Applications of network theory to human population genetics:

from pathways to genotype networks

Giovanni Marco Dall'Olio

Pompeu Fabra University, Barcelona

Advisors: Jaume Bertranpetit and Hafid Laayouni

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Acknowledgments

● I would like to thank:– My PhD supervisors, Jaume Bertranpetit and Hafid

Laayouni

– My committee: Dr. Mauro Santos, Dr. Ricard Solé, Prof. Guido Barbujani, Dr. Ferran Casals, Dra. Yolanda Espinosa

– The Evolutionary Systems Biology group at UPF

– The Institut of Biologia Evolutiva

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Topics

● Context and motivations● My research:

– Annotating the N-Glycosylation pathway

– Pathway approach on the N-Glycosylation pathway

– The Genotype Network Approach

– The Human Selection Browser and Biostar

● Conclusions

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Context of the thesis

● The first anatomically modern humans appeared about 200,000 years ago

● How can we understand the signals of genetic adaptation in our genome, since then?

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Factors that influenced recent human evolution

Agriculture

DiseasesNew climates

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The opportunity

● We have access to large datasets of human sequences

● Better annotations on gene function and role

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Contributions

● Find applications of network theory to understand genetic adaptation in the human species

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Applications of network theory

● The Pathway approach

● The Genotype Network approach

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Topics

● Context and motivations● My research:

– Annotating the N-Glycosylation pathway

– Pathway approach on the N-Glycosylation pathway

– The Genotype Network Approach

– The Human Selection Browser and Biostar

● Conclusions

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The Pathway approach

● Genes are organized in pathways

● Any eventual selection constraint will be distributed among all the genes of a pathway

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Distribution of Selection forcesin a pathway

● Some positions of the pathway will be more likely to have stronger signals of selection

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Pathway Approach - outline

● Build a Network representation of a pathway

● Execute a test for positive selection on each gene

● Determine how the signals of selection are distributed on the network

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Pathway approach on the N-Glycosylation pathway

● Asparagine N-Glycosylation is a metabolic pathway for a type of protein modification

● The structure of this pathway is easy to represent as a network

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N-glycosylation - upstream part● Produces a single sugar called “N-Glycan precursor”● This sugar is required for the proper folding of most

membrane proteins

Adapted from Stanley, P., Schachter, H., & Taniguchi, N. (2009). N-Glycans. Essentials of Glycobiology.

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N-Glycosylation and protein folding

● The product of the upstream part of N-glycosylation is used as a signal to distinguish folded and unfolded proteins

Folded protein Un-Folded protein

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● Complex pathway composed by thousands of reactions

● Produces multiple glycans, important for cell-to-cell interactions

N-glycosylation - downstream part

Hossler, P., Mulukutla, B. C., & Hu, W.-S. (2007). Systems analysis of N-glycan processing in mammalian cells. PloS one, 2(1), e713. doi:10.1371/journal.pone.0000713

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Glycans on the cell surface

● The surface of a cell is similar to a forest of glycosylated proteins

● Each organism and cell has a specific repertoire of glycans

A. Doeer, Glycoproteomics. Nature Methods, 2011. doi:10.1038/nmeth.1821

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Annotating theN-Glycosylation pathway

● In order to build a correct network model for the N-Glycosylation pathway, we annotated it first in the Reactome database

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The N-Glycosylation pathwayin Reactome

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The KEGG entry for N-Glycosylation is incomplete

Downstream N-Glycosylationin KEGG

Real representationof downstream N-Glycosylation

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Another error for N-Glycosylationin KEGG

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Erroneous annotation in String

● There are two genes with the symbol ALG2:

– ALG2 (Asparagine Linked Glycosylation 2)

– ALG-2 (Apoptosis Linked Gene – 2)

● In String, these two were confused

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Ambigous interpretation of the term N-Glycosylation in GO

N-Glycosylated protein

Merged

N-Glycosylated pathway

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Annotating theN-Glycosylation pathway

● Annotated ~100 reactions in Reactome

● Fixed ~50 Gene Ontology terms

● Fixed key errors in String and KEGG

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Network structure of N-Glycosylation pathway

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Dataset used● The CEPH-HGDP 650,000 Illumina chip dataset● 940 individuals, from 50 human populations

