Complex Genetic Traits Research Articles

Valid inference for machine learning-assisted genome-wide association studies.

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which uses sophisticated ML techniques to impute phenotypes and then performs GWAS on the imputed outcomes, have become increasingly common in complex trait genetics research. However, the validity of ML-assisted GWAS associations has not been carefully evaluated. Here, we report pervasive risks for false-positive associations in ML-assisted GWAS and introduce Post-Prediction GWAS (POP-GWAS), a statistical framework that redesigns GWAS on ML-imputed outcomes. POP-GWAS ensures valid and powerful statistical inference irrespective of imputation quality and choice of algorithm, requiring only GWAS summary statistics as input. We employed POP-GWAS to perform a GWAS of bone mineral density derived from dual-energy X-ray absorptiometry imaging at 14 skeletal sites, identifying 89 new loci and revealing skeletal site-specific genetic architecture. Our framework offers a robust analytic solution for future ML-assisted GWAS.

Causality between neuroticism personality clusters and female reproductive diseases in European population: a two-sample bidirectional mendelian randomization study

BackgroundThe causality between neuroticism, a personality trait characterized by the tendency to experience negative emotions, and female reproductive diseases remains unclear. To provide evidence for the development of effective screening and prevention strategies, this study employed Mendelian randomization (MR) to investigate the causality between neuroticism clusters and female reproductive diseases.MethodsInstrumental variables were obtained from large-scale genome-wide association studies of populations of European descent involving three neuroticism clusters (depressed affect, worry, sensitivity to environmental stress, and adversity [SESA]) in the Complex Trait Genetics database and six female reproductive diseases (infertility, polycystic ovary syndrome [PCOS], spontaneous abortion, recurrent spontaneous abortion, endometriosis, and uterine fibroids) in the FinnGen database. The bidirectional two-sample MR analysis was conducted using the inverse variance-weighted, weighted median, and MR-Egger methods, whereas the sensitivity analysis was conducted using the Cochran’s Q-test, MR-Egger intercept, and leave-one-out analysis.ResultsIn the forward analysis, genetically predicted depressed affect and worry components of neuroticism significantly increased the risk of infertility (depressed affect: odds ratio [OR] = 1.399, 95% confidence interval [CI]: 1.054–1.856, p = 0.020; worry: OR = 1.587, 95% CI: 1.229–2.049, p = 0.000) and endometriosis (depressed affect: OR = 1.611, 95% CI: 1.234–2.102, p = 0.000; worry: OR = 1.812, 95% CI: 1.405–2.338, p = 0.000). Genetically predicted SESA component of neuroticism increased only the risk of endometriosis (OR = 1.524, 95% CI: 1.104–2.103, p = 0.010). In the reverse analysis, genetically predicted PCOS was causally associated with an increased risk of the worry component of neuroticism (Beta = 0.009, 95% CI: 0.003–0.016, p = 0.003).ConclusionsThe MR study showed that the three neuroticism personality clusters had definite causal effects on at least one specific female reproductive disease. Moreover, PCOS may increase the risk of the worry component of neuroticism. This finding suggests the need to screen for specific female reproductive diseases in populations with high neuroticism and assess the psychological status of patients with PCOS.

Short Tandem Repeats in the era of next-generation sequencing: from historical loci to population databases.

In this study, we explore the landscape of short tandem repeats (STRs) within the human genome through the lens of evolving technologies to detect genomic variations. STRs, which encompass approximately 3% of our genomic DNA, are crucial for understanding human genetic diversity, disease mechanisms, and evolutionary biology. The advent of high-throughput sequencing methods has revolutionized our ability to accurately map and analyze STRs, highlighting their significance in genetic disorders, forensic science, and population genetics. We review the current available methodologies for STR analysis, the challenges in interpreting STR variations across different populations, and the implications of STRs in medical genetics. Our findings underscore the urgent need for comprehensive STR databases that reflect the genetic diversity of global populations, facilitating the interpretation of STR data in clinical diagnostics, genetic research, and forensic applications. This work sets the stage for future studies aimed at harnessing STR variations to elucidate complex genetic traits and diseases, reinforcing the importance of integrating STRs into genetic research and clinical practice.

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

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 winterwheat-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.

