Phenotype Association Analysis Research Articles

The Study of Correlation and Genetic Divergence analysis between Yield and Yield related traits of Bread Wheat (Triticum Aestivum L.) Genotypes

Wheat is one of the best useful crops grown in Ethiopia. Evaluation of correlation and Genetic Divergence analysis provides the basis for increasing yield and successful breeding programme. In addition, some information is generated about correlation and genetic divergence analysis between yield and yield related traits of bread wheat germplasm in Ethiopia. Therefore this study was conducted to evaluate the extent of genetic correlation and Genetic Divergence analysis among yield and yield related traits of bread wheat genotypes. The study was carried out with the following objectives; to assess the association and Genetic Divergence analysis for yield and yield related traits like genotypic and phenotypic association and Genetic Divergence analysis of yield and yield related traits. To achieve the above objectives; total of 100 bread wheat (Triticum aestivum L.) genotypes were evaluated at Holeta Agricultural Research Centre in 2021 main cropping season. The experiment was conducted by using 10x10 alpha lattice designs. Data were subjected to analysis of variance which revealed that there was highly significant difference (p=0.01) among the genotypes for all characters studied. The highest grain yield (5620.76t ha) was recorded from Elit Ethiopia172/2020 and the lowest grain yield (1027.2t/ha) was recorded from 30HRWSN246/2019 genotype. Grain yield was positive and highly correlated with harvest index (r=0.810), thousand kernel weight (r=0.786), kernels per spike (r=0.716), hectoliter weight (r=0.631), Plant height(r=0.347), spikelets per spike (r=0.325), and spike length (r=0.214), and negative and significant correlation with fertile tillers per plant (r=-0.1600.25). The highest cluster was clusterI which consistes of 45 genotypes (31.30%), and the lowest cluster was cluster VI (6.30%) consists of 1 genotype.

Genotype and Phenotype Association Analysis Based on Multi-omics Statistical Data

Background: When using clinical data for multi-omics analysis, there are issues such as the insufficient number of omics data types and relatively small sample size due to the protection of patients' privacy, the requirements of data management by various institutions, and the relatively large number of features of each omics data. This paper describes the analysis of multi-omics pathway relationships using statistical data in the absence of clinical data. Methods: We proposed a novel approach to exploit easily accessible statistics in public databases. This approach introduces phenotypic associations that are not included in the clinical data and uses these data to build a three-layer heterogeneous network. To simplify the analysis, we decomposed the three-layer network into double two-layer networks to predict the weights of the inter-layer associations. By adding a hyperparameter β, the weights of the two layers of the network were merged, and then k-fold cross-validation was used to evaluate the accuracy of this method. In calculating the weights of the two-layer networks, the RWR with fixed restart probability was combined with PBMDA and CIPHER to generate the PCRWR with biased weights and improved accuracy. Results: The area under the receiver operating characteristic curve was increased by approximately 7% in the case of the RWR with initial weights. Conclusion: Multi-omics statistical data were used to establish genotype and phenotype correlation networks for analysis, which was similar to the effect of clinical multi-omics analysis.

Association analysis of antibiotic and disinfectant resistome in human and foodborne E. coli in Beijing, China

The widespread use of chemical disinfectants and antibiotics poses a major threat to food safety and human health. However, the mechanisms of co-transmission of antimicrobial resistance genes (ARGs) and biocides and metal resistance genes (BMRGs) of foodborne pathogens in the food chain is still unclear. This study isolated 343 E. coli strains from animal-derived foods in Beijing and incorporated online data of human-derived E. coli strains from NCBI. Our results demonstrated a relatively uniform distribution of strains from various regions in Beijing, indicating a lack of region-specific clustering. Additionally, predominant sequence types varied between food- and human-derived strains, suggesting a preference for different hosts and environments. Phenotypic association analysis showed that the chlorine disinfectants peroxides had a significant positive correlation with tetracyclines. Many more ARGs and BMRGs were enriched in human-associated E. coli compared with those in chicken- and pork-origin. The quaternary ammonium compounds (QACs) resistance gene qacEΔ1 had a strong correlation with aminoglycoside resistance gene aadA5, folate pathway antagonist resistance gene dfrA17, sul1 and macrolide resistance gene mph(A). The correlation results indicated a significant association between the copper resistance gene cluster pco and the silver resistance gene cluster sil. Coexistence of many resistance genes was observed within the qacEΔ1 gene structure, with qacEΔ1-sul1 being the most common combination. Our findings demonstrated that the epidemiological spread of resistance is affected by a combination of heavy metals, disinfectants and antibiotic use, suggesting that the prevention and control strategies of antimicrobial resistance need to be multifaceted and comprehensive.

