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STUDY QUESTION Can a genome-wide association study (GWAS) meta-analysis, including a large sample of young premenopausal women from a founder population from Northern Finland, identify novel genetic variants for circulating anti-Müllerian hormone (AMH) levels and provide insights into single-nucleotide polymorphism enrichment in different biological pathways and tissues involved in AMH regulation? SUMMARY ANSWER The meta-analysis identified a total of six loci associated with AMH levels at P  <   5 × 10−8, three of which were novel in or near CHEK2 , BMP4 , and EIF4EBP1 , as well as highlighted significant enrichment in renal system vasculature morphogenesis, and the pituitary gland as the top associated tissue in tissue enrichment analysis. WHAT IS KNOWN ALREADY AMH is expressed by preantral and small antral stage ovarian follicles in women, and variation in age-specific circulating AMH levels has been associated with several health conditions. However, the biological mechanisms underlying the association between health conditions and AMH levels are not yet fully understood. Previous GWAS have identified loci associated with AMH levels in pre-menopausal women, in or near MCM8 , AMH , TEX41 , and CDCA7. STUDY DESIGN, SIZE, DURATION We performed a GWAS meta-analysis for circulating AMH level measurements in 9668 pre-menopausal women. PARTICIPANTS/MATERIALS, SETTING, METHODS We performed a GWAS meta-analysis in which we combined 2619 AMH measurements (at age 31 years) from a prospective founder population cohort (Northern Finland Birth Cohort 1966, NFBC1966) with a previous GWAS meta-analysis that included 7049 pre-menopausal women (age range 15–48 years) (N = 9668). NFBC1966 AMH measurements were quantified using an automated assay. We annotated the genetic variants, combined different data layers to prioritize potential candidate genes, described significant pathways and tissues enriched by the GWAS signals, identified plausible regulatory roles using colocalization analysis, and leveraged publicly available summary statistics to assess genetic and phenotypic correlations with multiple traits. MAIN RESULTS AND THE ROLE OF CHANCE Three novel genome-wide significant loci were identified. One of these is in complete linkage disequilibrium with c.1100delC in CHEK2 , which is found to be 4-fold enriched in the Finnish population compared to other European populations. We propose a plausible regulatory effect of some of the GWAS variants linked to AMH, as they colocalize with GWAS signals associated with gene expression levels of BMP4 , TEX41 , and EIFBP41. Gene set analysis highlighted significant enrichment in renal system vasculature morphogenesis, and tissue enrichment analysis ranked the pituitary gland as the top association. LARGE SCALE DATA The GWAS meta-analysis summary statistics are available for download from the GWAS Catalogue with accession number GCST90428625. LIMITATIONS, REASONS FOR CAUTION This study only included women of European ancestry and the lack of sufficiently sized relevant tissue data in gene expression datasets hinders the assessment of potential regulatory effects in reproductive tissues. WIDER IMPLICATIONS OF THE FINDINGS Our results highlight the increased power of founder populations and larger sample sizes to boost the discovery of novel trait-associated variants underlying variation in AMH levels, which aided the characterization of GWAS signals enrichment in different biological pathways and plausible genetic regulatory effects linked with AMH level variation for the first time. STUDY FUNDING/COMPETING INTEREST(S) This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the MATER Marie Sklodowska-Curie Grant Agreement No. 813707 and Oulu University Scholarship Foundation and Paulon Säätiö Foundation. (N.P.-G.), Academy of Finland, Sigrid Jusélius Foundation, Novo Nordisk, University of Oulu, Roche Diagnostics (T.T.P.). This work was supported by the Estonian Research Council Grant 1911 (R.M.). J.R. was supported by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreements No. 874739 (LongITools), 824989 (EUCAN-Connect), 848158 (EarlyCause), and 733206 (LifeCycle). U.V. was supported by the Estonian Research Council grant PRG (PRG1291). The NFBC1966 received financial support from University of Oulu Grant No. 24000692, Oulu University Hospital Grant No. 24301140, and ERDF European Regional Development Fund Grant No. 539/2010 A31592. T.T.P. has received grants from Roche, Perkin Elmer, and honoraria for scientific presentations from Gedeon Richter, Exeltis, Astellas, Roche, Stragen, Astra Zeneca, Merck, MSD, Ferring, Duodecim, and Ajaton Terveys. For all other authors, there are no competing interests. [ABSTRACT FROM AUTHOR]

Identifying genuine polymorphic variants is a significant challenge in sequence data analysis, although detecting low-frequency variants in sequence data is essential for estimating demographic parameters and investigating genetic processes, such as selection, within populations. Arbuscular mycorrhizal (AM) fungi are multinucleate organisms, in which individual nuclei collectively operate as a population, and the extent of genetic variation across nuclei has long been an area of scientific interest. In this study, we investigated the patterns of polymorphism discovery and the alternate allele frequency distribution by comparing polymorphism discovery in 2 distinct genomic sequence datasets of the AM fungus model species, Rhizophagus irregularis strain DAOM197198. The 2 datasets used in this study are publicly available and were generated either from pooled spores and hyphae or amplified single nuclei from a single spore. We also estimated the intraorganismal variation within the DAOM197198 strain. Our results showed that the 2 datasets exhibited different frequency patterns for discovered variants. The whole-organism dataset showed a distribution spanning low-, intermediate-, and high-frequency variants, whereas the single-nucleus dataset predominantly featured low-frequency variants with smaller proportions in intermediate and high frequencies. Furthermore, single nucleotide polymorphism density estimates within both the whole organism and individual nuclei confirmed the low intraorganismal variation of the DAOM197198 strain and that most variants are rare. Our study highlights the methodological challenges associated with detecting low-frequency variants in AM fungal whole-genome sequence data and demonstrates that alternate alleles can be reliably identified in single nuclei of AM fungi. [ABSTRACT FROM AUTHOR]

