Genome-wide Association Study Approach Research Articles

Genome-wide association studies for milk production traits and persistency of first calving Holstein cattle in Türkiye

The study presents a comprehensive investigation into the genetic determinants of 100-day milk yield (100DMY), 305-day milk yield (305DMY), total milk yield (TMY), and persistency using first lactation records of 374 Holstein heifers reared in a private farm at Çanakkale province of Türkiye, employing a genome-wide association study (GWAS) approach. The research underscores the substantial genetic component underlying these economically important traits through detailed descriptive statistics and heritability estimations. The estimated moderate to high heritabilities (0.32–0.54) for milk production traits suggest the feasibility of targeted genetic improvement strategies. By leveraging GWAS, the study identifies many significant and suggestively significant single nucleotide polymorphisms (SNP) associated with studied traits. Noteworthy genes have identified in this analysis include BCAS3, MALRD1, CTNND2, DOCK1, TMEM132C, NRP1, CNTNAP2, GPRIN2, PLEKHA5, GLRA1, SCN7A, HHEX, KTM2C, RAB40C, RAB11FIP3, and FXYD6. These findings provide valuable understandings of the genetic background of milk production and persistency in Holstein cattle, shedding light on specific genomic regions and candidate genes playing pivotal roles in these traits. This research contributes valuable knowledge to the field of dairy cattle genetics and informs future breeding efforts to improve milk production sustainability and efficiency in Holstein cattle populations.

Identification of Candidate Genes for Soybean Storability via GWAS and WGCNA Approaches

Soybean (Glycine max (L.) Merr.) is an important crop for both food and feed, playing a significant role in agricultural production and the human diet. During long-term storage, soybean seeds often exhibit reduced quality, decreased germination, and lower seedling vigor, ultimately leading to significant yield reductions in soybean crops. Seed storage tolerance is a complex quantitative trait controlled by multiple genes and is also influenced by environmental factors during seed formation, harvest, and storage. This study aimed to evaluate soybean germplasms for their storage tolerance, identify quantitative trait nucleotides (QTNs) associated with seed storage tolerance traits, and screen for candidate genes. The storage tolerance of 168 soybean germplasms was evaluated, and 23,156 high-quality single nucleotide polymorphism (SNP) markers were screened and analyzed through a genome-wide association study (GWAS). Ultimately, 14 QTNs were identified as being associated with seed storage tolerance and were distributed across the eight chromosomes of soybean, with five QTNs (rs25887810, rs27941858, rs33981296, rs44713950, and rs18610980) being newly reported loci in this study. In the linkage disequilibrium regions of these SNPs, 256 genes were identified. By combining GWAS and weighted gene co-expression network analysis (WGCNA), eight hub genes (Glyma.03G058300, Glyma.04G1921100, Glyma.04G192600, Glyma.04G192900, Glyma.07G002000, Glyma.08G329400, Glyma.16G074600, Glyma.16G091400) were jointly identified. Through the analysis of expression patterns, two candidate genes (Glyma.03G058300, Glyma.16G074600) potentially involved in seed storage tolerance were ultimately identified. Additionally, haplotype analysis revealed that natural variations in Glyma.03G058300 could affect seed storage tolerance. The findings of this research provide a theoretical foundation for understanding the regulatory mechanism underlying soybean storage.

Genome-Wide and Exome-Wide Association Study Identifies Genetic Underpinning of Comorbidity between Myocardial Infarction and Severe Mental Disorders.

Myocardial Infarction (MI) and severe mental disorders (SMDs) are two types of highly prevalent and complex disorders and seem to have a relatively high possibility of mortality. However, the contributions of common and rare genetic variants to their comorbidity arestill unclear. We conducted a combined genome-wide association study (GWAS) and exome-wide association study (EWAS) approach. Using gene-based and gene-set association analyses based on the results of GWAS, we found the common genetic underpinnings of nine genes (GIGYF2, KCNJ13, PCCB, STAG1, HLA-C, HLA-B, FURIN, FES, and SMG6) and nine pathways significantly shared between MI and SMDs. Through Mendelian randomization analysis, we found that twenty-seven genes were potential causal genes for SMDs and MI. Based on the exome sequencing data of MI and SMDs patients from the UK Biobank, we found that MUC2 was exome-wide significant in the two diseases. The gene-set analyses of the exome-wide association study indicated that pathways related to insulin processing androgen catabolic process and angiotensin receptor binding may be involved in the comorbidity between SMDs and MI. We also found that six candidate genes were reported to interact with known therapeutic drugs based on the drug-gene interaction information in DGIdb. Altogether, this study revealed the overlap of common and rare genetic underpinning between SMDs and MI and may provide useful insights for their mechanism study and therapeutic investigations.