Multiple genetic polymorphisms are associated with the risk of metabolic syndrome, fatty liver, and airflow limitation: A Taiwan Biobank study

BackgroundLinks have been reported between the airflow limitation and both metabolic syndrome (MetS) and fatty liver (FL). Additionally, associations between genetic factors and risks of MetS, FL, and airflow limitation have been identified separately in different studies. Our study aims to simultaneously explore the association between specific single nucleotide polymorphisms (SNPs) of certain genes and the risk of the three associated diseases. MethodsIn this retrospective cross-sectional nationwide study, 150,709 participants from the Taiwan Biobank (TWB) were enrolled. We conducted a genotype–phenotype association analysis of nine SNPs on seven genes (ApoE-rs429358, MBOAT7-rs641738, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, TM6SF2-rs58542926, and IFNL4-rs368234815) using data from the TWB1.0 and TWB2.0 genotype dataset. Participants underwent a series of assessments including questionnaires, blood examinations, abdominal ultrasounds, and spirometry examinations. ResultsMetS was associated with FL and airflow limitation. ApoE-rs429358, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, and TM6SF2-rs58542926 were significantly associated with the risk of MetS. The cumulative impact of T alleles of ApoE-rs429358 and TM6SF2-rs58542926 on the risk of FL was observed (p-value for trend < 0.001). Individuals without MetS and airflow limitation carrying LEPR-rs1805096 G_G genotype exhibited a reduction in the forced expiratory volume in 1 s percentage prediction (Coefficient −35, 95 % confidence interval (CI) −69.7– −0.4), low forced vital capacity percentage prediction (Coefficient −41.6, 95 % CI −82.6- −0.6), and low vital capacity percentage prediction (Coefficient −42.2, 95 % CI −84.2- −0.1). ConclusionsMetS significantly correlated with FL and airflow limitation. Multiple SNPs were notably associated with MetS. Specifically, T alleles of ApoE-rs429358 and TM6SF2-rs58542926 cumulatively increased the risk of FL. LEPR-rs1805096 shows a trend-wise association with pulmonary function, which is significant in patients without MetS or airflow limitation.

Transcriptome Analysis of Sesame (Sesamum indicum L.) Reveals the LncRNA and mRNA Regulatory Network Responding to Low Nitrogen Stress.

Nitrogen is one of the important factors restricting the development of sesame planting and industry in China. Cultivating sesame varieties tolerant to low nitrogen is an effective way to solve the problem of crop nitrogen deficiency. To date, the mechanism of low nitrogen tolerance in sesame has not been elucidated at the transcriptional level. In this study, two sesame varieties Zhengzhi HL05 (ZZ, nitrogen efficient) and Burmese prolific (MD, nitrogen inefficient) in low nitrogen were used for RNA-sequencing. A total of 3964 DEGs (differentially expressed genes) and 221 DELs (differentially expressed lncRNAs) were identified in two sesame varieties at 3d and 9d after low nitrogen stress. Among them, 1227 genes related to low nitrogen tolerance are mainly located in amino acid metabolism, starch and sucrose metabolism and secondary metabolism, and participate in the process of transporter activity and antioxidant activity. In addition, a total of 209 pairs of lncRNA-mRNA were detected, including 21 pairs of trans and 188 cis. WGCNA (weighted gene co-expression network analysis) analysis divided the obtained genes into 29 modules; phenotypic association analysis identified three low-nitrogen response modules; through lncRNA-mRNA co-expression network, a number of hub genes and cis/trans-regulatory factors were identified in response to low-nitrogen stress including GS1-2 (glutamine synthetase 1-2), PAL (phenylalanine ammonia-lyase), CHS (chalcone synthase, CHS), CAB21 (chlorophyll a-b binding protein 21) and transcription factors MYB54, MYB88 and NAC75 and so on. As a trans regulator, lncRNA MSTRG.13854.1 affects the expression of some genes related to low nitrogen response by regulating the expression of MYB54, thus responding to low nitrogen stress. Our research is the first to provide a more comprehensive understanding of DEGs involved in the low nitrogen stress of sesame at the transcriptome level. These results may reveal insights into the molecular mechanisms of low nitrogen tolerance in sesame and provide diverse genetic resources involved in low nitrogen tolerance research.

