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Local ancestry, defined as the genetic ancestry at a genomic location of an admixed individual, is widely used as a genetic marker in genetic association and evolutionary genetics studies. Many methods have been developed to infer the local ancestries in a set of unrelated individuals, a few of them have been extended to small nuclear families, but none can be applied to large (e.g. three-generation) pedigrees. In this study, we developed a method, FamANC, that can improve the accuracy of local ancestry inference in large pedigrees by: (1) using an existing algorithm to infer local ancestries for all individuals in a family, assuming (contrary to fact) they are unrelated, and (2) improving its accuracy by correcting inference errors using pedigree structure. Applied on African-American pedigrees from the Cleveland Family Study, FamANC was able to correct all identified Mendelian errors and most of double crossovers. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) is a growing public health threat globally and several studies suggest a role of host genetic susceptibility in increased TB risk. As part of a household contact study in Kampala, Uganda, we have taken a unique approach to the study of genetic susceptibility to TB by developing an intermediate phenotype model for TB susceptibility, analyzing levels of tumor necrosis factor-α (TNFα) in response to culture filtrate as the phenotype. In the present study, we analyzed candidate genes related to TNFα regulation and found that interleukin (IL)-10, interferon-gamma receptor 1 ( IFNGR1), and TNFα receptor 1 ( TNFR1) genes were linked and associated to both TB and TNFα. We also show that these associations are with progression to active disease and not susceptibility to latent infection. This is the first report of an association between TB and TNFR1 in a human population and our findings for IL-10 and IFNGR1 replicate previous findings. By observing pleiotropic effects on both phenotypes, we show construct validity of our intermediate phenotype model, which enables the characterization of the role of these genetic polymorphisms on TB pathogenesis. This study further illustrates the utility of such a model for disentangling complex traits. [ABSTRACT FROM AUTHOR]

Sarcoidosis, a systemic granulomatous disease, likely results from both environmental agents and genetic susceptibility. Sarcoidosis is more prevalent in women and, in the United States, African Americans are both more commonly and more severely affected than Caucasians. We report a follow up of the first genome scan for sarcoidosis susceptibility genes in African Americans. Both the genome scan and the present study comprise 229 African American nuclear families ascertained through two or more sibs with sarcoidosis. Regions studied included those which reached a significance in the genome scan of 0.01 (2p25, 5q11, 5q35, 9q34, 11p15 and 20q13), 0.05 (3p25 and 5p15–13) or which replicated previous findings (3p14–11). We performed genotyping with additional markers in the same families used in the genome scan. We examined multi-locus models for epistasis and performed model-based linkage analysis on subsets of the most linked families to characterize the underlying genetic model. The strongest signal was at marker D5S407 ( P=0.005) on 5q11.2, using both full and half sibling pairs. Our results support, in an African American population, a sarcoidosis susceptibility gene on chromosome 5q11.2, and a gene protective for sarcoidosis on 5p15.2. These fine mapping results further prioritize the importance of candidate regions on chromosomes 2p25, 3p25, 5q35, 9q34, 11p15 and 20q13 for African Americans. Additionally, our results suggest joint action of the effects of putative genes on chromosome 3p14–11 and 5p15.2. We conclude that multiple susceptibility loci for sarcoidosis exist in African Americans and that some may have interdependent effects on disease pathogenesis. [ABSTRACT FROM AUTHOR]

The article focuses on gene-gene interaction detection in diseases without statistical interaction. It references the article "Detecting gene-gene interactions that underlie human diseases," by H. J. Cordell in the 2009 issue. It tackles the need to jointly interpret statistical interaction and effect terms. It tells that statistical interaction may not be engaged with statistical models since it is population-level and since non-additivity's presence and magnitude are scale and model reliant.