local ancestry inference and its implication in searching for selection evidence in recent admixed

125 Pages·2016·2.22 MB·English

by  Heming Wang

LOCAL ANCESTRY INFERENCE AND ITS IMPLICATION
IN SEARCHING FOR SELECTION EVIDENCE IN
RECENT ADMIXED POPULATION

by
HEMING WANG

Submitted in partial fulfillment of the requirements
For the degree of Doctor of Philosophy

Dissertation Advisor: Dr. Xiaofeng Zhu

Department of Epidemiology and Biostatistics
CASE WESTERN RESERVE UNIVERSITY

January, 2017
CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of
Heming Wang
candidate for the degree of Ph.D.*.

Committee Chair
Xiaofeng Zhu

Committee Member
Robert C. Elston

Committee Member
Nathan J. Morris

Committee Member
Xiang Zhang

Date of Defense
8/31/2016

*We also certify that written approval has been obtained
for any proprietary material contained therein.
Table of Contents
CHAPTER 1: LITERATURE REVIEW AND SPECIFIC AIMS .................... 10
1.1 Genetic Admixture ............................................................................................. 10
1.2 Local ancestry .................................................................................................... 11
1.3 Selection ............................................................................................................. 24
1.4 Fitness epistasis .................................................................................................. 29
1.5 Specific Aims ..................................................................................................... 39
CHAPTER 2: IMPROVING LOCAL ANCESTRY INFERENCE
ACCURACY IN LARGE PEDIGREES ....................................................................... 41
2.1 Introduction ........................................................................................................ 41
2.2 Methods .............................................................................................................. 43
2.2.1 Local ancestry inference error .................................................................... 43
2.2.2 The FamANC algorithm ............................................................................. 46
2.2.3 Parameter estimation ................................................................................... 50
2.2.4 Simulation ................................................................................................... 57
2.2.5 Application .................................................................................................. 59
2.3 Results ................................................................................................................ 60
2.3.1 Simulation ................................................................................................... 60
2.3.2 Application to the CFS................................................................................ 62
2.4 Discussion .......................................................................................................... 65
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CHAPTER 3: TESTING NATURAL SELECTION IN AFRICAN
AMERICANS BY EXAMINING EXCESS OF LOCAL ANCESTRY ..................... 68
3.1 Introduction ........................................................................................................ 68
3.2 Sample and local ancestry inference .................................................................. 68
3.3 Results ................................................................................................................ 70
3.3.1 Variance components .................................................................................. 72
3.3.2 Effective population size............................................................................. 73
3.4 Conclusion .......................................................................................................... 73
CHAPTER 4: GENOME-WIDE SURVEY IN AFRICAN AMERICANS
DEMOSTRATES POTENTIAL EPISTASIS OF FITNESS IN THE HUMAN
GENOME………… ........................................................................................................ 75
4.1 Introduction ........................................................................................................ 75
4.2 Methods .............................................................................................................. 77
4.3 Results ................................................................................................................ 81
4.3.1 Simulation ................................................................................................... 81
4.3.2 Applying to real data ................................................................................... 86
4.3.3 Impact of biases introduced by systematic errors ....................................... 96
4.3.4 Candidate genes .......................................................................................... 97
4.4 Discussion .......................................................................................................... 98
CHAPTER 5: DISCUSSION AND FUTURE WORK ..................................... 112
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List of Tables
Table 1-1．The combination of haplotypes (H , H , H ) in a parent-child pair given their
1 2 3
genotypes. ......................................................................................................................... 22
Table 1-2．The combination of haplotypes (H , H , H , H ) in a parent-child trio given
1 2 3 4
their genotypes. ................................................................................................................. 23
Table 1-3．An example of epistasis defined by Bateson ................................................. 30
Table 1-4．Penetrance table for two diallelic loci in a general model............................. 31
Table 1-5．Penetrance table for two loci in a heterogeneity model ................................ 32
Table 1-6. Genotype counts of two unlinked SNPs. ......................................................... 35
Table 1-7. Genotype proportions of two unlinked SNPs. ................................................. 36
Table 2-1. The probability of an inferred local ancestry given the true local ancestry. ... 45
Table 2-2. The probability of the jth child’s local ancestry given his/her parents’ local
ancestries. .......................................................................................................................... 51
Table 2-3. The probability of the father’s local ancestry is being in error but not leading
to Mendelian errors in a family of 2 parents and m children. ........................................... 52
Table 2-4. The probability of the jth child’s local ancestry is being in error but not leading
to Mendelian errors in a family of 2 parents and m children. ........................................... 53
Table 2-5. The probability of the jth child’s local ancestry given the father’s local ancestry
when the mother is missing............................................................................................... 55
Table 2-6. The probability of the father’s local ancestry is being in error but not leading
to Mendelian errors in a family of 1 parent and m children .............................................. 55
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Table 2-7. The probability of the jth child’s local ancestry is being in error leading to
Mendelian errors in a family of 1 parents and m children ................................................ 56
Table 2-8. Local ancestry errors before and after applying FamANC. ............................ 62
Table 3-1. Variance components in CARe, FBPP, WHI and a larger African-American
dataset comprising five cohorts. ....................................................................................... 73
Table 4-1. Distributions of p-value and estimate in three simulated datasets using
Model (4.1), Model (4.2), and Model (4.3). ........𝛽...1.......................................................... 86
Table 4-2. Replications among CARe, FBPP, and WHI. ................................................. 91
Table 4-3. Top pairwise local ancestry correlated regions in the meta-analysis of the
CARe, FBPP and WHI (p-value < 10-5). .......................................................................... 95
Table 4-4. Correlations between ancestral markers in candidate genes. .......................... 97