Genetic diversity of grain yield traits and identification of a grain weight gene SiTGW6 in foxtail millet.

Agronomic traits were evaluated in 1250 foxtail millet accessions, and a crucial gene SiTGW6 governing grain yield was identified. Elite haplotypes and dCAPS markers developed for SiTGW6 facilitate molecular breeding. A comprehensive evaluation of phenotypic characteristics and genetic diversity in germplasm resources are important for gene discovery and breeding improvements. In this study, we conducted a comprehensive evaluation of 1250 foxtail millet varieties, assessing seven grain yield-related traits and fourteen common agronomic traits over two years. Principal component analysis, correlation analysis, and cluster analysis revealed a strong positive correlation between 1000-grain weight and grain width with grain yield, emphasizing their importance in foxtail millet breeding. Additionally, we found that panicle weight positively correlated with 1000-grain weight but negatively correlated with branch and tiller numbers, indicating selection factors during domestication and breeding. Using this information, we identified 27 germplasm resources suitable for high-yield foxtail millet breeding. Furthermore, through an integration of haplotype variations and phenotype association analysis, we pinpointed a crucial gene, SiTGW6, responsible for governing grain yield in foxtail millet. SiTGW6 encodes an IAA-glucose hydrolase, primarily localized in the cytoplasm and predominantly expressed in flowering panicles. Employing RNAseq analysis, we identified 1439 differentially expressed genes across various SiTGW6 haplotypes. Functional enrichment analysis indicating that SiTGW6 regulates grain yield through the orchestration of auxin and glucan metabolism, as well as plant hormone signaling pathways. Additionally, we have identified elite haplotypes and developed dCAPS markers for SiTGW6, providing valuable technical tools to facilitate molecular breeding efforts in foxtail millet.

Exome sequencing identifies genes associated with sleep-related traits.

Sleep is vital for human health and has a moderate heritability. Previous genome-wide association studies have limitations in capturing the role of rare genetic variants in sleep-related traits. Here we conducted a large-scale exome-wide association study of eight sleep-related traits (sleep duration, insomnia symptoms, chronotype, daytime sleepiness, daytime napping, ease of getting up in the morning, snoring and sleep apnoea) among 450,000 participants from UK Biobank. We identified 22 new genes associated with chronotype (ADGRL4, COL6A3, CLK4 and KRTAP3-3), daytime sleepiness (ST3GAL1 and ANKRD12), daytime napping (PLEKHM1, ANKRD12 and ZBTB21), snoring (WDR59) and sleep apnoea (13 genes). Notably, 20 of these genes were confirmed to be significantly associated with sleep disorders in the FinnGen cohort. Enrichment analysis revealed that these discovered genes were enriched in circadian rhythm and central nervous system neurons. Phenotypic association analysis showed that ANKRD12 was associated with cognition and inflammatory traits. Our results demonstrate the value of large-scale whole-exome analysis in understanding the genetic architecture of sleep-related traits and potential biological mechanisms.

The Alzheimer’s Disease Sequencing Project – Follow Up Study (ADSP‐FUS): APOE genotype status and demographic characteristics across datasets

