Genetic Basis Of Complex Traits Research Articles

Genetic insight into a polygenic trait using a novel genome-wide association approach in a wild amphibian population.

Body size variation is central in the evolution of life-history traits in amphibians, but the underlying genetic architecture of this complex trait is still largely unknown. Herein, we studied the genetic basis of body size and fecundity of the alternative morphotypes in a wild population of the Greek smooth newt (Lissotriton graecus). By combining a genome-wide association approach with linkage disequilibrium network analysis, we were able to identify clusters of highly correlated loci thus maximizing sequence data for downstream analysis. The putatively associated variants explained 12.8% to 44.5% of the total phenotypic variation in body size and were mapped to genes with functional roles in the regulation of gene expression and cell cycle processes. Our study is the first to provide insights into the genetic basis of complex traits in newts and provides a useful tool to identify loci potentially involved in fitness-related traits in small data sets from natural populations in non-model species.

Whole genome sequencing of mouse lines divergently selected for fatness (FLI) and leanness (FHI) revealed several genetic variants as candidates for novel obesity genes

BackgroundAnalysing genomes of animal model organisms is widely used for understanding the genetic basis of complex traits and diseases, such as obesity, for which only a few mouse models exist, however, without their lean counterparts.ObjectiveTo analyse genetic differences in the unique mouse models of polygenic obesity (Fat line) and leanness (Lean line) originating from the same base population and established by divergent selection over more than 60 generations.MethodsGenetic variability was analysed using WGS. Variants were identified with GATK and annotated with Ensembl VEP. g.Profiler, WebGestalt, and KEGG were used for GO and pathway enrichment analysis. miRNA seed regions were obtained with miRPathDB 2.0, LncRRIsearch was used to predict targets of identified lncRNAs, and genes influencing adipose tissue amount were searched using the IMPC database.ResultsWGS analysis revealed 6.3 million SNPs, 1.3 million were new. Thousands of potentially impactful SNPs were identified, including within 24 genes related to adipose tissue amount. SNP density was highest in pseudogenes and regulatory RNAs. The Lean line carries SNP rs248726381 in the seed region of mmu-miR-3086-3p, which may affect fatty acid metabolism. KEGG analysis showed deleterious missense variants in immune response and diabetes genes, with food perception pathways being most enriched. Gene prioritisation considering SNP GERP scores, variant consequences, and allele comparison with other mouse lines identified seven novel obesity candidate genes: 4930441H08Rik, Aff3, Fam237b, Gm36633, Pced1a, Tecrl, and Zfp536.ConclusionWGS revealed many genetic differences between the lines that accumulated over the selection period, including variants with potential negative impacts on gene function. Given the increasing availability of mouse strains and genetic polymorphism catalogues, the study is a valuable resource for researchers to study obesity.

Genome-Wide Association Study (GWAS) for Identifying SNPs and Genes Related to Phosphate-Induced Phenotypic Traits in Tomato (Solanum lycopersicum L.)

Phosphate (P) is a crucial macronutrient for normal plant growth and development. The P availability in soils is a limitation factor, and understanding genetic factors playing roles in plant adaptation for improving P uptake is of great biological importance. Genome-wide association studies (GWAS) have become indispensable tools in unraveling the genetic basis of complex traits in various plant species. In this study, a comprehensive GWAS was conducted on diverse tomato (Solanum lycopersicum L.) accessions grown under normal and low P conditions for two weeks. Plant traits such as shoot height, primary root length, plant biomass, shoot inorganic content (SiP), and root inorganic content (RiP) were measured. Among several models of GWAS tested, the Bayesian-information and linkage disequilibrium iteratively nested keyway (BLINK) models were used for the identification of single nucleotide polymorphisms (SNPs). Among all the traits analyzed, significantly associated SNPs were recorded for PB, i.e., 1 SNP (SSL4.0CH10_49261145) under control P, SiP, i.e., 1 SNP (SSL4.0CH08_58433186) under control P and 1 SNP (SSL4.0CH08_51271168) under low P and RiP i.e., 2 SNPs (SSL4.0CH04_37267952 and SSL4.0CH09_4609062) under control P and 1 SNP (SSL4.0CH09_3930922) under low P condition. The identified SNPs served as genetic markers pinpointing regions of the tomato genome linked to P-responsive traits. The novel candidate genes associated with the identified SNPs were further analyzed for their protein-protein interactions using STRING. The study provided novel candidate genes, viz. Solyc10g050370 for PB under control, Solyc08g062490, and Solyc08g062500 for SiP and Solyc09g010450, Solyc09g010460, Solyc09g010690, and Solyc09g010710 for RiP under low P condition. These findings offer a glimpse into the genetic diversity of tomato accessions’ responses to P uptake, highlighting the potential for tailored breeding programs to develop P-efficient tomato varieties that could adapt to varying soil conditions, making them crucial for sustainable agriculture and addressing global challenges, such as soil depletion and food security.

