Genetic basis of maize stalk strength decoded via linkage and association mapping.

Stalk lodging is a severe problem that limits maize production worldwide, although little attention has been given to its genetic basis. Here we measured rind penetrometer resistance (RPR), an effective index for stalk lodging, in a multi-parent population of 1948 recombinant inbred lines (RILs) and an association population of 508 inbred lines (AMP508). Linkage and association mapping identified 53 and 29 single quantitative trait loci (QTLs) and 50 and 19 pairs of epistatic interactions for RPR in the multi-parent population and AMP508 population, respectively. Phenotypic variation explained by all identified epistatic QTLs (up to ~5%) was much less than that explained by all single additive QTLs (up to ~33% in the multi-parent population and ~ 60% in the AMP508 population). Among all detected QTLs, only eight single QTLs explained >10% of phenotypic variation in single RIL populations. Alleles that increased RPR were enriched in tropical/subtropical (TST) groups from the AMP508 population. Based on genome-wide association studies in both populations, we identified 137 candidate genes affecting RPR, which were assigned to multiple biological processes, such as the biosynthesis of cell wall components. Sixty-six candidate genes were cross-validated by multiple methods or populations. Most importantly, 23 candidate genes were upregulated or downregulated in high-RPR lines relative to low-RPR lines, supporting the associations between candidate genes and RPR. These findings reveal the complex nature of the genetic basis underlying RPR and provide loci or candidate genes for developing elite varieties that are resistant to stalk lodging via molecular breeding.

Unraveling coevolutionary dynamics using ecological genomics.

Coevolutionary interactions, from the delicate co-dependency in mutualistic interactions to the antagonistic relationship of hosts and parasites, are a ubiquitous driver of adaptation. Surprisingly, little is known about the genomic processes underlying coevolution in an ecological context. However, species comprise genetically differentiated populations that interact with temporally variable abiotic and biotic environments. We discuss the recent advances in coevolutionary theory and genomics as well as shortcomings, to identify coevolving genes that take into account this spatial and temporal variability of coevolution, and propose a practical guide to understand the dynamic of coevolution using an ecological genomics lens.

Natural variation identifies new effectors of water-use efficiency in Arabidopsis.

Water-use efficiency (WUE) is the ratio of biomass produced per unit of water consumed; thus, it can be altered by genetic factors that affect either side of the ratio. In the present study, we exploited natural variation for WUE to discover loci affecting either biomass accumulation or water use as factors affecting WUE. Genome-wide association studies (GWAS) using integrated WUE measured through carbon isotope discrimination (δ13C) of Arabidopsis thaliana accessions identified genomic regions associated with WUE. Reverse genetic analysis of 70 candidate genes selected based on the GWAS results and transcriptome data identified 25 genes affecting WUE as measured by gravimetric and δ13C analyses. Mutants of four genes had higher WUE than wild type, while mutants of the other 21 genes had lower WUE. The differences in WUE were caused by either altered biomass or water consumption (or both). Stomatal density (SD) was not a primary cause of altered WUE in these mutants. Leaf surface temperatures indicated that transpiration differed for mutants of 16 genes, but generally biomass accumulation had a greater effect on WUE. The genes we identified are involved in diverse cellular processes, including hormone and calcium signaling, meristematic activity, photosynthesis, flowering time, leaf/vasculature development, and cell wall composition; however, none of them had been previously linked to WUE. Thus, our study successfully identified effectors of WUE that can be used to understand the genetic basis of WUE and improve crop productivity.

Gene-level association analysis of bivariate ordinal traits with functional regressions.