AbstractBackgroundThe ADSP‐FUS is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for Alzheimer Disease (AD) by expanding the ADSP beyond non‐Hispanic Whites of European Ancestry (NHW‐EA) populations. Given the lack of diversity in the ADSP, the ADSP‐FUS was designed to whole genome sequence (WGS) existing ethnically diverse and unique cohorts. The upcoming phase ADSP‐ FUS 2.0: The Diverse Population Initiative, focuses on inclusion of Hispanic/Latino (HL), non‐Hispanic Black with African Ancestry (NHB‐AA), and Asian populations.MethodsADSP‐FUS cohorts consist of studies of AD, dementia, and age‐related conditions. Clinical classifications are assigned based on standard criteria from clinical measures and history, as well as additional neuropathologic data. In addition to production of WGS, genome‐wide array and APOE genotyping is acquired or performed for all ADSP‐FUS samples.ResultsThe ADSP‐FUS currently consists of 38 cohorts comprised of ∼40,000 individuals, with plan to sequence &gt;100,000 individuals from diverse ancestries. Genotyping, sequencing, and clinical adjudication has been performed on 23,428 participants (cases N = 6,961, median age = 73; controls N = 13,007, median age = 72; ADRD N = 3,460, median age = 77. More participants are female (62.3%) than male and are evenly distributed across cases (61.0%), controls (63.1%), and ADRD (61.8%). As expected, the most prevalent APOE genotype is APOE 3/3 (% by cases/controls for 2/2 = 0.2,0.4; 2/3 = 4.3, 8.2; 2/4 = 2.2, 1.8; 3/3 = 43.8, 64.4; 3/4 = 39.5, 23.0; 4/4 = 10.1, 2.2). These proportions vary greatly between ethnicities, with the highest for APOE 4/4 observed in Asian participants (8.8%) and the lowest in Hispanic participants (2.5%), for example. Mean Braak stage for AD cases is higher (5.1+1.2) than controls (2.6+1.3) and ADRD participants (3.5+1.6).ConclusionThe results provide an overview of features of ADSP‐FUS cohorts. As the ADSP‐FUS expands in size and diversity, this genomic resource, available via NIAGADS, will be integrated with ADSP programs focused on phenotype harmonization, association analyses, functional genomics, and machine learning. In concert with these programs, the ADSP‐FUS will accelerate the identification and understanding of potential genetic risk and protective variants for AD across all populations with the target of developing new treatments that are globally effective.

Effect of SIRT1 gene single-nucleotide polymorphisms on susceptibility to type 1 diabetes in a Han Chinese population

AimsSIRT1 deficiency has been associated with diabetes, and a variant of the SIRT1 gene has been found to be involved in human autoimmune diabetes; however, it is unclear whether this genetic variation exists in Han Chinese with type 1 diabetes (T1D) and whether it contributes to development of T1D. Therefore, we aimed to explore the association of the SIRT1 gene single-nucleotide polymorphisms (SNPs) rs10997866 and rs3818292 in a Han Chinese population with T1D.MethodsThis study recruited 2653 unrelated Han Chinese individuals, of whom 1289 had T1D and 1364 were healthy controls. Allelic and genotypic distributions of SIRT1 polymorphisms (rs10997866 and rs3818292) were determined by MassARRAY. Basic characteristics, genotype and allele frequencies of selected SNPs were compared between the T1D patients and healthy controls. Further genotype–phenotype association analysis of the SNPs was performed on the T1D patients divided into three groups according to genotype. Statistical analyses included the chi-square test, Mann‒Whitney U test, Kruskal‒Wallis H test and logistic regression.ResultsThe allelic (G vs. A) and genotypic (GA vs. AA) distributions of SIRT1 rs10997866 were significantly different in T1D patients and healthy controls (P = 0.039, P = 0.027), and rs10997866 was associated with T1D susceptibility under dominant, overdominant and additive models (P = 0.026, P = 0.030 and P = 0.027, respectively). Moreover, genotype–phenotype association analysis showed the GG genotype of rs10997866 and the GG genotype of rs3818292 to be associated with higher titers of IA-2A (P = 0.013 and P = 0.038, respectively).ConclusionSIRT1 rs10997866 is significantly associated with T1D susceptibility, with the minor allele G conferring a higher risk of T1D. Moreover, SIRT1 gene rs10997866 and rs3818292 correlate with the titer of IA-2A in Han Chinese individuals with T1D.

