A tutorial on statistical methods for population association studies

Intergenerational neurotoxicity of polystyrene nanoplastics in offspring mice is mediated by dysfunctional microbe-gut-brain axis

Nanoplastics (NPs) are ubiquitous in daily life, posing potential risks to the environment and human. While their negative effects on parental organisms have been extensively studied, intergenerational effects are still in the early stages of investigation. Here, we aimed to investigate the impact of maternal exposure to an environmentally relevant level of polystyrene NPs (PSNPs, 100 nm) during gestation and lactation (∼32 days, 50 μg/mouse/day) on neurotoxicity mediated by the microbe-gut-brain axis in offspring mice. Maternal PSNPs exposure significantly increased brain TNF-α level and microglia by 1.43 and 1.48 folds respectively, compared to control, accompanied by nuclear pyknosis and cell vacuolization in cortex and hippocampus. Targeted neurotransmitter metabolomics analysis revealed dysregulation in dopamine and serotonin metabolism. Specifically, dopamine levels increased significantly from 0.007 ng/L to 0.015 ng/L, while N-acetylseroton and 3,4-dihydroxyphenylacetic acid decreased significantly from 0.002 and 0.929 ng/L to 0.001 and 0.680 ng/L, respectively. Through a combination of 16S rRNA sequencing and biochemical analysis, we discovered that maternal PSNPs exposure led to a depletion of anti-inflammatory bacteria and an enrichment of pro-inflammatory bacteria resulting in intestinal barrier damage, elevated levels of lipopolysaccharide in blood, and subsequent activation of neuroinflammation. Meanwhile, gut bacteria dysbiosis interfered with communication between gut and brain by dysregulating neurotransmitter synthesis, as evidenced by significant associations between neurotransmitter-related bacteria (Akkermansia, Family_XIII_AD3011_group, Lachnoclostridium) and dopamine/serotonin related metabolites. Furthermore, transcriptional alterations in dopamine and serotonin related pathways were observed in the enteric nervous system, suggesting abnormal signal transduction from gut to brain contributes to neurotoxicity. This study provides new insights into NPs-induced neurotoxicity within the context of microbe-gut-brain axis and highlights the risk of cerebral dysfunction in offspring with maternal NPs exposure.

Trait association and prediction through integrative k-mer analysis

Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

gwid: an R package and Shiny application for Genome-Wide analysis of IBD data

Summary

Genome-wide identity by descent (gwid) is an R package developed for the analysis of identity-by-descent (IBD) data pertaining to dichotomous traits. This package offers a set of tools to assess differential IBD levels for the two states of a binary trait, yielding informative and meaningful results. Furthermore, it provides convenient functions to visualize the outcomes of these analyses, enhancing the interpretability and accessibility of the results. To assess the performance of the package, we conducted an evaluation using real genotype data derived from the SNPs to investigate rheumatoid arthritis susceptibility from the Marshfield Clinic Personalized Medicine Research Project.

Availability and implementation

gwid is available as an open-source R package. Release versions can be accessed on CRAN (https://cran.r-project.org/package=gwid) for all major operating systems. The development version is maintained on GitHub (https://github.com/soroushmdg/gwid) and full documentation with examples and workflow templates is provided via the package website (http://tinyurl.com/gwid-tutorial). An interactive R Shiny dashboard is also developed (https://tinyurl.com/gwid-shiny).

Quantifying higher-order epistasis: beware the chimera

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Drivers of genomic diversity and phenotypic development in early phases of domestication in Hermetia illucens

The black soldier fly (BSF), Hermetia illucens, has the ability to efficiently bioremediate organic waste into usable bio-compounds. Understanding the impact of domestication and mass rearing on fitness and production traits is therefore important for sustainable production. This study aimed to assess patterns of genomic diversity and its association to phenotypic development across early generations of mass rearing under two selection strategies: selection for greater larval mass (SEL lines) and no direct artificial selection (NS lines). Genome-wide single nucleotide polymorphism (SNP) data were generated using 2bRAD sequencing, while phenotypic traits relating to production and population fitness were measured. Declining patterns of genomic diversity were observed across three generations of captive breeding, with the lowest diversity recorded for the F3 generation of both selection lines, most likely due to founder effects. The SEL cohort displayed statistically significantly greater larval weight com the NS lines with pronounced genetic and phenotypic directional changes across generations. Furthermore, lower genetic and phenotypic diversity, particularly for fitness traits, were evident for SEL lines, illustrating the trade-off between selecting for mass and the resulting decline in population fitness. SNP-based heritability was significant for growth, but was low or non-significant for fitness traits. Genotype-phenotype correlations were observed for traits, but individual locus effect sizes where small and very few of these loci demonstrated a signature for selection. Pronounced genetic drift, due to small effective population sizes, is likely overshadowing the impacts of selection on genomic diversity and consequently phenotypic development. The results hold particular relevance for genetic management and selective breeding for BSF in future.

A genome-wide association study on growth traits of Korean commercial pig breeds using Bayesian methods

OBJECTIVE

This study aims to identify the significant regions and candidate genes of growth-related traits (adjusted backfat thickness [ABF], average daily gain [ADG], and days to 90 kg [DAYS90]) in Korean commercial GGP pig (Duroc, Landrace, and Yorkshire) populations.

METHODS

A genome-wide association study (GWAS) was performed using single-nucleotide polymorphism (SNP) markers for imputation to Illumina PorcineSNP60. The BayesB method was applied to calculate thresholds for the significance of SNP markers. The identified windows were considered significant if they explained ≥1% genetic variance.

RESULTS

A total of 28 window regions were related to genetic growth effects. Bayesian GWAS revealed 28 significant genetic regions including 52 informative SNPs associated with growth traits (ABF, ADG, DAYS90) in Duroc, Landrace, and Yorkshire pigs, with genetic variance ranging from 1.00% to 5.46%. Additionally, 14 candidate genes with previous functional validation were identified for these traits.

CONCLUSION

The identified SNPs within these regions hold potential value for future markerassisted or genomic selection in pig breeding programs. Consequently, they contribute to an improved understanding of genetic architecture and our ability to genetically enhance pigs. SNPs within the identified regions could prove valuable for future marker-assisted or genomic selection in pig breeding programs.

