Mendelian randomization in family data

The phrase "mendelian randomization" has become associated with the use of genetic polymorphisms to uncover causal relationships between phenotypic variables. The statistical methods useful in mendelian randomization are known as instrumental variable techniques. We present an approach to instrumental variable estimation that is useful in family data and is robust to the use of weak instruments. We illustrate our method to measure the causal influence of low-density lipoprotein on high-density lipoprotein, body mass index, triglycerides, and systolic blood pressure. We use the Framingham Heart Study data as distributed to participants in the Genetics Analysis Workshop 16

A Variance Component Based Multi-marker Association Test Using Family and Unrelated Data

Background: Incorporating family data in genetic association studies has become increasingly appreciated, especially for its potential value in testing rare variants. We introduce here a variance-component based association test that can test multiple common or rare variants jointly using both family and unrelated samples. Results: The proposed approach implemented in our R package aggregates or collapses the information across a region based on genetic similarity instead of genotype scores, which avoids the power loss when the effects are in different directions or have different association strengths. The method is also able to effectively leverage the LD information in a region and it can produce a test statistic with an adaptively estimated number of degrees of freedom. Our method can readily allow for the adjustment of non-genetic contributions to the familial similarity, as well as multiple covariates. Conclusions: We demonstrate through simulations that the proposed method achieves good performance in terms of Type I error control and statistical power. The method is implemented in the R package “fassoc”, which provides a useful tool for data analysis and exploration

Comparison of univariate and multivariate linkage analysis of traits related to hypertension

Complex traits are often manifested by multiple correlated traits. One example of this is hypertension (HTN), which is measured on a continuous scale by systolic blood pressure (SBP). Predisposition to HTN is predicted by hyperlipidemia, characterized by elevated triglycerides (TG), low-density lipids (LDL), and high-density lipids (HDL). We hypothesized that the multivariate analysis of TG, LDL, and HDL would be more powerful for detecting HTN genes via linkage analysis compared with univariate analysis of SBP. We conducted linkage analysis of four chromosomal regions known to contain genes associated with HTN using SBP as a measure of HTN in univariate Haseman-Elston regression and using the correlated traits TG, LDL, and HDL in multivariate Haseman-Elston regression. All analyses were conducted using the Framingham Heart Study data. We found that multivariate linkage analysis was better able to detect chromosomal regions in which the angiotensinogen, angiotensin receptor, guanine nucleotide-binding protein 3, and prostaglandin I2 synthase genes reside. Univariate linkage analysis only detected the AGT gene. We conclude that multivariate analysis is appropriate for the analysis of multiple correlated phenotypes, and our findings suggest that it may yield new linkage signals undetected by univariate analysis

Controls on soil carbon sequestration and dynamics: lessons from land-use change

Includes bibliographical references (pages 82-83).Soil carbon (C) dynamics and sequestration are controlled by interactions of chemical, physical and biological factors. These factors include biomass quantity and quality, physical environment and the biota. Management can alter these factors in ways that alter C dynamics. We have focused on a range of managed sites with documented land use change from agriculture or grassland to forest. Our results suggest that interactions of soil type, plant and environment impact soil C sequestration. Above and below ground C storage varied widely across sites. Results were related to plant type and calcium on sandy soils in our Northern sites. Predictors of sequestration were more difficult to detect over the temperature range of 12.4°C in the present study. Accrual of litter under pines in the moist Mississippi site limited C storage in a similar manner to our dry Nebraska site. Pre-planting heterogeneity of agricultural fields such as found in Illinois influences C contents. Manipulation of controls on C sequestration such as species planted or amelioration of soil quality before planting within managed sites could increase soil C to provide gains in terrestrial C storage. Cost effective management would also improve soil C pools positively affecting soil fertility and site productivity.Publisher version: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3380508

miR-196b target screen reveals mechanisms maintaining leukemia stemness with therapeutic potential.

We have shown that antagomiR inhibition of miRNA miR-21 and miR-196b activity is sufficient to ablate MLL-AF9 leukemia stem cells (LSC) in vivo. Here, we used an shRNA screening approach to mimic miRNA activity on experimentally verified miR-196b targets to identify functionally important and therapeutically relevant pathways downstream of oncogenic miRNA in MLL-r AML. We found Cdkn1b (p27Kip1) is a direct miR-196b target whose repression enhanced an embryonic stem cell–like signature associated with decreased leukemia latency and increased numbers of leukemia stem cells in vivo. Conversely, elevation of p27Kip1 significantly reduced MLL-r leukemia self-renewal, promoted monocytic differentiation of leukemic blasts, and induced cell death. Antagonism of miR-196b activity or pharmacologic inhibition of the Cks1-Skp2–containing SCF E3-ubiquitin ligase complex increased p27Kip1 and inhibited human AML growth. This work illustrates that understanding oncogenic miRNA target pathways can identify actionable targets in leukemia