Genetic-Phenotype Analysis of Bifidobacterium bifidum and Its Glycoside Hydrolase Gene Distribution at Different Age Groups

Human gut microbiota interfere with host development and aging. Bifidobacterium is a microbial genus found in the human digestive tract that has probiotic activities such as improving constipation and enhancing immunity. The species and numbers present change with age, but there has been limited research on probiotic gut microbiota at specific ages. This study analyzed the distribution of 610 bifidobacteria in subjects in several age groups (0-17, 18-65, and 66-108 y) using 486 fecal samples and determined the distribution of glycoside hydrolases based on genetic analysis of strains representing 85% of the Bifidobacterium species abundance in each age group. 6'-Sialyllactose is a major component of acidic breast milk oligosaccharides, which can promote human neurogenesis and bifidobacteria growth. Using genotypic and phenotypic association analysis, we investigated the utilization of 6'-sialyllactose by six B. bifidum strains isolated from subjects 0-17 and 18-65 y. A comparative genomic analysis of the six B. bifidum strains revealed differences in genomic features across age groups. Finally, the safety of these strains was evaluated by antibiotic gene and drug resistance phenotype analysis. Our results reveal that the distribution of glycoside hydrolase genes in B. bifidum varies with age, thus affecting the phenotypic results. This provides important insights for the design and application of probiotic products for different ages.

DeepPheWAS: an R package for phenotype generation and association analysis for phenome-wide association studies.

DeepPheWAS is an R package for phenome wide association studies that creates clinically-curated composite phenotypes, and integrates quantitative phenotypes from primary care data, longitudinal trajectories of quantitative measures, disease progression, and drug response phenotypes. Tools are provided for efficient analysis of association with any genetic input, under any genetic model, with optional sex-stratified analysis, and for developing novel phenotypes. The DeepPheWAS R package is freely available under GNU general public licence v3.0 from at https://github.com/Richard-Packer/DeepPheWAS. Supplementary methods and results are available at Bioinformatics online.

CRISPR/Cas9-Mediated Enrichment Coupled to Nanopore Sequencing Provides a Valuable Tool for the Precise Reconstruction of Large Genomic Target Regions.

Complete and accurate identification of genetic variants associated with specific phenotypes can be challenging when there is a high level of genomic divergence between individuals in a study and the corresponding reference genome. We have applied the Cas9-mediated enrichment coupled to nanopore sequencing to perform a targeted de novo assembly and accurately reconstruct a genomic region of interest. This approach was used to reconstruct a 250-kbp target region on chromosome 5 of the common bean genome (Phaseolus vulgaris) associated with the shattering phenotype. Comparing a non-shattering cultivar (Midas) with the reference genome revealed many single-nucleotide variants and structural variants in this region. We cut five 50-kbp tiled sub-regions of Midas genomic DNA using Cas9, followed by sequencing on a MinION device and de novo assembly, generating a single contig spanning the whole 250-kbp region. This assembly increased the number of Illumina reads mapping to genes in the region, improving their genotypability for downstream analysis. The Cas9 tiling approach for target enrichment and sequencing is a valuable alternative to whole-genome sequencing for the assembly of ultra-long regions of interest, improving the accuracy of downstream genotype-phenotype association analysis.

Prediction and association analyses of skin phenotypes in Japanese females using genetic, environmental, and physical features.

Skin characteristics show great variation from person to person and are affected by multiple factors, including genetic, environmental, and physical factors, but details of the involvement and contributions of these factors remain unclear. We aimed to characterize genetic, environmental, and physical factors affecting 16 skin features by developing models to predict personal skin characteristics. We analyzed the associations of skin phenotypes with genetic, environmental, and physical features in 1472 Japanese females aged 20-80 years. We focused on 16 skin characteristics, including melanin, brightness/lightness, yellowness, pigmented spots, wrinkles, resilience, moisture, barrier function, texture, and sebum amount. As genetic factors, we selected 74 single-nucleotide polymorphisms of genes related to skin color, vitamin level, hormones, circulation, extracellular matrix (ECM) components and ECM-degrading enzymes, inflammation, and antioxidants. Histories of ultraviolet (UV) exposure and smoking as environmental factors and age, height, and weight as physical factors were acquired by means of a questionnaire. A linear association with age was prominent for increase in the area of crow's feet, increase in number of pigmented spots, decrease in forehead sebum, and increase in VISIA wrinkle parameters. Associations were analyzed by constructing linear regression models for skin feature changes and logistic regression models to predict whether subjects show lower or higher skin measurement values in the same age groups. Multiple genetic factors, history of UV exposure and smoking, and body mass index were statistically selected for each skin characteristic. The most important association found for skin spots, such as lentigines and wrinkles, was adolescent sun exposure. Genetic, environmental, and physical factors associated with interindividual differences of the selected skin features were identified. The developed models should be useful to predict the skin characteristics of individuals and their age-related changes.

