Current Genome-wide Association Studies Research Articles

GWAS advancements to investigate disease associations and biological mechanisms.

Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

Genome-wide association study of brain functional and structural networks

Abstract Imaging genetics studies with large samples have identified many genes associated with brain functions and structures, but little is known about genes associated with brain functional and structural network properties. The current genome-wide association study examined graph theory measures of brain structural and functional networks with 497 healthy Chinese participants (17–28 years). Four genes (TGFB3, LGI1, TSPAN18, and FAM155A) were identified to be significantly associated with functional network global efficiency, and two (NLRP6 and ICE2) with structural network global efficiency. Meta-analysis of structural and functional brain network property confirmed the four functional-related genes and revealed two more (RBFOX1 and WWOX). They were reported to be significantly associated with regional brain structural or functional measurements in the UK Biobank project; and showed differential gene expression level between low and high structure–function coupling regions according to Allen Human Brain Atlas gene expression data. Taken together, our results suggest that brain structural and functional networks had shared and unique genetic bases, consistent with the notion of many-to-many structure–function coupling of the brain.

Summary of single nucleotide polymorphisms in filaggrin associated with atopic dermatitis

AbstractBackgroundLoss of function mutations in the filaggrin gene (FLG) play an important role in the pathogenesis of atopic dermatitis (AD). However, FLG is structurally challenging to sequence using conventional high‐throughput techniques. Genome‐wide association studies (GWAS) chips and imputation panels are also not designed to detect most of these mutations. Furthermore, bioinformatics tools have variable sensitivity for identification of loss of function variants. Targeted sequencing is often performed for AD but requires a comprehensive list of potential variants.ObjectivesThis study sought to compile all published FLG single nucleotide polymorphisms (SNPs) in AD and characterize the methods for assessing the associated phenotype.MethodsWe searched nine electronic databases for studies that reported measures of association between FLG and AD. Data regarding FLG SNPs and participant demographics were extracted. The identified SNPs were compared to those available in the National Human Genome Research Institute‐European Bioinformatics Institute (NHGRI‐EBI) GWAS Catalogue and the 1000Genomes reference panel.ResultsWe identified 168 SNPs in FLG that have been associated with AD, with the most studied being R501X, 2282del4, R2447X, 3321delA, S3247X and p.S2554 in European and Asian ancestries. A total of 153 of these SNPs are not available from GWAS studies, and 78 are not included in the 1000Genomes reference panel.ConclusionsBecause FLG is a complex gene, current GWAS chips do not capture most of the polymorphisms that have been associated with AD.

A benchmark study on current GWAS models in admixed populations.

The performances of popular genome-wide association study (GWAS) models have not been examined yet in a consistent manner under the scenario of genetic admixture, which introduces several challenging aspects: heterogeneity of minor allele frequency (MAF), wide spectrum of case-control ratio, varying effect sizes, etc. We generated a cohort of synthetic individuals (N = 19234) that simulates (i) a large sample size; (ii) two-way admixture (Native American and European ancestry) and (iii) a binary phenotype. We then benchmarked three popular GWAS tools [generalized linear mixed model associated test (GMMAT), scalable and accurate implementation of generalized mixed model (SAIGE) and Tractor] by computing inflation factors and power calculations under different MAFs, case-control ratios, sample sizes and varying ancestry proportions. We also employed a cohort of Peruvians (N = 249) to further examine the performances of the testing models on (i) real genetic and phenotype data and (ii) small sample sizes. In the synthetic cohort, SAIGE performed better than GMMAT and Tractor in terms of type-I error rate, especially under severe unbalanced case-control ratio. On the contrary, power analysis identified Tractor as the best method to pinpoint ancestry-specific causal variants but showed decreased power when the effect size displayed limited heterogeneity between ancestries. In the Peruvian cohort, only Tractor identified two suggestive loci (P-value $\le 1\ast{10}^{-5}$) associated with Native American ancestry. The current study illustrates best practice and limitations for available GWAS tools under the scenario of genetic admixture. Incorporating local ancestry in GWAS analyses boosts power, although careful consideration of complex scenarios (small sample sizes, imbalance case-control ratio, MAF heterogeneity) is needed.

