Title: | Genetic Association Analysis |
Version: | 0.0.3 |
Date: | 2025-3-24 |
Maintainer: | Jing Hua Zhao <jinghuazhao@hotmail.com> |
Description: | It gathers information, meta-data and scripts in a two-part Henry-Stewart talk by Zhao (2009, <doi:10.69645/DCRY5578>), which showcases analysis in aspects such as testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with trait using genetic and statistical models as well as Bayesian implementation, power calculation in study design and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications. |
License: | MIT + file LICENSE |
URL: | https://jinghuazhao.github.io/gaawr2/, https://github.com/jinghuazhao/gaawr2 |
BugReports: | https://github.com/jinghuazhao/gaawr2/issues |
Encoding: | UTF-8 |
Depends: | R (≥ 3.5.0) |
Imports: | dplyr, gap, gap.datasets, ggplot2, survival, Rdpack |
RdMacros: | Rdpack |
LazyData: | Yes |
LazyLoad: | Yes |
LazyDataCompression: | xz |
VignetteBuilder: | knitr |
Suggests: | BLR, BGLR, biomaRt, bookdown, Cairo, EnsDb.Hsapiens.v75, ensembldb, GMMAT, HardyWeinberg, haplo.stats, httr, httpuv, jsonlite, kableExtra, knitr, MCMCglmm, plumber, powerEQTL, R2jags, regress, seqminer, SNPassoc, testthat, tidyr |
RoxygenNote: | 7.3.2 |
NeedsCompilation: | no |
Packaged: | 2025-03-24 14:21:47 UTC; jhz22 |
Author: | Jing Hua Zhao [aut, cre] (ORCID: <https://orcid.org/0000-0002-1463-5870>, ORCID: <https://orcid.org/0000-0003-4930-3582>), Benjamin Altmann [ctb], Brian Ripley [ctb] |
Repository: | CRAN |
Date/Publication: | 2025-03-24 15:00:09 UTC |
Genetic Association Analysis
Description
It gathers information, meta-data and scripts in a two-part Henry-Stewart talk by Zhao (2009, doi:10.69645/DCRY5578), which showcases analysis in aspects such as testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with trait using genetic and statistical models as well as Bayesian implementation, power calculation in study design and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications.
Details
Available data and function are listed in the following table.
Objects | Description |
Dataset | |
DiaHealth | A Bangladeshi dataset for Type 2 diabetes prediction |
diabetes | A diabetes dataset |
Functions | |
welcome | An enhanced welcome |
We can add references such as Francois (2014).
Usage
Vignettes on package usage:
Genetic Association Analysis with R (II),
vignette("gaawr2")
.Web facilities,
vignette("web")
.
Author(s)
Jing Hua Zhao in collaboration with other colleagues.
References
Romain Francois (2014). bibtex: bibtex parser. R package version 0.4.0.
See Also
Useful links:
Report bugs at https://github.com/jinghuazhao/gaawr2/issues
Examples
welcome(3)
DiaHealth
Description
A Bangladeshi dataset for Type 2 diabetes prediction.
Usage
DiaHealth
Format
A data frame with 5,437 patients and 14 variables on demographics, clinical parameters, and medical history.
age
Years (age of the person).
gender
Categorical variable (Female, Male).
pulse_rate
Beats per minute (bpm).
systolic_bp
SBP in millimeters of mercury (mmHg).
diastolic_bp
DBP (mmHg).
glucose
Milligrams per deciliter (mg/dL).
height
Meter (m).
weight
Kilogram (kg).
bmi
Body mass index (BMI).
family_diabetes
Family history of diabetes.
hypertensive
Hypertension.
family_hypertension
Family history of hypertension.
cardiovascular_disease
CVD.
stroke
Stroke.
diabetic
Diabetic.
Details
Key features include age, gender, pulse rate, blood pressure (systolic and diastolic), glucose level, BMI, and family history of diabetes and related conditions like hypertension and cardiovascular disease. The dataset is labeled with a binary outcome indicating whether each patient has diabetes. This rich dataset is designed to support the development and evaluation of machine learning models for diabetes detection, management, and treatment.
Source
Prama TT, Zaman M, Sarker F, Mamun KA. (2024), “DiaHealth: A Bangladeshi Dataset for Type 2 Diabetes Prediction ”, Mendeley Data, V1, doi: 10.17632/7m7555vgrn.1
See Also
Examples
data(DiaHealth)
knitr::kable(head(DiaHealth,5),caption="Five individauls in DiaHealth")
Diabetes Dataset
Description
A diabetes dataset on 1,000 patients.
Usage
diabetes
Format
A data frame with 1,000 rows and 14 variables:
ID
Unique identifier for each patient (unitless).
No_Pation
Patient number (unitless).
Gender
Categorical variable (Female, Male).
AGE
Years (age of the person).
Urea
Chief nitrogenous end product of the metabolic breakdown of proteins in milligrams per deciliter (mg/dL).
Cr
Creatinine ratio (Cr) (mg/dL).
HbA1c
Hemoglobin A1c (HbA1c) % (percentage).
Chol
Cholesterol (Chol) (mg/dL).
TG
Triglycerides (TG) (mg/dL).
HDL
High-density lipoprotein (HDL) (mg/dL).
LDL
Low-density lipoprotein (LDL) (mg/dL).
VLDL
Very-low-density lipoprotein (VLDL) (mg/dL).
BMI
Body mass index (BMI).
CLASS
Class (the patient's diabetes disease class may be Diabetic, Non-Diabetic, or Predict-Diabetic).
Details
The data were collected from the Iraqi society, as they data were acquired from the laboratory of Medical City Hospital and (the Specializes Center for Endocrinology and Diabetes-Al-Kindy Teaching Hospital).
Source
Rashid A (2020), “Diabetes Dataset”, Mendeley Data, V1, doi: 10.17632/wj9rwkp9c2.1.
See Also
Examples
data(diabetes)
knitr::kable(head(diabetes,5),caption="Five individuals in diabetes data")
An enhanced welcome
Description
It prints a welcome message, saying number of times.
Usage
welcome(n)
Arguments
n |
The number of times (>1 integer) to welcome the user. |
Value
Prints a welcome message to the console.
Examples
welcome(3)