Systematic development of ionizable lipid nanoparticles for placental mRNA delivery using a design of experiments approach.

Ionizable lipid nanoparticles (LNPs) have gained attention as mRNA delivery platforms for vaccination against COVID-19 and for protein replacement therapies. LNPs enhance mRNA stability, circulation time, cellular uptake, and preferential delivery to specific tissues compared to mRNA with no carrier platform. However, LNPs are only in the beginning stages of development for safe and effective mRNA delivery to the placenta to treat placental dysfunction.

Squamous Cell Carcinoma in Situ Achieves Tumor Clearance in More Mohs Stages Than Invasive Squamous Cell Carcinoma.

Background: Squamous cell carcinoma in situ (SCCIS) has more subclinical lateral extension than invasive squamous cell carcinomas (SCC).

Objective: To determine whether it takes a greater number of Mohs stages for clearance of SCCIS compared with SCC and whether the difference in final defect size and clinical size is larger in SCCIS than SCC.

Methods: All Mohs micrographic surgery cases of SCCIS and SCC performed between January 2011 and December 2021 were identified.

Lipid Nanoparticle Composition Drives mRNA Delivery to the Placenta.

Ionizable lipid nanoparticles (LNPs) have gained attention as mRNA delivery platforms for vaccination against COVID-19 and for protein replacement therapies. LNPs enhance mRNA stability, circulation time, cellular uptake, and preferential delivery to specific tissues compared to mRNA with no carrier platform. However, LNPs have yet to be developed for safe and effective mRNA delivery to the placenta as a method to treat placental dysfunction.

Designing strategies for eradication of Helicobacter pylori based on prevalence patterns of infection and antibiotic resistance in a low-income, medically underserved community in the United States.

Background: Regional variation in Helicobacter pylori resistance patterns is a significant contributing factor for the ineffectiveness of traditional treatments. To improve treatment outcomes, we sought to create an individualized, susceptibility-driven therapeutic approach among our patient population, which is one of the poorest in the nation. It is medically underserved, minority-predominant and has high incidence of H pylori infection.

A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies.

Background: Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway.

Methods: We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information.

Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.

Dissecting the genetic mechanism underlying a complex disease hinges on discovering gene-environment interactions (GXE). However, detecting GXE is a challenging problem especially when the genetic variants under study are rare. Haplotype-based tests have several advantages over the so-called collapsing tests for detecting rare variants as highlighted in recent literature.

Cytokines increase engraftment of human acute myeloid leukemia cells in immunocompromised mice but not engraftment of human myelodysplastic syndrome cells.

Patient-derived xenotransplantation models of human myeloid diseases including acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms are essential for studying the biology of the diseases in pre-clinical studies. However, few studies have used these models for comparative purposes. Previous work has shown that acute myeloid leukemia blasts respond to human hematopoietic cytokines whereas myelodysplastic syndrome cells do not.

Investigating multiple sclerosis genetic susceptibility on the founder population of east-central Sardinia via association and linkage analysis of immune-related loci.

Background: A wealth of single-nucleotide polymorphisms (SNPs) responsible for multiple sclerosis (MS) susceptibility have been identified; however, they explain only a fraction of MS heritability.

Objectives: We contributed to discovery of new MS susceptibility SNPs by studying a founder population with high MS prevalence.

Methods: We analyzed ImmunoChip data from 15 multiplex families and 94 unrelated controls from the Nuoro Province, Sardinia, Italy.

A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data.

We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure.

Evaluation of logistic Bayesian LASSO for identifying association with rare haplotypes.

It has been hypothesized that rare variants may hold the key to unraveling the genetic transmission mechanism of many common complex traits. Currently, there is a dearth of statistical methods that are powerful enough to detect association with rare haplotypes. One of the recently proposed methods is logistic Bayesian LASSO for case-control data.

Population-based association and gene by environment interactions in Genetic Analysis Workshop 18.

In the past decade, genome-wide association studies have been successful in identifying genetic loci that play a role in many complex diseases. Despite this, it has become clear that for many traits, investigation of single common variants does not give a complete picture of the genetic contribution to the phenotype. Therefore a number of new approaches are currently being investigated to further the search for susceptibility loci or regions.

A confidence set inference method for identifying SNPs that regulate quantitative phenotypes.

Aims: We introduce a family-based confidence set inference (CSI) method that can be used in preliminary genome-wide association studies to obtain confidence sets of SNPs that contribute a specific percentage to the additive genetic variance of quantitative traits.

Methods: Developed in the framework of generalized linear mixed models, the method utilizes data from outbred families of arbitrary size and structure. Through our own simulation study and analysis of the Genetics Analysis Workshop 16 simulated data, we study the properties of our method and compare its performance to that of the family association method described by Chen and Abecasis [Am J Hum Genet 2007;81:913-926].

Confidence set of putative quantitative trait loci in whole genome scans with application to the Genetic Analysis Workshop 17 simulated data.

As genetic maps become more highly dense, the ability to sufficiently localize putative disease loci becomes an achievable goal. This has prompted an increased interest in methods for constructing confidence intervals for the location of variants that contribute to a trait. Such intervals are important because, by reducing the number of candidate loci, they can help in the design of cost-effective and time-efficient follow-up studies.

Multiple testing in high-throughput sequence data: experiences from Group 8 of Genetic Analysis Workshop 17.