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List of Figures
Figure 1-1. The two models of population admixture ...................................................... 11
Figure 1-2. Schematic of local ancestries in an admixed individual genome for the first
three generations ............................................................................................................... 12
Figure 1-3. Inferring local ancestries using (a) HMM and (b) MHMM ........................... 16
Figure 1-4. Schematic of sampling offspring from representative parental haplotypes ... 18
Figure 1-5. Illustration of 2 dimensional PC space of 3 ancestral populations. ............... 20
Figure 1-6. Schematic of the Dobzhansky-Muller Model ................................................ 37
Figure 2-1. Conceptual model of the FamANC algorithm. .............................................. 46
Figure 2-2. Three simulated pedigrees.............................................................................. 58
Figure 2-3. Cutting a large pedigree into smaller informative pedigrees. ........................ 60
Figure 2-4. The probability of numbers of individuals with ancestry errors in each
simulated pedigree with error rate 0.01. ........................................................................... 61
Figure 2-5. Pedigree characteristics in the CFS ................................................................ 63
Figure 2-6. Local ancestries on chromosome 22 before and after applying FamANC in a
family of 11 individuals from the CFS. ............................................................................ 64
Figure 2-7. An example of local ancestry errors that cannot be corrected by FamANC. . 67
Figure 3-1. Distributions of global ancestries in CARe, FBPP, and WHI........................ 70
Figure 3-2. Average local ancestries across the genome in CARe, FBPP, and WHI. ...... 71
Figure 4-1. Distributions of true and estimated global ancestry in simulated data........... 81
Figure 4-2. Distributions of p-value and estimate in simulated data using model (4.1)
................................................................................𝛽..1......................................................... 83
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Figure 4-3. Distributions of p-value and estimate in simulated data using model (4.2)
................................................................................𝛽..1......................................................... 84
Figure 4-4. Distributions of p-value and estimate in simulated data using model (4.3)
................................................................................𝛽..1......................................................... 85
Figure 4-5. Distributions of p-value and estimate in real data using model (4.1) ....... 87
Figure 4-6. Distributions of p-value and estimate 𝛽1 in real data using model (4.2). ...... 88
Figure 4-7. Distributions of p-value and estimate 𝛽1 in real data using model (4.3) ....... 89
Figure 4-8. Correlations of local ancestries and th𝛽e 1corresponding statistical evidence .. 90
Figure 4-9. Correlation features and recent selection evidence of additional regions (p-
value < 10-6). ..................................................................................................................... 94

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Acknowledgements
First of all, I express my earnest gratitude to my advisor, Dr. Xiaofeng Zhu, for
his guidance and supports in my research and career development. He taught me
professional knowledge and skills, and encouraged me during the hard time in my
research. I thank Dr. Robert Elston for his supports and advice on my research and
training. I also thank my committee members Dr. Nathan Morris and Dr. Xiang Zhang
for their guidance and feedback on this dissertation work.
I would also like to thank the current and former colleagues and friends in the
Department of Epidemiology and Biostatistics, especially Dr. Tao Feng, Dr. Xuefeng
Wang, Dr. Xiaoqing Yu, Dr. Huaizhen Qin, Karen He, Dr. Xiaoyin Li, Dr.Yajuan Wang,
Dr. Jingjing Liang, Dr. Priya Shetty, and Dr. Xiangqing Sun, for their kind help in
statistical analyses, computational programing, and inspiring discussions. In addition, I
am grateful to my collaborators outside Case Western Reserve University, including Dr.
Susan Redline and Dr. Xihong Lin from Harvard University, Dr. Hua Tang from Stanford
University, Dr. Neil Risch from the University of California San Francisco, Dr. Arvinda
Chakravarti from the John Hopkins University, and Drs. Bamidele Tayo,and Richard
Cooper from Loyola University, and their lab members. They gave me tremendous help
and support in my research.
I appreciate the financial support from the National Institute of Health, the
National Heart, Lung, and Blood Institute, the National Human Genome Research
Institute, and the National Science Foundation in my PhD study. Finally, I dedicate this
dissertation is to my husband and my parents for their love, understanding and support.
7
Local Ancestry Inference and its Implication in Searching for
Selection Evidence in Recent Admixed Population
Abstract
by
HEMING WANG
There has been increasing interest in studying the ancestral spectrum in admixed
individuals, such as African Americans and Latino Americans. Investigating the different
ancestral proportions across an individual’s genome, i.e. the local ancestries, is important
in genetic studies. Many methods have been developed to infer the local ancestries in
admixed individuals from their genotype data. A few of them have attempted to extend
their methodologies to small nuclear families, but none can be applied to large pedigrees.
We developed a method named FamANC that is able to improve the accuracy of local
ancestry inference in large pedigrees by: 1) using existing software to infer local
ancestries for all individuals in a family, assuming they are unrelated, 2) improving
inference accuracy by correcting the inference errors. From our simulations, FamANC
reduced the local ancestry estimation errors by over 20%.
The second part of this dissertation is on searching for the epistasis of fitness
evidence using local ancestries. The role played by epistasis between alleles at unlinked
loci in shaping population fitness has been debated for many years. However, existing
evidence has been mainly accumulated from model organisms. In model organisms,
fitness epistasis can be systematically inferred by detecting non-independence of
genotypic values between loci in a population and confirmed through examining the
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