Development and evaluation of a haplotype reference panel of Zhikong scallop (Chlamys farreri) for genotype imputation

Single nucleotide polymorphisms (SNPs) across the genome are remarkably useful resources for investigating the genome architecture, studying the genetic basis of complex traits and implementation of genomic selection. However, information on high-density SNPs is unavailable for most aquaculture species owing to the high costs of whole genome sequencing. Genotype imputation is an attractive approach for obtaining large number of genomic variants at a low cost. In this study, the first haplotype reference panel was constructed for Chlamys farreri, an economically important mollusk species and a major contributor to aquaculture in China. A total of 174 scallops from six populations in the main scallop culture area were selected for whole-genome resequencing. More than 87.47 million SNPs were identified and 3,968,417 SNPs genome-wide were selected for constructing the haplotype reference panel for C. farreri. The number of SNPs per chromosome ranged from 125,000 to 279,981 with an average spacing of 242 bases. Several factors were evaluated based on the imputation performance of the reference panel. The imputation accuracies of 19 chromosomes ranged from 0.91 to 0.95, and there were no significant differences among them (p > 0.05). The imputation accuracies of populations could reach 0.95, 0.93, and 0.90 for ZY, PR, and CS respectively, which confirmed a positive relationship between the kinship within the population and the imputation accuracy. The imputation accuracies increased with sequencing depth and plateaued at 0.5×, reaching accuracies of 0.91–0.93 for artificially breeding populations. Taken together, we constructed a haplotype reference panel for C. farreri, which can provide a cost-effective way to extend the limited number of genotypes for boosting the power of downstream analyses such as genome-wide association studies and genomic selection.

Genetic Association Analysis of Copy Number Variations for Meat Quality in Beef Cattle.

Meat quality is an economically important trait for global food production. Copy number variations (CNVs) have been previously implicated in elucidating the genetic basis of complex traits. In this article, we detected a total of 112,198 CNVs and 10,102 CNV regions (CNVRs) based on the Bovine HD SNP array. Next, we performed a CNV-based genome-wide association analysis (GWAS) of six meat quality traits and identified 12 significant CNV segments corresponding to eight candidate genes, including PCDH15, CSMD3, etc. Using region-based association analysis, we further identified six CNV segments relevant to meat quality in beef cattle. Among these, TRIM77 and TRIM64 within CNVR4 on BTA29 were detected as candidate genes for backfat thickness (BFT). Notably, we identified a 34 kb duplication for meat color (MC) which was supported by read-depth signals, and this duplication was embedded within the keratin gene family including KRT4, KRT78, and KRT79. Our findings will help to dissect the genetic architecture of meat quality traits from the aspects of CNVs, and subsequently improve the selection process in breeding programs.

Host genomic variation shapes gut microbiome diversity in threespine stickleback fish.

A major focus of host-microbe research is to understand how genetic differences, of various magnitudes, among hosts translate to differences in their microbiomes. This has been challenging for animal hosts, including humans, because it is difficult to control environmental variables tightly enough to isolate direct genetic effects on the microbiome. Our work in stickleback fish is a significant contribution because our experimental approach allowed strict control over environmental factors, including standardization of the microbiome from the earliest stage of development and unrestricted co-housing of fish in a truly common environment. Furthermore, we measured host genetic variation over 2,000 regions of the stickleback genome, comparing this information and microbiome composition data among fish from very similar and very different genetic backgrounds. Our findings highlight how differences in the host genome influence microbiome diversity and make a case for future manipulative microbiome experiments that use host systems with naturally occurring genetic variation.

Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits.

The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.

Developmental coordination disorder: What can we learn from RI mice using motor learning tasks and QTL analysis.

Developmental Coordination Disorder (DCD) is a neurodevelopmental disorder of unknown etiology that affects one in 20 children. There is an indication that DCD has an underlying genetic component due to its high heritability. Therefore, we explored the use of a recombinant inbred family of mice known as the BXD panel to understand the genetic basis of complex traits (i.e., motor learning) through identification of quantitative trait loci (QTLs). The overall aim of this study was to utilize the QTL approach to evaluate the genome-to-phenome correlation in BXD strains of mice in order to better understand the human presentation of DCD. Results of this current study confirm differences in motor learning in selected BXD strains and strains with altered cerebellar volume. Five strains - BXD15, BXD27, BXD28, BXD75, and BXD86 - exhibited the most DCD-like phenotype when compared with other BXD strains of interest. Results indicate that BXD15 and BXD75 struggled primarily with gross motor skills, BXD28 primarily had difficulties with fine motor skills, and BXD27 and BXD86 strains struggled with both fine and gross motor skills. The functional roles of genes within significant QTLs were assessed in relation to DCD-like behavior. Only Rab3a (Ras-related protein Rab-3A) emerged as a high likelihood candidate gene for the horizontal ladder rung task. This gene is associated with brain and skeletal muscle development, but lacked nonsynonymous polymorphisms. This study along with Gill et al. (same issue) is the first studies to specifically examine the genetic linkage of DCD using BXD strains of mice.

Epistasis-aware genome-wide association studies provide insights into high-yield and high-quality rice efficient breeding

Marker-assisted selection (MAS) and genomic selection (GS) breeding have greatly improved the efficiency of rice breeding. Due to the influence of epistasis and gene pleiotropy, how to ensure the actual breeding effect of MAS and GS is still a difficult problem to overcome. In this study, 113 indica rice varieties (V) and their 565 testcross hybrids (TC) were used as materials to investigate the genetic basis of 12 quality traits and 9 agronomic traits. The original traits and general combining ability of parents, as well as the original traits and mid-parent heterosis of testcross hybrids were subjected to genome-wide association analysis. In total, 381 primary significantly associated loci (SAL) and 1759 secondary SALs that had epistatic interaction with these primary SALs were detected. 322 candidate genes located within or nearby the SALs were screened, of which 204 were cloned genes. A total of 39 MAS molecular modules that are beneficial for trait improvement was identified by pyramiding the superior haplotype of candidate genes and desirable epistatic allele of secondary SALs. All the SALs were used to construct genetic networks, in which 91 pleiotropic loci were investigated. Additionally, we estimated the accuracy of genomic prediction in parent varieties and testcross hybrids by incorporating no SALs, primary SALs, secondary SALs or epistatic effect SALs as covariates. Although the prediction accuracy of the four models was mostly not significantly different in the TC dataset, the incorporation of primary SALs, secondary SALs, and epistatic effect SALs significantly improved the prediction accuracy of 5(26%), 3(16%), and 11(58%) traits in the V dataset, respectively. These results suggested that SALs and epistatic effect SALs identified based on additive genotype can provide considerable predictive power for parental lines. These results provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data

Mapping cell type-specific gene expression quantitative trait loci (ct-eQTLs) is a powerful way to investigate the genetic basis of complex traits. A popular method for ct-eQTL mapping is to assess the interaction between the genotype of a genetic locus and the abundance of a specific cell type using a linear model. However, this approach requires transforming RNA-seq count data, which distorts the relation between gene expression and cell type proportions and results in reduced power and/or inflated type I error. To address this issue, we have developed a statistical method called CSeQTL that allows for ct-eQTL mapping using bulk RNA-seq count data while taking advantage of allele-specific expression. We validated the results of CSeQTL through simulations and real data analysis, comparing CSeQTL results to those obtained from purified bulk RNA-seq data or single cell RNA-seq data. Using our ct-eQTL findings, we were able to identify cell types relevant to 21 categories of human traits.

GWAS Characterization of the Genetic Regions Associated with Nine Important Agronomic Traits in Tomato (Solanum lycopersicum L.)