Significant variation in the performance of DNA methylation predictors across data preprocessing and normalization strategies

BackgroundDNA methylation (DNAm)-based predictors hold great promise to serve as clinical tools for health interventions and disease management. While these algorithms often have high prediction accuracy, the consistency of their performance remains to be determined. We therefore conduct a systematic evaluation across 101 different DNAm data preprocessing and normalization strategies and assess how each analytical strategy affects the consistency of 41 DNAm-based predictors.ResultsOur analyses are conducted in a large EPIC DNAm array dataset from the Jackson Heart Study (N = 2053) that included 146 pairs of technical replicate samples. By estimating the average absolute agreement between replicate pairs, we show that 32 out of 41 predictors (78%) demonstrate excellent consistency when appropriate data processing and normalization steps are implemented. Across all pairs of predictors, we find a moderate correlation in performance across analytical strategies (mean rho = 0.40, SD = 0.27), highlighting significant heterogeneity in performance across algorithms. Successful or unsuccessful removal of technical variation furthermore significantly impacts downstream phenotypic association analysis, such as all-cause mortality risk associations.ConclusionsWe show that DNAm-based algorithms are sensitive to technical variation. The right choice of data processing strategy is important to achieve reproducible estimates and improve prediction accuracy in downstream phenotypic association analyses. For each of the 41 DNAm predictors, we report its degree of consistency and provide the best performing analytical strategy as a guideline for the research community. As DNAm-based predictors become more and more widely used, our work helps improve their performance and standardize their implementation.

PSIII-4 Late-Breaking: Genetic Marker Association Study of Heifer Pregnancy in Red Angus Cattle

Abstract Heifer pregnancy (HPG) is lowly heritable, yet economically relevant in beef cattle production systems. Hence, identifying single nucleotide polymorphisms (SNP) associated with phenotype could increase the amount of information used in the genetic evaluation of the trait. Using RNA-sequence data from lactating Holstein cows, a set of SNP (n = 69) were discovered among embryo mortality conceptuses (Conceptus-SNP) associated with maternal recognition of pregnancy on day 16 after artificial insemination (AI). To further study these SNP, our objective was to conduct analyses to identify markers associated with HPG in Red Angus. Phenotypic records and genotypes were provided by the Red Angus Association of America (RAAA). For phenotypes, a total of 59,387 cattle records, from the June 2022 RAAA genetic evaluation, were used in the analysis. For genotype data, a 47,860 SNP panel (excluding SNP on the X-chromosome) were formed by combining the historical RAAA genotype data (96,162 animals with genotypes) and genotypes from whole genome sequence of 30 prominent Red Angus AI sires. Markers were mapped to the ARS-UCD1.2 bovine reference genome and then imputed to 47,860 SNP panel using findhap software. A subset of SNP (n = 2,169 SNP; Conceptus-SNP and the multi-breed selected reduced panel [MSRP]) were extracted from the imputed genotypes file. Using PLINK software, genotype quality control procedure was performed so that animals with call rate &amp;lt; 0.9 were removed. A marker was removed if: it had minor allele frequency &amp;lt; 0.01, not in Hardy–Weinberg equilibrium (P &amp;lt; 0.00001), or in high linkage disequilibrium (r2 &amp;gt; 0.95). The final genotype file contained 96,667 animals with 2,137 SNP. A Bayes Cπ single-step super hybrid model (π = 0.99), using the BOLT software package, was used to estimate marker’s posterior probability of inclusion (PPI), the marker significance criterion in predicting HPG. A marker’s PPI was calculated as the average probability from four different Gibbs sampling chains of 75,000 iterations that included 5,000 iterations as burn-in. Markers rs42576394 (18:60583784) and rs42942055 (14:34249373) had PPI of 25.5 and 7.1%, respectively; suggesting association with HPG. None of the Conceptus-SNP had a detectable association with HPG; however, these Conceptus-SNP may be much more informative for genotype to phenotype association analyses for the trait of stayability. Such a trait would be measured with lactating mature beef cows and potentially more similar to the lactating Holstein cows from which the markers were discovered.