In genetic studies, many phenotypes have multiple naturally ordered discrete values. The phenotypes can be correlated with each other. If multiple correlated ordinal traits are analyzed simultaneously, the power of analysis may increase significantly while the false positives can be controlled well. In this study, we propose bivariate functional ordinal linear regression (BFOLR) models using latent regressions with cumulative logit link or probit link to perform a gene-based analysis for bivariate ordinal traits and sequencing data. In the proposed BFOLR models, genetic variant data are viewed as stochastic functions of physical positions, and the genetic effects are treated as a function of physical positions. The BFOLR models take the correlation of the two ordinal traits into account via latent variables. The BFOLR models are built upon functional data analysis which can be revised to analyze the bivariate ordinal traits and high-dimension genetic data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Extensive simulation studies show that the likelihood ratio tests of the BFOLR models control type I errors well and have good power performance. The BFOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes, CFH and ARMS2, are found to strongly associate with eye drusen size, drusen area, age-related macular degeneration (AMD) categories, and AMD severity scale.

Transcriptional repression of MdMa1 by MdMYB21 in Ma locus decreases malic acid content in apple fruit.

Malic acid is a major organic acid component of apples and a crucial determinant of fruit organoleptic quality. A candidate gene for malic acid content, designated MdMa1, was previously identified in the Ma locus, which is a major quantitative trait locus (QTL) for apple fruit acidity located on the linkage group 16. Region-based association mapping to detect candidate genes in the Ma locus identified MdMa1 and an additional MdMYB21 gene putatively associated with malic acid. MdMYB21 was significantly associated with fruit malic acid content, accounting for ~7.48% of the observed phenotypic variation in the apple germplasm collection. Analyses of transgenic apple calli, fruits and tomatoes demonstrated that MdMYB21 negatively regulated malic acid accumulation. The apple fruit acidity-related MdMa1 and its tomato ortholog, SlALMT9, exhibited lower expression profiles in apple calli, mature fruits and tomatoes in which MdMYB21 was overexpressed, compared with their corresponding wild-type variety. MdMYB21 directly binds to the MdMa1 promoter and represses its expression. Interestingly, a 2-bp variation in the MdMYB21 promoter region altered its expression and regulation of its target gene, MdMa1, expression. Our findings not only demonstrate the efficiency of integrating QTL and association mapping in the identification of candidate genes controlling complex traits in apples, but also provide insights into the complex regulatory mechanism of fruit malic acid accumulation.

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.

Genome-wide identification of the key kinesin genes during fiber and boll development in upland cotton (Gossypium hirsutum L.).

Kinesin is a kind of motor protein, which interacts with microtubule filaments and regulates cellulose synthesis. Cotton fiber is a natural model for studying the cellular development and cellulose synthesis. Therefore, a systematic research of kinesin gene family in cotton (Gossypium spp.) will be beneficial for both understanding the function of kinesin protein and assisting the fiber improvement. Here, we aimed to identify the key kinesin genes present in cotton by combining genome-wide expression profile data, association mapping, and public quantitative trait loci (QTLs) in upland cotton (G. hirsutum L.). Results showed that 159 kinesin genes, including 15 genes of the kinesin-13 gene subfamily, were identified in upland cotton; of which 157 kinesin genes can be traced back to the diploid ancestors, G. raimondii and G. arboreum. Using a combined analysis of public QTLs and genome-wide expression profile information, there were 29 QTLs co-localized together with 28 kinesin genes in upland cotton, including 10 kinesin-13 subfamily genes. Genome-wide expression profile data indicated that, among the 28 co-localized genes, seven kinesin genes were predominantly expressed in fibers or ovules. By association mapping analysis, 30 kinesin genes were significantly associated with three fiber traits, among which a kinesin-13 gene, Ghir_A11G028430, was found to be associated with both cotton boll length and lint weight, and one kinesin-7 gene, Ghir_D04G017880 (Gh_Kinesin7), was significantly associated with fiber strength. In addition, two missense mutations were identified in the motor domain of the Gh_Kinesin7 protein. Overall, the kinesin gene family seemingly plays an important role in cotton fiber and boll development. The exploited kinesin genes will be beneficial for the genetic improvement of fiber quality and yield.

Genome-wide identification of the key Kinesin genes during fiber and boll development in upland cotton (Gossypium hirsutum L).