Understanding the genetic background of elite cultivated tomato germplasm resources in crossbreeding and revealing the genetic basis of complex traits are vital for better-targeted germplasm expansion and the creation of strong hybrids. Here, we obtained approximately 21 million single-nucleotide polymorphisms (SNPs) based on the sequencing of 212 cultivated tomato accessions and the population structure of which was revealed. More importantly, we found that target genes distributed on chromosomes 1, 5, 9, and 11 may be actively selected in breeding. In particular, the significant signals related to soluble sugar content (chr1_94170222, chr1_96273188, chr9_4167512, and chr11_55592768), fruit firmness (chr5_4384919 and chr5_5111452) and gray leaf spot resistance (chr11_8935252 and chr11_9707993) were also detected on the corresponding chromosomes, respectively. Overall, we reported 28 significant association signals for nine agronomic traits based on a mixed linear model (MLM), including 114 genes. Among these signals, 21 contained potential novel genes for six fruit traits. These novel candidate genes located in genomic regions without previously known loci or on different chromosomes explained approximately 16% of the phenotypic variance on average in cultivated tomatoes. These findings could accelerate the identification and validation of novel and known candidate genes and QTLs, improving the understanding of the genetic structure of complex quantitative traits. These results also provide a basis for tomato breeding and improvement.

Ancestry, diversity, and genetics of health-related traits in African-derived communities (quilombos) from Brazil.

Brazilian quilombos are communities formed by enslaved Africans and their descendants all over the country during slavery and shortly after its abolition. Quilombos harbor a great fraction of the largely unknown genetic diversity of the African diaspora in Brazil. Thus, genetic studies in quilombos have the potential to provide important insights not only into the African roots of the Brazilian population but also into the genetic bases of complex traits and human adaptation to diverse environments. This review summarizes the main results of genetic studies performed on quilombos so far. Here, we analyzed the patterns of African, Amerindian, European, and subcontinental ancestry (within Africa) of quilombos from the five different geographic regions of Brazil. In addition, uniparental markers (from the mtDNA and the Y chromosome) studies are analyzed together to reveal demographic processes and sex-biased admixture that occurred during the formation of these unique populations. Lastly, the prevalence of known malaria-adaptive African mutations and other African-specific variants discovered in quilombos, as well as the genetic bases of health-related traits, are discussed here, together with their implication for the health of populations of African descent.

Convergent Genomic Signatures of Cashmere Traits: Evidence for Natural and Artificial Selection.

Convergent evolution provides powerful opportunities to investigate the genetic basis of complex traits. The Tibetan antelope (Pantholops hodgsonii) and Siberian ibex (Capra sibirica) belong to different subfamilies in Bovidae, but both have evolved similar superfine cashmere characteristics to meet the cold temperature in plateau environments. The cashmere traits of cashmere goats underwent strong artificial selection, and some traces of domestication also remained in the genome. Hence, we investigated the convergent genomic signatures of cashmere traits between natural and artificial selection. We compared the patterns of convergent molecular evolution between Tibetan antelope and Siberian ibex by testing positively selected genes, rapidly evolving genes and convergent amino acid substitutions. In addition, we analyzed the selected genomic features of cashmere goats under artificial selection using whole-genome resequencing data, and skin transcriptome data of cashmere goats were also used to focus on the genes involved in regulating cashmere traits. We found that molecular convergent events were very rare, but natural and artificial selection genes were convergent enriched in similar functional pathways (e.g., ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway) in a variety of gene sets. Type IV collagen family genes (COL4A2, COL4A4, COL4A5, COL6A5, COL6A6) and integrin family genes (ITGA2, ITGA4, ITGA9, ITGB8) may be important candidate genes for cashmere formation and development. Our results provide a comprehensive approach and perspective for exploring cashmere traits and offer a valuable reference for subsequent in-depth research on the molecular mechanisms regulating cashmere development and fineness.

Comparative epigenomics reveals the impact of ruminant-specific regulatory elements on complex traits.

