workshop 2011

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Population Structure Analysisusing STRUCTURE software

Chang Bum Hong

kt Bioinformatics TF, hongiiv@gmail.com, twitter @hongiiv, hongiiv.tistory.com

Friday, August 12, 11

Genetic test

일반적으로 알콜을 섭취하게 되면 알콜은 아세트알데히드(얼굴을 붉게 만들고, 가슴도 콩닥 거리고, 구토를 일으키는 독성 물질)로 변하게 되고 이것이 다시 ALDH 에 의해 인체에 무해한 젖산으로 분해되는 과정을 거치게 됩니다. 이때 ALDH2라는 유전자가 바로 아세트알데히드가 조금이라도 생성되면 분해하는데 관여하게 이때 유전자형에 따라서 3가지 유형으로 나

타나게 됩니다.

23andMe

북서유럽

남동유럽

HGDP(Human Genome Diversity Project)

PASNP(Pan-Asian SNP Consortium)

SNP Individual PopulationPASNP 54,794 1,928 75

HGDP 2,834~ 1,056 52

HapMap 1,481,135 1,397 11

SGVP 268,667 292 3

Korean 58,625 159 10

China(Yanbian) 58,625 16 1

Japan(Kobe) 58,625 5 1

Korea-Japan 58,625 6 1

Vietnam 58,625 16 1

Korean-Vietnam 58,625 8 1

Cambodia 58,625 16 1

Mongol 58,625 16 1

East Asia - Public genotype data

a. Pan-Asian SNP Consortium(http://www4a.biotec.or.th/PASNP)

b. Singapore Genome Variation Project(http://www.nus-cme.org.sg/SGVP)

GyeongJu

UlSanGoryeong

YeonCheon

Cheonan

JeCheon

PyeongChang

MWaverage >70 year oldlong settlementAffymetrix 50K Xba

58,960 SNPs

China(Yanbian)Japan(Kobe)Korea-Japan

VietnamKorean-Vietnam

CambodiaMongol

Korean Data

GyeongJu

GoRyeong

Before QC 58,960 SNPs All Asian

Before QC 58,960 SNPsKorean

Missing genotype individuals

Relatedness between the 153 Korean(10 region) Individuals

PCA analysis using autosomal 46,559 SNP markers (n=153, Korean)

GyeongJu

GoRyeong

CheonAn

JeCheon

PyeongChang

YeonCheon

Korea-Vietnam Korea-Japan

Jeju Kobe JPT-HapMap

Yanbian

Mongol

CHB-HapMap

Vietnam

Cambodia

PCA analysis of East Asian descent

illustration of geographic correspondence of ethnic group

locations

Relationship between Eigenvector values and Latitude

R = 0.8621y = 36.65 + 166.33x

• A model-based clustering method (Pritchard et al. 2000)

• Free software (http://pritch.bsd.uchicago.edu/structure.html)

• Bayesian approach (MCMC: Markov Chain Mote Carlo)

• Detects the underlying genetic population among a set of individuals genotyped at multiple markers

• Computes the proportion of the genome of an individual originating from each inferred population (quantitative clustering method)

STRUCTURE software

• A matrix where the data for individuals are in rows, the loci are in column

• n consecutive rows have the data for each individual of n-ploid species

• Integer should be used for coding genotype

• Missing data should be indicated by a number which doesn’t occur elsewhere in the data (e.g. -1)

• The data file should be a text file (.txt) not an excel (.xls) for running STRUCTURE

Input data

Input format

Information of user-defined populationsLable : 각 개인의 고유한 ID로 숫자 또는 문자 어떤것이든 상관없다.(예, CEPH1334.10)PopID: 개인이 속한 민족의 고유한 번호 (예, 중국인(CHB)인 경우 5, 유럽인(CEU)인 경우 1과 같이 자신이 직접 부여) Flag: 해당 PopID 정보를 STRUCTURE 프로그램 실행시 사용할 것인가?(1= 사용한다, 2= 사용하지 않는다.)Location: 해당 개인의 위치정보(예, 동아시아(EAS)인경우 1번, 유럽(EURA)인 경우 2번과 같이 자신이 직접 부여)

genotype (1,2,5)AA = 11AB = 12BB = 22

missing = 55

MarkerName...Label PopID Flag Location Genotype...

1 consecutive rows for alleles

Input format (cont.)

Running STRUCTURE from a graphical interface, Front End

Importing input data into a project

Importing input data into a project (cont.)

Configuring a parameter set

Configuring a parameter set (cont.)

Length of Burnin Period : how long to run the simulation before collecting data to minimize the effect of the starting configuration, 목표함수로 수렴할 때까지의 반복 숫자Number of MCMC Reps after Burnin : how long to run the simulation after burnin to get accurate parameter estimates

Running STRUCTURE: a single run

Running STRUCTURE: a single run (cont.)

Running STRUCTURE: a batch run

Running STRUCTURE: a batch run (cont.)

Ln P(D): Estimated probability of Ks

• For very large data sets, the runtime of structure using default settings may become impractically slow

• reduced data sets (ex, pruned)

• get accurate results using much shorter runs than default (ex, small values of NUMREPS)

• download the source code and compile it on your machine (using 64-bit machine)

• use the command-line version of structure

Analysis of genome-wide SNP data

An example of MCMC convergence

Inference of true K(number of population)

• The log likelihood for each K, Ln P(D) = L(K)

• Two approaches to determine the best K

• Use of L(K) : When K is approaching a true value, L(K) plateaus and has high variance between runs

• Use of an ad hod quantity (∆K) : calculated based on the second order rate of change of the likelihood (∆K). The ∆K shows a clear peak at the true value of K

Q-metrixan individuals belongs to a subpopulation

Simulation Result

Simulation Result (cont.)

Enjoy running STRUCTURE

We may not always be able to know the TRUE value K, but we should aim for the smallest value of K

that captures the major structure in the data

Pritchard et al. (2000)

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