers across the complete sets of DNA, or genomes, of many people to find genetic variations associated with a particular phenotype.
Once associations are identified,
develop better strategies to detect, treat and prevent the disease.
find genetic variations that contribute to common, complex diseases, such as asthma, cancer, diabetes, heart disease and mental illnesses.

11. Potential of GWAS
Whole genome information, when combined with epidemiological, clinical and other phenotype data, offers the potential for
increased understanding of basic biological processes affecting human health,
improvement in the prediction of disease and patient care,
the promise of personalized medicine.

12. Potential of GWAS

13. How to do GWAS What do you need?
The human genome reference
A map of human genetic variation
A set of technologies that can quickly and accurately analyze whole or partial (exome) samples for genetic variants
This is typically accomplished using low coverage genome (or exome) sequencing (4-20X)
A typically GWAS is based on a case-control design in which SNPs are genotyped across a population….

14. How to do GWAS
A typically GWAS is based on a case-control design in which SNPs are genotyped across a population….
And the strength of association between each SNP and the disease in question is calculated

15. How to do GWAS
A typically GWAS is based on a case-control design in which SNPs are genotyped across a population….
And the strength of association between each SNP and the disease in question is calculated
Usually visualized via a Manhattan plot in which SNPS from each chromosome are plotted along with their association value

16. The basic idea The A allele is associated (4/14, 29%)
with individuals exhibiting the disease phenotype
The basic idea G G G A A G A A A A G G A A A G G A G G G G G G G G

17. Age-related macular degeneration
Study cohort – 2172 unrelated individuals of European descent, at least 60 years old
1238 with AMD, 934 controls
Each individual harbors two alleles
2476 AMD alleles
1868 non-AMD alleles
Null hypothesis – Alleles will be randomly distributed in the population, i.e. no association of any alleles with AMD
Alternative hypothesis – Some allele will be positively associated with AMD

18. Age-related macular degeneration
Single SNP identified by GWAS, rs
4344 alleles recovered, two variants C/T
X2 test suggests association, p=1.2 x 10-62
Allele
Cases with AMD
Controls
Total Alleles C
1522
670
2192 T
954
1198
2152
Total alleles
2476
1868
4344

19. Age-related macular degeneration
bin/hgTracks?db=hg38&lastVirtModeType=default&lastVirtModeExtraState= &virtModeType=default&virtMode=0&nonVirtPosition=&position=chr1%3A &hgsid= _LVSGMKr7pmucs4DrZ7CkYbaFVbni
;v=rs ;vdb=variation;vf=762175

20. Complex traits vs. Mendelian traits
Traits for which a molecular cause is known (2002)
Complex trait – any phenotype that does not exhibit classic Mendelian inheritance attributable to a single locus
Although these traits may exhibit familial tendencies
Why would a trait be non-Mendelian?
Codominance, incomplete dominance
Multiple alleles
Polygenic characteristics
Environmental effects

21. GWAS in practice (2007)

22. GWAS in practice

23. GWAS in practice

25. GWAS in practice

26. GWAS in practice

27. GWAS in practice (2014)

28. Post-GWAS: Finding the causal locus
GWAS is really just a starting point – it typically narrows it the causal region down to a few million or a few hundred thousand bp
SNPs occur every bp
If the locus is narrowed down to 500,000 bp, that’s ~2500 SNPs
One way to proceed – identify genes in the region and determine plausibility
Location of SNPs and function of the locus
Relatively easy in some cases
Not so much in others (regulatory function?)
Functional annotation databases exist to classify genes according to roles in the cell

29. Association signals in the IL23R gene region on chromosome 1p31
Association signals in the IL23R gene region on chromosome 1p31. (A) Genomic locations of genes on chromosome 1p31 between 67,260,000 and 67,580,000 base pairs (Build 35). (B) The negative log10 association P-values (Cochran-Mantel-Haenszel chi-square test) from the combined Jewish and non-Jewish case-control cohorts are plotted for genotyped markers in the region.

30. GWAS is promising
Many diseases and traits are influenced by genetic factors
i.e., they are caused by sequence variants in the genome
Over 12 millions SNPs are known in the genome
i.e., some SNPs will be directly or indirectly associated with causal variants
The cost of SNP Genotyping is reduced
i.e., it is affordable to genotype a large number of SNPs in the genome
Large numbers of cases and controls are available
i.e., there is statistical power to detect variants with modest effect

31. GWAS is challenging
Many diseases and traits are influenced by genetic factors
But probably due to multiple modest risk variants
They confer a stronger risk when they interact
True associated SNPs are not necessary highly significant
Too many SNPs are evaluated
False positives due to multiple tests
Single studies tend to be underpowered
False negatives
Considerable heterogeneity among studies
Phenotypic and genetic heterogeneity
False positives due to population stratification
Xu, 2007

33. Components of a GWAS (simple)

34. Components of a GWAS (not so simple)