Genomic insights of leafminer resistance in spinach through GWAS approach and genomic prediction

The Leafminers, representing a diverse group of insects from various genera within the Agromyzidae family, pose a significant threat to spinach (Spinacia oleracea L.) production. This study aimed to identify single nucleotide polymorphism (SNP) markers associated with leafminer resistance through a genome-wide association study (GWAS) and to evaluate the prediction accuracy (PA) for selecting resistant spinach using genomic prediction (GP). Using a dataset of 84 301 SNPs obtained from whole-genome resequencing, seven GWAS models, including BLINK, FarmCPU, MLM, and MLMM in GAPIT 3, as well as MLM, GLM, and SMR in TASSEL 5, were employed to perform GWAS on a panel of 286 USDA spinach germplasm accessions. Three SNP markers, namely 1_115279256_C_T, 3_157082529_C_T, and 4_168510908_T_G on chromosomes 1, 3, and 4, respectively, were identified as associated with leafminer resistance. In the 30 kb flanking regions of these markers, four candidate genes (SOV1g031330, SOV1g031340, SOV4g047270, and SOV4g047280), encoding LOB domain-containing protein, KH domain-containing protein, were discovered. Nodulin-like domain-containing protein, and SAM domain-containing protein, were discovered. The PA for leafminer resistance selection was estimated using ten different SNP sets, including two GWAS-derived marker sets (three and 51 SNPs) and eight random marker sets (ranging from 51 to 10 K SNPs) analyzed by seven GP models. The findings emphasized the superior performance of GWAS-derived SNP sets, reaching a PA of up to 0.79 using the cBLUP model. Notably, this research marks the pioneering application of GP in the context of insect resistance, providing a significant advancement in the understanding and management of leafminer resistance in spinach cultivation.

Dissecting the genetic architecture of sunflower disc diameter using genome-wide association study.

Sunflower (Helianthus annuus L.) plays an essential role in meeting the demand for edible oil worldwide. The yield of sunflower seeds encompasses several component traits, including the disc diameter. Over three consecutive years, 2019, 2020, and 2022, we assessed phenotypic variation in disc diameter across a diverse set of sunflower accessions (N = 342) in replicated field trials. Upon aggregating the phenotypic data from multiple years, we estimated the broad sense heritability (H 2) of the disc diameter trait to be 0.88. A subset of N = 274 accessions was genotyped by using the tunable genotyping-by-sequencing (tGBS) method, resulting in 226,779 high-quality SNPs. Using these SNPs and the disc diameter phenotype, we conducted a genome-wide association study (GWAS) employing two statistical approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU). The MLM and farmCPU GWAS approaches identified 106 and 8 significant SNPs located close to 53 and 21 genes, respectively. The MLM analysis identified two significant peaks: a prominent signal on chromosome 10 and a relatively weaker signal on chromosome 16, both of which were also detected by farmCPU. The genetic loci associated with disc diameter, as well as the related candidate genes, present promising avenues for further functional validation and serve as a basis for sunflower oil yield improvement.

Biochemical Defense Arsenal, Genes/QTLs and Transcripts for Imparting Anthracnose Resistance in Common bean (Phaseolus vulgaris L.)

