The genetic analysis of complex traits is one of the major challenges facing biomedical researchers today. These traits are often common in the population and, hence, account for a large portion of the health care burden. Their manifestation is the result of numerous factors, both genetic and environmental, that often interact in complex ways, making the identification of risk factors extremely difficult. Novel genetic mapping techniques that take advantage of both the study design and characteristics of the available data may prove instrumental in identifying and characterizing the genetic components of these traits. The broad, long-term objective of this proposal is to develop statistical and computational methods for the identification of genetic risk factors of complex traits. We focus primarily on methods that are applicable in arbitrary pedigrees, seeking to maximize the amount of information extracted from the pedigree regardless of its size and complexity. Genetic mapping in families, particularly large ones, has both particular advantages and challenges. For instance, with attention turning towards the search for genetic variants that extend beyond just those that are common with moderate to large effect, studies involving related individuals may prove particularly effective at finding relatively rare variants. That is, given an individual with a rare trait-influencing variant, it is likely that other individuals who are related will also have the same variant and be similarly affected. This effect is likely to be more pronounced with larger family sizes. However, families, especially those with large pedigrees, pose significant computational and methodological challenges. At the same time, rapid technological advances have dramatically increased the amounts and types of genomic data available to researchers, increasing the challenge even more. Our goal, then, is to develop methods that make use of these rich types of data while tackling the computational difficulties associated with general pedigrees.
The specific aims of this proposal are (1) the development of a method for estimating genomic sharing in a region between arbitrarily related individuals given dense SNP data with high LD between SNPs;(2) the development of methods to integrate family information into the analysis of SNP and CNP genotyping array data;(3) to develop methods for mapping of binary traits in potentially hundreds of individuals who may all be related while allowing for covariates and other disease susceptibility factors;and (4) to develop methods for mapping of multiple correlated quantitative traits, that is, finding genes with pleiotropic effects. All of our methods will be implemented in user-friendly software that will be made freely available.

Public Health Relevance

Common complex disorders, such as diabetes and cardiovascular disease, are a consequence of many genetic and non-genetic risk factors and account for a tremendous proportion of health care expenditures. Here, we seek to develop novel methodologies to find genetic variants that predispose people to these disorders by seeking to fully leverage the characteristics of genomic data acquired through recent technological developments.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG002899-08
Application #
8264592
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Brooks, Lisa
Project Start
2003-07-01
Project End
2013-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
8
Fiscal Year
2012
Total Cost
$386,100
Indirect Cost
$138,600
Name
University of Chicago
Department
Genetics
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Yao, Tsung-Chieh; Du, Gaixin; Han, Lide et al. (2014) Genome-wide association study of lung function phenotypes in a founder population. J Allergy Clin Immunol 133:248-55.e1-10
Parker, Clarissa C; Carbonetto, Peter; Sokoloff, Greta et al. (2014) High-resolution genetic mapping of complex traits from a combined analysis of F2 and advanced intercross mice. Genetics 198:103-16
Cheng, Riyan; Parker, Clarissa C; Abney, Mark et al. (2013) Practical considerations regarding the use of genotype and pedigree data to model relatedness in the context of genome-wide association studies. G3 (Bethesda) 3:1861-7
Stanhope, Stephen A; Abney, Mark (2012) GLOGS: a fast and powerful method for GWAS of binary traits with risk covariates in related populations. Bioinformatics 28:1553-4
Han, Lide; Abney, Mark (2011) Identity by descent estimation with dense genome-wide genotype data. Genet Epidemiol 35:557-67
Papachristou, Charalampos; Ober, Carole; Abney, Mark (2011) Genetic variance components estimation for binary traits using multiple related individuals. Genet Epidemiol 35:291-302
Kosova, Gülüm; Pickrell, Joseph K; Kelley, Joanna L et al. (2010) The CFTR Met 470 allele is associated with lower birth rates in fertile men from a population isolate. PLoS Genet 6:e1000974
Kosova, Gulum; Abney, Mark; Ober, Carole (2010) Colloquium papers: Heritability of reproductive fitness traits in a human population. Proc Natl Acad Sci U S A 107 Suppl 1:1772-8
Abney, Mark (2009) A graphical algorithm for fast computation of identity coefficients and generalized kinship coefficients. Bioinformatics 25:1561-3
Abney, Mark (2008) Identity-by-descent estimation and mapping of qualitative traits in large, complex pedigrees. Genetics 179:1577-90

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