Estimation of hereditary fructose intolerance prevalence in the Chinese population

BackgroundHereditary fructose intolerance (HFI) caused by aldolase B reduction or deficiency that results in fructose metabolism disorder. The disease prevalence in the Chinese population is unknown, which impedes the formulation of HFI screening and diagnosis strategies.Materials and methodsBy searching a local cohort (Chinese Children’s Rare Disease Genetic Testing Clinical Collaboration System, CCGT) and public databases (ClinVar and Human Gene Mutation Database) and reviewing HFI-related literature, we manually curated ALDOB pathogenic or likely pathogenic (P/LP) variants according to ACMG guidelines. Allele frequency (AF) information from the local database CCGT and the public databases HuaBiao and gnomAD for ALDOB P/LP variants was used to estimate and the HFI prevalence in the Chinese population and other populations by the Bayesian framework. We collected the genotype and clinical characteristics of HFI patients from the CCGT database and published literature to study genotype–phenotype relationships.ResultIn total, 81 variants of ALDOB were curated as P/LP. The estimated Chinese HFI prevalence was approximately 1/504,678, which was much lower than that for non-Finland European (1/23,147), Finnish in Finland (1/55,539), admixed American (1/132,801) and Ashkenazi Jewish (1/263,150) populations. By analyzing the genetic characteristics of ALDOB in the Chinese population, two variants (A338V, A338G) had significantly higher AFs in the Chinese population than in the non-Finland European population from gnomAD (all P values < 0.05). Five variants (A150P, A175D, N335K, R60*, R304Q) had significantly lower AFs (all P values < 0.1). The genotype–phenotype association analyses were based on 68 reported HFI patients from a literature review and the CCGT database. The results showed that patients carrying homozygous variant sites (especially A150P) were more likely to present nausea, and patients carrying two missense variant sites were more likely to present aversion to sweets and fruit (all P values < 0.05). Our research reveals that some gastrointestinal symptoms seem to be associated with certain genotypes.ConclusionThe prevalence of HFI in the Chinese population is extremely low, and there is no need to add HFI testing to the current newborn screening programs if medical costs are considered. A genetic testing strategy is suggested for early diagnosis of HFI.

Early marker of ocular neurodegeneration in children and adolescents with type 1 diabetes: the contributing role of polymorphisms in mir146a and mir128a genes

BackgroundEarly ocular neurodegenerative signs of diabetic neuropathy (DN) can be found in children and adolescents with type 1 diabetes (T1D). No data are available on the potential role of polymorphisms in miRNAs genes in predisposing T1D subjects to these signs.AimsTo determine whether MIR146A rs2910164 and MIR128A rs11888095 polymorphisms are associated with early retinal and corneal neurodegenerative changes in pediatric patients with T1D.MethodsA total of 140 T1D children/adolescents underwent spectral domain-optical coherence tomography (SD-OCT) and in vivo confocal microscopy (IVCM) with measurement of retinal and corneal nerve fiber parameters. Risk factors for diabetes complications (diabetes duration, blood pressure, HbA1c) were recorded. Genotyping of rs2910164 and rs1188095 SNPs and genotype–phenotype association analysis were performed.ResultsThe C allele of rs2910164 in MIR146A was associated with higher values of IVCM parameters and minimum rim width (MRW) of the peripapillary region of optic nerve head measured in the retina, whereas the T allele of rs1188095 in MIR128A was associated with a significant impairment of them. Multiple regression analysis showed that MIR146A and MIR128A polymorphisms were significantly associated with corneal nerve fiber length (beta = 0.225 and − 0.204, respectively) and other IVCM parameters, independently from age, diabetes duration, HbA1c and systolic blood pressure percentile. Similar results were found for MRW (beta = 0.213 and − 0.286, respectively).ConclusionsThese results provide new insight into the genetic predisposition to DN showing that two polymorphisms in MIR146A and MIR128A genes could significantly contribute to the development of early ocular preclinical signs of DN.

Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype-phenotype interactions.