Kinesin is a kind of motor protein, which interacts with microtubule filaments and regulates cellulose synthesis. Cotton fiber is a natural model for studying the cellular development and cellulose synthesis. Therefore, a systematic research of Kinesin gene family in cotton (Gossypium spp.) will be beneficial for both understanding the function of Kinesin protein and assisting the fiber improvement. Here, we aimed to identify the key Kinesin genes present in cotton by combining genome-wide expression profile data, association mapping, and public quantitative trait loci (QTLs) in upland cotton (Gossypium hirsutum L.). Results showed that 159 Kinesin genes, including 15 genes of the Kinesin-13 gene subfamily, were identified in upland cotton; of which 157 Kinesin genes can be traced back to the diploid ancestors, G. raimondii and G. arboreum. Using a combined analysis of public QTLs and genome-wide expression profile information, there were 29 QTLs co-localized together with 28 Kinesin genes in upland cotton, including 10 Kinesin-13 subfamily genes. Genome-wide expression profile data indicated that, among the 28 co-localized genes, seven Kinesin genes were predominantly expressed in fibers or ovules. By association mapping analysis, 30 Kinesin genes were significantly associated with three fiber traits, among which a Kinesin-13 gene, Ghir_A11G028430, was found to be associated with both cotton boll length and lint weight, and one Kinesin-7 gene, Ghir_D04G017880 (Gh_Kinesin7), was significantly associated with fiber strength. In addition, two missense mutations were identified in the motor domain of the Gh_Kinesin7 protein. Overall, the Kinesin gene family seemingly plays an important role in cotton fiber and boll development. The exploited Kinesin genes will be beneficial for the genetic improvement of fiber quality and yield.

Genome-wide association study of drought tolerance in wheat (Triticum aestivum L.) identifies SNP markers and candidate genes.

Drought stress poses a severe threat to global wheat production, necessitating an in-depth exploration of the genetic basis for drought tolerance associated traits. This study employed a 90 K SNP array to conduct a genome-wide association analysis, unravelling genetic determinants of key traits related to drought tolerance in wheat, namely plant height, root length, and root and shoot dry weight. Using the mixed linear model (MLM) method on 125 wheat accessions subjected to both well-watered and drought stress treatments, we identified 53 SNPs significantly associated with stress susceptibility (SSI) and tolerance indices (STI) for the targeted traits. Notably, chromosomes 2A and 3B stood out with ten and nine associated markers, respectively. Across 17 chromosomes, 44 unique candidate genes were pinpointed, predominantly located on the distal ends of 1A, 1B, 1D, 2A, 3A, 3B, 4A, 6A, 6B, 7A, 7B, and 7D chromosomes. These genes, implicated in diverse functions related to plant growth, development, and stress responses, offer a rich resource for future investigation. A clustering pattern emerged, notably with seven genes associated with SSI for plant height and four genes linked to both STI of plant height and shoot dry weight, converging on specific regions of chromosome arms of 2AS and 3BL. Additionally, shared genes encoding polygalacturonase, auxilin-related protein 1, peptide deformylase, and receptor-like kinase underscored the interconnectedness between plant height and shoot dry weight. In conclusion, our findings provide insights into the molecular mechanisms governing wheat drought tolerance, identifying promising genomic loci for further exploration and crop improvement strategies.

Genome-wide association study reveals marker-trait associations for major agronomic traits in proso millet (Panicum miliaceum L.).

