TABLE 2.

Methods and software for identifying beneficial genetic variation in crop wild relatives.

Approach	Method	Statistics/strategy	Tool	References
Phenotype-independent approach	Genome scan	Standard population genetics statistics and kNN-based scans	popGenome	Pfeifer et al., 2014
		PCA-based scans for selection	pcadapt	Privé et al., 2020
		Composite test for selective sweeps	RAiSD	Alachiotis and Pavlidis, 2018
		Supervised machine learning	diploS/HIC	Kern and Schrider, 2018
		Convolutional neural network	ImaGene	Torada et al., 2019
	Genome–environment association	Fst-based genome scan	BayeScEnv	De Villemereuil and Gaggiotti, 2015
		Latent factor mixed models	LFMM2	Caye et al., 2019
		Bayesian framework for allele frequencies—environment correlation	Bayenv2	Günther and Coop, 2013
Phenotype-dependent approach	QTL mapping	Different models to map QTLS, implemented in R	R/qtl	Broman et al., 2003
		QTL mapping tool in polyploids	GWASploy	Rosyara et al., 2016
		Pooled data, univariate and multivariate QTL mapping	MultiQTL	MultiQTL Ltd., www.multiQTL.com
	Genome-wide association studies	Not requiring a reference genome	KmerGWAS	Voichek and Weigel, 2020
		GAPIT	GAPIT	Tang et al., 2016
		Univariate, multivariate, and sparse Bayesian linear mixed models	GEMMA	Zhou and Stephens, 2014
	Expression analysis	Now include a full pipeline to analyze RNA-Seq data	RSEM	Original paper: Li and Dewey, 2011
		Web-based tool to integrate results of different expression analysis packages	IDEAMEX	Jiménez-Jacinto et al., 2019