Can polygenic risk scores help explain disease prevalence differences around the world? A worldwide investigation

Complex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. The extent to which genetic risk, as identified by Genome Wide Association Study (GWAS), correlates to disease prevalence in different populations has not been investigated systematically. Here, we studied 14 different complex disorders and explored whether polygenic risk scores (PRS) based on current GWAS correlate to disease prevalence within Europe and around the world. A clear variation in GWAS-based genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders. We found that for four out of the 14 studied disorders, PRS significantly correlates to disease prevalence within Europe. We also found significant correlations between worldwide disease prevalence and PRS for eight of the studied disorders with Multiple Sclerosis genetic risk having the highest correlation to disease prevalence. Based on current GWAS results, the across population differences in genetic risk for certain disorders can potentially be used to understand differences in disease prevalence and identify populations with the highest genetic liability. The study highlights both the limitations of PRS based on current GWAS but also the fact that in some cases, PRS may already have high predictive power. This could be due to the genetic architecture of specific disorders or increased GWAS power in some cases.

Genomic approaches to explore susceptibility and pathogenesis of alcohol use disorder and alcohol-associated liver disease.

Excessive alcohol use is a major risk factor for the development of an alcohol use disorder (AUD) and contributes to a wide variety of other medical illnesses, including alcohol-associated liver disease (ALD). Both AUD and ALD are complex and causally interrelated diseases, and multiple factors other than alcohol consumption are implicated in the disease pathogenesis. While the underlying pathophysiology of AUD and ALD is complex, there is substantial evidence for a genetic susceptibility of both diseases. Current genome-wide association studies indicate that the genes associated with clinical AUD only poorly overlap with the genes identified for heavy drinking and, in turn, neither overlap with the genes identified for ALD. Uncovering the main genetic factors will enable us to identify molecular drivers underlying the pathogenesis, discover potential targets for therapy, and implement patient care early in disease progression. In this review, we described multiple genomic approaches and their implications to investigate the susceptibility and pathogenesis of both AUD and ALD. We concluded our review with a discussion of the knowledge gaps and future research on genomic studies in these 2 diseases.

Identifying risk loci for FTD and shared genetic component with ALS: A large-scale multitrait association analysis

Current genome-wide association studies of frontotemporal dementia (FTD) are underpowered due to limited samples. Further, common genetic etiologies between FTD and amyotrophic lateral sclerosis (ALS) remain unknown. Using the largest summary statistics of FTD (3526 cases and 9402 controls) and ALS (27,205 cases and 110,881 controls), we found a significant genetic correlation between them (rˆg = 0.637, P = 0.032) and identified 190 FTD-related variants within 5 loci (3p22.1, 5q35.1, 9p21.2, 19p13.11, and 20q13.13). Among these, ALS and FTD had causal variants in 9p21.2 and 19p13.11. Moreover, MOBP (3p22.1), C9orf72 (9p21.2), MOB3B (9p21.2), UNC13A (19p13.11), SLC9A8 (20q13.13), SNAI1 (20q13.13), and SPATA2 (20q13.13) were discovered by both SNP- and gene-level analyses, which together discovered 15 FTD-associated genes, with 10 not detected before (IFNK, RNF114, SLC9A8, SPATA2, SNAI1, SCFD1, POLDIP2, TMEM97, G2E3, and PIGW). Functional analyses showed these genes were enriched in heart left ventricle, kidney cortex, and some brain regions. Overall, this study provides insights into genetic determinants of FTD and shared genetic etiology underlying FTD and ALS.

Associations of polygenic risk scores with risks of stroke and its subtypes in Chinese

Background and purposePrevious studies, mostly focusing on the European population, have reported polygenic risk scores (PRSs) might achieve risk stratification of stroke. We aimed to examine the association strengths of...

P-347 the causal effects of serum lipids and apolipoproteins on endometriosis: a two-sample mendelian randomization analysis