Genomic prediction within and across maize landrace derived populations using haplotypes

Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.

Association of rs4516035 Polymorphism with Osteoporosis in the Southeastern Iranian Population: A Case-Control Study

BACKGROUND

Genetic polymorphisms are known to play a crucial role in the development of osteoporosis. Vitamin D3 regulates bone homeostasis through the vitamin D receptor (VDR). Reduced VDR activity increases osteoporosis risk. Study Design: A case-control study.

METHODS

This case-control study investigated the potential association between six single-nucleotide polymorphisms (SNPs) within the VDR gene (rs11568820, rs4516035, rs2228570, rs1544410, rs7975232, and rs731236) and the occurrence of osteoporosis in Kerman province. The genotypes of the SNPs were analyzed using polymerase chain reaction-restriction fragment length polymorphism, tetra primer amplification refractory mutation system-PCR, and sequencing in two groups of osteoporosis patients (n=40) and controls (n=42). Additionally, the levels of calcium and vitamin D3 in the serum of the patients were measured, and the in silico analysis of the VDR structure and interaction was performed using I-TASSER, ProSA, PROCHECK, GeneMANIA, GTEx, and GPS 6.0.

RESULTS

None of the patients exhibited calcium or vitamin D3 deficiencies. Among the six SNPs, only the T allele in rs4516035, which leads to a shorter variant called VDRA, showed a significant association with susceptibility to osteoporosis (odds ratio=3.061, P=0.007). The in silico analysis demonstrated that the 3D structure, expression, and post-transcriptional modification of VDRA are distinct from those of the more extended variant, VDRB1. VDRB1 is upregulated in sun-exposed skin, and its interactions with its partners differ from those of VDRA.

CONCLUSION

Despite adequate vitamin D levels, the VDRA variant, which has lower activity, could increase the predisposition to osteoporosis in the studied population. These findings clarify the importance of genetic screening for personalized medicine and the effectiveness of prevention and treatment strategies.

Clinical Strategies in Gene Screening Counseling for the Healthy General Population

The burgeoning interest in precision medicine has propelled an increase in the use of genome tests for screening purposes within the healthy population. Gene screening tests aim to pre-emptively identify those individuals who may be genetically predisposed to certain diseases. However, as genetic screening becomes more commonplace, it is essential to acknowledge the unique challenges it poses. A prevalent issue in this regard is the occurrence of falsepositive results, which can lead to unnecessary additional tests or treatments, and psychological distress. Additionally, the interpretation of genomic variants is based on current research evidence, and can accordingly change as new research findings emerge, potentially altering the clinical significance of these variants. Conversely, a further prominent concern regards false assurances in genetic testing, as genetic tests can yield false-negative results, potentially posing a significant clinical risk. Moreover, the results obtained for the same disease can vary among different genetic testing services, due to differences in the types of variants assessed, the scope of tests, analytical methods, and the algorithms used for predicting diseases. Consequently, whereas genetic testing holds significant promise for the future of medicine, it poses unique challenges. If conducted without a full understanding of its implications, genetic testing may fail to achieve its purpose potentially hindering effective health management. Therefore, to ensure a comprehensive understanding of the implications of genetic testing within the general population, sufficient discussion and careful consideration should be given to counseling based on gene test results.

Large-scale gene expression alterations introduced by structural variation drive morphotype diversification in Brassica oleracea

Brassica oleracea, globally cultivated for its vegetable crops, consists of very diverse morphotypes, characterized by specialized enlarged organs as harvested products. This makes B. oleracea an ideal model for studying rapid evolution and domestication. We constructed a B. oleracea pan-genome from 27 high-quality genomes representing all morphotypes and their wild relatives. We identified structural variations (SVs) among these genomes and characterized these in 704 B. oleracea accessions using graph-based genome tools. We show that SVs exert bidirectional effects on the expression of numerous genes, either suppressing through DNA methylation or promoting probably by harboring transcription factor-binding elements. The following examples illustrate the role of SVs modulating gene expression: SVs promoting BoPNY and suppressing BoCKX3 in cauliflower/broccoli, suppressing BoKAN1 and BoACS4 in cabbage and promoting BoMYBtf in ornamental kale. These results provide solid evidence for the role of SVs as dosage regulators of gene expression, driving B. oleracea domestication and diversification.

Geographic and seasonal variation of the for gene reveal signatures of local adaptation in Drosophila melanogaster

In the early 1980s, the observation that Drosophila melanogaster larvae differed in their foraging behaviour laid the foundation for the work that would later lead to the discovery of the foraging gene (for) and its associated foraging phenotypes, rover and sitter. Since then, the molecular characterization of the for gene and our understanding of the mechanisms that maintain its phenotypic variants in the laboratory have progressed enormously. However, the significance and dynamics of such variation are yet to be investigated in nature. With the advent of next-generation sequencing, it is now possible to identify loci underlying the adaptation of populations in response to environmental variation. Here, I present the results of a genotype-environment association analysis that quantifies variation at the for gene among samples of D. melanogaster structured across space and time. These samples consist of published genomes of adult flies collected worldwide, and at least twice per site of collection (during spring and fall). Both an analysis of genetic differentiation based on Fs⁢t values and an analysis of population structure revealed an east-west gradient in allele frequency. This gradient may be the result of spatially varying selection driven by the seasonality of precipitation. These results support the hypothesis that different patterns of gene flow as expected under models of isolation by distance and potentially isolation by environment are driving genetic differentiation among populations. Overall, this study is essential for understanding the mechanisms underlying the evolution of foraging behaviour in D. melanogaster.