Anthracnose (ANT), caused by Colletotrichum lindemuthianum, is the most devastating disease affecting common bean (Phaseolus vulgaris L.), leading to significant yield losses in the Western Himalayas. The study provides a comprehensive understanding of ANT resistance via trait phenotyping, biochemical profiling, genome-wide association studies (GWASs), and RNA sequencing. The assessment of bean association mapping panel in different environments revealed a diverse spectrum of resistance levels. Biochemical analysis revealed distinctive defense responses against ANT infection among different genotypes. GWAS approach identified 24 significant marker‒trait associations (MTAs) distributed across all 11 bean chromosomes. Notably, 03 MTAs (BMr205, BMr269 and BMr244) present on chromosome Pv07 were validated for ANT, and the remaining MTAs were novel MTAs for ANT. Transcriptome sequencing of resistant (PBG-3) and susceptible (PBG-26) genotypes under mock and 120-hour post inoculation conditions revealed key differentially expressed genes, such as leucine-rich repeat domain-containing protein (PHAVU_007G087700g), NB-ARC domain-containing protein (PHAVU_003G002500g) and transcription factors pivotal for disease resistance. The expression patterns of four genes (PHAVU_007G087700g, PHAVU_003G002500g, PHAVU_007G056100g and PHAVU_003G003000g) were validated through quantitative reverse transcription polymerase chain reaction (qRT‒PCR). Furthermore, the integration of GWAS-identified candidate genes with transcriptomics and cross-referencing with previous studies validated overlapping regions and common candidate genes, enriching our understanding of the genetic basis of ANT resistance. Therefore, the results offer a holistic perspective on ANT resistance in common bean, providing a foundation for targeted breeding efforts. The identified potential candidate genes and associated pathways will contribute valuable insights into the development of ANT resistant common bean varieties.

Genome-wide association study between SARS-CoV-2 single nucleotide polymorphisms and virus copies during infections.

Significant variations have been observed in viral copies generated during SARS-CoV-2 infections. However, the factors that impact viral copies and infection dynamics are not fully understood, and may be inherently dependent upon different viral and host factors. Here, we conducted virus whole genome sequencing and measured viral copies using RT-qPCR from 9,902 SARS-CoV-2 infections over a 2-year period to examine the impact of virus genetic variation on changes in viral copies adjusted for host age and vaccination status. Using a genome-wide association study (GWAS) approach, we identified multiple single-nucleotide polymorphisms (SNPs) corresponding to amino acid changes in the SARS-CoV-2 genome associated with variations in viral copies. We further applied a marginal epistasis test to detect interactions among SNPs and identified multiple pairs of substitutions located in the spike gene that have non-linear effects on viral copies. We also analyzed the temporal patterns and found that SNPs associated with increased viral copies were predominantly observed in Delta and Omicron BA.2/BA.4/BA.5/XBB infections, whereas those associated with decreased viral copies were only observed in infections with Omicron BA.1 variants. Our work showcases how GWAS can be a useful tool for probing phenotypes related to SNPs in viral genomes that are worth further exploration. We argue that this approach can be used more broadly across pathogens to characterize emerging variants and monitor therapeutic interventions.

Integrated GWAS, linkage, and transcriptome analysis to identify genetic loci and candidate genes for photoperiod sensitivity in maize.

Maize photosensitivity and the control of flowering not only are important for reproduction, but also play pivotal roles in the processes of domestication and environmental adaptation, especially involving the utilization strategy of tropical maize in high-latitude regions. In this study, we used a linkage mapping population and an inbred association panel with the photoperiod sensitivity index (PSI) phenotyped under different environments and performed transcriptome analysis of T32 and QR273 between long-day and short-day conditions. The results showed that PSIs of days to tasseling (DTT), days to pollen shedding (DTP), and days to silking (DTS) indicated efficacious interactions with photoperiod sensitivity for maize latitude adaptation. A total of 48 quantitative trait loci (QTLs) and 252 quantitative trait nucleotides (QTNs) were detected using the linkage population and the inbred association panel. Thirteen candidate genes were identified by combining the genome-wide association study (GWAS) approach, linkage analysis, and transcriptome analysis, wherein five critical candidate genes, MYB163, bif1, burp8, CADR3, and Zm00001d050238, were significantly associated with photoperiod sensitivity. These results would provide much more abundant theoretical proofs to reveal the genetic basis of photoperiod sensitivity, which would be helpful to understand the genetic changes during domestication and improvement and contribute to reducing the barriers to use of tropical germplasm.

BLESS: bagged logistic regression for biomarker identification.

The traditional single nucleotide polymorphism (SNP)-wise approach in genome-wide association studies is focused on examining the marginal association between each SNP with the outcome separately and applying multiple testing adjustments to the resulting p-values to reduce false positives. However, the approach suffers a lack of power in identifying biomarkers. We design an ensemble machine learning approach to aggregate results from logistic regression models based on multiple subsamples, which helps to identify biomarkers from high-dimensional genomic data. We use different methods to analyze a genome-wide association study from the Alzheimer's Disease Neuroimaging Initiative. The SNP-wise approach does not identify any significant signal, while our novel approach provides a list of ranked SNPs associated with the cognitive functions of interests.

