The human global population has expanded more than 1000-fold in the last 400 generations, resulting in a state that is profoundly out of equilibrium with respect to genetic variation. The recent growth produces a large excess of rare variation, which has important consequences for finding genes that underlie complex disease risk. Our overall objective is to develop and test methods of population genetic analysis to understand the role of rapid population expansion in shaping patterns of genetic variation.
In Aim 1 we will develop theoretical approaches to understand how and why explosive growth impacts patterns of genetic variation. We will also derive the analytical implications of using samples that are so large as to violate assumptions of the neutral coalescent. We have shown how large samples can result in multiple mergers, and so both rapid growth and large sample sizes distort the topology of the gene genealogies of a sample so as to make standard coalescent theory invalid. We will replace this with new methods that generate the appropriate sample site frequency spectrum under models with both rapid growth and large samples. Given large data sets, we want to make inference about population genetic parameters, and such estimates generally require an appropriate model relating population size and mutation rates to levels of variation.
In Aim 2 we will develop novel statistical and computational inference methods to accommodate growing populations and apply them to large-scale data. We will thoroughly test our inference methods using simulation data generated under appropriate demographic models.
This aim will generate novel software packages with broad utility for the community.
In Aim 3 we will learn how natural selection in a rapidly growing population impacts population genetic variation and the architecture of complex traits. This goal will be accomplished through extensive forward-in-time simulations. Among other things, results will tell us conditions under which rapid growth inflates the individual mutation load. By developing an understanding of the way that such rapid growth has impacted genetic variation in humans, we anticipate that these results will provide a more accurate picture of the expected genetic architecture of disease risk, which will in turn guide methods for improved association testing.

Public Health Relevance

This project will develop methods of population genetic analysis to understand the role of recent rapid population expansion in shaping patterns of variation in human populations. Improved methods for genetic inference in the face of such rapid growth will be developed, correcting the misapplication of standard methods which were developed for stable populations. Rapid population expansion dramatically inflates the abundance of rare variants in the population, and the impact of this on the genetic architecture of human disease risk will be quantified.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM108805-01
Application #
8613540
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Eckstrand, Irene A
Project Start
2014-05-10
Project End
2018-04-30
Budget Start
2014-05-10
Budget End
2015-04-30
Support Year
1
Fiscal Year
2014
Total Cost
$588,893
Indirect Cost
$117,674
Name
Cornell University
Department
Biochemistry
Type
Schools of Arts and Sciences
DUNS #
872612445
City
Ithaca
State
NY
Country
United States
Zip Code
14850
Ye, Kaixiong; Gao, Feng; Wang, David et al. (2017) Dietary adaptation of FADS genes in Europe varied across time and geography. Nat Ecol Evol 1:167
Reppell, M; Zöllner, S (2017) An efficient algorithm for generating the internal branches of a Kingman coalescent. Theor Popul Biol :
Koch, Evan; Novembre, John (2017) A Temporal Perspective on the Interplay of Demography and Selection on Deleterious Variation in Humans. G3 (Bethesda) 7:1027-1037
Terhorst, Jonathan; Kamm, John A; Song, Yun S (2017) Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat Genet 49:303-309
Kamm, John A; Terhorst, Jonathan; Song, Yun S (2017) Efficient computation of the joint sample frequency spectra for multiple populations. J Comput Graph Stat 26:182-194
Kothapalli, Kumar S D; Ye, Kaixiong; Gadgil, Maithili S et al. (2016) Positive Selection on a Regulatory Insertion-Deletion Polymorphism in FADS2 Influences Apparent Endogenous Synthesis of Arachidonic Acid. Mol Biol Evol 33:1726-39
Gao, Feng; Keinan, Alon (2016) Explosive genetic evidence for explosive human population growth. Curr Opin Genet Dev 41:130-139
Kamm, John A; Spence, Jeffrey P; Chan, Jeffrey et al. (2016) Two-Locus Likelihoods Under Variable Population Size and Fine-Scale Recombination Rate Estimation. Genetics 203:1381-99
Pinto, Yishay; Gabay, Orshay; Arbiza, Leonardo et al. (2016) Clustered mutations in hominid genome evolution are consistent with APOBEC3G enzymatic activity. Genome Res 26:579-87
Waldman, Yedael Y; Biddanda, Arjun; Dubrovsky, Maya et al. (2016) The genetic history of Cochin Jews from India. Hum Genet 135:1127-43

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