Under the parent grant, we have developed methods for the analysis of trait and marker data observed on members of pedigree structures, to aid in the identification of genes contributing to increased risk of com- plex traits, including cardiovascular, neurological and behavioral disorders. These methods include the development of Monte Carlo methods to infer gene descent in extended pedigrees, given data at a dense genome screen of markers, and the use of these gene descent patterns in joint multilocus linkage and segregation analysis of complex traits. The proposed new research direction addresses methods for the use of modern dense genomic marker data available on individuals. The methods we have employed with success to infer identical-by-descent (ibd) genome segments among pedigree members will be combined with new methods to infer ibd among individuals and families not known to be related. This approach has the potential to combine the linkage detection power of family-based studies with the resolution and sample-size advantages of population-based studies. We propose to develop methods to infer ibd of genome segments in the 106 bp range, within and among members of populations using dense SNP marker data (e.g. 500K SNP chips). We will investigate the accuracy and power of these methods, and their robustness to population subdivision, stratification, and admixture. We will investigate the impact on ibd inferences of meiotic map heterogeneity (recombination hot-spots), linkage disequilibrium, and copy number variants. We will extend the methods to make use of within-pedigree ibd and resulting haplotype (phase) information, to detect ibd genome segments among members of different pedigrees. We will extend methods for genetic analyses of trait data conditional on the inferred joint pattern of ibd among observed pedigree members to accommodate also between-pedigree ibd. Methods will be evaluated pri- marily on data sets in which the ibd is simulated, but in which real-data chromosomes are used to found the simulated population. Additional testing and evaluation will be done using real data sets including collections of pedigrees segregating cardiovascular or behavioral disorders. These real data sets include several on which are available genome-wide SNP genotype screens or more localized multigene haplo- types. Finally, software will be developed that implements these methods within the MORGAN-3 software package of the parent grant. The software will be documented and released for use by practitioners.
Resolution of the genetic and environmental components of a disease trait is an important step in de- veloping methods for prevention and treatment. Identification of genes through family-based designs has increased understanding of the biology underlying many complex human traits, but the ability to use family-based designs efficiently in genetic studies has encountered both statistical and computational challenges. On the other hand, population-based association studies are challenged by genetic and pop- ulation heterogeneities. New computational and statistical approaches for the combined analysis of data on pedigrees and in populations can make maximal use of genetic data and lead to better methods for resolving the genetic basis of complex traits.
| Wang, Bowen; Sverdlov, Serge; Thompson, Elizabeth (2017) Efficient Estimation of Realized Kinship from Single Nucleotide Polymorphism Genotypes. Genetics 205:1063-1078 |
| Sverdlov, Serge; Thompson, Elizabeth (2017) Combinatorial Methods for Epistasis and Dominance. J Comput Biol 24:267-279 |
| Blue, Elizabeth M; Brown, Lisa A; Conomos, Matthew P et al. (2016) Estimating relationships between phenotypes and subjects drawn from admixed families. BMC Proc 10:357-362 |
| Saad, Mohamad; Nato Jr, Alejandro Q; Grimson, Fiona L et al. (2016) Identity-by-descent estimation with population- and pedigree-based imputation in admixed family data. BMC Proc 10:295-301 |
| Wijsman, Ellen M (2016) Family-based approaches: design, imputation, analysis, and beyond. BMC Genet 17 Suppl 2:9 |
| Raffa, Jesse D; Thompson, Elizabeth A (2016) Power and Effective Study Size in Heritability Studies. Stat Biosci 8:264-283 |
| Glazner, Chris; Thompson, Elizabeth (2015) Pedigree-Free Descent-Based Gene Mapping from Population Samples. Hum Hered 80:21-35 |
| Nato Jr, Alejandro Q; Chapman, Nicola H; Sohi, Harkirat K et al. (2015) PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers. Bioinformatics 31:3790-8 |
| Blue, Elizabeth M; Sun, Lei; Tintle, Nathan L et al. (2014) Value of Mendelian laws of segregation in families: data quality control, imputation, and beyond. Genet Epidemiol 38 Suppl 1:S21-8 |
| Cheung, Charles Y K; Marchani Blue, Elizabeth; Wijsman, Ellen M (2014) A statistical framework to guide sequencing choices in pedigrees. Am J Hum Genet 94:257-67 |
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