Association of Variants in Innate Immune Genes TLR4 and TLR5 with Reproductive and Milk Production Traits in Czech Simmental Cattle

Bovine genes TLR4 and TLR5, which encode antibacterial toll-like receptors, were screened for polymorphisms in Czech Red Pied (Czech Simmental) cattle to identify variants associated with reproduction, udder health, and milk production traits. Variants were discovered by hybrid resequencing of 164 bulls using HiSeq X-Ten and PacBio technologies and then individually genotyped. Nominal p-values < 0.05 for associations were detected in 18 combinations between 14 polymorphisms and 15 traits using one-way analysis of variance (ANOVA). The TLR4 variants g.610C>T (rs43578094) and g.10310T>G (rs8193072) in reference AC000135.1 were strictly associated with the index of early reproductive disorders and maternal calving ease, respectively, at false discovery rate (FDR) < 0.05. A highly permissive false discovery rate cutoff of 0.6 separated seventeen combinations in both genes comprising eight positives. In the case of the TLR4 variant g.9422T>C (rs8193060), indications were obtained for the association with as many as four reproductive traits: incidence of cystic ovaries, early reproductive disorders, calving ease, and production longevity. The permissive FDR interpretation for the TLR5 data indicated associations with cyst incidence and early reproduction disorders with maternal calving ease. Moreover, three TLR5 polymorphisms correlated with milk production traits. The discrepancy of the observed associations with the predicted impacts of the SNPs on protein function points to the role of haplotypes. Nevertheless, this question should be resolved on a larger scale. The observed associations are endorsed by independent evidence from the published functional roles in other species and by the published QTL mapping data.

Exploring genetic diversity and ascertaining genetic loci associated with important fruit quality traits in apple (Malus × domestica Borkh.)

Genetic diversity is the primary source of variability in any crop improvement program, and the diverse germplasm of any crop species represents an important genetic resource for gene or allele mining to meet future needs. Huge genetic and phenotypic diversity is present in the apple gene pool, even though, breeding programs have been mainly focused on a few traits of interests, which have resulted in the reduction of the diversity in the cultivated lines of apple. Therefore, the present study was carried out on 70 diverse apple genotypes with the objective of analyzing the genetic diversity and to identify the genetic loci associated with important fruit quality traits. A total of 140 SSR primers were used to characterize the 70 genotypes of apples, out of which only 88 SSRs were found to be polymorphic. The PIC values varied from 0.03 to 0.75. The value of MI, EMR, and RP varied from 0.03 to 3.5, 0.5 to 5.0, and 1.89 to 6.74, respectively. The dendrogram and structure analysis divided all the genotypes into two main groups. In addition to this, large phenotypic variability was observed for the fruit quality traits under study indicated the suitability of the genotypes for association studies. Altogether 71 novel MTAs were identified for 10 fruit quality traits, of which 15 for fruit length, 15 for fruit diameter, 12 for fruit weight, 2 for total sugar, 2 for TSS, 4 for reducing sugar, 5 for non-reducing sugar, 5 for fruit firmness, 5 for fruit acidity and 6 for anthocyanin, respectively. Consistent with the physicochemical evaluation of traits, there was a significant correlation coefficient among different fruit quality characters, and many common markers were found to be associated with these traits (fruit diameter, length, TSS, total sugar, acidity and anthocyanin, respectively) by using the different modeling techniques (GLM, MLM). The inferred genetic structure, diversity pattern and the identified MTAs will be serving as resourceful grounds for better predictions and understanding of apple genome towards efficient conservation and utilization of apple germplasm for facilitating genetic improvement of fruit quality traits. Furthermore, these findings also suggested that association mapping could be a viable alternative to the conventional QTL mapping approach in apple.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01382-w.

Genome-Wide Association Study of Fiber Diameter in Alpacas

The aim of this study was the identification of candidate genomic regions associated with fiber diameter in alpacas. DNA samples were collected from 1011 female Huacaya alpacas from two geographical Andean regions in Peru (Pasco and Puno), and three alpaca farms within each region. The samples were genotyped using an Affymetrix Custom Alpaca genotyping array containing 76,508 SNPs. After the quality controls, 960 samples and 51,742 SNPs were retained. Three association study methodologies were performed. The GWAS based on a linear model allowed us to identify 11 and 35 SNPs (-log10(p-values) > 4) using information on all alpacas and alpacas with extreme values of fiber diameter, respectively. The haplotype and marker analysis method allowed us to identify nine haplotypes with standardized haplotype heritability higher than six standard deviations. The selection signatures based on cross-population extended haplotype homozygosity (XP-EHH) allowed us to identify 180 SNPs with XP-EHH values greater than |3|. Four candidate regions with adjacent SNPs identified via two association methods of analysis are located on VPA6, VPA9, VPA29 and one chromosomally unassigned scaffold. This study represents the first analysis of alpaca whole genome association with fiber diameter, using a recently assembled alpaca SNP microarray.

Genome-wide analysis of cold imbibition stress in soybean, Glycine max

In Canada, the length of the frost-free season necessitates planting crops as early as possible to ensure that the plants have enough time to reach full maturity before they are harvested. Early planting carries inherent risks of cold water imbibition (specifically less than 4°C) affecting seed germination. A marker dataset developed for a previously identified Canadian soybean GWAS panel was leveraged to investigate the effect of cold water imbibition on germination. Seed from a panel of 137 soybean elite cultivars, grown in the field at Ottawa, ON, over three years, were placed on filter paper in petri dishes and allowed to imbibe water for 16 hours at either 4°C or 20°C prior to being transferred to a constant 20°C. Observations on seed germination, defined as the presence of a 1 cm radicle, were done from day two to seven. A three-parameter exponential rise to a maximum equation (3PERM) was fitted to estimate germination, time to the one-half maximum germination, and germination uniformity for each cultivar. Genotype-by-sequencing was used to identify SNPs in 137 soybean lines, and using genome-wide association studies (GWAS - rMVP R package, with GLM, MLM, and FarmCPU as methods), haplotype block analysis, and assumed linkage blocks of ±100 kbp, a threshold for significance was established using the qvalue package in R, and five significant SNPs were identified on chromosomes 1, 3, 4, 6, and 13 for maximum germination after cold water imbibition. Percent of phenotypic variance explained (PVE) and allele substitution effect (ASE) eliminated two of the five candidate SNPs, leaving three QTL regions on chromosomes 3, 6, and 13 (Chr3-3419152, Chr6-5098454, and Chr13-29649544). Based on the gene ontology (GO) enrichment analysis, 14 candidate genes whose function is predicted to include germination and cold tolerance related pathways were identified as candidate genes. The identified QTLs can be used to select future soybean cultivars tolerant to cold water imbibition and mitigate risks associated with early soybean planting.