Abstract Study question Is the risk of endometriosis associated with genetically predicted levels of different blood lipid and apolipoprotein traits? Summary answer Our Mendelian randomization (MR) analysis indicated that causal associations existed between some serum lipid and apolipoprotein levels and endometriosis occurrence in European ancestry. What is known already Endometriosis is a chronic gynecological disease with a high prevalence among reproductive-aged women. Recently, many observational, epidemiological studies have investigated the causal effect of different serum lipid and apolipoprotein parameters on the occurrence and progression of endometriosis. However, the results of these studies remained controversial. Moreover, no different subtypes of endometriosis were investigated in previous observational studies, thus detailed information about the associations was insufficient. Study design, size, duration We performed a two-sample MR analysis in the largest available genetic datasets. To correctly employ the MR method, the following assumptions are required to be met: (a) the genetic variants must be strongly associated with the exposures; (b) the single-nucleotide polymorphisms (SNPs) are not correlated with the confounders that are related to the exposure and the outcome; (c) the SNPs can influence the outcome solely through the exposure and no other biological pathways are concerned. Participants/materials, setting, methods Summary-level data for genetic variants of different serum lipid and apolipoprotein traits were derived from the UK Biobank and EBI database. Instrumental variables (IVs) associated with endometriosis and all the subtypes were acquired from the FinnGen biobank. The MR method is an accurate way to determine the underlying causality between the exposure and the outcome, which can minimize the effect of potential confounders and avoid reverse causality. Main results and the role of chance Our results demonstrated that five serum lipid-related traits were causally linked to lower odds of endometriosis, including very low-density lipoprotein (VLDL) cholesterol (OR: 1.231, 95% CI: 1.035-1.463, p = 0.019), cholesterol in chylomicron (CM) and extremely large VLDL (OR: 1.290, 95% CI: 1.013-1.644, p = 0.039), total cholesterol (TC) (OR: 1.250, 95% CI: 1.001-1.562, p = 0.049), apolipoprotein B (OR: 1.273, 95% CI: 1.022-1.587, p = 0.031), the ratio of apolipoprotein B to apolipoprotein A1 (OR: 1.163, 95% CI: 1.011-1.337, p = 0.035), while high-density lipoprotein cholesterol (HDL) (OR: 0.853, 95% CI: 0.754-0.963, p = 0.011) and apolipoprotein A1 (OR: 0.874, 95% CI: 0.770-0.993, p = 0.038) showed the opposite. Subgroup analyses about subtypes of endometriosis suggested the causal association of several serum lipid and apolipoprotein levels with peritoneal endometriosis. Neither pleiotropy nor heterogeneity was found in our study. No evidence was detected on the causal relations of low-density lipoprotein cholesterol (LDL) (OR: 1.014, 95% CI: 0.859-1.197, p = 0.868 and OR: 1.029, 95% CI: 0.906-1.169, p = 0.659, respectively) and triglyceride (TG) (OR: 1.138, 95% CI: 0.978-1.324, p = 0.095) with endometriosis. Limitations, reasons for caution Several limitations do exist in this study. We could not control the uniformity of sex between the exposure and the outcome, due to a lack of suitable summary-level data. Furthermore, it is possible that some of the null findings were due to limited statistical power from current genome-wide association study. Wider implications of the findings Our study found strong evidence for the possibility that aberrant lipid metabolism is causally involved in the pathogenesis of endometriosis, especially for endometriosis of pelvic peritoneum, and shed new insights on targeting serum lipid and apolipoprotein levels as a potential novel strategy for the prevention and treatment of endometriosis. Trial registration number not applicable

Summary statistics-based association test for identifying the pleiotropic effects with set of genetic variants.

Traditional genome-wide association study focuses on testing one-to-one relationship between genetic variants and complex human diseases or traits. While its success in the past decade, this one-to-one paradigm lacks efficiency because it does not utilize the information of intrinsic genetic structure and pleiotropic effects. Due to privacy reasons, only summary statistics of current genome-wide association study data are publicly available. Existing summary statistics-based association tests do not consider covariates for regression model, while adjusting for covariates including population stratification factors is a routine issue. In this work, we first derive the correlation coefficients between summary Wald statistics obtained from linear regression model with covariates. Then, a new test is proposed by integrating three-level information including the intrinsic genetic structure, pleiotropy, and the potential information combinations. Extensive simulations demonstrate that the proposed test outperforms three other existing methods under most of the considered scenarios. Real data analysis of polyunsaturated fatty acids further shows that the proposed test can identify more genes than the compared existing methods. Code is available at https://github.com/bschilder/ThreeWayTest.

Predicting late-stage age-related macular degeneration by integrating marginally weak SNPs in GWA studies.