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level. Synopsis: A diallel hybrid panel from diverse parental natural yeast isolates is used to investigate the effects of low- frequency single nucleotide polymorphisms (SNPs) and structural variants (SVs) on gene expression variation in a population. The impact of low-frequency and structural variants is assessed using a diallel panel. Diallel SVs are fully cataloged using long-read sequencing and combined parental types. SVs contribute less than SNPs to expression variation and exhibit lower effect size. Low-frequency variants are overrepresented in associations compared to common ones. A diallel hybrid panel from diverse parental natural yeast isolates is used to investigate the effects of low- frequency single nucleotide polymorphisms (SNPs) and structural variants (SVs) on gene expression variation in a population. [ABSTRACT FROM AUTHOR]

Abstract Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Low-frequency mutations associated with drug resistance have been related to virologic failure in subjects with no history of pre-treatment and recent HIV diagnosis. In total, 78 antiretroviral treatment (ART)-naïve subjects with a recent HIV diagnosis were selected and followed by CD4+ T lymphocytes and viral load tests to detect virologic failure. We sequenced the basal samples retrospectively using next-generation sequencing (NGS), looking for low-frequency mutations that had not been detected before using the Sanger sequencing method (SSM) and describing the response to ART. Twenty-two subjects developed virologic failure (VF), and thirteen of them had at least one drug-resistance mutation associated with Reverse Transcriptase Inhibitors (RTI) and Protease Inhibitors (PIs) at frequency levels ≤ 1%, not detected previously in their basal genotyping test. No resistance mutations were observed to Integrase Strand Transfer Inhibitors (INSTIs). We identified a possible cause of VF in ART-naïve subjects with low-frequency mutations detected. To our knowledge, this is the first evaluation of pre-existing drug resistance for HIV-1 minority variants carried out on ART-naïve people living with HIV/AIDS (PLWHA) by analyzing the HIV-1 pol gene using NGS in the country.

Whole-exome sequencing (WES) in families with an unexplained tendency for venous thromboembolism (VTE) may favor detection of low-frequency variants in genes with known contribution to hemostasis or associated with VTE-related phenotypes. WES analysis in six family members, three of whom affected by documented VTE, filtered for MAF < 0.04 in 192 candidate genes, revealed 22 heterozygous (16 missense and six synonymous) variants in patients. Functional prediction by multi-component bioinformatics tools, implemented by a database/literature search, including ClinVar annotation and QTL analysis, prioritized 12 missense variants, three of which (CRP Leu61Pro, F2 Asn514Lys and NQO1 Arg139Trp) were present in all patients, and the frequent functional variants FGB Arg478Lys and IL1A Ala114Ser. Combinations of prioritized variants in each patient were used to infer functional protein interactions. Different interaction patterns, supported by high-quality evidence, included eight proteins intertwined in the "acute phase" (CRP, F2, SERPINA1 and IL1A) and/or in the "fibrinogen complex" (CRP, F2, PLAT, THBS1, VWF and FGB) significantly enriched terms. In a wide group of candidate genes, this approach highlighted six low-frequency variants (CRP Leu61Pro, F2 Asn514Lys, SERPINA1 Arg63Cys, THBS1 Asp901Glu, VWF Arg1399His and PLAT Arg164Trp), five of which were top ranked for predicted deleteriousness, which in different combinations may contribute to disease susceptibility in members of this family. [ABSTRACT FROM AUTHOR]

Low-frequency mutations associated with drug resistance have been related to virologic failure in subjects with no history of pre-treatment and recent HIV diagnosis. In total, 78 antiretroviral treatment (ART)-naïve subjects with a recent HIV diagnosis were selected and followed by CD4+ T lymphocytes and viral load tests to detect virologic failure. We sequenced the basal samples retrospectively using next-generation sequencing (NGS), looking for low-frequency mutations that had not been detected before using the Sanger sequencing method (SSM) and describing the response to ART. Twenty-two subjects developed virologic failure (VF), and thirteen of them had at least one drug-resistance mutation associated with Reverse Transcriptase Inhibitors (RTI) and Protease Inhibitors (PIs) at frequency levels ≤ 1%, not detected previously in their basal genotyping test. No resistance mutations were observed to Integrase Strand Transfer Inhibitors (INSTIs). We identified a possible cause of VF in ART-naïve subjects with low-frequency mutations detected. To our knowledge, this is the first evaluation of pre-existing drug resistance for HIV-1 minority variants carried out on ART-naïve people living with HIV/AIDS (PLWHA) by analyzing the HIV-1 pol gene using NGS in the country. [ABSTRACT FROM AUTHOR]