A Large-Scale Candidate-Gene Association Mapping for Drought Tolerance and Agronomic Traits in Sugarcane

Dissection of the genetic loci controlling drought tolerance traits with a complex genetic inheritance is important for drought-tolerant sugarcane improvement. In this study, we conducted a large-scale candidate gene association study of 649 candidate genes in a sugarcane diversity panel to identify genetic variants underlying agronomic traits and drought tolerance indices evaluated in plant cane and ratoon cane under water-stressed (WS) and non-stressed (NS) environments. We identified 197 significant marker-trait associations (MTAs) in 141 candidate genes associated with 18 evaluated traits with the Bonferroni correction threshold (α = 0.05). Out of the total, 95 MTAs in 78 candidate genes and 62 MTAs in 58 candidate genes were detected under NS and WS conditions, respectively. Most MTAs were found only in specific water regimes and crop seasons. These MTAs explained 7.93-30.52% of phenotypic variation. Association mapping results revealed that 34, 59, and 104 MTAs involved physiological and molecular adaptation, phytohormone metabolism, and drought-inducible genes. They identified 19 pleiotropic genes associated with more than one trait and many genes related to drought tolerance indices. The genetic and genomic resources identified in this study will enable the combining of yield-related traits and sugar-related traits with agronomic value to optimize the yield of sugarcane cultivars grown under drought-stressed and non-stressed environments.

Whole genome sequencing of human Borrelia burgdorferi isolates reveals linked blocks of accessory genome elements located on plasmids and associated with human dissemination

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 B. burgdorferi (Bb) isolates derived from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bb isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bb isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ~900 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination in humans and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, have increased rates of dissemination in humans. OspC type A strains possess a unique set of strongly linked genetic elements including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. These features of OspC type A strains reflect a broader paradigm across Bb isolates, in which near-clonal genotypes are defined by strain-specific clusters of linked genetic elements, particularly those encoding surface-exposed lipoproteins. These clusters of genes are maintained by strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework

Association testing has been widely used to study the relationship between genetic variants and phenotypes. Most association testing methods are genotype-based, i.e. first estimate genotype and then regress phenotype on estimated genotype and other variables. Directly testing methods based on next generation sequencing (NGS) data without genotype calling have been proposed and shown advantage over genotype-based methods in the scenarios when genotype calling is not accurate. NGS data-based single-variant testing have been proposed including our previously proposed single-variant testing method, i.e. UNC combo method [1]. NGS data-based group testing methods for continuous phenotype have also been proposed by us using a linear model framework which can handle continuous responses [2]. In this paper, we extend our linear model-based framework to a generalized linear model-based framework so that the methods can handle other types of responses especially binary responses which is commonly-faced in association studies. We have conducted extensive simulation studies to evaluate the performance of different estimators and compare our estimators with their corresponding genotype-based methods. We found that all methods have Type I errors controlled, and our NGS data-based testing methods have better performance than their corresponding genotype-based methods in the literature for other types of responses including binary responses (logistic regression) and count responses (Poisson regression especially when sequencing depth is low. In conclusion, we have extended our previous linear model (LM) framework to a generalized linear model (GLM) framework and derived NGS data-based testing methods for a group of genetic variants. Compared with our previously proposed LM-based methods [2], the new GLM-based methods can handle more complex responses (for example, binary responses and count responses) in addition to continuous responses. Our methods have filled the literature gap and shown advantage over their corresponding genotype-based methods in the literature.

Medicago truncatula quantitative resistance to a new strain of Verticillium alfalfae from Iran revealed by a genome-wide association study

Verticillium wilt is a major threat to many crops, among them alfalfa (Medicago sativa). The model plant Medicago truncatula, a close relative of alfalfa was used to study the genetic control of resistance towards a new Verticillium alfalfae isolate. The accidental introduction of pathogen strains through global trade is a threat to crop production and such new strains might also be better adapted to global warming. Isolates of V. alfalfae were obtained from alfalfa fields in Iran and characterized. The Iranian isolate AF1 was used in a genome-wide association study (GWAS) involving 242 accessions from the Mediterranean region. Root inoculations were performed with conidia at 25°C and symptoms were scored regularly. Maximum Symptom Score and Area under Disease Progess Curve were computed as phenotypic traits to be used in GWAS and for comparison to a previous study with French isolate V31.2 at 20°C. This comparison showed high correlation with a shift to higher susceptibility, and similar geographical distribution of resistant and susceptible accessions to AF1 at 25°C, with resistant accessions mainly in the western part. GWAS revealed 30 significant SNPs linked to resistance towards isolate AF1. None of them were common to the previous study with isolate V31.2 at 20°C. To confirm these loci, the expression of nine underlying genes was studied. All genes were induced in roots following inoculation, in susceptible and resistant plants. However, in resistant plants induction was higher and lasted longer. Taken together, the use of a new pathogen strain and a shift in temperature revealed a completely different genetic control compared to a previous study that demonstrated the existence of two major QTLs. These results can be useful for Medicago breeding programs to obtain varieties better adapted to future conditions.

Genetic Polymorphisms of ENPP2 Are Possibly Associated with Pain Severity and Opioid Dose Requirements in Patients with Inflammatory Pain Conditions: Clinical Observation Study

Autotaxin, encoded by the ENPP2 gene, is a known key element of neuropathic pain; however, its involvement in nociceptive pain processing remains unclear. We explored the associations between postoperative pain intensity, 24-h postoperative opioid dose requirements, and 93 ENNP2-gene single-nucleotide polymorphisms (SNPs) in 362 healthy patients who underwent cosmetic surgery using the dominant, recessive, and genotypic models. Next, we validated the associations between relevant SNPs on the one hand and pain intensity and daily opioid dosages on the other in 89 patients with cancer-related pain. In this validation study, a Bonferroni correction for multiplicity was applied on all relevant SNPs of the ENPP2 gene and their respective models. In the exploratory study, three models of two SNPs (rs7832704 and rs2249015) were significantly associated with postoperative opioid doses, although the postoperative pain intensity was comparable. In the validation study, the three models of the two SNPs were also significantly associated with cancer pain intensity (p < 0.017). Patients with a minor allele homozygosity complained of more severe pain compared with patients with other genotypes when using comparable daily opioid doses. Our findings might suggest that autotaxin is associated with nociceptive pain processing and the regulation of opioid requirements.

A Genome-Wide Association Study to Identify Novel Candidate Genes Related to Low-Nitrogen Tolerance in Cucumber (Cucumis sativus L.)

