Fruit cracking and firmness DNA test development and evaluation in sweet cherry

One application of DNA-informed breeding,which has potential to increase the effectiveness of traditional breeding methods,is the use of DNAbased diagnostic tests to estimate genetic potential of breeding individuals.In sweet cherry(Prunus avium L.),cracked or soft fruit are major industry challenges.Recent research detected two quantitative trait loci(QTLs)for fruit cracking and firmness differing in trait levels associated with QTL haplotypic variation.Also,a DNA test for cracking(Pav-G5Crack-SSR),using two simple sequence repeat(SSR)markers,was previously developed but not yet validated on breeding germplasm.In addition to SSR markers,single nucleotide polymorphism(SNP)markers can be used for developing locus-specific DNA tests and run as simple assays such as high-resolution melting(HRM).The objective of this research was to develop and evaluate the predictiveness of DNA tests for fruit cracking and firmness in sweet cherry.Unselected seedlings from pedigreeconnected families were screened with the Pav-G5Crack-SSR DNA test.DNA tests were also created from four SNP markers with HRM assays,using two years of cracking and firmness data for evaluation.Pav-G5Crack-SSR explained 12–15%of the cracking phenotypic variance,while Pav-G1Crack-SNP and Pav-G5Crack-SNP(which targeted the same QTL as Pav-G5Crack-SSR)together explained 16%–30%of the cracking phenotypic variance.Pav-G1Firm-SNP and Pav-G3Firm-SNP together explained 22%–28%of the firmness phenotypic variance.All three DNA tests can be implemented in breeding programs to enhance effectiveness in breeding for decreased cracking incidence and increased fruit firmness in sweet cherry.

Evolutionary genomics of climatic adaptation and resilience to climate change in alfalfa

Given the escalating impact of climate change on agriculture and food security,gaining insights into the evolutionary dynamics of climatic adaptation and uncovering climate-adapted variation can empower the breeding of climate-resilient crops to face future climate change.Alfalfa(Medicago sativa subsp.sativa),the queen of forages,shows remarkable adaptability across diverse global environments,making it an excellent model for investigating species responses to climate change.In this study,we performed population genomic analyses using genome resequencing data from 702 accessions of 24 Medicago species to unravel alfalfa’s climatic adaptation and genetic susceptibility to future climate change.We found that interspecific genetic exchange has contributed to the gene pool of alfalfa,particularly enriching defense and stress-response genes.Intersubspecific introgression between M.sativa subsp.falcata(subsp.falcata)and alfalfa not only aids alfalfa’s climatic adaptation but also introduces genetic burden.A total of 1671 genes were associated with climatic adaptation,and 5.7%of them were introgressions from subsp.falcata.By integrating climate-associated variants and climate data,we identified populations that are vulnerable to future climate change,particularly in higher latitudes of the Northern Hemisphere.These findings serve as a clarion call for targeted conservation initiatives and breeding efforts.We also identified preadaptive populations that demonstrate heightened resilience to climate fluctuations,illuminating a pathway for future breeding strategies.Collectively,this study enhances our understanding about the local adaptation mechanisms of alfalfa and facilitates the breeding of climate-resilient alfalfa cultivars,contributing to effective agricultural strategies for facing future climate change.

Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes

Dear Editor,Pan-genomes with high quality de novo assemblies are shifting the paradigm of biology research in genome evolution,speciation,and function annotation(Shi et al.,2023).An all-vs.-all comparison across assemblies potentially overcomes the limitation of mapping short reads to a single assembly in cataloging polymorphisms,especially large insertions and deletions(indels)contributing to phenotypic variations through altering gene structure or expression(Chen et al.,2021).

rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study

Along with the develoipment of high-throughput sequencing technologies,both sample size and SNP number are increasing rapidly in genome-wide association studies(GWAS),and the associated computation is more challenging than ever.Here,we present a memory-efficient,visualization-enhanced,and parallel-accelerated R package called“r MVP”to address the need for improved GWAS computation.r MVP can 1)effectively process large GWAS data,2)rapidly evaluate population structure,3)efficiently estimate variance components by Efficient Mixed-Model Association e Xpedited(EMMAX),Factored Spectrally Transformed Linear Mixed Models(Fa ST-LMM),and Haseman-Elston(HE)regression algorithms,4)implement parallel-accelerated association tests of markers using general linear model(GLM),mixed linear model(MLM),and fixed and random model circulating probability unification(Farm CPU)methods,5)compute fast with a globally efficient design in the GWAS processes,and 6)generate various visualizations of GWASrelated information.Accelerated by block matrix multiplication strategy and multiple threads,the association test methods embedded in r MVP are significantly faster than PLINK,GEMMA,and Farm CPU_pkg.r MVP is freely available at https://github.com/xiaolei-lab/r MVP.

GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction

Genome-wide association study(GWAS)and genomic prediction/selection(GP/GS)are the two essential enterprises in genomic research.Due to the great magnitude and complexity of genomic and phenotypic data,analytical methods and their associated software packages are frequently advanced.GAPIT is a widely-used genomic association and prediction integrated tool as an R package.The first version was released to the public in 2012 with the implementation of the general linear model(GLM),mixed linear model(MLM),compressed MLM(CMLM),and genomic best linear unbiased prediction(g BLUP).The second version was released in 2016 with several new implementations,including enriched CMLM(ECMLM)and settlement of MLMs under progressively exclusive relationship(SUPER).All the GWAS methods are based on the single-locus test.For the first time,in the current release of GAPIT,version 3 implemented three multi-locus test methods,including multiple loci mixed model(MLMM),fixed and random model circulating probability unification(Farm CPU),and Bayesian-information and linkage-disequilibrium iteratively nested keyway(BLINK).Additionally,two GP/GS methods were implemented based on CMLM(named compressed BLUP;c BLUP)and SUPER(named SUPER BLUP;s BLUP).These new implementations not only boost statistical power for GWAS and prediction accuracy for GP/GS,but also improve computing speed and increase the capacity to analyze big genomic data.Here,we document the current upgrade of GAPIT by describing the selection of the recently developed methods,their implementations,and potential impact.All documents,including source code,user manual,demo data,and tutorials,are freely available at the GAPIT website(http://zzlab.net/GAPIT).

Genetics-inspired data-driven approaches explain and predict crop performance fluctuations attributed to changing climatic conditions

Dear Editor,Genetics-focused approaches have been widely used to uncover major genetic variants associated with performance variation.Selecting,manipulating,and editing genetic variants significantly improve crop performance.Meanwhile,the genetic component explains a portion of performance variation,and the environ-mental component contributes to the remaining,often large,portion(Laidig et al.,2017;Bonecke et al.,2020;Li et al.,2021).To ensure superior and robust performance,elite varieties are extensively tested across multiple years and locations.These extensive performance records,coupled with climatic profiles,could be leveraged to understand climate's impact on agriculture through approaches parallel to quantitative genetics approaches(Figure 1A).

Genome Assembly of Alfalfa Cultivar Zhongmu-4 and Identification of SNPs Associated with Agronomic Traits

Alfalfa(Medicago sativa L.)is the most important legume forage crop worldwide with high nutritional value and yield.For a long time,the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits.We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4,a cultivar widely cultivated in China,and a comprehensive database of genomic variations based on resequencing of 220 germplasms.Approximate 2.74 Gb contigs(N50 of 2.06 Mb),accounting for 88.39%of the estimated genome,were assembled,and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes.A total of 34,922 allelic genes were identified from the allele-aware genome.We observed the expansion of gene families,especially those related to the nitrogen metabolism,and the increase of repetitive elements including transposable elements,which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula.Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe,suggesting that geography may influence alfalfa genetic divergence during local adaption.Genome-wide association studies identified 101 single nucleotide polymorphisms(SNPs)associated with 27 agronomic traits.Two candidate genes were predicted to be correlated with fall dormancy and salt response.We believe that the alleleaware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.

Genome-wide association study of the backfat thickness trait in two pig populations

Backfat thickness is a good predictor of carcass lean content,an economically important trait,and a main breeding target in pig improvement.In this study,the candidate genes and genomic regions associated with the tenth rib backfat thickness trait were identified in two independent pig populations,using a genome-wide association study of porcine 60K SNP genotype data applying the compressed mixed linear model(CMLM)statistical method.For each population,30 most significant single-nucleotide polymorphisms(SNPs)were selected and SNP annotation implemented using Sus scrofa Build 10.2.In the first population,25 significant SNPs were distributed on seven chromosomes,and SNPs on SSC1 and SSC7 showed great significance for fat deposition.The most significant SNP(ALGA0006623)was located on SSC1,upstream of the MC4R gene.In the second population,27 significant SNPs were recognized by annotation,and 12 SNPs on SSC12 were related to fat deposition.Two haplotype blocks,M1GA0016251-MARC0075799 and ALGA0065251-MARC0014203-M1GA0016298-ALGA0065308,were detected in significant regions where the PIPNC1 and GH1 genes were identified as contributing to fat metabolism.The results indicated that genetic mechanism regulating backfat thickness is complex,and that genome-wide associations can be affected by populations with different genetic backgrounds.