Introduction: Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of blindness in developed countries. Current genome-wide association studies (GWAS) for late-stage age-related macular degeneration are mainly single-marker-based approaches, which investigate one Single-Nucleotide Polymorphism (SNP) at a time and postpone the integration of inter-marker Linkage-disequilibrium (LD) information in the downstream fine mappings. Recent studies showed that directly incorporating inter-marker connection/correlation into variants detection can help discover novel marginally weak single-nucleotide polymorphisms, which are often missed in conventional genome-wide association studies, and can also help improve disease prediction accuracy. Methods: Single-marker analysis is performed first to detect marginally strong single-nucleotide polymorphisms. Then the whole-genome linkage-disequilibrium spectrum is explored and used to search for high-linkage-disequilibrium connected single-nucleotide polymorphism clusters for each strong single-nucleotide polymorphism detected. Marginally weak single-nucleotide polymorphisms are selected via a joint linear discriminant model with the detected single-nucleotide polymorphism clusters. Prediction is made based on the selected strong and weak single-nucleotide polymorphisms. Results: Several previously identified late-stage age-related macular degeneration susceptibility genes, for example, BTBD16, C3, CFH, CFHR3, HTARA1, are confirmed. Novel genes DENND1B, PLK5, ARHGAP45, and BAG6 are discovered as marginally weak signals. Overall prediction accuracy of 76.8% and 73.2% was achieved with and without the inclusion of the identified marginally weak signals, respectively. Conclusion: Marginally weak single-nucleotide polymorphisms, detected from integrating inter-marker linkage-disequilibrium information, may have strong predictive effects on age-related macular degeneration. Detecting and integrating such marginally weak signals can help with a better understanding of the underlying disease-development mechanisms for age-related macular degeneration and more accurate prognostics.

Polygenic risk scores for complex diseases: Where are we now?

Polygenic risk scores for complex diseases: Where are we now?

GIFT: new method for the genetic analysis of small gene effects involving small sample sizes

Small gene effects involved in complex/omnigenic traits remain costly to analyse using current genome-wide association studies (GWAS) because of the number of individuals required to return meaningful association(s), a.k.a. study power. Inspired by field theory in physics, we provide a different method called genomic informational field theory (GIFT). In contrast to GWAS, GIFT assumes that the phenotype is measured precisely enough and/or the number of individuals in the population is too small to permit the creation of categories. To extract information, GIFT uses the information contained in the cumulative sums difference of gene microstates between two configurations: (i) when the individuals are taken at random without information on phenotype values, and (ii) when individuals are ranked as a function of their phenotypic value. The difference in the cumulative sum is then attributed to the emergence of phenotypic fields. We demonstrate that GIFT recovers GWAS, that is, Fisher’s theory, when the phenotypic fields are linear (first order). However, unlike GWAS, GIFT demonstrates how the variance of microstate distribution density functions can also be involved in genotype–phenotype associations when the phenotypic fields are quadratic (second order). Using genotype–phenotype simulations based on Fisher’s theory as a toy model, we illustrate the application of the method with a small sample size of 1000 individuals.

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

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

Genetically predicted tobacco consumption and risk of intracranial aneurysm: a Mendelian randomization study.

Several observational studies have suggested that tobacco consumption is a risk factor for intracranial aneurysms (IAs). We here genetically predict the causal association between specific smoking features and biomarkers for smokers and IA risk. The Mendelian randomization (MR) analysis considered summary statistics from the largest current genome-wide association studies of smoking and IA. The inverse-variance weighted (IVW) method, weighted median method, MR-RAPS, and multiple variants Mendelian randomization (MVMR) were performed to estimate the effect of different smoking features and drinking in IA. We observed significant causal effects of smoking on the risk of both aneurysmal subarachnoid hemorrhage (aSAH) and unruptured IA (uIA). The ORs of IAs based on the IVW method were 1.890 (95% CI 1.486-2.405) of ever smoking regularly. MVMR analysis afforded odds ratios of 1.685 (95% CI 1.136-2.501). In the further subgroup analysis, a similar causal relationship was observed in aSAH. Moreover, our analyses suggested that higher blood cotinine level and cadmium increases aSAH risk, and ORs were 1.235 (95%CI 1.009-1.186) and 1.235 (95%CI 1.046-1.458), respectively. Our study suggests that ever smoking regularly is associated with the IA risk, which includes both uIA and aSAH. Besides, higher blood cadmium and cotinine level may increases IA and aSAH risk. Thus, tobacco control should be promoted as primordial prevention for IAs, and screening for patients with a smoking history is emphasized.

Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke.

