Hensley Benjamin (stoolvelvet06)

com/algbio/panvc-founders. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online. Placentally-transferred maternal IgG protects against pathogens in early life, yet vertically-transmitted infections can interfere with transplacental IgG transfer. Although human cytomegalovirus (HCMV) is the most common placentally-transmitted viral infection worldwide, the impact of congenital HCMV (cCMV) infection on transplacental IgG transfer has been underexplored. We evaluated total and antigen-specific maternal and cord blood IgG levels and transplacental IgG transfer efficiency in a U.S-based cohort of 93 mother-infant pairs including 27 cCMV-infected and 66 cCMV-uninfected pairs, of which 29 infants were born to HCMV-seropositive non-transmitting mothers and 37 to HCMV-seronegative mothers. Controls were matched on sex, race/ethnicity, maternal age, and delivery year. Transplacental IgG transfer efficiency was decreased by 23% (95% CI 10-36%, p=0.0079) in cCMV-infected pairs and 75% of this effect (95% CI 28-174%, p=0.0085) was mediated by elevated maternal IgG levels (i.e., hypergammaglobulinemia) in HCMV-transmitting women. Despite reduced transfer efficiency, IgG levels were similar in cord blood from infants with and without cCMV infection. Our results indicate that cCMV infection moderately reduces transplacental IgG transfer efficiency due to maternal hypergammaglobulinemia; however, infants with and without cCMV infection had similar antigen-specific IgG levels, suggesting comparable protection from maternal IgG acquired via transplacental transfer. Our results indicate that cCMV infection moderately reduces transplacental IgG transfer efficiency due to maternal hypergammaglobulinemia; however, infants with and without cCMV infection had similar antigen-specific IgG levels, suggesting comparable protection from maternal IgG acquired via transplacental transfer. Identification and interpretation of noncoding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis (RA) and asthma (ATH) as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. The R package OWAS that implements our method is available at https//github.com/shuangsong0110/OWAS. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online. Age at onset is useful in identifying chronic back patients at an increased risk of axial spondyloarthritis (axSpA).