Whole-exome sequencing (WES) in families with an unexplained tendency for venous thromboembolism (VTE) may favor detection of low-frequency variants in genes with known contribution to hemostasis or associated with VTE-related phenotypes. WES analysis in six family members, three of whom affected by documented VTE, filtered for MAF < 0.04 in 192 candidate genes, revealed 22 heterozygous (16 missense and six synonymous) variants in patients. Functional prediction by multi-component bioinformatics tools, implemented by a database/literature search, including ClinVar annotation and QTL analysis, prioritized 12 missense variants, three of which (CRP Leu61Pro, F2 Asn514Lys and NQO1 Arg139Trp) were present in all patients, and the frequent functional variants FGB Arg478Lys and IL1A Ala114Ser. Combinations of prioritized variants in each patient were used to infer functional protein interactions. Different interaction patterns, supported by high-quality evidence, included eight proteins intertwined in the “acute phase” (CRP, F2, SERPINA1 and IL1A) and/or in the “fibrinogen complex” (CRP, F2, PLAT, THBS1, VWF and FGB) significantly enriched terms. In a wide group of candidate genes, this approach highlighted six low-frequency variants (CRP Leu61Pro, F2 Asn514Lys, SERPINA1 Arg63Cys, THBS1 Asp901Glu, VWF Arg1399His and PLAT Arg164Trp), five of which were top ranked for predicted deleteriousness, which in different combinations may contribute to disease susceptibility in members of this family.

Multiple Sclerosis (MS) is a complex multifactorial autoimmune disease, whose sex- and age-adjusted prevalence in Sardinia (Italy) is among the highest worldwide. To date, 233 loci were associated with MS and almost 20% of risk heritability is attributable to common genetic variants, but many low-frequency and rare variants remain to be discovered. Here, we aimed to contribute to the understanding of the genetic basis of MS by investigating potentially functional rare variants. To this end, we analyzed thirteen multiplex Sardinian families with Immunochip genotyping data. For five families, Whole Exome Sequencing (WES) data were also available. Firstly, we performed a non-parametric Homozygosity Haplotype analysis for identifying the Region from Common Ancestor (RCA). Then, on these potential disease-linked RCA, we searched for the presence of rare variants shared by the affected individuals by analyzing WES data. We found: (i) a variant (43181034 T > G) in the splicing region on exon 27 of CUL9; (ii) a variant (50245517 A > C) in the splicing region on exon 16 of ATP9A; (iii) a non-synonymous variant (43223539 A > C), on exon 9 of TTBK1; (iv) a non-synonymous variant (42976917 A > C) on exon 9 of PPP2R5D; and v) a variant (109859349-109859354) in 3′UTR of MYO16.

The zinc transporter 8 (ZnT8) plays an essential role in zinc homeostasis inside pancreatic β cells, its function is related to the stabilization of insulin hexameric form. Genome-wide association studies (GWAS) have established a positive and negative relationship of ZnT8 variants with type 2 diabetes mellitus (T2DM), exposing a dual and controversial role. The first hypotheses about its role in T2DM indicated a higher risk of developing T2DM for loss of function; nevertheless, recent GWAS of ZnT8 loss-of-function mutations in humans have shown protection against T2DM. With regard to the ZnT8 role in T2DM, most studies have focused on rodent models and common high-risk variants; however, considerable differences between human and rodent models have been found and the new approaches have included lower-frequency variants as a tool to clarify gene functions, allowing a better understanding of the disease and offering possible therapeutic targets. Therefore, this review will discuss the physiological effects of the ZnT8 variants associated with a major and lower risk of T2DM, emphasizing the low- and rare-frequency variants. [ABSTRACT FROM AUTHOR]

Population genomics is a fast‐developing discipline with promising applications in a growing number of life sciences fields. Advances in sequencing technologies and bioinformatics tools allow population genomics to exploit genome‐wide information to identify the molecular variants underlying traits of interest and the evolutionary forces that modulate these variants through space and time. However, the cost of genomic analyses of multiple populations is still too high to address them through individual genome sequencing. Pooling individuals for sequencing can be a more effective strategy in Single Nucleotide Polymorphism (SNP) detection and allele frequency estimation because of a higher total coverage. However, compared to individual sequencing, SNP calling from pools has the additional difficulty of distinguishing rare variants from sequencing errors, which is often avoided by establishing a minimum threshold allele frequency for the analysis. Finding an optimal balance between minimizing information loss and reducing sequencing costs is essential to ensure the success of population genomics studies. Here, we have benchmarked the performance of SNP callers for Pool‐seq data, based on different approaches, under different conditions, and using computer simulations and real data. We found that SNP callers performance varied for allele frequencies up to 0.35. We also found that SNP callers based on Bayesian (SNAPE‐pooled) or maximum likelihood (MAPGD) approaches outperform the two heuristic callers tested (VarScan and PoolSNP), in terms of the balance between sensitivity and FDR both in simulated and sequencing data. Our results will help inform the selection of the most appropriate SNP caller not only for large‐scale population studies but also in cases where the Pool‐seq strategy is the only option, such as in metagenomic or polyploid studies. [ABSTRACT FROM AUTHOR]

Rare genetic variants in yeast explain a large amount of phenotypic variation in a complex trait like growth.

