bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2020.05.29.114140

Marker-assisted selection of trees with MALE STERALITY 1 in Cryptomeria japonica D. Don

Moriguchi, Y.·Ueno, S.·Hasegawa, Y.·Tadama, T.·Watanabe, M.·Saito, R.·Hirayama, S.·Iwai, J.·Konno, Y.
View original source ↗Export metadata ↓

Abstract

Practical use of marker-assisted selection (MAS) is limited in conifers because of the difficulty with developing markers due to a rapid decrease in linkage disequilibrium, the limited genomic information available, and the diverse genetic backgrounds among breeding material collections. First, in this study, two families were produced by artificial crossing between two male-sterile trees, Shindai11 and Shindai12, and a plus tree, Suzu-2 (Ms1/ms1) (S11-S and S12-S families, respectively). The segregation ratio between male-sterile and male-fertile trees did not deviate significantly from the expected 1:1 ratio in either family. These results clearly suggested that the male-sterile gene of Shindai11 and Shindai12 is MALE STERALITY 1 (MS1). Because some markers reported previously have not been linkage mapped, we constructed a partial linkage map of the region encompassing MS1 using the S11-S and S12-S families. For the S11-S and S12-S families, 19 and 18 markers were mapped onto the partial linkage maps of MS1 region, respectively. There was collinearity (conserved gene order) between the two partial linkage maps. Two markers (CJt020762_ms1-1 and reCj19250_2335) were mapped to the same position as the MS1 locus on both maps. Of these markers, we used CJt020762 for MAS in this study. According to the MAS results for 650 trees from six prefectures of Japan (603 trees from breeding materials and 47 trees from the Ishinomaki natural population), five trees in Niigata Prefecture and one tree in Yamagata Prefecture had heterozygous ms1-1, and three trees in Miyagi Prefecture had heterozygous ms1-2. The results obtained in this study suggested that there may be geographical hotspots for the ms1-1 and ms1-2 alleles. Because MAS can be used effectively to reduce the labor and time required for selection of trees with a male-sterile gene, the number of breeding materials should increase in the future.

Source connections

Explore related subjects

geneticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Moriguchi, Y., Ueno, S., Hasegawa, Y., Tadama, T., Watanabe, M., Saito, R., Hirayama, S., Iwai, J., Konno, Y.. 2020-05-31. Marker-assisted selection of trees with MALE STERALITY 1 in Cryptomeria japonica D. Don. https://doi.org/10.1101/2020.05.29.114140

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗
bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗
bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