Current genome-wide association studies of ischemic stroke have focused primarily on late-onset disease. As a complement to these studies, we sought to identify the contribution of common genetic variants to risk of early-onset ischemic stroke. We performed a meta-analysis of genome-wide association studies of early-onset stroke (EOS), ages 18-59 years, using individual-level data or summary statistics in 16,730 cases and 599,237 nonstroke controls obtained across 48 different studies. We further compared effect sizes at associated loci between EOS and late-onset stroke (LOS) and compared polygenic risk scores (PRS) for venous thromboembolism (VTE) between EOS and LOS. We observed genome-wide significant associations of EOS with 2 variants in ABO, a known stroke locus. These variants tag blood subgroups O1 and A1, and the effect sizes of both variants were significantly larger in EOS compared with LOS. The odds ratio (OR) for rs529565, tagging O1, was 0.88 (95% confidence interval [CI]: 0.85-0.91) in EOS vs 0.96 (95% CI: 0.92-1.00) in LOS, and the OR for rs635634, tagging A1, was 1.16 (1.11-1.21) for EOS vs 1.05 (0.99-1.11) in LOS; p-values for interaction = 0.001 and 0.005, respectively. Using PRSs, we observed that greater genetic risk for VTE, another prothrombotic condition, was more strongly associated with EOS compared with LOS (p = 0.008). The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with EOS than with LOS, supporting a stronger role of prothrombotic factors in EOS.

Pinpointing novel risk loci for Lewy body dementia and the shared genetic etiology with Alzheimer’s disease and Parkinson’s disease: a large-scale multi-trait association analysis

BackgroundThe current genome-wide association study (GWAS) of Lewy body dementia (LBD) suffers from low power due to a limited sample size. In addition, the genetic determinants underlying LBD and the shared genetic etiology with Alzheimer’s disease (AD) and Parkinson’s disease (PD) remain poorly understood.MethodsUsing the largest GWAS summary statistics of LBD to date (2591 cases and 4027 controls), late-onset AD (86,531 cases and 676,386 controls), and PD (33,674 cases and 449,056 controls), we comprehensively investigated the genetic basis of LBD and shared genetic etiology among LBD, AD, and PD. We first conducted genetic correlation analysis using linkage disequilibrium score regression (LDSC), followed by multi-trait analysis of GWAS (MTAG) and association analysis based on SubSETs (ASSET) to identify the trait-specific SNPs. We then performed SNP-level functional annotation to identify significant genomic risk loci paired with Bayesian fine-mapping and colocalization analysis to identify potential causal variants. Parallel gene-level analysis including GCTA-fastBAT and transcriptome-wide association analysis (TWAS) was implemented to explore novel LBD-associated genes, followed by pathway enrichment analysis to understand underlying biological mechanisms.ResultsPairwise LDSC analysis found positive genome-wide genetic correlations between LBD and AD (rg = 0.6603, se = 0.2001; P = 0.0010), between LBD and PD (rg = 0.6352, se = 0.1880; P = 0.0007), and between AD and PD (rg = 0.2136, se = 0.0860; P = 0.0130). We identified 13 significant loci for LBD, including 5 previously reported loci (1q22, 2q14.3, 4p16.3, 4q22.1, and 19q13.32) and 8 novel biologically plausible genetic associations (5q12.1, 5q33.3, 6p21.1, 8p23.1, 8p21.1, 16p11.2, 17p12, and 17q21.31), among which APOC1 (19q13.32), SNCA (4q22.1), TMEM175 (4p16.3), CLU (8p21.1), MAPT (17q21.31), and FBXL19 (16p11.2) were also validated by gene-level analysis. Pathway enrichment analysis of 40 common genes identified by GCTA-fastBAT and TWAS implicated significant role of neurofibrillary tangle assembly (GO:1902988, adjusted P = 1.55 × 10−2).ConclusionsOur findings provide novel insights into the genetic determinants of LBD and the shared genetic etiology and biological mechanisms of LBD, AD, and PD, which could benefit the understanding of the co-pathology as well as the potential treatment of these diseases simultaneously.

Plasma biomarkers and genetics in the diagnosis and prediction of Alzheimer's disease.