Genome-wide association studies (GWAS) allow to dissect complex traits and map genetic variants, which often explain relatively little of the heritability. One potential reason is the preponderance of undetected low-frequency variants. To increase their allele frequency and assess their phenotypic impact in a population, we generated a diallel panel of 3025 yeast hybrids, derived from pairwise crosses between natural isolates and examined a large number of traits. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a third is governed by non-additive effects, with complete dominance having a key role. By performing GWAS on the diallel panel, we found that associated variants with low frequency in the initial population are overrepresented and explain a fraction of the phenotypic variance as well as an effect size similar to common variants. Overall, we highlighted the relevance of low-frequency variants on the phenotypic variation.

In light of the complex nature of multiple sclerosis (MS) and the recently estimated contribution of low-frequency variants into disease, decoding its genetic risk components requires novel variant prioritization strategies. We selected, by reviewing MS Genome Wide Association Studies (GWAS), 107 candidate loci marked by intragenic single nucleotide polymorphisms (SNPs) with a remarkable association (p-value ≤ 5 × 10-6). A whole exome sequencing (WES)-based pilot study of SNPs with minor allele frequency (MAF) ≤ 0.04, conducted in three Italian families, revealed 15 exonic low-frequency SNPs with affected parent-child transmission. These variants were detected in 65/120 Italian unrelated MS patients, also in combination (22 patients). Compared with databases (controls gnomAD, dbSNP150, ExAC, Tuscany-1000 Genome), the allelic frequencies of C6orf10 rs16870005 and IL2RA rs12722600 were significantly higher (i.e., controls gnomAD, p = 9.89 × 10-7 and p < 1 × 10-20). TET2 rs61744960 and TRAF3 rs138943371 frequencies were also significantly higher, except in Tuscany-1000 Genome. Interestingly, the association of C6orf10 rs16870005 (Ala431Thr) with MS did not depend on its linkage disequilibrium with the HLA-DRB1 locus. Sequencing in the MS cohort of the C6orf10 3′ region revealed 14 rare mutations (10 not previously reported). Four variants were null, and significantly more frequent than in the databases. Further, the C6orf10 rare variants were observed in combinations, both intra-locus and with other low-frequency SNPs. The C6orf10 Ser389Xfr was found homozygous in a patient with early onset of the MS. Taking into account the potentially functional impact of the identified exonic variants, their expression in combination at the protein level could provide functional insights in the heterogeneous pathogenetic mechanisms contributing to MS.

In light of the complex nature of multiple sclerosis (MS) and the recently estimated contribution of low-frequency variants into disease, decoding its genetic risk components requires novel variant prioritization strategies. We selected, by reviewing MS Genome Wide Association Studies (GWAS), 107 candidate loci marked by intragenic single nucleotide polymorphisms (SNPs) with a remarkable association (p -value ≤ 5 × 10-6). A whole exome sequencing (WES)-based pilot study of SNPs with minor allele frequency (MAF) ≤ 0.04, conducted in three Italian families, revealed 15 exonic low-frequency SNPs with affected parent-child transmission. These variants were detected in 65/120 Italian unrelated MS patients, also in combination (22 patients). Compared with databases (controls gnomAD, dbSNP150, ExAC, Tuscany-1000 Genome), the allelic frequencies of C6orf10 rs16870005 and IL2RA rs12722600 were significantly higher (i.e., controls gnomAD, p = 9.89 × 10-7 and p < 1 × 10-20). TET2 rs61744960 and TRAF3 rs138943371 frequencies were also significantly higher, except in Tuscany-1000 Genome. Interestingly, the association of C6orf10 rs16870005 (Ala431Thr) with MS did not depend on its linkage disequilibrium with the HLA-DRB1 locus. Sequencing in the MS cohort of the C6orf10 3′ region revealed 14 rare mutations (10 not previously reported). Four variants were null, and significantly more frequent than in the databases. Further, the C6orf10 rare variants were observed in combinations, both intra-locus and with other low-frequency SNPs. The C6orf10 Ser389Xfr was found homozygous in a patient with early onset of the MS. Taking into account the potentially functional impact of the identified exonic variants, their expression in combination at the protein level could provide functional insights in the heterogeneous pathogenetic mechanisms contributing to MS. [ABSTRACT FROM AUTHOR]

Purpose: Anthracyclines are widely used chemotherapeutic drugs that can cause progressive and irreversible cardiac damage and fatal heart failure. Several genetic variants associated with anthracycline-induced cardiotoxicity (AIC) have been identified, but they explain only a small proportion of the interindividual differences in AIC susceptibility.Methods: In this study, we evaluated the association of low-frequency variants with risk of chronic AIC using the Illumina HumanExome BeadChip array in a discovery cohort of 61 anthracycline-treated breast cancer patients with replication in a second independent cohort of 83 anthracycline-treated pediatric cancer patients, using gene-based tests (SKAT-O).Results: The most significant associated gene in the discovery cohort was ETFB (electron transfer flavoprotein beta subunit) involved in mitochondrial β-oxidation and ATP production (P = 4.16 × 10−4) and this association was replicated in an independent set of anthracycline-treated cancer patients (P = 2.81 × 10−3). Within ETFB, we found that the missense variant rs79338777 (p.Pro52Leu; c.155C > T) made the greatest contribution to the observed gene association and it was associated with increased risk of chronic AIC in the two cohorts separately and when combined (OR 9.00, P = 1.95 × 10−4, 95% CI 2.83–28.6).Conclusions: We identified and replicated a novel gene, ETFB, strongly associated with chronic AIC independently of age at tumor onset and related to anthracycline-mediated mitochondrial dysfunction. Although experimental verification and further studies in larger patient cohorts are required to confirm our finding, we demonstrated that exome array data analysis represents a valuable strategy to identify novel genes contributing to the susceptibility to chronic AIC. [ABSTRACT FROM AUTHOR]