Transcriptomic signatures and genomic regions associated with growth in the flatfish Senegalese sole

Growth performance is a complex genetic trait that interacts with several environmental variables and life-history attributes, most notably sex and reproduction. Thus, unveiling the genetic architecture of growth remains as a major challenge particularly in flatfish in which sexual dimorphism seems to be a common feature. In this study, we investigated the gene expression profiles and genomic windows associated with growth in the Senegalese sole (Solea senegalensis), which exhibits a female-biased size dimorphism. For this purpose, three fast-growing (FG) and two slow-growing (SG) genetic families were selected according to their breeding values for weight at harvest. Principal component analysis (PCA) using 17 morphometric traits showed that main variation (92.5%) was explained by size and shape. While phenotypic variation clearly separated the FG and SG groups, some differences associated with sex were still observable. Females exhibited greater weight, higher ventral body heights and less elliptical body shapes than males supporting sexual dimorphism both in size and shape. RNA-seq analysis identified 693, 12645 and 1059 differentially expressed transcripts (DETs) between FG and SG in muscle, liver, and brain, respectively. It should be noted a major effect of sex in muscle and liver with 7428 and 15715 DETs, respectively. In these two tissues, >50% of DETs for growth were also co-regulated by sex. Functional analysis using DETs for growth and sex unveiled intricate interactions between vitellogenesis, reproductive status, and growth-related transcriptional networks, particularly in muscle and liver tissues. Main transcriptional enriched pathways associated with growth were closely related to energy provision, cell cycle and signaling. Analysis of sex-specific and growth×sex interacting DETs between males and females highlighted lipid metabolism, macrophagy regulation, cell cycle, DNA and RNA metabolism, ribosome biogenesis, energy provision and maintainance of cellular homeostasis as the most relevant pathways driving sexual dimorphism. A variants analysis using RNA pool-seq data identified eleven genomic windows associated with growth distributed across the genome. Main gene candidates (pptc7, taz, abcd1, tfe3, nfix, ptcd2, prkaa1), whose expression was highly modified by growth, were involved in mitochondrial homeostasis, regulation of mitochondrial activity, energy production and regulation of musculoskeletal system. All these data improve our understanding of the intricate genetic architecture governing growth in sole.

Plant Synthetic Promoters

This article examines the structure and functions of the plant synthetic promoters frequently used to precisely regulate complex regulatory routes. It details the composition of native promoters and their interacting proteins to provide a better understanding of the tasks associated with synthetic promoter development. The production of synthetic promoters is performed by relatively small libraries produced generally by basic molecular or genetic engineering methods such as cis-element shuffling or domain swapping. The article also describes the preparation of large-scale libraries supported by synthetic DNA fragments, directed evolution, and machine or deep-learning methodologies. The broader application of novel, synthetic promoters reduces the prevalence of homology-based gene silencing or improves the stability of transgenes. A particularly interesting group of synthetic promoters are bidirectional forms, which can enable the expression of up to eight genes by one regulatory element. The introduction and controlled expression of several genes after one transgenic event strongly decreases the frequency of such problems as complex segregation patterns and the random integration of multiple transgenes. These complications are commonly observed during the transgenic crop development enabled by traditional, multistep transformation using genetic constructs containing a single gene. As previously tested DNA promoter fragments demonstrate low complexity and homology, their abundance can be increased by using orthogonal expression systems composed of synthetic promoters and trans-factors that do not occur in nature or arise from different species. Their structure, functions, and applications are rendered in the article. Among them are presented orthogonal systems based on transcription activator-like effectors (dTALEs), synthetic dTALE activated promoters (STAPs) and dCas9-dependent artificial trans-factors (ATFs). Synthetic plant promoters are valuable tools for providing precise spatiotemporal regulation and introducing logic gates into the complex genetic traits that are important for basic research studies and their application in crop plant development. Precisely regulated metabolic routes are less prone to undesirable feedback regulation and energy waste, thus improving the efficiency of transgenic crops.