MAIN CONCLUSION: The pilot-scale genome-wide association study in the US proso millet identified twenty marker-trait associations for five morpho-agronomic traits identifying genomic regions for future studies (e.g. molecular breeding and map-based cloning). Proso millet (Panicum miliaceum L.) is an ancient grain recognized for its excellent water-use efficiency and short growing season. It is an indispensable part of the winter wheat-based dryland cropping system in the High Plains of the USA. Its grains are endowed with high nutritional and health-promoting properties, making it increasingly popular in the global market for healthy grains. There is a dearth of genomic resources in proso millet for developing molecular tools to complement conventional breeding for developing high-yielding varieties. Genome-wide association study (GWAS) is a widely used method to dissect the genetics of complex traits. In this pilot study of the first-ever GWAS in the US proso millet, 71 globally diverse genotypes of 109 the US proso millet core collection were evaluated for five major morpho-agronomic traits at two locations in western Nebraska, and GWAS was conducted to identify single nucleotide polymorphisms (SNPs) associated with these traits. Analysis of variance showed that there was a significant difference among the genotypes, and all five traits were also found to be highly correlated with each other. Sequence reads from genotyping-by-sequencing (GBS) were used to identify 11,147 high-quality bi-allelic SNPs. Population structure analysis with those SNPs showed stratification within the core collection. The GWAS identified twenty marker-trait associations (MTAs) for the five traits. Twenty-nine putative candidate genes associated with the five traits were also identified. These genomic regions can be used to develop genetic markers for marker-assisted selection in proso millet breeding.

Linkage and association mapping in multi-parental populations reveal the genetic basis of carotenoid variation in maize kernels.

Carotenoids are indispensable to plants and critical components of the human diet. The carotenoid metabolic pathway is conserved across plant species, but our understanding of the genetic basis of carotenoid variation remains limited for the seeds of most cereal crops. To address this issue, we systematically performed linkage and association mapping for eight carotenoid traits using six recombinant inbred line (RIL) populations. Single linkage mapping (SLM) and joint linkage mapping (JLM) identified 77 unique additive QTLs and 104 pairs of epistatic QTLs. Among these QTLs, we identified 22 overlapping hotspots of additive and epistatic loci, highlighting the important contributions of some QTLs to carotenoid levels through additive or epistatic mechanisms. A genome-wide association study based on all RILs detected 244 candidate genes significantly associated with carotenoid traits, 23 of which were annotated as carotenoid pathway genes. Effect comparisons suggested that a small number of loci linked to pathway genes have substantial effects on carotenoid variation in our tested populations, but many loci not associated with pathway genes also make important contributions to carotenoid variation. We identified ZmPTOX as the causal gene for a QTL hotspot (Q10/JLM10/GWAS019); this gene encodes a putative plastid terminal oxidase that produces plastoquinone-9 used by two enzymes in the carotenoid pathway. Natural variants in the promoter and second exon of ZmPTOX were found to alter carotenoid levels. This comprehensive assessment of the genetic mechanisms underlying carotenoid variation establishes a foundation for rewiring carotenoid metabolism and accumulation for efficient carotenoid biofortification.

Genetic underpinnings of arthropod community distributions in Populus trichocarpa.

Community genetics seeks to understand the mechanisms by which natural genetic variation in heritable host phenotypes can encompass assemblages of organisms such as bacteria, fungi, and many animals including arthropods. Prior studies that focused on plant genotypes have been unable to identify genes controlling community composition, a necessary step to predict ecosystem structure and function as underlying genes shift within plant populations. We surveyed arthropods within an association population of Populus trichocarpa in three common gardens to discover plant genes that contributed to arthropod community composition. We analyzed our surveys with traditional single-trait genome-wide association analysis (GWAS), multitrait GWAS, and functional networks built from a diverse set of plant phenotypes. Plant genotype was influential in structuring arthropod community composition among several garden sites. Candidate genes important for higher level organization of arthropod communities had broadly applicable functions, such as terpenoid biosynthesis and production of dsRNA binding proteins and protein kinases, which may be capable of targeting multiple arthropod species. We have demonstrated the ability to detect, in an uncontrolled environment, individual genes that are associated with the community assemblage of arthropods on a host plant, further enhancing our understanding of genetic mechanisms that impact ecosystem structure.

Polymorphisms in two key anthocyanic genes of clivia (Clivia miniata L.) reveal evidence of selection and possible association with flower pigmentation.