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Methods used

● The FST index → measure of population

differentiation● The iHS test → identification of signals of

recent positive selection

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FST

– Population differentiation

● FST is a measure of

population differentiation

● If the FST between two

population is 1, it means that the two populations are fixed for different alleles

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Signatures of population differentiation in the N-Glycosylation pathway

FST signals are concentrated

in the downstream part, and in the substrates biosynthesis

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Population Differentiationand network position

● Node degree correlates with the distribution of F

ST signals

● Genes with high FST are

generally more connected

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IHS and Long range haplotypes

● A selective sweep may cause the appearance of long homozygous haplotypes at a high frequency

● Example: a long homozygous haplotype present in the LCT gene in North-European populations

Vitti et al, Trends in genetics, 2012

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iHS: Compares the Extended Haplotype Homozygosity decay (EHH decay) between ancestral and derived allele

Voight et al., PLoS Genetics 2006

IHS and Long range haplotypes:

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Signatures of selection in the N-Glycosylation pathway

No difference in the distribution of iHS signals between upstream

and downstream

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Signatures of selection in the N-Glycosylation pathway

GCS1: redirects to protein folding quality control

MAN2A1: redirects to Complex GlycansMGAT3:

redirects to Hybrid Glycans

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● There is a difference in the patterns of population differentiation between the two parts of the N-Glycosylation pathway

● Signals of positive selection are more likely on key genes

● One of the few works applying the pathway approach on human genetics

Pathway approach on N-Glycosylation

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Topics

● Context and motivations● My research:

– Annotating the N-Glycosylation pathway

– Pathway approach on the N-Glycosylation pathway

– The Genotype Network Approach

– The Human Selection Browser and Biostar

● Conclusions

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The Genotype Network approach

● Genotype Networks have been used to study the “innovability” and evolvability of a genetic system

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The Genotype Network approach

● Genotype Networks have been used to study the “innovability” and evolvability of a genetic system

● Never applied to population genetics data, because they require too much data!

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Genotype Networks - theory

● John Maynard-Smith: the concept of a Protein Space, which is explored by populations

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Genotype Networks - theory

● John Maynard-Smith: the concept of a Protein Space, which is explored by populations

“if evolution by natural selection is to occur, functional proteins [or DNA sequences] must form a continuous network which can be traversed by unit mutational steps without passing through non- functional intermediates”

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Neutralism and Selectionism

● Neutralism: most mutations are neutral or deleterious

● Selectionism: positive mutations drive evolution

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Genotype Networks help recoincile Neutralism and Selectionism

● Cycles of Neutral evolution, alterned by cycles of Selection

● Even neutral or negative mutations can beneficial on the long run, because they allow to explore the genotype space

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The Genotype Network - definitions

● The Genotype Space of a region of 5 SNPs can be represented as a network

● Each node is a possible genotype, and edge connect nodes with only one difference

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The Genotype Network - definitions

● Green nodes are sequences observed in a population

● This is the Genotype Network of a population

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Average Path Length of a Genotype Network

● This figure represents two populations

● The yellow one has an higher Average Path Length than the blue one

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Average Degree

● This population has an high Average Degree

● It is more robust to mutations

● This population has a low Average Degree

● Mutations are more likely to fall outside the Genotype Network

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Dataset analyzed● 1000genomes data, phase 1● 850 individuals genotyped, grouped into three

continental groups (AFR, EUR and ASN)

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The VCF2Space library

● Suite of Python scripts to calculate Genotype Networks from a VCF file

● ~400,000 lines of code

● ~350 unit tests

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Splitting the genome into windows of 11 SNPs

● Less than 11 SNPs -> networks are too small and condensed

● More than 11 SNPs -> networks are too large and sparse

Small network Large network

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Why windows of 11 SNPs?

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Genotype Network properties of the human genome

http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19&hubUrl=http://bioevo.upf.edu/~gdallolio/genotype_space/hub.txt

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Coding & Non-Coding regions● Coding regions have higher average path

length and degree than non coding regions

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Genotype Networks and Selection (simulated data)

Selection

Neutral

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● Coding networks: high average path lenght and degree

● Non coding networks: low average path lenght and degree

● Recent selection: lower average path lenght and degree

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Genotype Network:currently under review..

