Vazquez-Santiago, M. | Search bioRxiv

bioRxiv2020

Reduced placental transfer of antibodies against microbial and vaccine antigens in HIV-infected women in Mozambique

Antibody transplacental transfer is essential for conferring protection in newborns against infectious diseases. This transfer may be affected by gestational age and maternal infections, although the effects are not consistent across studies. We measured total IgG and IgG subclasses by quantitative suspension array technology against fourteen pathogens and vaccine antigens, including target of maternal immunization, in 341 delivering HIV- and HIV+ mother-infant pairs from southern Mozambique. Maternal antibody levels were the main determinant of cord antibody levels. HIV broadly reduced the placental transfer and cord levels of IgG and IgG1, but also IgG2 to half of the antigens. Plasmodium falciparum exposure and prematurity were negatively associated with cord antibody levels and placental transfer but this was antigen-subclass dependent. These findings suggest maternal infections may impact the efficacy of maternal immunization and confirm the lower transfer of antibodies as one of the causes underlying increased susceptibility to infections in HIV-exposed infants.

immunology↗

bioRxiv2017

lme4qtl: Linear Mixed Models With Flexible Covariance Structure For Genetic Studies Of Related Individuals

BackgroundQuantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.\n\nResultsTo address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.\n\nConclusionsOur software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl.

genetics↗