Motivation: A central goal of current biology is to establish a complete functional link between the genotype and phenotype, known as the so-called genotype–phenotype map. With the continuous development of high-throughput technology and the decline in sequencing costs, multi-omics analysis has become more widely employed. While this gives us new opportunities to uncover the correlation mechanisms between single-nucleotide polymorphism (SNP), genes, and phenotypes, multi-omics still faces certain challenges, specifically: 1) When the sample size is large enough, the number of omics types is often not large enough to meet the requirements of multi-omics analysis; 2) each omics’ internal correlations are often unclear, such as the correlation between genes in genomics; 3) when analyzing a large number of traits (p), the sample size (n) is often smaller than p, n << p, hindering the application of machine learning methods in the classification of disease outcomes. Results: To solve these issues with multi-omics and build a robust classification model, we propose a graph-embedded deep neural network (G-EDNN) based on expression quantitative trait loci (eQTL) data, which achieves sparse connectivity between network layers to prevent overfitting. The correlation within each omics is also considered such that the model more closely resembles biological reality. To verify the capabilities of this method, we conducted experimental analysis using the GSE28127 and GSE95496 data sets from the Gene Expression Omnibus (GEO) database, tested various neural network architectures, and used prior data for feature selection and graph embedding. Results show that the proposed method could achieve a high classification accuracy and easy-to-interpret feature selection. This method represents an extended application of genotype–phenotype association analysis in deep learning networks.

Genetic Variants at the APOE Locus Predict Cardiometabolic Traits and Metabolic Syndrome: A Taiwan Biobank Study.

Several apolipoprotein genes are located at the APOE locus on chromosome 19q13.32. This study explored the genetic determinants of cardiometabolic traits and metabolic syndrome at the APOE locus in a Taiwanese population. A total of 81,387 Taiwan Biobank (TWB) participants were enrolled to undergo genotype–phenotype analysis using data from the Axiom Genome-Wide CHB arrays. Regional association analysis with conditional analysis revealed lead single-nucleotide variations (SNVs) at the APOE locus: APOE rs7412 and rs429358 for total, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) cholesterol levels; CLPTM1 rs3786505 and rs11672748 for LDL and HDL cholesterol levels; and APOC1 rs438811 and APOE-APOC1 rs439401 for serum triglyceride levels. Genotype–phenotype association analysis revealed a significant association of rs429358 and rs438811 with metabolic syndrome and of rs7412, rs438811, and rs439401 with serum albumin levels (p < 0.0015). Stepwise regression analysis indicated that CLPTM1 variants were independently associated with LDL and HDL cholesterol levels (p = 3.10 × 10−15 for rs3786505 and p = 1.48 × 10−15 for rs11672748, respectively). APOE rs429358 and APOC1 rs438811 were also independently associated with metabolic syndrome (p = 2.29 × 10−14) and serum albumin levels (p = 3.80 × 10−6), respectively. In conclusion, in addition to APOE variants, CLPTM1 is a novel candidate locus for LDL and HDL cholesterol levels at the APOE gene region in Taiwan. Our data also indicated that APOE and APOC1 variants were independently associated with metabolic syndrome and serum albumin levels, respectively. These results revealed the crucial role of genetic variants at the APOE locus in predicting cardiometabolic traits and metabolic syndrome.

Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize.

The root-associated microbiome (rhizobiome) affects plant health, stress tolerance, and nutrient use efficiency. However, it remains unclear to what extent the composition of the rhizobiome is governed by intraspecific variation in host plant genetics in the field and the degree to which host plant selection can reshape the composition of the rhizobiome. Here, we quantify the rhizosphere microbial communities associated with a replicated diversity panel of 230 maize (Zea mays L.) genotypes grown in agronomically relevant conditions under high N (+N) and low N (-N) treatments. We analyze the maize rhizobiome in terms of 150 abundant and consistently reproducible microbial groups and we show that the abundance of many root-associated microbes is explainable by natural genetic variation in the host plant, with a greater proportion of microbial variance attributable to plant genetic variation in -N conditions. Population genetic approaches identify signatures of purifying selection in the maize genome associated with the abundance of several groups of microbes in the maize rhizobiome. Genome-wide association study was conducted using the abundance of microbial groups as rhizobiome traits, and n=622 plant loci were identified that are linked to the abundance of n=104 microbial groups in the maize rhizosphere. In 62/104 cases, which is more than expected by chance, the abundance of these same microbial groups was correlated with variation in plant vigor indicators derived from high throughput phenotyping of the same field experiment. We provide comprehensive datasets about the three-way interaction of host genetics, microbe abundance, and plant performance under two N treatments to facilitate targeted experiments toward harnessing the full potential of root-associated microbial symbionts in maize production.