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Topics

● Context and motivations● My research:

– Annotating the N-Glycosylation pathway

– Pathway approach on the N-Glycosylation pathway

– The Genotype Network Approach

– The Human Selection Browser and Biostar

● Conclusions

57

Other works: The Human Selection Browser

● We applied 21 tests for positive selection to the 1,000 Genomes dataset

– FST, CLR, iHS, etc...

● This dataset will be published and made freely available as a genome browser

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Other works: Biostar● An online forum for bioinformatics

● About 150,000 visits per month

● Helped thousands of bioinformaticians!

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Topics

● Context and motivations● My research:

– Annotating the N-Glycosylation pathway

– Pathway approach on the N-Glycosylation pathway

– The Genotype Network Approach

– The Human Selection Browser and Biostar

● Conclusions

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Conclusions (I)

● We developed two applications of network theory to the study of human population genetics.

● We produced a network model of the N-Glycosylation pathway, contributing it to the Reactome database and improving the annotations in other databases.

● We showed that the downstream part of the N-Glycosylation pathway shows more signatures of genetic differentiation than the upstream part. This is compatible with the role and structure of this part of the pathway.

● We showed that key genes of the N-Glycosylation pathway, such as GCS1, MGAT3 and MAN2A1, show signatures of recent positive selection in human populations.

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Conclusions (II)

● We produced a suite of Python scripts, called VCF2Space, to apply the concept of Genotype Networks to Single Nucleotide Polimorphism data

● Our genome-wide application of Genotype Networks showed that coding regions tend to have networks with higher average degree and path length than non-coding regions

● We contributed positively to the bioinformatics community, providing resources such as the 1000 Genomes Selection Browser and Biostar

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Figures credits● Slide 5:

humans: http://blogs.ancestry.com/ancestry/ star trek: http://en.wikipedia.org/wiki/Star_Trek:_The_Original_Series

● Slide 6:Malaria: http://science.psu.edu/news-and-events/2012-news/Read7-2012Climates: http://www.ancienteco.com/2012/03/climate-change-drives-human-evolution.htmlAgriculture: http://en.wikipedia.org/wiki/History_of_agriculture

● Slide 7:

– 1000 Genomes, CEPH-HGDP panel, UK10K, Hapmap websites

● Slide 14:

– Cover of Science, 23 March 2001

● Slide 15:

– Adapted from Stanley, P., Schachter, H., & Taniguchi, N. (2009). N-Glycans. Essentials of Glycobiology.

● Slide 17:

– Glycosylation, downstream: Hossler, P., Mulukutla, B. C., & Hu, W.-S. (2007). Systems analysis of N-glycan processing in mammalian cells. PloS one, 2(1), e713. doi:10.1371/journal.pone.0000713

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Figures credits● Slide 27:

http://www.cephb.fr/en/hgdp/diversity.php/

● Slide 29:http://www.rationalskepticism.org

● Slide 32Adapted from Vitti et al, 2012

● Slide 42:

– wikipedia

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The Pathway approach

Stronger Selection on Genes with high connectivity or upstream of a pathway

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N-glycosylation – how does it work

● All the N-glycans are generated from a single sugar with a very conserved structure, called N-glycan precursor

N-glycan precursor

Signal for folded proteins

Millions ofdifferent

glycans

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The FST test

Almost all the highest signals of F

ST are in

genes of the downstream part

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The iHS test

GCS1 in EUR

MAN2A1 in SSAFR and EASIA

MGAT3 in EASIA

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Combining p-values

● Fisher's combination test

● ZF follows a χ2(2K)

distribution● SNPs from the same

gene may violate the assumption of independency, but still the method is robust to errorsFrom Peng et al, Eur J Hum Genet. 2010

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Comparing upstream and downstream N-Glycosylation

● χ2 test comparing the number of events observed in the each part of the pathway, against what is the number expected if there were no pathway structure

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How to convert genotypes to networks

● Two haplotypes per individual● Reference allele → 0; Alternative allele → 1

Individual 1 AC AC AA GG TT TG CA TG

haplotype a 0 0 0 0 0 0 0 0

haplotype b 1 1 0 0 0 1 1 1

Ancestral alleles: A A A G T T C T