The emerging HIV-1 resistance epidemic is threatening the impressive global advances in HIV-1 infection treatment and prevention achieved in the last decade. Next-generation sequencing is improving our ability to understand, diagnose and prevent HIV-1 resistance, being increasingly cost-effective and more accessible. However, NGS still faces a number of limitations that need to be addressed to enable its widespread use. Here, we will review the main NGS platforms available for HIV-1 diagnosis, the factors affecting the clinical utility of NGS testing and the evidence supporting −or not- ultrasensitive genotyping over Sanger sequencing for routine HIV-1 diagnosis. Now that global HIV-1 eradication might be within our reach, making NGS accessible also to LMICs has become a priority. Reductions in sequencing costs, particularly in library preparation, and accessibility to low-cost, robust but simplified automated bioinformatic analyses of NGS data will remain essential to end the HIV-1 pandemic. [ABSTRACT FROM AUTHOR]

Aim To explain the missing heritability after the genome-wide association studies era, sequencing studies allow the identification of low-frequency variants with a stronger effect on disease risk. Common variants in the interleukin 10 gene ( IL10) have been consistently associated with Behçet's disease ( BD) and the goal of this study is to investigate the role of low-frequency IL10 variants in BD susceptibility. Methods To identify IL10 low-frequency variants, a discovery group of 50 Portuguese BD patients were Sanger-sequenced in a 7.7 kb genomic region encompassing the complete IL10 gene, 0.9 kb upstream and 2 kb downstream, and two conserved regions in the putative promoter. To assess if the novel variants are BD- and/or Portuguese-specific, they were assayed in an additional group of BD patients (26 Portuguese and 964 Iranian) and controls (104 Portuguese and 823 Iranian). Results Rare IL10 coding variants were not detected in BD patients, but we identified 28 known single nucleotide polymorphisms with minor allele frequencies ranging from 0.010 to 0.390, and five novel non-coding variants in five heterozygous cases. ss836185595, located in the IL10 3′ untranslated region, was also detected in one Iranian control individual and therefore is not specific to BD. The remaining novel IL10 variants (ss836185596 and ss836185602 in intron 3, ss836185598 and ss836185604 in the putative promoter region) were not found in the replication dataset. Conclusion This study highlights the importance of screening the whole gene and regulatory regions when searching for novel variants associated with complex diseases, and the need to develop bioinformatics tools to predict the functional impact of non-coding variants and statistical tests which incorporate these predictions. [ABSTRACT FROM AUTHOR]

Over the last 10 years substantial progress has been made in our understanding of the genetic basis for type 2 diabetes and related traits. These developments have been facilitated by technological advancements that have allowed comprehensive genome-wide assessments of the impact of common genetic variation on disease risk. Current efforts are now focused on extending this to genetic variants in the rare and low-frequency spectrum by capitalising on next-generation sequencing technologies. This review discusses the important contributions that studies in isolated populations are making to this effort for diabetes and metabolic disease, drawing on specific examples from populations in Greece and Greenland. This review summarises a presentation given at the 'Exciting news in genetics of diabetes' symposium at the 2015 annual meeting of the EASD, with topics presented by Eleftheria Zeggini and Torben Hansen, and an overview by the Session Chair, Anna Gloyn. [ABSTRACT FROM AUTHOR]

The study of rare variants in monogenic forms of autoimmune disease has offered insight into the aetiology of more complex pathologies. Research in complex autoimmune disease initially focused on sequencing candidate genes, with some early successes, notably in uncovering low-frequency variation associated with Type 1 diabetes mellitus. However, other early examples have proved difficult to replicate, and a recent study across six autoimmune diseases, re-sequencing 25 autoimmune disease-associated genes in large sample sizes, failed to find any associated rare variants. The study of rare and low-frequency variation in autoimmune diseases has been made accessible by the inclusion of such variants on custom genotyping arrays (e.g. Immunochip and Exome arrays). Whole-exome sequencing approaches are now also being utilised to uncover the contribution of rare coding variants to disease susceptibility, severity and treatment response. Other sequencing strategies are starting to uncover the role of regulatory rare variation. [ABSTRACT FROM AUTHOR]

Human immunodeficiency virus (HIV) exhibits remarkable diversity in its genomic makeup and exists in any given individual as a complex distribution of closely related but nonidentical genomes called a viral quasispecies, which is subject to genetic variation, competition, and selection. This viral diversity clinically manifests as a selection of mutant variants based on viral fitness in treatment-naive individuals and based on drug-selective pressure in those on antiretroviral therapy (ART). The current standard-of-care ART consists of a combination of antiretroviral agents, which ensures maximal viral suppression while preventing the emergence of drug-resistant HIV variants. Unfortunately, transmission of drug-resistant HIV does occur, affecting 5% to >20% of newly infected individuals. To optimize therapy, clinicians rely on viral genotypic information obtained from conventional population sequencing-based assays, which cannot reliably detect viral variants that constitute <20% of the circulating viral quasispecies. These low- frequency variants can be detected by highly sensitive genotyping methods collectively grouped under the moniker of deep sequencing. Low-frequency variants have been correlated to treatment failures and HIV transmission, and detection of these variants is helping to inform strategies for vaccine development. Here, we discuss the molecular virology of HIV, viral hetero-geneity, drug-resistance mutations, and the application of deep sequencing technologies in research and the clinical care of HIV-infected individuals. [ABSTRACT FROM AUTHOR]