Cucumber is one of the most important vegetables, and nitrogen is essential for the growth and fruit production of cucumbers. It is crucial to develop cultivars with nitrogen limitation tolerance or high nitrogen efficiency for green and efficient development in cucumber industry. To reveal the genetic basis of cucumber response to nitrogen starvation, a genome-wide association study (GWAS) was conducted on a collection of a genetically diverse population of cucumber (Cucumis sativus L.) comprising 88 inbred and DH accessions including the North China type, the Eurasian type, the Japanese and South China type mixed subtype, and the South China type subtype. Phenotypic evaluation of six traits under control (14 mM) and treatment (3.5 mM) N conditions depicted the presence of broad natural variation in the studied population. The GWAS results showed that there were significant differences in the population for nitrogen limitation treatment. Nine significant loci were identified corresponding to six LD blocks, three of which overlapped. Sixteen genes were selected by GO annotation associated with nitrogen. Five low-nitrogen stress tolerance genes were finally identified by gene haplotype analysis: CsaV3_3G003630 (CsNRPD1), CsaV3_3G002970 (CsNRT1.1), CsaV3_4G030260 (CsSnRK2.5), CsaV3_4G026940, and CsaV3_3G011820 (CsNPF5.2). Taken together, the experimental data and identification of candidate genes presented in this study offer valuable insights and serve as a useful reference for the genetic enhancement of nitrogen limitation tolerance in cucumbers.

Whole genome sequencing of Borrelia burgdorferi isolates reveals linked clusters of plasmid-borne accessory genome elements associated with virulence

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 patient-derived B. burgdorferi sensu stricto ( Bbss ) isolates from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bbss isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bbss isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ∼800 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, are associated with increased rates of dissemination. OspC type A strains possess a unique constellation of strongly linked genetic changes including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. The patterns of OspC type A strains typify a broader paradigm across Bbss isolates, in which genetic structure is defined by correlated groups of strain-variable genes located predominantly on plasmids, particularly for expression of surface-exposed lipoproteins. These clusters of genes are inherited in blocks through strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

Review: Status and prospects of association mapping in grapevine

Thanks to current advances in sequencing technologies, novel bioinformatics tools, and efficient modeling solutions, association mapping has become a widely accepted approach to unravel the link between genotype and phenotype diversity in numerous crops. In grapevine, this strategy has been used in the last decades to understand the genetic basis of traits of agronomic interest (fruit quality, crop yield, biotic and abiotic resistance), of special relevance nowadays to improve crop resilience to cope with future climate scenarios. Genome-wide association studies have identified many putative causative loci for different traits, some of them overlapping well-known causal genes identified by conventional quantitative trait loci studies in biparental progenies, and/or validated by functional approaches. In addition, candidate-gene association studies have been useful to pinpoint the causal mutation underlying phenotypic variation for several traits of high interest in breeding programs (like berry color, seedlessness, and muscat flavor), information that has been used to develop highly informative and useful markers already in use in marker-assisted selection processes. Thus, association mapping has proved to represent a valuable step towards high quality and sustainable grape production. This review summarizes current applications of association mapping in grapevine research and discusses future prospects in view of current viticulture challenges.

Combining genome-wide association study based on low-coverage whole genome sequencing and transcriptome analysis to reveal the key candidate genes affecting meat color in pigs

Meat color is an attractive trait that influences consumers' purchase decisions at the point of sale. To decipher the genetic basis of meat color traits, we performed a genome-wide association study based on low-coverage whole-genome sequencing. In total, 669 (Pietrain × Duroc) × (Landrace × Yorkshire) pigs were genotyped using low-coverage whole-genome sequencing. Single nucleotide polymorphism (SNP) calling and genotype imputation were performed using the BaseVar + STITCH channel. Six individuals with an average depth of 12.05× whole-genome resequencing were randomly selected to assess the accuracy of imputation. Heritability evaluation and genome-wide association study for meat color traits were conducted. Functional enrichment analysis of the candidate genes from genome-wide association study and integration analysis with our previous transcriptome data were conducted. The imputation accuracy parameters, allele frequency R² , concordance rate, and dosage R² were 0.959, 0.952, and 0.933, respectively. The heritability values of a*_45 min , b*_45 min , L*_45 min , C*, and H⁰ were 0.19, 0.11, 0.06, 0.16, and 0.26, respectively. In total, 3884 significant SNPs and 15 QTL, corresponding to 382 genes, were associated with meat color traits. Functional enrichment analysis revealed that 10 genes were the potential candidates for regulating meat color. Moreover, integration analysis revealed that DMRT2, EFNA5, FGF10, and COL11A2 were the most promising candidates affecting meat color. In summary, this study provides new insights into the molecular basis of meat color traits, and provides a new theoretical basis for the molecular breeding of meat color traits in pigs.

Association between ADAM33 Single-Nucleotide Polymorphisms and Treatment Response to Inhaled Corticosteroids and a Long-Acting Beta-Agonist in Asthma

ADAM33 has been linked to airway structural changes in patients with asthma, leading to airway hyperresponsiveness, narrowing, and ultimately poor treatment responsiveness. This study aimed to evaluate the genetic association of ADAM33 SNPs with asthma, disease severity, and treatment responsiveness to ICS+LABA in the South Indian population. In this case-control study (486 controls and 503 cases), we performed genotyping using MassArray for six SNPs of ADAM33, namely rs2280091, rs2787094, rs3918396, rs67044, rs2853209, and rs3918392. We studied the association with asthma and treatment responsiveness to ICS+LABA, using genotype, allele frequency distribution, and haplotype analysis. A significant clinical finding of the study was that certain patients in the disease severity group (moderate and mild) showed poor or no improvement after a three-month follow-up of regular ICS+LABA therapy. Of the studied ADAM33 SNPs, rs2853209 showed an association with asthma. The further analysis of asthma patients according to disease severity suggested an association between moderate disease and the minor allele "T" for rs2853209. The homozygous minor allele of SNP rs2787094 was found to be associated with poorer lung function and the least lung-function improvement after three months of ICS+LABA therapy. The haplotype analysis of six SNPs showed a significant association between the rs2853209 and rs3918396 blocks and asthma. ADAM33 gene polymorphism has clinical relevance in terms of disease association and response to treatment. SNP rs2853209 seemed most relevant to asthma, and SNP rs2787094 could be a genetic marker for predicting response to ICS+LABA therapy in the study population.