Insights into the genetic basis of complex traits and disease in both human and livestock species have been achieved over the past decade through detection of genetic variants in genome-wide association studies(GWAS). A majority of such variants were found located in noncoding genomic regions, and though the involvement of numerous regulatory elements (REs) has been predicted across multiple tissues in domesticated animals, their evolutionary conservation and effects on complex traits have not been fully elucidated, particularly in ruminants. Here, we systematically analyzed 137 epigenomic and transcriptomic datasets of six mammals, including cattle, sheep, goats, pigs, mice, and humans, and then integrated them with large-scale GWAS of complex traits. Using 40 ChIP-seq datasets of H3K4me3 and H3K27ac, we detected 68,479, 58,562, 63,273, 97,244, 111,881, and 87,049 REs in the liver of cattle, sheep, goats, pigs, humans and mice, respectively. We then systematically characterized the dynamic functional landscapes of these REs by integrating multi-omics datasets, including gene expression, chromatin accessibility, and DNA methylation. We identified a core set (n = 6359) of ruminant-specific REs that are involved in liver development, metabolism, and immune processes. Genes with more complex cis-REs exhibited higher gene expression levels and stronger conservation across species. Furthermore, we integrated expression quantitative trait loci (eQTLs) and GWAS from 44 and 52 complex traits/diseases in cattle and humans, respectively. These results demonstrated that REs with different degrees of evolutionary conservation across species exhibited distinct enrichments for GWAS signals of complex traits. We systematically annotated genome-wide functional REs in liver across six mammals and demonstrated the evolution of REs and their associations with transcriptional output and conservation. Detecting lineage-specific REs allows us to decipher the evolutionary and genetic basis of complex phenotypes in livestock and humans, which may benefit the discovery of potential biomedical models for functional variants and genes of specific human diseases.

Genome‐wide association and genomic selection in aquaculture

AbstractRecent advancements in genomic technologies have led to the discovery and application of DNA‐markers [e.g. single nucleotide polymorphisms (SNPs)] for the genetic improvement of several aquaculture species. The identification of specific genomic regions associated with economically important traits, using, for example, genome‐wide association studies (GWAS), has allowed the discovery and incorporation of markers linked to quantitative trait loci (QTL) into aquaculture breeding programs through marker‐assisted selection (MAS). However, most of the traits of economic relevance are expected to be controlled by many QTLs, each one explaining only a small proportion of the genetic variation. For traits under polygenic control, prediction of the genetic merit of animals based on the sum of effects at positions across the entire genome (i.e. genomic estimated breeding values, GEBV, which are used for what has become known as genomic selection), has been demonstrated to speed the rate of genetic gain for several traits in aquaculture breeding. The aim of this review was to provide an overview of the development and application of genomic technologies in uncovering the genetic basis of complex traits and accelerating the genetic progress in aquaculture species, as well as providing future perspectives about the deployment of novel molecular technologies for selective breeding in coming years.

Novel candidate loci for morpho-agronomic and seed quality traits detected by targeted genotyping-by-sequencing in common bean.

Phaseolus vulgaris L., known as common bean, is one of the most important grain legumes cultivated around the world for its immature pods and dry seeds, which are rich in protein and micronutrients. Common bean offers a cheap food and protein sources to ameliorate food shortage and malnutrition around the world. However, the genetic basis of most important traits in common bean remains unknown. This study aimed at identifying QTL and candidate gene models underlying twenty-six agronomically important traits in common bean. For this, we assembled and phenotyped a diversity panel of 200P. vulgaris genotypes in the greenhouse, comprising determinate bushy, determinate climbing and indeterminate climbing beans. The panel included dry beans and snap beans from different breeding programmes, elite lines and landraces from around the world with a major focus on accessions of African, European and South American origin. The panel was genotyped using a cost-conscious targeted genotyping-by-sequencing (GBS) platform to take advantage of highly polymorphic SNPs detected in previous studies and in diverse germplasm. The detected single nucleotide polymorphisms (SNPs) were applied in marker-trait analysis and revealed sixty-two quantitative trait loci (QTL) significantly associated with sixteen traits. Gene model identification via a similarity-based approach implicated major candidate gene models underlying the QTL associated with ten traits including, flowering, yield, seed quality, pod and seed characteristics. Our study revealed six QTL for pod shattering including three new QTL potentially useful for breeding. However, the panel was evaluated in a single greenhouse environment and the findings should be corroborated by evaluations across different field environments. Some of the detected QTL and a number of candidate gene models only elucidate the understanding of the genetic nature of these traits and provide the basis for further studies. Finally, the study showed the possibility of using a limited number of SNPs in performing marker-trait association in common bean by applying a highly scalable targeted GBS approach. This targeted GBS approach is a cost-efficient strategy for assessment of the genetic basis of complex traits and can enable geneticists and breeders to identify novel loci and targets for marker-assisted breeding more efficiently.