Plasma biomarkers for Alzheimer's disease-related pathologies have undergone rapid developments during the past few years, and there are now well-validated blood tests for amyloid and tau pathology, as well as neurodegeneration and astrocytic activation. To define Alzheimer's disease with biomarkers rather than clinical assessment, we assessed prediction of research-diagnosed disease status using these biomarkers and tested genetic variants associated with the biomarkers that may reflect more accurately the risk of biochemically defined Alzheimer's disease instead of the risk of dementia. In a cohort of Alzheimer's disease cases [n = 1439, mean age 68 years (standard deviation = 8.2)] and screened controls [n = 508, mean age 82 years (standard deviation = 6.8)], we measured plasma concentrations of the 40 and 42 amino acid-long amyloid-β (Aβ) fragments (Aβ40 and Aβ42, respectively), tau phosphorylated at amino acid 181 (P-tau181), neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) using state-of-the-art Single molecule array (Simoa) technology. We tested the relationships between the biomarkers and Alzheimer's disease genetic risk, age at onset and disease duration. We also conducted a genome-wide association study for association of disease risk genes with these biomarkers. The prediction accuracy of Alzheimer's disease clinical diagnosis by the combination of all biomarkers, APOE and polygenic risk score reached area under receiver operating characteristic curve (AUC) = 0.81, with the most significant contributors being ε4, Aβ40 or Aβ42, GFAP and NfL. All biomarkers were significantly associated with age in cases and controls (P < 4.3 × 10-5). Concentrations of the Aβ-related biomarkers in plasma were significantly lower in cases compared with controls, whereas other biomarker levels were significantly higher in cases. In the case-control genome-wide analyses, APOE-ε4 was associated with all biomarkers (P = 0.011-4.78 × 10-8), except NfL. No novel genome-wide significant single nucleotide polymorphisms were found in the case-control design; however, in a case-only analysis, we found two independent genome-wide significant associations between the Aβ42/Aβ40 ratio and WWOX and COPG2 genes. Disease prediction modelling by the combination of all biomarkers indicates that the variance attributed to P-tau181 is mostly captured by APOE-ε4, whereas Aβ40, Aβ42, GFAP and NfL biomarkers explain additional variation over and above APOE. We identified novel plausible genome wide-significant genes associated with Aβ42/Aβ40 ratio in a sample which is 50 times smaller than current genome-wide association studies in Alzheimer's disease.

Imputation Performance in Latin American Populations: Improving Rare Variants Representation With the Inclusion of Native American Genomes.

Current Genome-Wide Association Studies (GWAS) rely on genotype imputation to increase statistical power, improve fine-mapping of association signals, and facilitate meta-analyses. Due to the complex demographic history of Latin America and the lack of balanced representation of Native American genomes in current imputation panels, the discovery of locally relevant disease variants is likely to be missed, limiting the scope and impact of biomedical research in these populations. Therefore, the necessity of better diversity representation in genomic databases is a scientific imperative. Here, we expand the 1,000 Genomes reference panel (1KGP) with 134 Native American genomes (1KGP + NAT) to assess imputation performance in Latin American individuals of mixed ancestry. Our panel increased the number of SNPs above the GWAS quality threshold, thus improving statistical power for association studies in the region. It also increased imputation accuracy, particularly in low-frequency variants segregating in Native American ancestry tracts. The improvement is subtle but consistent across countries and proportional to the number of genomes added from local source populations. To project the potential improvement with a higher number of reference genomes, we performed simulations and found that at least 3,000 Native American genomes are needed to equal the imputation performance of variants in European ancestry tracts. This reflects the concerning imbalance of diversity in current references and highlights the contribution of our work to reducing it while complementing efforts to improve global equity in genomic research.

Assessment of cold stress tolerance in maize through quantitative trait locus, genome-wide association study and transcriptome analysis

Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The detailed that low temperature in maize seedlings altogether restricts germination and seedlings' development and destabilizes the cancer prevention agent safeguard component. Cold pressure adversely influences root morphology, photosystem II (PS II) effectiveness, chlorophyll substance, and leaf region. A short scene of low temperature stress (for example, under 10 °C for 7 days) during the V6–V9 maize development stages can fundamentally defer the anthesis commencement. Among the morphological reactions by focused on maize plants, low temperature stress causes strange tuft development in maize, along these lines influencing the fertilization and grain filling measures. Hence, problematic temperatures can cause a genuine yield decrease if happening at basic conceptive stages, as plants allocate over half of their photosynthesis to foster grains during this stage until physiological development. Low temperature stress fundamentally diminishes the plant stature and absolute yield biomass of maize. Leaf improvement turns out to be delayed in chilly focused on plants because of a drawn-out cell cycle and diminished pace of mitosis. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.