1. Partitioning of genomic variance using prior biological information
Acknowledgements:
Supervisor
Per – implemented AI-REML in DMU; core element in analysis.
Aim:
Next slide: Introduce complex traits.
Partitioning of genomic variance using prior biological information
Stefan McKinnon Edwards, Per Madsen & Peter Sørensen

2. Aim
"Develop an integrative genomics approach for evaluating the joint contribution of scattered genetic variants."

3. Complex traits Just explain what is shown, left to right:
Mendelian traits: rinkled peas, blood type, dry or wet earwax
Infinitesimal model: Used in linear mixed models, assume all genetic variants are the same.
Complex traits are regulated by interaction of multiple genes. Contribution to complex traits from many genetic variants, not just one.
Complex traits are somewhere between two extremes.
(Examples: obesity, height, susceptibility to infections.)
Next slide: Example of interaction of genes: KEGG pathway

4. KEGG pathway: Immune System
Pathway from Kyoto Encyclopedia of Genes and Genomes on Bovine genome.
Collection of several pathways. Each pathway is basically a set of genes that can be associated to a proces.
These pathways are scattered throughout the genome. Contribution from almost all LD blocks.
Next slide: Models

5. Linear mixed models 𝐲=𝐠+𝐞 𝐲=𝐠1+𝐠2+𝐞 Empirical statistics:
Simple model – null hypothesis, and Partitioned model
LMM assumes normality (we don't know) and uncorrelated groups (can see are not).
A partition is tested against the null hypothesis, using likelihood ratio. But these are not necessarily chi-squared distributed.
We derive empirical statistics for testing with large suite of random gene sets.
Next slide: Data, then results
𝐲=𝐠+𝐞
𝐲=𝐠1+𝐠2+𝐞
Empirical statistics:
Likelihood ratio (LR)
Proportion of explained genetic variance

6. Data 4,497 genotyped bulls. 637,951 markers per bull.
Mastitis (udder infections in first lactation period) in this presentation.
Next slide: Results
4,497 genotyped bulls.
637,951 markers per bull.
Health traits derived from daughters.

7. Results: Random gene sets
Summary of tested KEGG pathways against random gene sets.
x-axis: No. of SNPs
y-axis:
Colours. LR95 is 95% percentile of likelihood ratios.
Lines
Some KEGG pathways are significant on both tests, others only one.
Immune System (8274 SNPs), collectively contributes no more than random gene set.
Next slide: Summary of pathways in several traits.

8. Results: Significant KEGG pathways
Pathways that pass both LR test and explain more than random gene sets.
Immune System has large variability to combat pathogens.
Health traits share a pathway in Excretory System that explains a lot.
Fat yield has causative quantitative trait nucleotide in DGAT1.
4 pathways that explain most in fat yield contains this gene.
Proportions are not additive; gene sets are overlapping.

9. Conclusions 10-12% of pathways significant
150 pathways; pathways found in each trait.
10-12% of pathways significant
Results can be plugged into genetic prediction framework
Opened the black box