Attachment #255521 for bug #283036

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PORTNAME=	gemma
DISTVERSIONPREFIX=	v
DISTVERSION=	0.98.5
CATEGORIES=	biology

MAINTAINER=	jwb@FreeBSD.org
COMMENT=	Genome-wide Efficient Mixed Model Association
WWW=		https://xiangzhou.github.io/software/ \
		https://github.com/genetics-statistics/GEMMA/

LICENSE=	GPLv3
LICENSE_FILE=	${WRKSRC}/LICENSE

Lines 1-3 Link Here

(-)b/biology/gemma/distinfo (-3 / +3 lines)
1	TIMESTAMP = 1609514940	1	TIMESTAMP = 1732864105
2	SHA256 (genetics-statistics-GEMMA-0.98.3_GH0.tar.gz) = 8c27874634269f52a194a41048e70c17e2128563f56bb8ef59338a93147c61ba	2	SHA256 (genetics-statistics-GEMMA-v0.98.5_GH0.tar.gz) = 3ed336deee29e370f96ec8f1a240f7b62550e57dcd1694245ce7ec8f42241677
3	SIZE (genetics-statistics-GEMMA-0.98.3_GH0.tar.gz) = 49572695	3	SIZE (genetics-statistics-GEMMA-v0.98.5_GH0.tar.gz) = 51259250




GEMMA is a software toolkit for fast application of linear mixed models (LMMs)
and related models to genome-wide association studies (GWAS) and other
large-scale data sets.

Key features:

1.  Fast assocation tests implemented using the univariate linear mixed model
    (LMM). In GWAS, this can correct for population structure and sample
    non-exchangeability. It also provides estimates of the proportion of
    variance in phenotypes explained by available genotypes (PVE), often called
    "chip heritability" or "SNP heritability".
2.  Fast association tests for multiple phenotypes implemented using a
    multivariate linear mixed model (mvLMM). In GWAS, this can correct for
    population structure and sample (non)exchangeability - jointly in multiple
    complex phenotypes.
3.  Bayesian sparse linear mixed model (BSLMM) for estimating PVE, phenotype
    prediction, and multi-marker modeling in GWAS.
4.  Estimation of variance components ("chip/SNP heritability") partitioned by
    different SNP functional categories from raw (individual-level) data or
    summary data. For raw data, HE regression or the REML AI algorithm can be
    used to estimate variance components when individual-level data are
    available. For summary data, GEMMA uses the MQS algorithm to estimate
    variance components.

Return to bug 283036

Lines 1-11 Link Here

(-)b/biology/gemma/Makefile (-3 / +4 lines)
1	PORTNAME= gemma	1	PORTNAME= gemma
2	DISTVERSION= 0.98.3	2	DISTVERSIONPREFIX= v
3	PORTREVISION= 5	3	DISTVERSION= 0.98.5
4	CATEGORIES= biology	4	CATEGORIES= biology
5		5
6	MAINTAINER= jwb@FreeBSD.org	6	MAINTAINER= jwb@FreeBSD.org
7	COMMENT= Genome-wide Efficient Mixed Model Association	7	COMMENT= Genome-wide Efficient Mixed Model Association
8	WWW= https://github.com/genetics-statistics/GEMMA	8	WWW= https://xiangzhou.github.io/software/ \
		9	https://github.com/genetics-statistics/GEMMA/
9		10
10	LICENSE= GPLv3	11	LICENSE= GPLv3
11	LICENSE_FILE= ${WRKSRC}/LICENSE	12	LICENSE_FILE= ${WRKSRC}/LICENSE

Lines 1-3 Link Here

(-)b/biology/gemma/pkg-descr (-1 / +20 lines)
1	GEMMA is a software toolkit for fast application of linear mixed models (LMMs)	1	GEMMA is a software toolkit for fast application of linear mixed models (LMMs)
2	and related models to genome-wide association studies (GWAS) and other	2	and related models to genome-wide association studies (GWAS) and other
3	large-scale data sets.	3	large-scale data sets.
4	-	4
		5	Key features:
		6
		7	1. Fast assocation tests implemented using the univariate linear mixed model
		8	(LMM). In GWAS, this can correct for population structure and sample
		9	non-exchangeability. It also provides estimates of the proportion of
		10	variance in phenotypes explained by available genotypes (PVE), often called
		11	"chip heritability" or "SNP heritability".
		12	2. Fast association tests for multiple phenotypes implemented using a
		13	multivariate linear mixed model (mvLMM). In GWAS, this can correct for
		14	population structure and sample (non)exchangeability - jointly in multiple
		15	complex phenotypes.
		16	3. Bayesian sparse linear mixed model (BSLMM) for estimating PVE, phenotype
		17	prediction, and multi-marker modeling in GWAS.
		18	4. Estimation of variance components ("chip/SNP heritability") partitioned by
		19	different SNP functional categories from raw (individual-level) data or
		20	summary data. For raw data, HE regression or the REML AI algorithm can be
		21	used to estimate variance components when individual-level data are
		22	available. For summary data, GEMMA uses the MQS algorithm to estimate
		23	variance components.