Influenza viruses exhibit considerable diversity between hosts. Additionally, different quasispecies can be found within the same host. High-throughput sequencing technologies can be used to sequence a patient-derived virus population at sufficient depths to identify low-frequency variants (LFV) present in a quasispecies, but many challenges remain for reliable LFV detection because of experimental errors introduced during sample preparation and sequencing. High genomic copy numbers and extensive sequencing depths are required to differentiate false positive from real LFV, especially at low allelic frequencies (AFs). This study proposes a general approach for identifying LFV in patient-derived samples obtained during routine surveillance. Firstly, validated thresholds were determined for LFV detection, whilst balancing both the cost and feasibility of reliable LFV detection in clinical samples. Using a genetically well-defined population of influenza A viruses, thresholds of at least 10 4 genomes per microlitre and AF of ≥5 % were established as detection limits. Secondly, a subset of 59 retained influenza A (H3N2) samples from the 2016-2017 Belgian influenza season was composed. Thirdly, as a proof of concept for the added value of LFV for routine influenza monitoring, potential associations between patient data and whole genome sequencing data were investigated. A significant association was found between a high prevalence of LFV and disease severity. This study provides a general methodology for influenza LFV detection, which can also be adopted by other national influenza reference centres and for other viruses such as SARS-CoV-2. Additionally, this study suggests that the current relevance of LFV for routine influenza surveillance programmes might be undervalued.

Highlights: [•] We analyzed protein-changing and low-frequency variants of the type 1 diabetes candidate genes in Japanese. [•] The A908V variant in the GLIS3 gene was discovered to be strongly protective against type 1 diabetes. [•] The A908V variant may affect autoimmunity to the GLIS3 protein itself. [ABSTRACT FROM AUTHOR]

ims/hypothesis: Rare mutations in the gene HNF4A, encoding the transcription factor hepatocyte nuclear factor 4α (HNF-4A), account for ~5% of cases of MODY and more frequent variants in this gene may be involved in multifactorial forms of diabetes. Two low-frequency, non-synonymous variants in HNF4A (V255M, minor allele frequency [MAF] ~0.1%; T130I, MAF ~3.0%)-known to influence downstream HNF-4A target gene expression-are of interest, but previous type 2 diabetes association reports were inconclusive. We aimed to evaluate the contribution of these variants to type 2 diabetes susceptibility through large-scale association analysis. Methods: We genotyped both variants in at least 5,745 cases and 14,756 population controls from the UK and Denmark. We also undertook an expanded association analysis that included previously reported and novel genotype data obtained in Danish, Finnish, Canadian and Swedish samples. A meta-analysis incorporating all published association studies of the T130I variant was subsequently carried out in a maximum sample size of 14,279 cases and 26,835 controls. Results: We found no association between V255M and type 2 diabetes in either the initial ( p = 0.28) or the expanded analysis ( p = 0.44). However, T130I demonstrated a modest association with type 2 diabetes in the UK and Danish samples (additive per allele OR 1.17 [95% CI 1.08-1.28]; p = 1.5 × 10), which was strengthened in the meta-analysis (OR 1.20 [95% CI 1.10-1.30]; p = 2.1 × 10). Conclusions/interpretation: Our data are consistent with T130I as a low-frequency variant influencing type 2 diabetes risk, but are not conclusive when judged against stringent standards for genome-wide significance. This study exemplifies the difficulties encountered in association testing of low-frequency variants. [ABSTRACT FROM AUTHOR]

Re-sequencing of the human genome by next-generation sequencing (NGS) has been widely applied to discover pathogenic genetic variants and/or causative genes accounting for various types of diseases including cancers. The advances in NGS have allowed the sequencing of the entire genome of patients and identification of disease-associated variants in a reasonable timeframe and cost. The core of the variant identification relies on accurate variant calling and annotation. Numerous algorithms have been developed to elucidate the repertoire of somatic and germline variants. Each algorithm has its own distinct strengths, weaknesses, and limitations due to the difference in the statistical modeling approach adopted and read information utilized. Accurate variant calling remains challenging due to the presence of sequencing artifacts and read misalignments. All of these can lead to the discordance of the variant calling results and even misinterpretation of the discovery. For somatic variant detection, multiple factors including chromosomal abnormalities, tumor heterogeneity, tumor-normal cross contaminations, unbalanced tumor/normal sample coverage, and variants with low allele frequencies add even more layers of complexity to accurate variant identification. Given the discordances and difficulties, ensemble approaches have emerged by harmonizing information from different algorithms to improve variant calling performance. In this chapter, we first introduce the general scheme of variant calling algorithms and potential challenges at distinct stages. We next review the existing workflows of variant calling and annotation, and finally explore the strategies deployed by different callers as well as their strengths and caveats. Overall, NGS-based variant identification with careful consideration allows reliable detection of pathogenic variant and candidate variant selection for precision medicine., (© 2022. Springer Nature Switzerland AG.)

