Genetic Basis Of Complex Traits Research Articles

A path integral approach for allele frequency dynamics under polygenic selection.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change. Predicting allele frequency changes under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Analysis of cis-regulatory changes underlying phenotype divergence shaped by domestication in pigs

BackgroundCis-regulatory elements (CREs) are regions of DNA that regulate the expression of nearby genes. Changes in these elements can lead to phenotypic variations and adaptations in different populations. However, the regulatory dynamics underlying the local adaptation of traits remain poorly understood in Chinese and Western pigs. By comparing the chromatin accessibility profiles of skeletal muscle, liver, and fat between these two pig populations, we aimed to identify key regulatory elements that could explain phenotypic differences observed between the two groups.ResultsOur results revealed that the genome-wide chromatin accessibility profiles were largely similar at a qualitative level within tissues. However, we also identified local regions that exhibited quantitative differences, most of which occurred in liver tissue. Interestingly, we found that most of the increased chromatin accessibility in the livers of Chinese pigs was associated with tissue-specific openness. Furthermore, we observed a positive correlation between the ATAC-seq signal at the transcript start site (TSS) and the expression levels of nearby genes. Motif enrichment analysis revealed NR2F1 as a key regulator in Chinese pigs. Differentially expressed genes (DEGs) in Chinese pigs showed enrichment for NR2F1 response targets. One of the genes regulated by NR2F1 in Chinese pigs, NPC1, harbored a high alternative allelic frequency in the intron region.ConclusionOverall, our study provides valuable insights into the regulatory dynamics underlying phenotypic variation in pigs. By elucidating the role of CREs in driving phenotypic variation, we can better understand the genetic basis of complex traits and potentially identify targets for genetic improvement in livestock breeding programs.

Copy Number Variation and Selection Signal: Exploring the Domestication History and Phenotype Differences Between Duroc and the Chinese Native Ningxiang Pigs.

The Ningxiang pig, one of the well-known Chinese native pig breeds, has the advantages of tender meat, high intramuscular fat (IMF) content, and roughage tolerance, compared to the commercial lean pig breeds. The genetic basis for complex traits in Ningxiang pigs has been previously studied through other genetic markers, such as Single Nucleotide Polymorphism (SNP), while the characteristics of copy number variation (CNV) and the selection signal have not been investigated yet. In this study, GGP 50 k genotyping data of 2242 Ningxiang pigs (NX) and 1137 Duroc pigs (Duroc) were involved in CNV atlas construction and selection signals identification. Annotations of genes and quantitative trait locus (QTLs) were performed on the target candidate regions, as follows: (1) 162 CNVs were detected in Ningxiang pigs, while 326 CNVs were detected in Duroc pigs, and there are 21 copy number variation regions (CNVRs) shared between them; (2) The CNVRs of Duroc are more abundant, with 192 CNVRs, accounting for 1.61% of the entire genome, while those of Ningxiang pigs only have 98 CNVRs, accounting for 0.49%; (3) The QTLs annotated on CNVs and selected regions of Ningxiang pigs were mainly associated with meat quality and fertility. In contrast, the Duroc QTLs' notes relate primarily to the carcass and immunity, and explain why they have a higher slaughter rate and immunity; (4) There is a presence of high-frequency acquired CNVs, specifically in Ningxiang pigs, with 24 genes significantly enriched in the sensory receptor-related pathway in this region; (5) Based on the CNVs atlas, candidate genes such as 3 inositol 1,4,5-triphosphate receptor, type 3 (ITPR3), forkhead box protein K2 (FOXK2), G-protein coupled estrogen receptor 1 (GPER1), Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), triosephosphate isomerase 1 (TPI1), and other candidate genes related to fat deposition and differentiation were screened. In general, this study improved our knowledge about copy number variation and selection signal information of Ningxiang pigs, which can not only further explain the genetic differences between Chinese native and Western commercial pig breeds, but also provide new materials for the analysis of the genetic basis of complex traits.

Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits.

Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9-48%), year (4-52%), and genotype-by-year interactions (14-36%) to varying extents throughout development. Genome-wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

Dog10K: an integrated Dog10K database summarizing canine multi-omics.

The diversity observed in canine breed phenotypes, together with their risk for heritabily disorders of relevance to dogs and humans, makes the species an ideal subject for studies aimed at understanding the genetic basis of complex traits and human biomedical models. Dog10K is an ongoing international collaboration that aims to uncover the genetic basis of phenotypic diversity, disease, behavior, and domestication history of dogs. To best present and make the extensive data accessible and user friendly, we have established the Dog10K (http://dog10k.kiz.ac.cn/) database, a comprehensive-omics resource summarizing multiple types of data. This database integrates single nucleotide variants (SNVs) from 1987 canine genomes, de-novo mutations (DNMs) from 43 dog breeds with>40×sequence, RNA-seq data of 105057 single nuclei from hippocampus, 74067 single cells from leukocytesand 30 blood samples from published canid studies. We provide clear visualization, statistics, browse, searching, and downloading functions for all data. We have integrated three analysis tools, Selscan, LiftOver and AgeConversion, to aid researchers in custom exploration of the comprehensive-omics data. The Dog10K database will serve as a foundational platform for analyzing, presenting and utilizing canine multi-omics data.

Investigative power of genomic informational field theory relative to genome-wide association studies for genotype-phenotype mapping.