The use of high-throughput sequence data in genetic epidemiology allows the investigation of common and rare variants in the entire genome, thus increasing the amount of information and the potential number of statistical tests performed within one study. As a consequence, the problem of multiple testing may become even more pressing than in previous studies. As an important challenge, the exact number of statistical tests depends on the actual statistical method used.

Rising prevalence of asthma is sex-specific in a US farming population.

Background: Asthma prevalence is increasing worldwide in most populations, likely due to a combination of heritable factors and environmental changes. Curiously, however, some European farming populations are protected from asthma, which has been attributed to their traditional lifestyles and farming practices.

Objective: We conducted population-based studies of asthma and atopy in the Hutterites of South Dakota, a communal farming population, to assess temporal trends in asthma and atopy prevalence and describe the risk factors for asthma.

Genetic variance components estimation for binary traits using multiple related individuals.

Understanding and modeling genetic or nongenetic factors that influence susceptibility to complex traits has been the focus of many genetic studies. Large pedigrees with known complex structure may be advantageous in epidemiological studies since they can significantly increase the number of factors whose influence on the trait can be estimated. We propose a likelihood approach, developed in the context of generalized linear mixed models, for modeling dichotomous traits based on data from hundreds of individuals all of whom are potentially correlated through either a known pedigree or an estimated covariance matrix.

Accounting for disease model uncertainty in mapping heterogeneous traits--a Bayesian model averaging approach.

Background: Locus heterogeneity, wherein a disease can be caused in different individuals by different genes and/or environmental factors, is a ubiquitous feature of complex traits. A Bayesian approach has been proposed to account for variable rates of heterogeneity across families in a parametric linkage analysis setup [Biswas and Lin: J Am Stat Assoc 2006;101:1341-1351]. As with any parametric approach, its application requires specification of the disease model, which limits its practical utility.

Confidence intervals for putative quantitative trait loci - development and applications of new linkage methods.

A new method for constructing confidence intervals for the location of putative genes regulating expression levels (quantitative traits) is proposed. This method is suitable for the "intermediate" fine-mapping step usually performed between the initial whole-genome screening and the follow-up fine mapping step as a means of reducing the size of the region where the latter is performed. Assuming the existence of a single quantitative trait locus (QTL) in the region/chromosome identified by the genome scan, the method constructs a confidence region for its true position by testing each location in the chromosome to see if it can be the trait locus.

Summary of Genetic Analysis Workshop 15: Group 9 linkage analysis of the CEPH expression data.

Group 9 participants carried out linkage analysis of the Centre d'Etude de Polymorphism Humain (CEPH) expression data, using strategies that ranged from focused investigation of a small number of traits to full genome scans of all available traits. Results from five key areas encompass the most important results within and across the 17 participating groups. First, both extensive genetic heterogeneity and poor predictability of mapping results based on heritability have key implications for study design.

A comparison of methods for intermediate fine mapping.

The arrival of highly dense genetic maps at low cost has geared the focus of linkage analysis studies toward developing methods for placing putative trait loci in narrow regions with high confidence. This shift has led to a new analytic scheme that expands the traditional two-stage protocol of preliminary genome scan followed by fine mapping through inserting a new stage in between the two. The goal of this new "intermediate" fine mapping stage is to isolate disease loci to narrow intervals with high confidence so that association studies can be more focused, efficient, and cost-effective.

Interval estimation of disease loci: development and applications of new linkage methods.

Three variants of the confidence set inference (CSI) procedure were proposed and applied to both the simulated and the Collaborative Study on the Genetics of Alcoholism (COGA) data. For each of the two applications, we first performed a preliminary genome scan study based on the microsatellite markers using the GENEHUNTER+ software to identify regions that potentially harbor disease loci. For each such region, we estimated the sibling identity-by-descent sharing probability distribution at the putative disease locus.

Microsatellites versus Single-Nucleotide Polymorphisms in confidence interval estimation of disease loci.

With cost-effective high-throughput Single Nucleotide Polymorphism (SNP) arrays now becoming widely available, it is highly anticipated that SNPs will soon become the choice of markers in whole genome screens. This optimism raises a great deal of interest in assessing whether dense SNP maps offer at least as much information as their microsatellite (MS) counterparts. Factors considered to date include information content, strength of linkage signals, and effect of linkage disequilibrium.

A two-step procedure for constructing confidence intervals of trait loci with application to a rheumatoid arthritis dataset.

Preliminary genome screens are usually succeeded by fine mapping analyses focusing on the regions that signal linkage. It is advantageous to reduce the size of the regions where follow-up studies are performed, since this will help better tackle, among other things, the multiplicity adjustment issue associated with them. We describe a two-step approach that uses a confidence set inference procedure as a tool for intermediate mapping (between preliminary genome screening and fine mapping) to further localize disease loci.

A confidence set inference procedure for gene mapping using markers with incomplete polymorphism.

A recent approach for gene mapping based on confidence set inference (CSI) promises several advantages, including avoidance of corrections for multiple tests, availability of confidence intervals with known statistical properties, and sufficient localizations of disease genes. This paper proposes an extended CSI procedure that can handle markers with incomplete polymorphism, thereby increasing the applicability of the set of CSI methods in practical situations. Simulation studies show that the new procedure retains the main advantages of the original CSI.

Linkage analysis of the simulated data - evaluations and comparisons of methods.

The goal of this study is to evaluate, compare, and contrast several standard and new linkage analysis methods. First, we compare a recently proposed confidence set approach with MAPMAKER/SIBS. Then, we evaluate a new Bayesian approach that accounts for heterogeneity.