Genome-wide association analysis and admixture mapping in a Puerto Rican cohort supports an Alzheimer disease risk locus on chromosome 12.

Hispanic/Latino populations are underrepresented in Alzheimer Disease (AD) genetic studies. Puerto Ricans (PR), a three-way admixed (European, African, and Amerindian) population is the second-largest Hispanic group in the continental US. We aimed to conduct a genome-wide association study (GWAS) and comprehensive analyses to identify novel AD susceptibility loci and characterize known AD genetic risk loci in the PR population. Our study included Whole Genome Sequencing (WGS) and phenotype data from 648 PR individuals (345 AD, 303 cognitively unimpaired). We used a generalized linear-mixed model adjusting for sex, age, population substructure, and genetic relationship matrix. To infer local ancestry, we merged the dataset with the HGDP/1000G reference panel. Subsequently, we conducted univariate admixture mapping (AM) analysis. We identified suggestive signals within the SLC38A1 and SCN8A genes on chromosome 12q13. This region overlaps with an area of linkage of AD in previous studies (12q13) in independent data sets further supporting. Univariate African AM analysis identified one suggestive ancestral block (p = 7.2×10-6) located in the same region. The ancestry-aware approach showed that this region has both European and African ancestral backgrounds and both contributing to the risk in this region. We also replicated 11 different known AD loci -including APOE- identified in mostly European studies, which is likely due to the high European background of the PR population. PR GWAS and AM analysis identified a suggestive AD risk locus on chromosome 12, which includes the SLC38A1 and SCN8A genes. Our findings demonstrate the importance of designing GWAS and ancestry-aware approaches and including underrepresented populations in genetic studies of AD.

Genomic insights into oxalate content in spinach: A genome-wide association study and genomic prediction approach

Oxalate content in spinach is a key trait of interest due to its relevance to human health. Understanding the genetic basis of it can facilitate the development of spinach varieties with reduced oxalate levels. In pursuit of understanding the genetic determinants, a diverse panel comprising 288 spinach accessions underwent thorough phenotyping of oxalate content and were subjected to whole-genome resequencing, resulting in a comprehensive dataset encompassing 14 386 single-nucleotide polymorphisms (SNPs). Leveraging this dataset, we conducted a genome-wide association study (GWAS) to identify noteworthy SNPs associated with oxalate content. Furthermore, we employed genomic prediction (GP) via cross-prediction, utilizing five GP models, to assess genomic estimated breeding values (GEBVs) for oxalate content. The observed normal distribution and the wide range of oxalate content, exceeding 600.0 mg · 100 g−1, underscore the complex and quantitative nature of this trait, likely influenced by multiple genes. Additionally, our analysis revealed distinct stratification, delineating the population into four discernible subpopulations. Furthermore, GWAS analysis employing five models in GAPIT 3 and TASSEL 5 unveiled nine significant SNPs (four SNPs on chromosome 1 and five on chromosome 5) associated with oxalate content. These loci exhibited associations with six candidate genes, which might have potential contribution to oxalate content regulation. Remarkably, our GP models exhibited notable predictive abilities, yielding average accuracies of up to 0.51 for GEBV estimation. The integration of GWAS and GP approaches offers a holistic comprehension of the genetic underpinnings of oxalate content in spinach. These findings offered a promising avenue for the development of spinach cultivars and hybrids optimized for oxalate levels, promoting consumer health.

Identification of genetic variants associated with clinical features of sickle cell disease

Sickle cell disease (SCD) is an inherited blood disorder marked by homozygosity of hemoglobin S, which is a defective hemoglobin caused by a missense mutation in the β-globin gene. However, clinical phenotypes of SCD vary among patients. To investigate genetic variants associated with various clinical phenotypes of SCD, we genotyped DNA samples from 520 SCD subjects and used a genome-wide association study (GWAS) approach to identify genetic variants associated with phenotypic features of SCD. For HbF levels, the previously reported 2p16.1 locus (BCL11A) reached genome significance (rs1427407, P = 8.58 × 10–10) in our GWAS as expected. In addition, we found a new genome-wide significance locus at 15q14 (rs8182015, P = 2.07 × 10–8) near gene EMC7. GWAS of acute chest syndrome (ACS) detected a locus (rs79915189, P = 3.70 × 10–8) near gene IDH2 at 15q26.1. The SNP, rs79915189, is also an expression quantitative trait locus (eQTL) of IDH2 in multiple tissues. For vasoocclusive episode (VOE), GWAS detected multiple significant signals at 2p25.1 (rs62118798, P = 4.27 × 10–8), 15q26.1 (rs62020555, P = 2.04 × 10–9) and 15q26.3 (rs117797325, P = 4.63 × 10–8). Our findings provide novel insights into the genetic mechanisms of SCD suggesting that common genetic variants play an important role in the presentation of the clinical phenotypes of patients with SCD.