Members of the genus Clivia show considerable variation in flower pigmentation and morphology. Such variation is affected by mutations that emerge in candidate flower development genes over time. Besides population history, mutations can further illuminate the effects of demographic events in populations in addition to population genetic parameters including selection, recombination, and linkage disequilibrium (LD). The current study aimed to find sequence variants in 2 anthocyanin biosynthetic genes (DFR and bHLH) of Clivia miniata and use the data to assess population genetic factors from a random collection of orange/red- and yellow-flowered specimens. Overall, average nucleotide diversity in the 2 anthocyanin genes was moderate (π = 0.00646), whereas haplotypes differed significantly (Hd ≥ 0.9). Gene evolution was seemingly driven by mutations (CmiDFR) or recombinations (CmibHLH001). LD decayed swiftly within the analyzed gene regions and supported the feasibility of assessing trait-variant associations via the association/linkage mapping approach. In the end, most associations were found to be spurious, but 1 haplotype in CmibHLH001 showed a promising correlation to the orange/red flower phenotype in Clivia specimens. In all, the present study is the first to measure gene-level diversity in C. miniata-data that had never been reported so far. Furthermore, the study also identified allelic and haplotypic variants that may be beneficial in future association genetic studies of Clivia. Such studies, however, consider large diverse populations to control for statistical bias intrinsic to the analysis of small datasets.

Development of miRNA-SSR and target-SSR markers from yield-associate genes and their applicability in the assessment of genetic diversity and association mapping in rice (Oryza sativa L.).

The gene-derived functional markers are considered effective to use in marker-assisted breeding and genetic diversity analysis. As of now, no functional markers have been identified from miRNAs regulating yield traits. The miRNAs play a key role as regulators in controlling the candidate genes involved in grain yield improvement in rice. In this study, 13 miRNA-SSR and their target gene SSR markers were mined from 29 yield-responsive miRNA along with their 29 target genes in rice. The validation of these markers showed that four miRNA-SSRs and one target gene SSR markers had shown polymorphism among 120 diverse rice genotypes. The PIC values ranged from 0.25 (OsARF18-SSR) to 0.72 (miR408-SSR, miR172b-SSR, and miR396f-SSR) with an average value of 0.57. These polymorphic markers grouped 120 rice genotypes into 3 main clusters based on the levels of high genetic diversity. These markers also showed significant association with key yield traits. Among all, miR172b-SSR showed a strong association with plant height in two seasons. This investigation suggests that this new class of molecular markers has great potential in the characterization of rice germplasm by genetic diversity and population structure and in marker-assisted breeding for the development of high-yielding varieties. Supplementary information: The online version contains supplementary material available at 10.1007/s11032-024-01462-z.

Genotyping SNPs in lignin biosynthesis gene (CAD1) and transcription factors (MYB1 and MYB2) exhibits association with wood density in teak (Tectona grandis L.f.).

BACKGROUND: Teak (Tectona grandis L.f.), an important source of tropical timber with immense economic value, is a highly outcrossing forest tree species. 150 unrelated accessions of teak (Tectona grandis L.f.) plus trees assembled as clones at National Teak Germplasm Bank, Chandrapur, Maharashtra, India was investigated for association mapping of candidate lignin biosynthesis gene (CAD1) and transcription factors (MYB1 and MYB2). METHODS AND RESULTS: The CAD1, MYB1 and MYB2 were amplified using specifically designed primers. The amplified sequences were then sequenced and genotyped for 112 SNPs/11 indels. We evaluated the association between SNPs and wood density in teak accessions using GLM and MLM statistical models, with Bonferroni correction applied. The teak accessions recorded an average wood density of 416.69 kg.m-3 (CV 4.97%) and comprised of three loosely structured admixed sub-populations (K = 3), containing 72.05% genetic variation within sub-populations with low intragenic LD (0-21% SNP pairs) at P < 0.05 and high LD decay (33-934 bp) at R2 = 0.1. GLM and MLM models discounting systematic biases (Q and K matrices) to avoid false discovery revealed five loci at rare variants (MAF 0.003) and three loci at common variants (MAF 0.05) to be significantly (P < 0.05) associated with the wood density. However, the stringent Bonferroni correction (4.06-7.04 × 10-4) yielded only a single associated locus (B1485C/A) from exon of MYB1 transcription factor, contributing to about 10.35% phenotypic variation in wood density trait. CONCLUSION: Scored SNP locus (B1485C/A) can be developed as a molecular probe for selection of improved planting stock with proven wood density trait for a large-scale teak plantation.

Linkage Mapping and Genome-Wide Association Study Identified Two Peanut Late Leaf Spot Resistance Loci, PLLSR-1 and PLLSR-2, Using Nested Association Mapping.

Identification of candidate genes and molecular markers for late leaf spot (LLS) disease resistance in peanut (Arachis hypogaea) has been a focus of molecular breeding for the U.S. industry-funded peanut genome project. Efforts have been hindered by limited mapping resolution due to low levels of genetic recombination and marker density available in traditional biparental mapping populations. To address this, a multi-parental nested association mapping population has been genotyped with the peanut 58K single-nucleotide polymorphism (SNP) array and phenotyped for LLS severity in the field for 3 years. Joint linkage-based quantitative trait locus (QTL) mapping identified nine QTLs for LLS resistance with significant phenotypic variance explained up to 47.7%. A genome-wide association study identified 13 SNPs consistently associated with LLS resistance. Two genomic regions harboring the consistent QTLs and SNPs were identified from 1,336 to 1,520 kb (184 kb) on chromosome B02 and from 1,026.9 to 1,793.2 kb (767 kb) on chromosome B03, designated as peanut LLS resistance loci, PLLSR-1 and PLLSR-2, respectively. PLLSR-1 contains 10 nucleotide-binding site leucine-rich repeat disease resistance genes. A nucleotide-binding site leucine-rich repeat disease resistance gene, Arahy.VKVT6A, was also identified on homoeologous chromosome A02. PLLSR-2 contains five significant SNPs associated with five different genes encoding callose synthase, pollen defective in guidance protein, pentatricopeptide repeat, acyl-activating enzyme, and C2 GRAM domains-containing protein. This study highlights the power of multi-parent populations such as nested association mapping for genetic mapping and marker-trait association studies in peanuts. Validation of these two LLS resistance loci will be needed for marker-assisted breeding.

A Quantitative Trait Locus on Maize Chromosome 5 Is Associated with Root-Knot Nematode Resistance.

This study provides the first report of a quantitative trait locus (QTL) in maize (Zea mays) for resistance to the southern root-knot nematode (SRKN) (Meloidogyne incognita). The SRKN can feed on the roots of maize in the U.S. Southern Coastal Plain region and can cause yield losses of 30% or more in heavily infested fields. Increases in SRKN density in the soil may reduce the yield for subsequently planted susceptible crops. The use of maize hybrids with resistance to SRKN could prevent an increase in SRKN density, yet no genetic regions have been identified that confer host resistance. In this study, a B73 (susceptible) × Ky21 (resistant) S5 recombinant inbred line (RIL) population was phenotyped for total number of eggs (TE) and root weight. This population had been genotyped using single-nucleotide polymorphisms (SNPs). By utilizing the SNP data with the phenotype data, a single QTL was identified on chromosome 5 that explained 15% of the phenotypic variation (PV) for the number of eggs and 11% of the PV for the number of eggs per gram of root (EGR). Plants that were homozygous for the Ky21 allele for the most associated marker PZA03172.3 had fewer eggs and fewer EGR than the plants that were homozygous or heterozygous for the B73 allele. Thus, the first QTL for SRKN resistance in maize has been identified and could be incorporated into maize hybrids.

Identification of superior haplotypes for flowering time in pigeonpea through candidate gene-based association study of a diverse minicore collection.