Prioritized candidate causal haplotype blocks in plant genome-wide association studies.

Genome wide association studies (GWAS) can play an essential role in understanding genetic basis of complex traits in plants and animals. Conventional SNP-based linear mixed models (LMM) that marginally test single nucleotide polymorphisms (SNPs) have successfully identified many loci with major and minor effects in many GWAS. In plant, the relatively small population size in GWAS and the high genetic diversity found in many plant species can impede mapping efforts on complex traits. Here we present a novel haplotype-based trait fine-mapping framework, HapFM, to supplement current GWAS methods. HapFM uses genotype data to partition the genome into haplotype blocks, identifies haplotype clusters within each block, and then performs genome-wide haplotype fine-mapping to prioritize the candidate causal haplotype blocks of trait. We benchmarked HapFM, GEMMA, BSLMM, GMMAT, and BLINK in both simulated and real plant GWAS datasets. HapFM consistently resulted in higher mapping power than the other GWAS methods in high polygenicity simulation setting. Moreover, it resulted in smaller mapping intervals, especially in regions of high LD, achieved by prioritizing small candidate causal blocks in the larger haplotype blocks. In the Arabidopsis flowering time (FT10) datasets, HapFM identified four novel loci compared to GEMMA's results, and the average mapping interval of HapFM was 9.6 times smaller than that of GEMMA. In conclusion, HapFM is tailored for plant GWAS to result in high mapping power on complex traits and improved on mapping resolution to facilitate crop improvement.

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Towards smart and sustainable development of modern berry cultivars in Europe.

Fresh berries are a popular and important component of the human diet. The demand for high-quality berries and sustainable production methods is increasing globally, challenging breeders to develop modern berry cultivars that fulfill all desired characteristics. Since 1994, research projects have characterized genetic resources, developed modern tools for high-throughput screening, and published data in publicly available repositories. However, the key findings of different disciplines are rarely linked together, and only a limited range of traits and genotypes has been investigated. The Horizon2020 project BreedingValue will address these challenges by studying a broader panel of strawberry, raspberry and blueberry genotypes in detail, in order to recover the lost genetic diversity that has limited the aroma and flavor intensity of recent cultivars. We will combine metabolic analysis with sensory panel tests and surveys to identify the key components of taste, flavor and aroma in berries across Europe, leading to a high-resolution map of quality requirements for future berry cultivars. Traits linked to berry yields and the effect of environmental stress will be investigated using modern image analysis methods and modeling. We will also use genetic analysis to determine the genetic basis of complex traits for the development and optimization of modern breeding technologies, such as molecular marker arrays, genomic selection and genome-wide association studies. Finally, the results, raw data and metadata will be made publicly available on the open platform Germinate in order to meet FAIR data principles and provide the basis for sustainable research in the future.

Leveraging the local genetic structure for trans-ancestry association mapping

Over the past two decades, genome-wide association studies (GWASs) have successfully advanced our understanding of the genetic basis of complex traits. Despite the fruitful discovery of GWASs, most GWAS samples are collected from European populations, and these GWASs are often criticized for their lack of ancestry diversity. Trans-ancestry association mapping (TRAM) offers an exciting opportunity to fill the gap of disparities in genetic studies between non-Europeans and Europeans. Here, we propose a statistical method, LOG-TRAM, to leverage the local genetic architecture for TRAM. By using biobank-scale datasets, we showed that LOG-TRAM can greatly improve the statistical power of identifying risk variants in under-represented populations while producing well-calibrated p values. We applied LOG-TRAM to the GWAS summary statistics of various complex traits/diseases from BioBank Japan, UK Biobank, and African populations. We obtained substantial gains in power and achieved effective correction of confounding biases in TRAM. Finally, we showed that LOG-TRAM can be successfully applied to identify ancestry-specific loci and the LOG-TRAM output can be further used for construction of more accurate polygenic risk scores in under-represented populations.