Multiple Sclerosis (MS) is a complex multifactorial autoimmune disease, whose sex- and age-adjusted prevalence in Sardinia (Italy) is among the highest worldwide. To date, 233 loci were associated with MS and almost 20% of risk heritability is attributable to common genetic variants, but many low-frequency and rare variants remain to be discovered. Here, we aimed to contribute to the understanding of the genetic basis of MS by investigating potentially functional rare variants. To this end, we analyzed thirteen multiplex Sardinian families with Immunochip genotyping data. For five families, Whole Exome Sequencing (WES) data were also available. Firstly, we performed a non-parametric Homozygosity Haplotype analysis for identifying the Region from Common Ancestor (RCA). Then, on these potential disease-linked RCA, we searched for the presence of rare variants shared by the affected individuals by analyzing WES data. We found: ( i ) a variant (43181034 T > G) in the splicing region on exon 27 of CUL9 ; ( ii ) a variant (50245517 A > C) in the splicing region on exon 16 of ATP9A ; ( iii ) a non-synonymous variant (43223539 A > C), on exon 9 of TTBK1 ; ( iv ) a non-synonymous variant (42976917 A > C) on exon 9 of PPP2R5D ; and v ) a variant (109859349-109859354) in 3'UTR of MYO16.

In a recent paper in this journal, the use of variance-stabilising transformation techniques was proposed to overcome the problem of inadequacy in normality approximation when testing association for a low-frequency variant in a case-control study. It was shown that tests based on the variance-stabilising transformations are more powerful than Fisher's exact test while controlling for type I error rate. Earlier in the journal, another study had shown that the likelihood ratio test (LRT) is superior to Fisher's exact test, Wald's test, and Pearson's χ² test in testing association for low-frequency variants. Thus, it is of interest to make a direct comparison between the LRT and the tests based on the variance-stabilising transformations. In this commentary, we show that the LRT and the variance-stabilising transformation-based tests have comparable power greater than Fisher's exact test, Wald's test, and Pearson's χ² test. [ABSTRACT FROM AUTHOR]

Rare genetic variants in yeast explain a large amount of phenotypic variation in a complex trait like growth., Competing Interests: LP No competing interests declared, (© 2019, Pallares.)

Current high-throughput sequencing technologies can generate sequence data and provide information on the genetic composition of samples at very high coverage. Deep sequencing approaches enable the detection of rare variants in heterogeneous samples, such as viral quasi-species, but also have the undesired effect of amplifying sequencing errors and artefacts. Distinguishing real variants from such noise is not straightforward. Variant callers that can handle pooled samples can be in trouble at extremely high read depths, while at lower depths sensitivity is often sacrificed to specificity. In this paper, we propose SiNPle (Simplified Inference of Novel Polymorphisms from Large coveragE), a fast and effective software for variant calling. SiNPle is based on a simplified Bayesian approach to compute the posterior probability that a variant is not generated by sequencing errors or PCR artefacts. The Bayesian model takes into consideration individual base qualities as well as their distribution, the baseline error rates during both the sequencing and the PCR stage, the prior distribution of variant frequencies and their strandedness. Our approach leads to an approximate but extremely fast computation of posterior probabilities even for very high coverage data, since the expression for the posterior distribution is a simple analytical formula in terms of summary statistics for the variants appearing at each site in the genome. These statistics can be used to filter out putative SNPs and indels according to the required level of sensitivity. We tested SiNPle on several simulated and real-life viral datasets to show that it is faster and more sensitive than existing methods. The source code for SiNPle is freely available to download and compile, or as a Conda/Bioconda package. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) allow to dissect complex traits and map genetic variants, which often explain relatively little of the heritability. One potential reason is the preponderance of undetected low-frequency variants. To increase their allele frequency and assess their phenotypic impact in a population, we generated a diallel panel of 3025 yeast hybrids, derived from pairwise crosses between natural isolates and examined a large number of traits. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a third is governed by non-additive effects, with complete dominance having a key role. By performing GWAS on the diallel panel, we found that associated variants with low frequency in the initial population are overrepresented and explain a fraction of the phenotypic variance as well as an effect size similar to common variants. Overall, we highlighted the relevance of low-frequency variants on the phenotypic variation., Competing Interests: TF, OA, JH, JP, EC, JS No competing interests declared, (© 2019, Fournier et al.)

