Dataset Information

Advantages and pitfalls in the application of mixed-model association methods.

ABSTRACT: Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structure by implicitly conditioning on associated loci other than the candidate locus. Numerous variations on the standard MLMA approach have recently been published, with a focus on reducing computational cost. These advances provide researchers applying MLMA methods with many options to choose from, but we caution that MLMA methods are still subject to potential pitfalls. Here we describe and quantify the advantages and pitfalls of MLMA methods as a function of study design and provide recommendations for the application of these methods in practical settings.

SUBMITTER: Yang J

PROVIDER: S-EPMC3989144 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Advantages and pitfalls in the application of mixed-model association methods.

Yang Jian J Zaitlen Noah A NA Goddard Michael E ME Visscher Peter M PM Price Alkes L AL

Nature genetics 20140201 2

Mixed linear models are emerging as a method of choice for conducting genetic association studies in humans and other organisms. The advantages of the mixed-linear-model association (MLMA) method include the prevention of false positive associations due to population or relatedness structure and an increase in power obtained through the application of a correction that is specific to this structure. An underappreciated point is that MLMA can also increase power in studies without sample structur ...[more]

PMID: 24473328

Similar Datasets

Project description:IntroductionDrowning is a global public health threat, disproportionately impacting low-income and middle-income countries. In the Philippines, it is estimated that more than 5200 people die from drowning per annum. This number is likely to be higher than currently estimated with the inclusion of disaster-related and transportation-related drowning. Drowning is preventable if appropriate preventive interventions are put in place which redress known risk factors.Methods and analysisThis study uses the PRECEDE-PROCEED model (PPM), an eight-step health promotion planning and evaluation model for building and improving intervention programmes. This mixed-methods study, which can be used in any location, will be implemented in Los Baňos, Laguna, Philippines, identified as an area of concern for drowning. Using the PPM, data on drowning will be collected from death records, community observation, key informant interviews, focus group discussions and community survey. A range of analytical methods will be used to explore drowning data including univariate and χ2 analyses, analysis of variance, relative risk and calculating rates using population data. The quantitative data and themes drawn from qualitative data will be used to populate the first four phases of the PPM. Following the data collection, the remaining stages of the PPM will be designed and implemented in the barangay (village) with the highest drowning rate.Ethics and disseminationThis study has obtained ethical clearance from the University of the Philippines Manila Research Ethics Board (UPMREB 2017-425-01). Study findings will be disseminated through workshops and presentations to the local community as well as through peer-reviewed literature and conference presentations. The PPM has rarely been applied to drowning prevention and it is the aim that the study described in this protocol is expanded across other areas of the Philippines and to other countries with a high drowning burden to inform prevention efforts.

Dataset Information

Advantages and pitfalls in the application of mixed-model association methods.

Publications

Advantages and pitfalls in the application of mixed-model association methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets