diff --git a/biology/gemma/Makefile b/biology/gemma/Makefile
index 855f3a86b669..6e1c6ef9c2b4 100644
--- a/biology/gemma/Makefile
+++ b/biology/gemma/Makefile
@@ -1,38 +1,39 @@
 PORTNAME=	gemma
-DISTVERSION=	0.98.3
-PORTREVISION=	5
+DISTVERSIONPREFIX=	v
+DISTVERSION=	0.98.5
 CATEGORIES=	biology
 
 MAINTAINER=	jwb@FreeBSD.org
 COMMENT=	Genome-wide Efficient Mixed Model Association
-WWW=		https://github.com/genetics-statistics/GEMMA
+WWW=		https://xiangzhou.github.io/software/ \
+		https://github.com/genetics-statistics/GEMMA/
 
 LICENSE=	GPLv3
 LICENSE_FILE=	${WRKSRC}/LICENSE
 
 LIB_DEPENDS=	libgsl.so:math/gsl
 
 USES=		blaslapack:openblas compiler:c++11-lang eigen:3 gmake \
 		localbase:ldflags
 USE_GITHUB=	yes
 GH_ACCOUNT=	genetics-statistics
 GH_PROJECT=	GEMMA
 
 MAKEFILE=	${FILESDIR}/Makefile
 # Assuming openblas is built with pthreads, not openmp
 CXXFLAGS+=	-I${LOCALBASE}/include/eigen3 -DOPENBLAS -pthread
 LDFLAGS+=	-lopenblas -pthread
 
 OPTIONS_DEFINE=	EXAMPLES
 
 pre-configure:
 	@${REINPLACE_CMD} -e 's|../bin/gemma|../gemma|' ${WRKSRC}/test/*.sh
 
 do-install-EXAMPLES-on:
 	@${MKDIR} ${STAGEDIR}${EXAMPLESDIR}
 	(cd ${WRKSRC}/example && ${COPYTREE_SHARE} . ${STAGEDIR}${EXAMPLESDIR})
 
 do-test:
 	(cd ${WRKSRC}/test && ${SH} test_suite.sh)
 
 .include <bsd.port.mk>
diff --git a/biology/gemma/distinfo b/biology/gemma/distinfo
index dde8eb92ac25..fd7346b8e723 100644
--- a/biology/gemma/distinfo
+++ b/biology/gemma/distinfo
@@ -1,3 +1,3 @@
-TIMESTAMP = 1609514940
-SHA256 (genetics-statistics-GEMMA-0.98.3_GH0.tar.gz) = 8c27874634269f52a194a41048e70c17e2128563f56bb8ef59338a93147c61ba
-SIZE (genetics-statistics-GEMMA-0.98.3_GH0.tar.gz) = 49572695
+TIMESTAMP = 1732864105
+SHA256 (genetics-statistics-GEMMA-v0.98.5_GH0.tar.gz) = 3ed336deee29e370f96ec8f1a240f7b62550e57dcd1694245ce7ec8f42241677
+SIZE (genetics-statistics-GEMMA-v0.98.5_GH0.tar.gz) = 51259250
diff --git a/biology/gemma/pkg-descr b/biology/gemma/pkg-descr
index 9db7086ea489..c4c81c9fa6f6 100644
--- a/biology/gemma/pkg-descr
+++ b/biology/gemma/pkg-descr
@@ -1,3 +1,23 @@
 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.