Causal relationship between resting-state networks and depression: a bidirectional two-sample mendelian randomization study

BackgroundCerebral resting-state networks were suggested to be strongly associated with depressive disorders. However, the causal relationship between cerebral networks and depressive disorders remains controversial. In this study, we aimed to investigate the effect of resting-state networks on depressive disorders using a bidirectional Mendelian randomization (MR) design.MethodsUpdated summary-level genome-wide association study (GWAS) data correlated with resting-state networks were obtained from a meta-analysis of European-descent GWAS from the Complex Trait Genetics Lab. Depression-related GWAS data were obtained from the FinnGen study involving participants with European ancestry. Resting-state functional magnetic resonance imaging and multiband diffusion imaging of the brain were performed to measure functional and structural connectivity in seven well-known networks. Inverse-variance weighting was used as the primary estimate, whereas the MR-Pleiotropy RESidual Sum and Outliers (PRESSO), MR-Egger, and weighted median were used to detect heterogeneity, sensitivity, and pleiotropy.ResultsIn total, 20,928 functional and 20,573 structural connectivity data as well as depression-related GWAS data from 48,847 patients and 225,483 controls were analyzed. Evidence for a causal effect of the structural limbic network on depressive disorders was found in the inverse variance–weighted limbic network (odds ratio, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$28.21$$\\end{document}; 95% confidence interval, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.32-239.54$$\\end{document}; \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext{P}=0.002$$\\end{document}), whereas the causal effect of depressive disorders on SC LN was not found(OR=1.0025; CI,1.0005-1.0046; P=0.012). No significant associations between functional connectivity of the resting-state networks and depressive disorders were found in this MR study.ConclusionsThese results suggest that genetically determined structural connectivity of the limbic network has a causal effect on depressive disorders and may play a critical role in its neuropathology.

Mapping quantitative trait loci associated with self-(in)compatibility in goji berries (Lycium barbarum)

BackgroundGoji (Lycium barbarum L.) is a perennial deciduous shrub widely distributed in arid and semiarid regions of Northwest China. It is highly valued for its medicinal and functional properties. Most goji varieties are naturally self-incompatible, posing challenges in breeding and cultivation. Self-incompatibility is a complex genetic trait, with ongoing debates regarding the number of self-incompatible loci. To date, no genetic mappings has been conducted for S loci or other loci related to self-incompatibility in goji.ResultsWe used genome resequencing to create a high-resolution map for detecting de novo single-nucleotide polymorphisms (SNP) in goji. We focused on 229 F1 individuals from self-compatible ‘13–19’ and self-incompatible ‘new 9’ varieties. Subsequently, we conducted a quantitative trait locus (QTL) analysis on traits associated with self-compatibility in goji berries. The genetic map consisted of 249,327 SNPs distributed across 12 linkage groups (LGs), spanning a total distance of 1243.74 cM, with an average interval of 0.002 cM. Phenotypic data related to self-incompatibility, such as average fruit weight, fruit rate, compatibility index, and comparable compatibility index after self-pollination and geitonogamy, were collected for the years 2021–2022, as well as for an extra year representing the mean data from 2021 to 2022 (2021/22). A total of 43 significant QTL, corresponding to multiple traits were identified, accounting for more than 11% of the observed phenotypic variation. Notably, a specific QTL on chromosome 2 consistently appeared across different years, irrespective of the relationship between self-pollination and geitonogamy. Within the localization interval, 1180 genes were annotated, including Lba02g01102 (annotated as an S-RNase gene), which showed pistil-specific expression. Cloning of S-RNase genes revealed that the parents had two different S-RNase alleles, namely S1S11 and S2S8. S-genotype identification of the F1 population indicated segregation of the four S-alleles from the parents in the offspring, with the type of S-RNase gene significantly associated with self-compatibility.ConclusionsIn summary, our study provides valuable insights into the genetic mechanism underlying self-compatibility in goji berries. This highlights the importance of further positional cloning investigations and emphasizes the importance of integration of marker-assisted selection in goji breeding programs.

The impact of exercise on gene regulation in association with complex trait genetics.

Endurance exercise training is known to reduce risk for a range of complex diseases. However, the molecular basis of this effect has been challenging to study and largely restricted to analyses of either few or easily biopsied tissues. Extensive transcriptome data collected across 15 tissues during exercise training in rats as part of the Molecular Transducers of Physical Activity Consortium has provided a unique opportunity to clarify how exercise can affect tissue-specific gene expression and further suggest how exercise adaptation may impact complex disease-associated genes. To build this map, we integrate this multi-tissue atlas of gene expression changes with gene-disease targets, genetic regulation of expression, and trait relationship data in humans. Consensus from multiple approaches prioritizes specific tissues and genes where endurance exercise impacts disease-relevant gene expression. Specifically, we identify a total of 5523 trait-tissue-gene triplets to serve as a valuable starting point for future investigations [Exercise; Transcription; Human Phenotypic Variation].