Genome-wide association studies of COVID-19: Connecting the dots

Genome-wide association studies (GWASs) are a research approach used to identify genetic variants associated with common diseases, like COVID-19. The lead genetic variants (n = 41) reported by the eleven largest COVID-19 GWASs are mapped to 22 different chromosomal regions. The loci 3q21.31 (LZTFL1 and chemokine receptor genes) and 9q34.2 (ABO), associated with disease severity and susceptibility to infection, respectively, were the most replicated findings across studies. Genes involved with mucociliary clearance (CEP97, FOXP4), viral-entry (ACE2, SLC6A20) and mucosal immunity (MIR6891) are associated with the risk of SARS-CoV-2 infection while genes of antiviral immune response (IFNAR2, OAS1), leukocyte trafficking (CCR9, CXCR6) and lung injury (DPP9, NOTCH4) are associated with severe disease. The biological processes underlying the risk of infection occur prominently, but not exclusively, in the upper airways whereas the severe COVID-19-associated processes in alveolar-capillary interface. The COVID-19 GWASs has unraveled key genetic mechanisms of SARS-CoV-2 pathogenesis, although the genetic basis of other COVID-19 related phenotypes (long COVID and neurological impairment) remains to be elucidated.

Gaussian graphical models with applications to omics analyses

Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical foundations of GGMs are introduced with the goal of enabling the researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed for high-dimensional applications such as genomics, proteomics, or metabolomics. The application of these methods is illustrated using a publicly available dataset of gene expression profiles from 578 participants with ovarian cancer in The Cancer Genome Atlas. Stand-alone code for the demonstration is available as an RMarkdown file at https://github.com/katehoffshutta/ggmTutorial.

Individual and combined effects of the GSTM1, GSTT1, and GSTP1 polymorphisms on type 2 diabetes mellitus risk: A systematic review and meta-analysis

Backgrounds: Compared with previously published meta-analyses, this is the first study to investigate the combined effects of glutathione-S-transferase polymorphisms (GSTM1, GSTT1 and GSTP1 IIe105Val) and type 2 diabetes mellitus (T2DM) risk; moreover, the credibility of statistically significant associations was assessed; furthermore, many new original studies were published. Objectives: To determine the relationship between GSTM1, GSTT1, and GSTP1 polymorphisms with T2DM risk. Methods: PubMed, Embase, Wanfang, and China National Knowledge Infrastructure Databases were searched. We quantify the relationship using crude odds ratios and their 95% confidence intervals Moreover, the Venice criteria, false-positive report probability (FPRP), and Bayesian false discovery probability (BFDP) were used to validate the significance of the results. Results: Overall, significantly increased T2DM risk was found between individual and combined effects of GSTM1, GSTT1, and GSTP1 polymorphisms on T2DM risk, but, combined effects of the GSTT1 and GSTP1 polymorphisms was not statistically significant. GSTT1 gene polymorphism significantly increases the risk of T2DM complications, while GSTM1 and GSTP1 polymorphisms had no statistical significance. The GSTM1 null genotype was linked to a particularly increased risk of T2DM in Caucasians; the GSTT1 null genotype was connected to a significantly higher risk of T2DM in Asians and Indians; and the GSTP1 IIe105Val polymorphism was related to a substantially increased T2DM risk in Indians. Moreover, the GSTM1 and GSTT1 double null genotype was associated with substantially increased T2DM risk in Caucasians and Indians; the combined effects of GSTM1 and GSTP1 polymorphisms was associated with higher T2DM risk in Caucasians. However, all significant results were false when the Venice criteria, FPRP, and BFDP test were used (any FPRP >0.2 and BFDP value >0.8). Conclusion: The current analysis strongly suggests that the individual and combined effects of GSTM1, GSTT1 and GSTP1 polymorphisms might not be connected with elevated T2DM risk.

New Distance-Based approach for Genome-Wide Association Studies

With the rise of genome-wide association studies (GWAS), the analysis of typical GWAS data sets with thousands of single-nucleotide polymorphisms (SNPs) has become crucial in biomedicine research. Here, we propose a new method to identify SNPs related to disease in case-control studies. The method, based on genetic distances between individuals, takes into account the possible population substructure, and avoids the issues of multiple testing. The method provides two ordered lists of SNPs; one with SNPs which minor alleles can be considered risk alleles for the disease, and another one with SNPs which minor alleles can be considered as protective. These two lists provide a useful tool to help the researcher to decide where to focus attention in a first stage.

Relationship between vitamin D receptor gene polymorphisms and second acid-fast bacilli smear-positive during treatment for tuberculosis patients

PURPOSE

Tuberculosis (TB) treatment is associated with Vitamin D. This study aimed to explore the relationship between Vitamin D receptor (VDR) gene polymorphisms and second acid-fast bacilli (AFB) smear-positive during treatment for TB patients.

METHODS

This was a cross-sectional study. Seven hundred and thirty-one TB patients whose single nucleotide polymorphism site (SNPs) of VDR gene were detected from December 2019 to December 2020 in XinJiang of China. The genotypic distributions in each group were tested separately for Hardy-Weinberg equilibrium. The tetragram test was used to construct haplotypes to evaluate the association between each haplotype and second AFB smear-positive occurrence.

RESULTS

No significant deviations were observed with all the four polymorphism sites in the genotypic distributions (P>0.05). Linkage disequilibrium (LD) analysis showed that there was LD between SNPs of VDR gene (r²=0.74, D`>0.9). Each haplotype was not considered to be the influencing factor of second AFB smear-positive.

CONCLUSIONS

There is no association between VDR gene polymorphism (ApaI, BsmI, FokI and TaqI) and second AFB smear-positive.