Canine RNF170 Single Base Deletion in a Naturally Occurring Model for Human Neuroaxonal Dystrophy.

Neuroaxonal dystrophy (NAD) is a group of inherited neurodegenerative disorders characterized primarily by the presence of spheroids (swollen axons) throughout the central nervous system. In humans, NAD is heterogeneous, both clinically and genetically. NAD has also been described to naturally occur in large animal models, such as dogs. A newly recognized disorder in Miniature American Shepherd dogs (MAS), consisting of a slowly progressive neurodegenerative syndrome, was diagnosed as NAD via histopathology. To describe the clinical and pathological phenotype together with the identification of the underlying genetic cause. Clinical and postmortem evaluations, together with a genome-wide association study and autozygosity mapping approach, followed by whole-genome sequencing. Affected dogs were typically young adults and displayed an abnormal gait characterized by pelvic limb weakness and ataxia. The underlying genetic cause was identified as a 1-bp (base pair) deletion in RNF170 encoding ring finger protein 170, which perfectly segregates in an autosomal recessive pattern. This deletion is predicted to create a frameshift (XM_038559916.1:c.367delG) and early truncation of the RNF170 protein (XP_038415844.1:(p.Ala123Glnfs*11)). The age of this canine RNF170 variant was estimated at ~30 years, before the reproductive isolation of the MAS breed. RNF170 variants were previously identified in human patients with autosomal recessive spastic paraplegia-85 (SPG85); this clinical phenotype shows similarities to the dogs described herein. We therefore propose that this novel MAS NAD could serve as an excellent large animal model for equivalent human diseases, particularly since affected dogs demonstrate a relatively long lifespan, which represents an opportunity for therapeutic trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Leveraging Functional Genomics for Understanding Beef Quality Complexities and Breeding Beef Cattle for Improved Meat Quality.

Consumer perception of beef is heavily influenced by overall meat quality, a critical factor in the cattle industry. Genomics has the potential to improve important beef quality traits and identify genetic markers and causal variants associated with these traits through genomic selection (GS) and genome-wide association studies (GWAS) approaches. Transcriptomics, proteomics, and metabolomics provide insights into underlying genetic mechanisms by identifying differentially expressed genes, proteins, and metabolic pathways linked to quality traits, complementing GWAS data. Leveraging these functional genomics techniques can optimize beef cattle breeding for enhanced quality traits to meet high-quality beef demand. This paper provides a comprehensive overview of the current state of applications of omics technologies in uncovering functional variants underlying beef quality complexities. By highlighting the latest findings from GWAS, GS, transcriptomics, proteomics, and metabolomics studies, this work seeks to serve as a valuable resource for fostering a deeper understanding of the complex relationships between genetics, gene expression, protein dynamics, and metabolic pathways in shaping beef quality.

Genetic variation in a heat shock transcription factor modulates cold tolerance in maize

Understanding how maize (Zea mays) responds to cold stress is crucial for facilitating breeding programs of cold-tolerant varieties. Despite extensive utilization of the genome-wide association study (GWAS) approach for exploring favorable natural alleles associated with maize cold tolerance, few studies have successfully identified candidate genes that contribute to maize cold tolerance. In this study, we used a diverse panel of inbred maize lines collected from different germplasm sources to perform a GWAS on variations in the relative injured area of maize true leaves during cold stress—a trait very closely correlated with maize cold tolerance. We identified HSF21, which encodes a B-class heat shock transcription factor (HSF) that positively regulates cold tolerance at both the seedling and germination stages. Natural variations in the promoter of the cold-tolerant HSF21Hap1 allele led to increased HSF21 expression under cold stress by inhibiting binding of the basic leucine zipper bZIP68 transcription factor, a negative regulator of cold tolerance. By integrating transcriptome deep sequencing, DNA affinity purification sequencing, and targeted lipidomic analysis, we revealed the function of HSF21 in regulating lipid metabolism homeostasis to modulate cold tolerance in maize. In addition, we found that HSF21 confers maize cold tolerance without incurring yield penalties. Collectively, this study establishes HSF21 as a key regulator that enhances cold tolerance in maize, providing valuable genetic resources for breeding of cold-tolerant maize varieties.