An exploration of causal relationships between nine neurological diseases and the risk of breast cancer: a Mendelian randomization study.

Some preceding researches have observed that certain neurological disorders, such as Alzheimer's disease and multiple sclerosis, may affect breast cancer risk. However, whether there are causal relationships between these neurological conditions and breast cancer is inconclusive. This study was designed to explore whether neurological disorders affected the risks of breast cancer overall and of the two subtypes (ER+ and ER-). In the course of this study, genome-wide association study (GWAS) data for nine neurological diseases (Alzheimer's disease, multiple sclerosis, Parkinson's disease, myasthenia gravis, generalized epilepsy, intracerebral haemorrhage, cerebral atherosclerosis, brain glioblastoma, and benign meningeal tumour) were collected from the Complex Trait Genetics lab and the MRC Integrative Epidemiology Unit, and single-nucleotide polymorphisms (SNPs) extensively associated with these neurological ailments had been recognized as instrumental variables (IVs). GWAS data on breast cancer were collected from the Breast Cancer Association Consortium (BCAC). Two-sample Mendelian randomization (MR) analyses as well as multivariable MR analyses were performed to determine whether these SNPs contributed to breast cancer risk. Additionally, the accuracy of the results was evaluated using the false discovery rate (FDR) multiple correction method. Both heterogeneity and pleiotropy were evaluated by analyzing sensitivities. According to the results of two-sample MR analyses, Alzheimer's disease significantly reduced the risks of overall (OR 0.925, 95% CI [0.871-0.982], P = 0.011) and ER+ (OR 0.912, 95% CI [0.853-0.975], P = 0.007) breast cancer, but there was a negative result in ER- breast cancer. However, after multiple FDR corrections, the effect of Alzheimer's disease on overall breast cancer was not statistically significant. In contrast, multiple sclerosis significantly increased ER+ breast cancer risk (OR 1.007, 95% CI [1.003-1.011], P = 0.001). In addition, the multivariable MR analyses showed that Alzheimer's disease significantly reduced the risk of ER+ breast cancer (IVW: OR 0.929, 95% CI [0.864-0.999], P=0.047; MR-Egger: OR 0.916, 95% CI [0.846-0.992], P=0.031); however, multiple sclerosis significantly increased the risk of ER+ breast cancer (IVW: OR 1.008, 95% CI [1.003-1.012], P=4.35×10-4; MR-Egger: OR 1.008, 95% CI [1.003-1.012], P=5.96×10-4). There were no significant associations between the remainder of the neurological diseases and breast cancer. This study found the trends towards a decreased risk of ER+ breast cancer in patients with Alzheimer's disease and an increased risk in patients with multiple sclerosis. However, due to the limitations of Mendelian randomization, we cannot determine whether there are definite causal relationships between neurological diseases and breast cancer risk. For conclusive evidences, more prospective randomized controlled trials will be needed in the future.

Revealing inheritance of a Xinjiang isolate BGTB-1 of Puccinia striiformis f. sp. tritici and the shift of pathogenicity from avirulence to virulence at heterozygous AvrYr5 locus

In China, Xinjiang is a relatively independent epidemic region of wheat stripe rust, caused by Puccinia striiformis f. sp. tritici, due to great genetic divergence of Xinjiang with other inland epidemic regions. In this region, race evolution was usually slower than inland populations. However, many new races have recently been found, and there needs to be more understanding of the virulence evolution of the Xinjiang population. So, in this study, a 65 sexual progenies, derived from a Xinjiang single-urediospore isolate BGTB-1 of P. striiformis f. sp. tritici by selfing on alternate host barberry (Berberis aggregata). It was phenotyped on the 25 single Yr lines, and genotyped by 19 kompetitive allele-specific PCR-single nucleotide polymorphism (KASP-SNP) markers. As a result, the 65 progenies were identified as 56 various virulence patterns (VPs), and neither of which was identical to the parental isolate, showed 100% virulence variation. Compared with the parental isolate, of all progenies, 39 (60.0%) had increased virulence, and 18 (27.7%) had decreased virulence. All progenies exhibited avirulence to Yr10, Yr15, Yr32, and YrTr1 loci, and avirulence and virulence segregation at the remaining 21 Yr resistance loci. The results showed avirulence to Yr5, Yr7, and Yr76 (A:V≍3:1) loci is controlled by a single dominant gene, and that to Yr6, Yr25, and Yr44 (A:V≍1:3) loci by a single recessive gene. However, avirulence to the remaining 15 resistant loci including Yr1, Yr2, Yr3, Yr4, Yr8, Yr9, Yr17, Yr26 (=Yr24), Yr28, Yr29, Yr43, YrSp, Yr27, YrA, and YrExp2, with various avirulence and virulence segregation ratios, is controlled by two genes with different gene effects, indicating complex genetic traits of the parental isolate. Totally, 65 dissimilar genotypes detected among progenies using overall molecular markers, by which a linkage map was constructed, with a genetic distance of 441.0 cM. Interestingly, although the parental isolate was avirulent to Yr5, but 17 progenies showed virulence, showing the change of pathogenicity from avirulence to virulence at this resistance locus. It was for the first time to report that progenies with virulence to Yr5 produced sexually from avirulent parental isolate at this resistance locus. To our knowledge, this study offers an insight into inheritance, sexual reproduction and virulence variation of P. striiformis f. sp. tritici in Xinjiang, facilitating us to understand evolution of the rust pathogen in this region and accounting for Xinjiang population distinguished form other inland populations. Additionally, it is necessary to further confirm the roles of sexual reproduction in the emergence of new races and affecting population genetic diversity of P. striiformis f. sp. tritici under natural conditions in Xinjiang.

HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait

BackgroundCotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS. Here, we introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS).ResultsTrichomes of 1250 images were annotated (AnnCoT) and a combination of six Feature Extractor modules and five Segmentation modules were tested alongside a range of loss functions and data augmentation techniques. HairNet2 was further validated on the dataset used to build HairNet (CotLeaf-1), a similar dataset collected in two subsequent seasons (CotLeaf-2), and a dataset collected on two genetically diverse populations (CotLeaf-X). The main findings of this study are that (1) leaf number, environment and image position did not significantly affect results, (2) although GHS and LTS mostly correlated for individual GHS classes, results at the genotype level revealed a strong LTS heterogeneity within a given GHS class, (3) LTS correlated strongly with expert scoring of individual images.ConclusionsHairNet2 is the first quantitative and scalable deep-learning model able to measure leaf hairiness. Results obtained with HairNet2 concur with the qualitative values used by breeders at both extremes of the scale (GHS 1-2, and 5-5+), but interestingly suggest a reordering of genotypes with intermediate values (GHS 3-4+). Finely ranking mild phenotypes is a difficult task for humans. In addition to providing assistance with this task, HairNet2 opens the door to selecting plants with specific leaf hairiness characteristics which may be associated with other beneficial traits to deliver better varieties.

Selective autophagy receptor‐encoding sequences in shallot transcriptome: In silico identification and expression patterns in response to asymptomatic shallot virus X infection

AbstractPlant host tolerance is a key plant defence response to parasites, including viruses. This complex genetic trait involves multiple molecular mechanisms and, in particular, selective autophagy. Experimental data on the role of various selective autophagy factors and, in particular, cargo receptors in plant tolerance to viral infection are extremely limited. In this communication, I present the results of in silico identification of sequences in the shallot transcriptome encoding homologues of several selective autophagy receptors (SARs) related to the immune response and their expression patterns in response to asymptomatic infection of shallot virus X, a member of the genus Allexivirus, subgenus Acarallexivirus, within the family Alphaflexiviridae. The results obtained, together with the relevant literature data, suggest that some SARs likely to be involved in the regulation of the unfolded protein response and programmed cell death may play an active role in the formation of the state of tolerance of the host plant to allexivirus infection.

Cross-Species Comparative DNA Methylation Reveals Novel Insights into Complex Trait Genetics among Cattle, Sheep, and Goats.

The cross-species characterization of evolutionary changes in the functional genome can facilitate the translation of genetic findings across species and the interpretation of the evolutionary basis underlying complex phenotypes. Yet, this has not been fully explored between cattle, sheep, goats, and other mammals. Here, we systematically characterized the evolutionary dynamics of DNA methylation and gene expression in 3 somatic tissues (i.e. brain, liver, and skeletal muscle) and sperm across 7 mammalian species, including 3 ruminant livestock species (cattle, sheep, and goats), humans, pigs, mice, and dogs, by generating and integrating 160 DNA methylation and transcriptomic data sets. We demonstrate dynamic changes of DNA hypomethylated regions and hypermethylated regions in tissue-type manner across cattle, sheep, and goats. Specifically, based on the phylo-epigenetic model of DNA methylome, we identified a total of 25,074 hypomethylated region extension events specific to cattle, which participated in rewiring tissue-specific regulatory network. Furthermore, by integrating genome-wide association studies of 50 cattle traits, we provided novel insights into the genetic and evolutionary basis of complex phenotypes in cattle. Overall, our study provides a valuable resource for exploring the evolutionary dynamics of the functional genome and highlights the importance of cross-species characterization of multiomics data sets for the evolutionary interpretation of complex phenotypes in cattle livestock.