Natural variation of HTH5 from wild rice, Oryza rufipogon Griff., is involved in conferring high-temperature tolerance at the heading stage

Global warming is a major abiotic stress factor, which limit rice production. Exploiting the genetic basis of the natural variation in heat resistance at different reproductive stages among diverse exotic Oryza germplasms can help breeding heat-resistant rice cultivars. Here, we identified a stable quantitative trait locus (QTL) for heat tolerance at the heading stage on chromosome 5 (qHTH5) in O. rufipogon Griff. The corresponding gene, HTH5, pertains to the pyridoxal phosphate-binding protein PLPBP (formerly called PROSC) family, which is predicted to encode pyridoxal phosphate homeostasis protein (PLPHP) localized to the mitochondrion. Overexpression of HTH5 increased the seed-setting rate of rice plants under heat stress at the heading stage, whereas suppression of HTH5 resulted in greater susceptibility to heat stress. Further investigation indicated that HTH5 reduces reactive oxygen species accumulation at high temperatures by increasing the heat-induced pyridoxal 5'-phosphate (PLP) content. Moreover, we found that two SNPs located in the HTH5 promoter region are involved with its expression level and associated with heat tolerance diversity. These findings suggest that the novel gene HTH5 might have great potential value for heightening rice tolerance to heat stress to the on-going threat of global warming.

Combinational Drug Repurposing from Genetic Networks Applied to Alzheimer’s Disease

Background: Human diseases are multi-factorial biological phenomena resulting from perturbations of numerous functional networks. The complex nature of human diseases explains frequently observed marginal or transitory efficacy of mono-therapeutic interventions. For this reason, combination therapy is being increasingly evaluated as a biologically plausible strategy for reversing disease state, fostering the development of dedicated methodological and experimental approaches. In parallel, genome-wide association studies (GWAS) provide a prominent opportunity for disclosing human-specific therapeutic targets and rational drug repurposing. Objective: In this context, our objective was to elaborate an integrated computational platform to accelerate discovery and experimental validation of synergistic combinations of repurposed drugs for treatment of common human diseases. Methods: The proposed approach combines adapted statistical analysis of GWAS data, pathway-based functional annotation of genetic findings using gene set enrichment technique, computational reconstruction of signaling networks enriched in disease-associated genes, selection of candidate repurposed drugs and proof-of-concept combinational experimental screening. Results: It enables robust identification of signaling pathways enriched in disease susceptibility loci. Therapeutic targeting of the disease-associated signaling networks provides a reliable way for rational drug repurposing and rapid development of synergistic drug combinations for common human diseases. Conclusion: Here we demonstrate the feasibility and efficacy of the proposed approach with an experiment application to Alzheimer’s disease.

Characteristics of white blood cell count in acute lymphoblastic leukemia: A COST LEGEND phenotype-genotype study

BACKGROUND

White blood cell count (WBC) as a measure of extramedullary leukemic cell survival is a well-known prognostic factor in acute lymphoblastic leukemia (ALL), but its biology, including impact of host genome variants, is poorly understood.

METHODS

We included patients treated with the Nordic Society of Paediatric Haematology and Oncology (NOPHO) ALL-2008 protocol (N = 2347, 72% were genotyped by Illumina Omni2.5exome-8-Bead chip) aged 1-45 years, diagnosed with B-cell precursor (BCP-) or T-cell ALL (T-ALL) to investigate the variation in WBC. Spline functions of WBC were fitted correcting for association with age across ALL subgroups of immunophenotypes and karyotypes. The residuals between spline WBC and actual WBC were used to identify WBC-associated germline genetic variants in a genome-wide association study (GWAS) while adjusting for age and ALL subtype associations.

RESULTS

We observed an overall inverse correlation between age and WBC, which was stronger for the selected patient subgroups of immunophenotype and karyotypes (ρ_BCP-ALL  = -.17, ρ_T-ALL  = -.19; p < 3 × 10^-4 ). Spline functions fitted to age, immunophenotype, and karyotype explained WBC variation better than age alone (ρ = .43, p << 2 × 10^-6 ). However, when the spline-adjusted WBC residuals were used as phenotype, no GWAS significant associations were found. Based on available annotation, the top 50 genetic variants suggested effects on signal transduction, translation initiation, cell development, and proliferation.

CONCLUSION

These results indicate that host genome variants do not strongly influence WBC across ALL subsets, and future studies of why some patients are more prone to hyperleukocytosis should be performed within specific ALL subsets that apply more complex analyses to capture potential germline variant interactions and impact on WBC.

Sex-dimorphic gene effects on survival outcomes in people with coronary artery disease

Background:

Ischemic coronary heart disease (IHD) is the leading cause of death worldwide. Genetic variation is presumed to be a major factor underlying sex differences for IHD events, including mortality. The purpose of this study was to identify sex-specific candidate genes associated with all-cause mortality among people diagnosed with coronary artery disease (CAD).

Methods:

We performed a sex-stratified, exploratory genome-wide association (GWAS) screen using existing data from CAD-diagnosed males (n = 510) and females (n = 174) who reported European ancestry from the Duke Catheterization Genetics biorepository. Extant genotype data for 785,945 autosomal SNPs generated with the Human Omni1-Quad BeadChip (Illumina, CA, USA) were analyzed using an additive inheritance model. We estimated instantaneous risk of all-cause mortality by genotype groups across the 11-year follow-up using Cox multivariate regression, covarying for age and genomic ancestry.

Results:

The top GWAS hits associated with all-cause mortality among people with CAD included 8 SNPs among males and 15 among females (p = 1 × 10⁻⁶ or 10⁻⁷), adjusted for covariates. Cross-sex comparisons revealed distinct candidate genes. Biologically relevant candidates included rs9932462 (EMP2/TEKT5) and rs2835913 (KCNJ6) among males and rs7217169 (RAP1GAP2), rs8021816 (PRKD1), rs8133010 (PDE9A), and rs12145981 (LPGAT1) among females.

Conclusions:

We report 20 sex-specific candidate genes having suggestive association with all-cause mortality among CAD-diagnosed subjects. Findings demonstrate proof of principle for identifying sex-associated genetic factors that may help explain differential mortality risk in people with CAD. Replication and meta-analyses in larger studies with more diverse samples will strengthen future work in this area.