Beyond the standard GWAS - a guide for plant biologists.

Classic genome-wide association studies (GWAS) look for associations between individual SNPs and phenotypes of interest. With the rapid progress of high-throughput genotyping and phenotyping technologies, GWAS have become increasingly powerful for detecting genetic determinants and their molecular mechanisms underpinning natural phenotypic variation. However, GWAS frequently yield results with neither expected nor promising loci, nor any significant associations. This is often because associations between SNPs and a single phenotype are confounded, for example with the environment, other traits, or complex genetic structures. Such confounding can mask true genotype-phenotype associations, or inflate spurious associations. To address these problems, numerous methods have been developed that go beyond the standard model. Such advanced GWAS models are flexible and can offer improved statistical power for understanding the genetics underlying complex traits. Despite this advantage, these models have not been widely adopted and implemented compared to the standard GWAS approach, partly because this literature is diverse and often technical. In this review, our aim is to provide an overview of the application and the benefits of various advanced GWAS models for handling complex traits and genetic structures, targeting plant biologists who wish to carry out GWAS more effectively.

P-521 Inter-individual variation in gonadotoxicity in female childhood cancer survivors – a genome-wide association study: results from the PanCareLIFE study

Abstract Study question To identify new variants associated with alkylating agent (AA) induced gonadotoxicity among adult female childhood cancer survivors (CCS) using a genome-wide association study approach (GWAS). Summary answer Upon meta-analysis of two independent cohorts, the variant rs78861946 was associated with lower AMH levels in CCS, but not separately in the smaller replication cohort. What is known already Female childhood cancer survivors are at risk of therapy-related gonadal impairment. Alkylating agents are well-established risk factors, and the inter-individual variability in gonadotoxicity may be explained by genetic polymorphisms. Study design, size, duration A genome-wide association study (GWAS) approach was used to discover new variants associated with AA-induced gonadotoxicity among adult female CCS. The discovery cohort included adult female CCS from the pan-European PanCareLIFE cohort (n = 743; median age: 25.8 years), excluding those who received bilateral ovarian irradiation, bilateral oophorectomy, CNS, total body irradiation, or stem cell transplantation. Results were replicated in the USA-based St. Jude Lifetime Cohort (n = 391; median age: 31.3 years) and combined in a meta-analysis. Participants/materials, setting, methods Anti-Müllerian hormone (AMH) levels served as a proxy for ovarian function in both the discovery and replication cohort. A GWAS was performed using a linear regression model adjusted for cyclophosphamide equivalent dose of alkylating agents, age at diagnosis, and age at study. Main results and the role of chance Three genome-wide significant (<5.0x10E-8 ) and 16 genome-wide suggestive (<5.0x10E-6 ) loci were associated with log-transformed AMH levels, adjusted for cyclophosphamide equivalent dose of alkylating agents, age at diagnosis, and age at study in the PanCareLIFE cohort. Based on effect allele frequency (EAF) (>0.01 if not genome-wide significant), p-value (<5.0 × 10E−6 ), and biological relevance, 15 SNPs were selected for replication in the independent replication cohort of the St. Jude Lifetime Cohort. None of the SNPs were statistically significantly associated with AMH levels in this smaller cohort. A meta-analysis indicated thatrs78861946 was associated at borderline genome-wide statistical significance (reference/effect allele: C/T;EAF: 0.04, Beta (SE): −0.484 (0.091), p-value= 9.39 × 10E−8). While the sample size of both the discovery and replication cohorts are large for CCS cohorts, they are very small compared to GWASs in the general population. The limited power is likely one of the reasons the identified SNPs were not statistically significant in our replication cohort of SJLIFE. Limitations, reasons for caution While AMH is a valuable surrogate marker for ovarian reserve, it remains a proxy for ovarian reserve and low AMH levels may still coincide with fertility potential and pregnancy. Wider implications of the findings Identification of risk-contributing variants such as rs78861946 may prove useful to better identify patients who are specifically susceptible for alkylating agent-induced gonadal toxicity. Future polygenic risk scores may aid individualized counselling regarding the treatment-related risk of gonadotoxicity and fertility preservation options for female childhood cancer survivors. Trial registration number not applicable

The benefits of permutation-based genome-wide association studies.