Genome-wide association studies for economically important traits in mink using copy number variation

Copy number variations (CNVs) are structural variants consisting of duplications and deletions of DNA segments, which are known to play important roles in the genetics of complex traits in livestock species. However, CNV-based genome-wide association studies (GWAS) have remained unexplored in American mink. Therefore, the purpose of the current study was to investigate the association between CNVs and complex traits in American mink. A CNV-based GWAS was performed with the ParseCNV2 software program using deregressed estimated breeding values of 27 traits as pseudophenotypes, categorized into traits of growth and feed efficiency, reproduction, pelt quality, and Aleutian disease tests. The study identified a total of 10,137 CNVs (6968 duplications and 3169 deletions) using the Affymetrix Mink 70K single nucleotide polymorphism (SNP) array in 2986 American mink. The association analyses identified 250 CNV regions (CNVRs) associated with at least one of the studied traits. These CNVRs overlapped with a total of 320 potential candidate genes, and among them, several genes have been known to be related to the traits such as ARID1B, APPL1, TOX, and GPC5 (growth and feed efficiency traits); GRM1, RNASE10, WNT3, WNT3A, and WNT9B (reproduction traits); MYO10, and LIMS1 (pelt quality traits); and IFNGR2, APEX1, UBE3A, and STX11 (Aleutian disease tests). Overall, the results of the study provide potential candidate genes that may regulate economically important traits and therefore may be used as genetic markers in mink genomic breeding programs.

Nitrate stimulates adventitious rooting by increasing auxin content and regulating auxin- and root development-related genes expression in apple

Adventitious root (AR) formation is critical for cutting survival and nutrient absorption re-establishment. This complex genetic trait involves the interplay of nitrogen, endogenous hormones, and several key genes. In this study, we treated GL-3 apple (Malus domestica) in vitro shoots with nitrate and ammonium to determine their impact on AR formation, hormonal content, and gene expression patterns. Nitrate treatment significantly promotes adventitious rooting by increasing cell division, differentiation, and AR primordia formation compared to ammonium treatment. Elevated indole-3-acetic acid (IAA), reduced abscisic acid, and zeatin riboside concentrations were consistently observed with nitrate, likely crucial for promoting ARs over ammonium. Furthermore, Malus domestica auxin resistance1 (MdAUX1) expression was induced, increasing IAA levels. MdIAA23 was upregulated. Further results indicate that the higher expression levels of Malusdomestica WUSCHEL-relatedHomeobox gene 11 (MdWOX11), Malus domestica lateral organ boundariesdomaingene 16 (MdLBD16), and MdLBD29, and increased cell cycle-related gene expressions, contribute to auxin-stimulated adventitious rooting under nitrate conditions. In conclusion, this study establishes that auxin content and associated genes related to root development and cell cycle contribute to superior ARs in response to nitrate.

SparsePro: An efficient fine-mapping method integrating summary statistics and functional annotations.

Identifying causal variants from genome-wide association studies (GWAS) is challenging due to widespread linkage disequilibrium (LD) and the possible existence of multiple causal variants in the same genomic locus. Functional annotations of the genome may help to prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. Classical fine-mapping methods conducting an exhaustive search of variant-level causal configurations have a high computational cost, especially when the underlying genetic architecture and LD patterns are complex. SuSiE provided an iterative Bayesian stepwise selection algorithm for efficient fine-mapping. In this work, we build connections between SuSiE and a paired mean field variational inference algorithm through the implementation of a sparse projection, and propose effective strategies for estimating hyperparameters and summarizing posterior probabilities. Moreover, we incorporate functional annotations into fine-mapping by jointly estimating enrichment weights to derive functionally-informed priors. We evaluate the performance of SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved improved power for fine-mapping with reduced computation time. We demonstrate the utility of SparsePro through fine-mapping of five functional biomarkers of clinically relevant phenotypes. In summary, we have developed an efficient fine-mapping method for integrating summary statistics and functional annotations. Our method can have wide utility in understanding the genetics of complex traits and increasing the yield of functional follow-up studies of GWAS. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.

