Package 'RankGWASK'

Compare RSS and SRS GWAS

Description

Compare top SNPs identified using RSS and SRS.

Usage

compareRSSvsSRS(
  data,
  phenotype = "Yield",
  ranking = "PlantHeight",
  set.size = 5
)

Arguments

data	Dataset.
phenotype	Phenotype variable.
ranking	Ranking variable.
set.size	RSS set size.

Value

A list of GWAS results.

Examples

data(gwas_data)
comp <- compareRSSvsSRS(gwas_data)

---

Example GWAS Dataset

Description

Simulated genome-wide association study (GWAS) dataset containing 500 individuals, 100 SNP markers, a Yield phenotype, and a PlantHeight ranking variable.

Usage

gwas_data

Format

A data frame with 500 rows and 103 variables.

The dataset contains:

• ID: Individual identifier.

• SNP1-SNP100: Genotype markers coded as 0, 1, or 2.

• Yield: Quantitative phenotypic trait.

• PlantHeight: Ranking variable used in Ranked Set Sampling (RSS).

Details

This dataset was generated for illustrating Genome-Wide Association Studies (GWAS) under Ranked Set Sampling (RSS) and Simple Random Sampling (SRS) designs. SNP markers are coded as 0, 1, or 2, representing genotype classes.

Source

Simulated data generated using simulateGWAS().

Examples

data(gwas_data)
dim(gwas_data)
names(gwas_data)[1:10]

---

RSS-Based GWAS

Description

Perform GWAS after Ranked Set Sampling.

Usage

rssGWAS(data, phenotype = "Yield", ranking = "PlantHeight", set.size = 5)

Arguments

data	Dataset.
phenotype	Phenotype column.
ranking	Ranking variable.
set.size	RSS set size.

Value

GWAS results.

Examples

data(gwas_data)
res <- rssGWAS(
  gwas_data,
  phenotype = "Yield",
  ranking = "PlantHeight"
)

---

Manhattan Plot

Description

Create Manhattan plot from GWAS results.

Usage

rssManhattan(results)

Arguments

results

Output from standardGWAS() or rssGWAS().

Value

Manhattan plot.

Examples

data(gwas_data)
res <- standardGWAS(gwas_data)
rssManhattan(res)

---

QQ Plot for GWAS

Description

Create QQ plot from GWAS results.

Usage

rssQQ(results)

Arguments

results

GWAS results.

Value

QQ plot.

Examples

data(gwas_data)
res <- standardGWAS(gwas_data)
rssQQ(res)

---

Ranked Set Sampling (RSS)

Description

Performs Ranked Set Sampling (RSS) by dividing data into sets, ranking within sets, and selecting order statistics.

Usage

rssSample(data, ranking, set.size = 5, m = 1, r = 1)

Arguments

data	A data frame.
ranking	Character string specifying ranking variable.
set.size	Integer: number of units per set.
m	Integer: number of cycles (default 1).
r	Integer: number of repetitions (default 1).

Value

A data frame containing RSS selected observations.

Examples

data(gwas_data)
rssSample(gwas_data, "PlantHeight", set.size = 5, m = 2, r = 1)

---

Simulate GWAS Data

Description

Generate simulated GWAS genotype and phenotype data.

Usage

simulateGWAS(n = 500, nSNP = 100, seed = 123)

Arguments

n	Number of individuals.
nSNP	Number of SNP markers.
seed	Random seed.

Value

A data frame containing simulated SNP markers, phenotype values and ranking variable.

Examples

sim <- simulateGWAS()
head(sim)

---

Simple Random Sampling

Description

Draw a simple random sample.

Usage

srsSample(data, n)

Arguments

data	Data frame.
n	Sample size.

Value

A sampled data frame.

---

Standard GWAS Analysis

Description

Perform single-marker genome-wide association analysis using linear regression.

Usage

standardGWAS(data, phenotype = "Yield")

Arguments

data	A data frame containing SNP markers and phenotype.
phenotype	Character string specifying the phenotype column.

Value

A data frame containing SNP effects, standard errors, and p-values sorted by significance.

Examples

data(gwas_data)
res <- standardGWAS(
  gwas_data,
  phenotype = "Yield"
)

head(res)

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