Identification of Secreted Protein Gene-Based SNP Markers Associated with Virulence Phenotypes of Puccinia striiformis f. sp. tritici, the Wheat Stripe Rust Pathogen

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a destructive disease that occurs throughout the major wheat-growing regions of the world. This pathogen is highly variable due to the capacity of virulent races to undergo rapid changes in order to circumvent resistance in wheat cultivars and genotypes and to adapt to different environments. Intensive efforts have been made to study the genetics of wheat resistance to this disease; however, no known avirulence genes have been molecularly identified in Pst so far. To identify molecular markers for avirulence genes, a Pst panel of 157 selected isolates representing 126 races with diverse virulence spectra was genotyped using 209 secreted protein gene-based single nucleotide polymorphism (SP-SNP) markers via association analysis. Nineteen SP-SNP markers were identified for significant associations with 12 avirulence genes: AvYr1, AvYr6, AvYr7, AvYr9, AvYr10, AvYr24, AvYr27, AvYr32, AvYr43, AvYr44, AvYrSP, and AvYr76. Some SP-SNPs were associated with two or more avirulence genes. These results further confirmed that association analysis in combination with SP-SNP markers is a powerful tool for identifying markers for avirulence genes. This study provides genomic resources for further studies on the cloning of avirulence genes, understanding the mechanisms of host–pathogen interactions, and developing functional markers for tagging specific virulence genes and race groups.

Cohort profile: genomic data for 26 622 individuals from the Canadian Longitudinal Study on Aging (CLSA)

PURPOSE

The Canadian Longitudinal Study on Aging (CLSA) Comprehensive cohort was established to provide unique opportunities to study the genetic and environmental contributions to human disease as well as ageing process. The aim of this report was to describe the genomic data included in CLSA.

PARTICIPANTS

A total of 26 622 individuals from the CLSA Comprehensive cohort of men and women aged 45-85 recruited between 2010 and 2015 underwent genome-wide genotyping of DNA samples collected from blood. Comprehensive quality control metrics were measured for genetic markers and samples, respectively. The genotypes were imputed to the TOPMed reference panel. Sex chromosome abnormalities were identified by copy number profiling. Classical human leukocyte antigen gene haplotypes were imputed at two-field (four-digit).

FINDINGS TO DATE

Of the 26 622 genotyped participants, 24 655 (92.6%) were identified as having European ancestry. These genomic data were linked to physical, lifestyle, medical, economic, environmental and psychosocial factors collected longitudinally in CLSA. The combined analysis, including CLSA genomic data, uncovered over 100 novel loci associated with key parameters to define glaucoma. The CLSA genomic dataset validated the contribution of a polygenic risk score to screen individuals with high fracture risk. It is also a valuable resource to directly identify common genetic variations associated with conditions related to complex traits. Taking advantage of the comprehensive interview and physical information collected in CLSA, this genomic dataset has been linked to psychosocial factors to investigate both the independent and interactive effects on cardiovascular disease.

FUTURE PLANS

The CLSA overall is ongoing. Follow-up data will continue to be collected from participants in the current genomic subcohort, including the DNA methylation and metabolomic data. Ongoing studies focus on elucidating the role of genetic factors in cognitive decline and cardiovascular diseases. This genomic data resource is available on request through the CLSA data access application process.

Distinct biological ages of organs and systems identified from a multi-omics study

Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian.

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator and a Bayesian sparse linear mixed model. In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.

Genetic architecture of variation in Arabidopsis thaliana rosettes

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Genotyping-by-sequencing and genomic selection applications in hexaploid triticale

Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n = 289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) > 0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r² > 0.18 (P < 0.05). Genome-wide LD declined rapidly to r² < 0.1 beyond 10 kb physical distance. The three sub-genomes (A, B, and R) showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement.

SNP and Haplotype Regional Heritability Mapping (SNHap-RHM): Joint Mapping of Common and Rare Variation Affecting Complex Traits

We describe a genome-wide analytical approach, SNP and Haplotype Regional Heritability Mapping (SNHap-RHM), that provides regional estimates of the heritability across locally defined regions in the genome. This approach utilises relationship matrices that are based on sharing of SNP and haplotype alleles at local haplotype blocks delimited by recombination boundaries in the genome. We implemented the approach on simulated data and show that the haplotype-based regional GRMs capture variation that is complementary to that captured by SNP-based regional GRMs, and thus justifying the fitting of the two GRMs jointly in a single analysis (SNHap-RHM). SNHap-RHM captures regions in the genome contributing to the phenotypic variation that existing genome-wide analysis methods may fail to capture. We further demonstrate that there are real benefits to be gained from this approach by applying it to real data from about 20,000 individuals from the Generation Scotland: Scottish Family Health Study. We analysed height and major depressive disorder (MDD). We identified seven genomic regions that are genome-wide significant for height, and three regions significant at a suggestive threshold (p-value < 1 × 10-5) for MDD. These significant regions have genes mapped to within 400 kb of them. The genes mapped for height have been reported to be associated with height in humans. Similarly, those mapped for MDD have been reported to be associated with major depressive disorder and other psychiatry phenotypes. The results show that SNHap-RHM presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of novel genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the "missing" heritability.

Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

The Contribution of Copy Number Variants and Single Nucleotide Polymorphisms to the Additive Genetic Variance of Carcass Traits in Cattle

The relative contributions of both copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) to the additive genetic variance of carcass traits in cattle is not well understood. A detailed understanding of the relative importance of CNVs in cattle may have implications for study design of both genomic predictions and genome-wide association studies. The first objective of the present study was to quantify the relative contributions of CNV data and SNP genotype data to the additive genetic variance of carcass weight, fat, and conformation for 945 Charolais, 923 Holstein-Friesian, and 974 Limousin sires. The second objective was to jointly consider SNP and CNV data in a least absolute selection and shrinkage operator (LASSO) regression model to identify genomic regions associated with carcass weight, fat, and conformation within each of the three breeds separately. A genomic relationship matrix (GRM) based on just CNV data did not capture any variance in the three carcass traits when jointly evaluated with a SNP-derived GRM. In the LASSO regression analysis, a total of 987 SNPs and 18 CNVs were associated with at least one of the three carcass traits in at least one of the three breeds. The quantitative trait loci (QTLs) corresponding to the associated SNPs and CNVs overlapped with several candidate genes including previously reported candidate genes such as MSTN and RSAD2, and several potential novel candidate genes such as ACTN2 and THOC1. The results of the LASSO regression analysis demonstrated that CNVs can be used to detect associations with carcass traits which were not detected using the set of SNPs available in the present study. Therefore, the CNVs and SNPs available in the present study were not redundant forms of genomic data.