Linear mixed models (LMMs) are a commonly used method for genome-wide association studies (GWAS) that aim to detect associations between genetic markers and phenotypic measurements in a population of individuals while accounting for population structure and cryptic relatedness. In a standard GWAS, hundreds of thousands to millions of statistical tests are performed, requiring control for multiple hypothesis testing. Typically, static corrections that penalize the number of tests performed are used to control for the family-wise error rate, which is the probability of making at least one false positive. However, it has been shown that in practice this threshold is too conservative for normally distributed phenotypes and not stringent enough for non-normally distributed phenotypes. Therefore, permutation-based LMM approaches have recently been proposed to provide a more realistic threshold that takes phenotypic distributions into account. In this work, we discuss the advantages of permutation-based GWAS approaches, including new simulations and results from a re-analysis of all publicly available Arabidopsis phenotypes from the AraPheno database.

Genome sequence analyses identify novel risk loci for multiple system atrophy.

Multiple system atrophy (MSA) is an adult-onset, sporadic synucleinopathy characterized by parkinsonism, cerebellar ataxia, and dysautonomia. The genetic architecture of MSA is poorly understood, and treatments are limited to supportive measures. Here, we performed a comprehensive analysis of whole genome sequence data from 888 European-ancestry MSA cases and 7,128 controls to systematically investigate the genetic underpinnings of this understudied neurodegenerative disease. We identified four significantly associated risk loci using a genome-wide association study approach. Transcriptome-wide association analyses prioritized USP38-DT, KCTD7, and lnc-KCTD7-2 as novel susceptibility genes for MSA within these loci, and single-nucleus RNA sequence analysis found that the associated variants acted as cis-expression quantitative trait loci for multiple genes across neuronal and glial cell types. In conclusion, this study highlights the role of genetic determinants in the pathogenesis of MSA, and the publicly available data from this study represent a valuable resource for investigating synucleinopathies.

Case-Case Genome-Wide Analyses Identify Subtype-Informative Variants That Confer Risk for Breast Cancer.

Breast cancer includes several subtypes with distinct characteristic biological, pathologic, and clinical features. Elucidating subtype-specific genetic etiology could provide insights into the heterogeneity of breast cancer to facilitate the development of improved prevention and treatment approaches. In this study, we conducted pairwise case-case comparisons among five breast cancer subtypes by applying a case-case genome-wide association study (CC-GWAS) approach to summary statistics data of the Breast Cancer Association Consortium. The approach identified 13 statistically significant loci and eight suggestive loci, the majority of which were identified from comparisons between triple-negative breast cancer (TNBC) and luminal A breast cancer. Associations of lead variants in 12 loci remained statistically significant after accounting for previously reported breast cancer susceptibility variants, among which, two were genome-wide significant. Fine mapping implicated putative functional/causal variants and risk genes at several loci, e.g., 3q26.31/TNFSF10, 8q22.3/NACAP1/GRHL2, and 8q23.3/LINC00536/TRPS1, for TNBC as compared with luminal cancer. Functional investigation further identified rs16867605 at 8q22.3 as a SNP that modulates the enhancer activity of GRHL2. Subtype-informative polygenic risk scores (PRS) were derived, and patients with a high subtype-informative PRS had an up to two-fold increased risk of being diagnosed with TNBC instead of luminal cancers. The CC-GWAS PRS remained statistically significant after adjusting for TNBC PRS derived from traditional case-control GWAS in The Cancer Genome Atlas and the African Ancestry Breast Cancer Genetic Consortium. The CC-GWAS PRS was also associated with overall survival and disease-specific survival among patients with breast cancer. Overall, these findings have advanced our understanding of the genetic etiology of breast cancer subtypes, particularly for TNBC. Significance: The discovery of subtype-informative genetic risk variants for breast cancer advances our understanding of the etiologic heterogeneity of breast cancer, which could accelerate the identification of targets and personalized strategies for prevention and treatment.