KEY MESSAGE: In current study candidate gene (261 genes) based association mapping on 144 pigeonpea accessions for flowering time and related traits and 29 MTAs producing eight superior haplotypes were identified. In the current study, we have conducted an association analysis for flowering-associated traits in a diverse pigeonpea mini-core collection comprising 144 accessions using the SNP data of 261 flowering-related genes. In total, 13,449 SNPs were detected in the current study, which ranged from 743 (ICP10228) to 1469 (ICP6668) among the individuals. The nucleotide diversity (0.28) and Watterson estimates (0.34) reflected substantial diversity, while Tajima's D (-0.70) indicated the abundance of rare alleles in the collection. A total of 29 marker trait associations (MTAs) were identified, among which 19 were unique to days to first flowering (DOF) and/or days to fifty percent flowering (DFF), 9 to plant height (PH), and 1 to determinate (Det) growth habit using 3 years of phenotypic data. Among these MTAs, six were common to DOF and/or DFF, and four were common to DOF/DFF along with the PH, reflecting their pleiotropic action. These 29 MTAs spanned 25 genes, among which 10 genes clustered in the protein-protein network analysis, indicating their concerted involvement in floral induction. Furthermore, we identified eight haplotypes, four of which regulate late flowering, while the remaining four regulate early flowering using the MTAs. Interestingly, haplotypes conferring late flowering (H001, H002, and H008) were found to be taller, while those involved in early flowering (H003) were shorter in height. The expression pattern of these genes, as inferred from the transcriptome data, also underpinned their involvement in floral induction. The haplotypes identified will be highly useful to the pigeonpea breeding community for haplotype-based breeding.

Genetic basis of variation in cocaine and methamphetamine consumption in outbred populations of Drosophila melanogaster.

We used Drosophila melanogaster to map the genetic basis of naturally occurring variation in voluntary consumption of cocaine and methamphetamine. We derived an outbred advanced intercross population (AIP) from 37 sequenced inbred wild-derived lines of the Drosophila melanogaster Genetic Reference Panel (DGRP), which are maximally genetically divergent, have minimal residual heterozygosity, are not segregating for common inversions, and are not infected with Wolbachia pipientis We assessed consumption of sucrose, methamphetamine-supplemented sucrose, and cocaine-supplemented sucrose and found considerable phenotypic variation for consumption of both drugs, in both sexes. We performed whole-genome sequencing and extreme quantitative trait locus (QTL) mapping on the top 10% of consumers for each replicate, sex, and condition and an equal number of randomly selected flies. We evaluated changes in allele frequencies among high consumers and control flies and identified 3,033 variants significantly (P < 1.9 × 10-8) associated with increased consumption, located in or near 1,962 genes. Many of these genes are associated with nervous system development and function, and 77 belong to a known gene-gene interaction subnetwork. We assessed the effects of RNA interference (RNAi) on drug consumption for 22 candidate genes; 17 had a significant effect in at least one sex. We constructed allele-specific AIPs that were homozygous for alternative candidate alleles for 10 single-nucleotide polymorphisms (SNPs) and measured average consumption for each population; 9 SNPs had significant effects in at least one sex. The genetic basis of voluntary drug consumption in Drosophila is polygenic and implicates genes with human orthologs and associated variants with sex- and drug-specific effects.

Interaction-integrated linear mixed model reveals 3D-genetic basis underlying Autism.

Genetic interactions play critical roles in genotype-phenotype associations. We developed a novel interaction-integrated linear mixed model (ILMM) that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems of searching for combinations. To demonstrate its utility, with 3D genomic interactions (assessed by Hi-C experiments) as a priori, we applied ILMM to whole-genome sequencing data for Autism Spectrum Disorders (ASD) and brain transcriptome data, revealing the 3D-genetic basis of ASD and 3D-expression quantitative loci (3D-eQTLs) for brain tissues. Notably, we reported a potential mechanism involving distal regulation between FOXP2 and DNMT3A, conferring the risk of ASD.