Identifying associations between phenotype and genotype is the fundamental basis of genetic analyses. Inspired by frequentist probability and the work of R. A. Fisher, genome-wide association studies (GWAS) extract information using averages and variances from genotype-phenotype datasets. Averages and variances are legitimated upon creating distribution density functions obtained through the grouping of data into categories. However, as data from within a given category cannot be differentiated, the investigative power of such methodologies is limited. Genomic informational field theory (GIFT) is a method specifically designed to circumvent this issue. The way GIFT proceeds is opposite to that of GWAS. Although GWAS determines the extent to which genes are involved in phenotype formation (bottom-up approach), GIFT determines the degree to which the phenotype can select microstates (genes) for its subsistence (top-down approach). Doing so requires dealing with new genetic concepts, a.k.a. genetic paths, upon which significance levels for genotype-phenotype associations can be determined. By using different datasets obtained in Ovis aries related to bone growth (dataset 1) and to a series of linked metabolic and epigenetic pathways (dataset 2), we demonstrate that removing the informational barrier linked to categories enhances the investigative and discriminative powers of GIFT, namely that GIFT extracts more information than GWAS. We conclude by suggesting that GIFT is an adequate tool to study how phenotypic plasticity and genetic assimilation are linked.NEW & NOTEWORTHY The genetic basis of complex traits remains challenging to investigate using classic genome-wide association studies (GWASs). Given the success of gene editing technologies, this point needs to be addressed urgently since there can only be useful editing technologies whether precise genotype-phenotype mapping information is available initially. Genomic informational field theory (GIFT) is a new mapping method designed to increase the investigative power of biological/medical datasets suggesting, in turn, the need to rethink the conceptual bases of quantitative genetics.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

A Path Integral Approach for Allele Frequency Dynamics Under Polygenic Selection.

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from , to in time ) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Dual-extraction modeling: A multi-modal deep-learning architecture for phenotypic prediction and functional gene mining of complex traits

Despite considerable advances in extracting crucial insights from bio-omics data to unravel the intricate mechanisms underlying complex traits, the absence of a universal multi-modal computational tool with robust interpretability for accurate phenotype prediction and identification of trait-associated genes remains a challenge. This study introduces the dual-extraction modeling (DEM) approach, a multi-modal deep-learning architecture designed to extract representative features from heterogeneous omics datasets, enabling the prediction of complex trait phenotypes. Through comprehensive benchmarking experiments, we demonstrate the efficacy of DEM in classification and regression prediction of complex traits. DEM consistently exhibits superior accuracy, robustness, generalizability, and flexibility. Notably, we establish its effectiveness in predicting pleiotropic genes that influence both flowering time and rosette leaf number, underscoring its commendable interpretability. In addition, we have developed user-friendly software to facilitate seamless utilization of DEM’s functions. In summary, this study presents a state-of-the-art approach with the ability to effectively predict qualitative and quantitative traits and identify functional genes, confirming its potential as a valuable tool for exploring the genetic basis of complex traits.

Genetic basis of phenotypic convergence in pig terminal sires using pathway-based selection signature detection methods.

The primary purpose of genetic improvement in lean pig breeds is to enhance production performance. Owing to their similar breeding directions, Duroc and Pietrain pigs are ideal models for investigating the phenotypic convergence underlying artificial selection. However, most important economic traits are controlled by a polygenic basis, so traditional strategies for detecting selection signatures may not fully reveal the genetic basis of complex traits. The pathway-based gene network analysis method utilizes each pathway as a unit, overcoming the limitations of traditional strategies for detecting selection signatures by revealing the selection of complex biological processes. Here, we utilized 13 122 398 high-quality SNPs from whole-genome sequencing data of 48 Pietrain pigs, 156 Duroc pigs and 36 European wild boars to detect selective signatures. After calculating FST and iHS scores, we integrated the pathway information and utilized the r/bioconductor graphite and signet packages to construct gene networks, identify subnets and uncover candidate genes underlying selection. Using the traditional strategy, a total of 47 genomic regions exhibiting parallel selection were identified. The enriched genes, including INO80, FZR1, LEPR and FAF1, may be associated with reproduction, fat deposition and skeletal development. Using the pathway-based selection signatures detection method, we identified two significant biological pathways and eight potential candidate genes underlying parallel selection, such as VTN, FN1 and ITGAV. This study presents a novel strategy for investigating the genetic basis of complex traits and elucidating the phenotypic convergence underlying artificial selection, by integrating traditional selection signature methods with pathway-based gene network analysis.

De novo prediction of functional effects of genetic variants from DNA sequences based on context-specific molecular information

Deciphering the functional effects of noncoding genetic variants stands as a fundamental challenge in human genetics. Traditional approaches, such as Genome-Wide Association Studies (GWAS), Transcriptome-Wide Association Studies (TWAS), and Quantitative Trait Loci (QTL) studies, are constrained by obscured the underlying molecular-level mechanisms, making it challenging to unravel the genetic basis of complex traits. The advent of Next-Generation Sequencing (NGS) technologies has enabled context-specific genome-wide measurements, encompassing gene expression, chromatin accessibility, epigenetic marks, and transcription factor binding sites, to be obtained across diverse cell types and tissues, paving the way for decoding genetic variation effects directly from DNA sequences only. The de novo predictions of functional effects are pivotal for enhancing our comprehension of transcriptional regulation and its disruptions caused by the plethora of noncoding genetic variants linked to human diseases and traits. This review provides a systematic overview of the state-of-the-art models and algorithms for genetic variant effect predictions, including traditional sequence-based models, Deep Learning models, and the cutting-edge Foundation Models. It delves into the ongoing challenges and prospective directions, presenting an in-depth perspective on contemporary developments in this domain.

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets.

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.

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.