Purpose: Anthracyclines are widely used chemotherapeutic drugs that can cause progressive and irreversible cardiac damage and fatal heart failure. Several genetic variants associated with anthracycline-induced cardiotoxicity (AIC) have been identified, but they explain only a small proportion of the interindividual differences in AIC susceptibility., Methods: In this study, we evaluated the association of low-frequency variants with risk of chronic AIC using the Illumina HumanExome BeadChip array in a discovery cohort of 61 anthracycline-treated breast cancer patients with replication in a second independent cohort of 83 anthracycline-treated pediatric cancer patients, using gene-based tests (SKAT-O)., Results: The most significant associated gene in the discovery cohort was ETFB (electron transfer flavoprotein beta subunit) involved in mitochondrial β-oxidation and ATP production (P = 4.16 × 10 -4 ) and this association was replicated in an independent set of anthracycline-treated cancer patients (P = 2.81 × 10 -3 ). Within ETFB, we found that the missense variant rs79338777 (p.Pro52Leu; c.155C > T) made the greatest contribution to the observed gene association and it was associated with increased risk of chronic AIC in the two cohorts separately and when combined (OR 9.00, P = 1.95 × 10 -4 , 95% CI 2.83-28.6)., Conclusions: We identified and replicated a novel gene, ETFB, strongly associated with chronic AIC independently of age at tumor onset and related to anthracycline-mediated mitochondrial dysfunction. Although experimental verification and further studies in larger patient cohorts are required to confirm our finding, we demonstrated that exome array data analysis represents a valuable strategy to identify novel genes contributing to the susceptibility to chronic AIC.

Genome-wide association studies have reported more than 100 independent common loci associated with breast cancer risk. The contribution of low-frequency or rare variants to breast cancer susceptibility has not been well explored. Thus, we applied exome chip to genotype >200 000 low-frequency and rare variants in 1064 breast cancer cases and 1125 cancer-free controls and subsequently validated promising associations in another 1040 breast cancer cases and 1240 controls. We identified two low-frequency nonsynonymous variants at FKBPL (rs200847762, OR = 0.34, 95% CI = 0.20-0.57, P = 4.31 × 10 -5 ) and ARPC1B (rs1045012, OR = 0.56, 95% CI = 0.43-0.74, P = 4.30 × 10 -5 ) associated with breast cancer risk. In stratification analyses, we found that the protective effect of rs200847762 was stronger in ER-positive breast cancer (OR = 0.18, 95% CI = 0.06-0.42) than that in ER-negative one (OR = 0.59, 95% CI = 0.31-1.05). Our findings indicate that low-frequency variants may also contribute to breast cancer susceptibility and genetic variants in 6p21.33 and 7q22.1 are important in breast carcinogenesis. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts., (© 2016 WILEY PERIODICALS, INC.)

Although genome-wide association studies (GWAS) have identified thousands of trait-associated genetic variants, there are relatively few findings on the X chromosome. For analysis of low-frequency variants (minor allele frequency <5%), investigators can use region- or gene-based tests where multiple variants are analyzed jointly to increase power. To date, there are no gene-based tests designed for association testing of low-frequency variants on the X chromosome. Here we propose three gene-based tests for the X chromosome: burden, sequence kernel association test (SKAT), and optimal unified SKAT (SKAT-O). Using simulated case-control and quantitative trait (QT) data, we evaluate the calibration and power of these tests as a function of (1) male:female sample size ratio; and (2) coding of haploid male genotypes for variants under X-inactivation. For case-control studies, all three tests are reasonably well-calibrated for all scenarios we evaluated. As expected, power for gene-based tests depends on the underlying genetic architecture of the genomic region analyzed. Studies with more (haploid) males are generally less powerful due to decreased number of chromosomes. Power generally is slightly greater when the coding scheme for male genotypes matches the true underlying model, but the power loss for misspecifying the (generally unknown) model is small. For QT studies, type I error and power results largely mirror those for binary traits. We demonstrate the use of these three gene-based tests for X-chromosome association analysis in simulated data and sequencing data from the Genetics of Type 2 Diabetes (GoT2D) study., (© 2015 WILEY PERIODICALS, INC.)

In genetic association studies a conventional test statistic is proportional to the correlation coefficient between the trait and the variant, with the result that it lacks power to detect association for low-frequency variants. Considering the link between the conventional association test statistics and the linkage disequilibrium measure r(2), we propose a test statistic analogous to the standardized linkage disequilibrium D' to increase the power of detecting association for low-frequency variants. By both simulation and real data analysis we show that the proposed D' test is more powerful than the conventional methods for detecting association for low-frequency variants in a genome-wide setting. The optimal coding strategy for the D' test and its asymptotic properties are also investigated. In summary, we advocate using the D' test in a dominant model as a complementary approach to enhancing the power of detecting association for low-frequency variants with moderate to large effect sizes in case-control genome-wide association studies.

In genome-wide association studies of binary traits, investigators typically use logistic regression to test common variants for disease association within studies, and combine association results across studies using meta-analysis. For common variants, logistic regression tests are well calibrated, and meta-analysis of study-specific association results is only slightly less powerful than joint analysis of the combined individual-level data. In recent sequencing and dense chip based association studies, investigators increasingly test low-frequency variants for disease association. In this paper, we seek to (1) identify the association test with maximal power among tests with well controlled type I error rate and (2) compare the relative power of joint and meta-analysis tests. We use analytic calculation and simulation to compare the empirical type I error rate and power of four logistic regression based tests: Wald, score, likelihood ratio, and Firth bias-corrected. We demonstrate for low-count variants (roughly minor allele count [MAC] < 400) that: (1) for joint analysis, the Firth test has the best combination of type I error and power; (2) for meta-analysis of balanced studies (equal numbers of cases and controls), the score test is best, but is less powerful than Firth test based joint analysis; and (3) for meta-analysis of sufficiently unbalanced studies, all four tests can be anti-conservative, particularly the score test. We also establish MAC as the key parameter determining test calibration for joint and meta-analysis., (© 2013 WILEY PERIODICALS, INC.)