Joint Mapping for Quantitative Trait Loci

A comprehensive computational framework for joint mapping, developed by Li (2016) , supports quantitative trait locus detection in structured genetic populations. It integrates robust phenotype summarization, computes genotype probabilities, and imputes missing markers for association and linkage mapping. Empirical significance thresholds are estimated via permutation testing coupled with stepwise regression. The framework enables genome-wide scans under both univariate and multivariate trait models, streamlining the discovery of complex genetic architectures.

JM4QTN: Joint Mapping for Quantitative Trait Loci

Overview

JM4QTN supports joint mapping and related analyses for quantitative trait loci (QTL). The package provides phenotype summaries, genotype probability and imputation (association vs linkage mapping), permutation-based thresholds with stepwise regression (via StepReg), and scan statistics comparing full vs reduced linear models (joint_map).

Key Features

Core analysis

• Permutation testing (permutation_test) for empirical p-value and LOD cutoffs under stepwise selection
• Skeleton model (skeleton_build / skeletion_build) fitting the selected model using those cutoffs
• Joint mapping scan (joint_map) over candidate terms vs the skeleton model

Genotypes and map

• Genotype probabilities / imputation (genotype_prob) for AM or LM, optional virtual markers
• Expected genotype distribution (expected_genotype_dist) and genotype frequencies (genotype_freq)
• Haldane map (haldane_map) for recombination

Phenotypes

• Phenotype statistics (pheno_stats): normality, ANOVA, least squares means (via lsmeans)

Installation

# Install from GitHub
if (!require("devtools")) install.packages("devtools")
devtools::install_github("JunhuiLi1017/JM4QTN")

## or install from CRAN
install.pakcages("JM4QTN")

# Load the package
library(JM4QTN)

Quick start

1. Phenotype analysis (pheno_stats)

Designed for multi-environment trials: columns E, B, R, G, then traits.

pheno_data <- data.frame(
  E = rep(c("Env1", "Env2"), each = 60),
  B = rep(c("B1", "B2"), each = 30, times = 2),
  R = rep(1:5, 24),
  G = factor(rep(1:12, 10)),
  Height = rnorm(120, 175, 8),
  Weight = rnorm(120, 75, 12)
)

results <- pheno_stats(pheno_data)
results$Height$normality_test
results$Height$ANOVA
results$Height$lsmeans

2. Genotype probabilities (genotype_prob)

genetic_map <- data.frame(
  marker = c("M1", "M2", "M3"),
  chr = c(1, 1, 1),
  pos = c(0, 10, 20)
)
geno_data <- matrix(sample(0:2, 15, TRUE), nrow = 5, ncol = 3,
                    dimnames = list(paste0("Ind", 1:5), c("M1", "M2", "M3")))

result_am <- genotype_prob(genetic_map, geno_data, method = "AM")
result_lm <- genotype_prob(genetic_map, geno_data, method = "LM",
                           croType = "F2", steps = 0)

3. Permutation thresholds, skeleton model, and joint mapping

Formula-based workflow with genotype columns bound into data (see package examples for joint_map).

pheno_data <- data.frame(
  Trait1 = rnorm(100, 100, 15),
  Trait2 = rnorm(100, 50, 8),
  Popu = rep(c("Pop1", "Pop2"), each = 50)
)
geno_data <- matrix(sample(0:2, 100 * 50, TRUE), nrow = 100, ncol = 50)
colnames(geno_data) <- paste0("M", 1:50)
data1 <- cbind(pheno_data, geno_data)

terms <- c("Popu", paste0(colnames(geno_data), ":Popu"))
formula1 <- reformulate(terms, response = "Trait1")

cut_off_list <- permutation_test(formula1, data1, n = 100, alpha = 0.1)

skeleton <- skeleton_build(
  formula1, data1,
  strategy = "bidirection", metric = "SL",
  cut_off_list = cut_off_list
)

results <- joint_map(
  formula1, data1, skeleton,
  include = "Popu", cut_off_list = cut_off_list
)
results$p_value
results$lod

4. Supporting utilities

haldane_map(0.1)   # genetic distance (Morgan) -> recombination fraction
genotype_freq("Fn", generation = 3, genotype_index = 1, recomb_aq = 0.1, recomb_qb = 0.2)
expected_genotype_dist("22", "F2", Gn = 2, x = 0.1, y = 0.2)

Function reference (exported)

Backward-compatible names (same package, alternate spelling): calculate_genotype_frequencies, calculate_expected_genotype_distribution, haldane_mapping_function — see individual help pages.

Data notes

• pheno_stats: expects E, B, R, G as the first four columns, then traits.
• genotype_prob: rows = individuals, columns = markers; column order must align with GeneticMap rows.
• joint_map / permutation_test: supply a single data frame containing phenotypes, population, and numeric genotype columns used in the formula.

Citation

@Manual{JM4QTN,
  title = {JM4QTN: Joint Mapping for Quantitative Trait Loci},
  author = {Junhui Li and Wenxin Liu},
  year = {2026},
  note = {R package version 1.0.0},
  url = {https://github.com/JunhuiLi1017/JM4QTN}
}

References

• Jiang, C. and Zeng, Z.B. (1995). Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics, 140(3), 1111-1127.
• Churchill, G.A. and Doerge, R.W. (1994). Empirical threshold values for quantitative trait mapping. Genetics, 138(3), 963-971.
• Haldane, J.B.S. (1919). The combination of linkage values and the calculation of distances between the loci of linked factors. Journal of Genetics, 8(3), 299-309.

Contributing

Issues and pull requests are welcome on GitHub.

License

This project is licensed under the GNU General Public License v2.0 - see the GNU GPLv2 license text for details.

Contact

• Maintainer: Junhui Li [email protected]
• GitHub: https://github.com/JunhuiLi1017/JM4QTN