Application and prospect of gene chip in genetic breeding of livestock and poultry.

Gene chip is a high-throughput technique for detecting specific DNA sequences by DNA or DNA-RNA complementary hybridization, among which SNP genotyping chips have been widely employed in the animal genetics and breeding, and have made great achievements in cattle (Bos taurus), pigs (Sus scrofa), sheep (Caprinae), chickens (Gallus gallus) and other livestock. However, genomic selection applied in production merely uses genomic information and cannot fully explain the molecular mechanism of complex traits genetics, which limits the accuracy of genomic selection. With the continuous progresses in epigenetic research, the development of commercial methylation chips and the application of the epigenome-wide association study (EWAS), DNA methylation has been extensively used to draw the causal connections between genetics and phenotypes. In the future, it is hopefully expected to develop methylation chips customized for livestock and poultry and explore methylation sites significantly related to economic traits of livestock and poultry through EWAS thereby extending the understanding of causal variation of complex traits. Combining methylation chips and SNP chips, we can capture the epigenomic and genomic information of livestock and poultry, interpret genetic variation more precisely, improve the accuracy of genome selection, and promote the fine evolution of molecular genetic breeding of livestock and poultry. In this review, we summarize the application of SNP chips and depict the prospects of the application of methylation chips in livestock and poultry. This review will provide valuable insights for further application of gene chips in farm animal breeding.

The genetics of Graves' disease.

Graves' disease (GD) is the commonest cause of hyperthyroidism and has a strong female preponderance. Everyday clinical practice suggests strong aggregation within families and twin studies demonstrate that genetic factors account for 60-80% of risk of developing GD. In this review, we collate numerous genetic studies and outline the discoveries over the years, starting with historic candidate gene studies and then exploring more recent genome-wide linkage and association studies, which have involved substantial cohorts of East Asian patients as well as those of European descent. Variants in genes including HLA, CTLA4, and PTPN22 have been shown to have substantial individual effects on disease susceptibility. In addition, we examine emerging evidence concerning the possibility that genetic variants may correlate with relevant clinical phenotypes including age of onset of GD, severity of thyrotoxicosis, goitre size and relapse of hyperthyroidism following antithyroid drug therapy, as well as thyroid eye disease. This review supports the inheritance of GD as a complex genetic trait, with a growing number of more than 80 susceptibility loci identified so far. Future implementation of more targeted clinical therapies requires larger studies investigating the influence of these genetic variants on the various phenotypes and different outcomes of conventional treatments.

Computing Acceleration to Genome-Wide Association Study Based on CPU/FPGA Heterogeneous System

Genome Wide Association Study (GWAS) reveals the influence of single nucleotide polymorphisms (SNP) and other genetic markers on the complex genetic disease traits, making a significant contribution to the prevention and treatment of genetic diseases. Since GWAS needs to calculate large numbers of pairwise tests, it is highly time-consuming and this limits its widespread promotion in many medical applications. To solve the above problem, many studies deploy GWAS on the CPU/GPU platform. By taking advantages of GPUs' high parallelism, the completion time of GWAS can be significantly decreased. However, GPUs have high energy consumption, which increases GWAS's operating cost. Considering FPGA has low power, high parallelism and programmability, this article aims to use FPGA to design a hardware accelerator dedicated to GWAS for the purpose of optimizing both completion time and energy cost of GWAS. First, the CPU/FPGA heterogenous computing system is built, and the computationally-intensive GWAS tasks such as the contingency table creation (CTC) and Kirkwood Superposition Approximation (KSA) screening are offloaded to run on FPGA. Then, a specific FPGA architectures dedicated to CTC and KSA tasks of GWAS are implemented by using a set of architectural design technologies such as hardware and software co-optimization, systolic array, loop unrolling, pipeline and parallelism. To evaluate the performance of FPGA, we compare its completion time, energy cost and performance per Watt with those of CPU and GPU. The results show the FPGA and GPU can respectively achieve 105--fold and 148--fold speedup compared with CPU. Although FPGA's computing performance is a bit lower than that of GPU, it has higher energy efficiency and its performance per Watt is 1.4 times that of GPU.