The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by beta diversity, that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome beta diversity with or without interacting with an environmental factor. We show that score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes P-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate P-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile-quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (P=0.0032) and weighted (P=0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.
By 24 months after HIV seroconversion, the oldest subjects and those with the highest HIV RNA levels during early chronic infection had experienced the most severe depletion of CD4+ cells. Subsequent declines in CD4+ cells varied little by early chronic HIV RNA level or age.
Seroprevalence of human T lymphotropic virus (HTLV) and human immunodeficiency virus type 1 (HIV-1) was determined among 7841 intravenous drug users (IVDUs) from drug treatment centers in Baltimore, Chicago, Los Angeles, New Jersey (Asbury Park and Trenton), New York City (Brooklyn and Harlem), Philadelphia, and San Antonio, Texas; 20.9% had evidence of HTLV infection, as determined using a p21e EIA for screening and p21e blot for confirmation. With a type-specific EIA and blot used in combination, HTLV-II was identified in 97.6% of HTLV-positive IVDUs whose sera could be subtyped. HIV-1 seroprevalence was 13.2%. HTLV-II without HIV-1 was most common in Los Angeles and San Antonio. HIV-1 without HTLV-II was most common in New York, New Jersey, and Baltimore. Dual infection was most common in New York and New Jersey. Logistic regression analysis revealed that seroprevalence of HTLV-II was significantly greater with HIV-1 infection and increasing age and among women, blacks, and Mexican-Americans. In conclusion, it appears that among US IVDUs, nearly all HTLV infection is attributable to HTLV-II, and HTLV-II infection is associated with HIV-1 and sociodemographic background.
PBMC that have high levels of HIV-1 replication and low levels of recent thymic emigrants are associated with a substantially increased risk of AIDS. It is not known if measurement of either TREC or 2-LTR circles will complement HIV-1 viral load as an estimation of the risk of AIDS for patients who are receiving highly effective anti-HIV therapy.
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