A haplotype map of the human genome. Nature. 2005;437:1299-1320. [PMID: 16255080 PMCID: PMC1880871 DOI: 10.1038/nature04226]

Perspectives and opportunities in forensic human, animal, and plant integrative genomics in the Pangenome era

The Human Pangenome Reference Consortium, the Chinese Pangenome Consortium, and other plant and animal pangenome projects have announced the completion of pilot work aimed at constructing high-quality, haplotype-resolved reference graph genomes representative of global ethno-linguistically different populations or different plant and animal species. These graph-based, gapless pangenome references, which are enriched in terms of genomic diversity, completeness, and contiguity, have the potential for enhancing long-read sequencing (LRS)-based genomic research, as well as improving mappability and variant genotyping on traditional short-read sequencing platforms. We comprehensively discuss the advancements in pangenome-based genomic integrative genomic discoveries across forensic-related species (humans, animals, and plants) and summarize their applications in variant identification and forensic genomics, epigenetics, transcriptomics, and microbiome research. Recent developments in multiplexed array sequencing have introduced a highly efficient and programmable technique to overcome the limitations of short forensic marker lengths in LRS platforms. This technique enables the concatenation of short RNA transcripts and DNA fragments into LRS-optimal molecules for sequencing, assembly, and genotyping. The integration of new pangenome reference coordinates and corresponding computational algorithms will benefit forensic integrative genomics by facilitating new marker identification, accurate genotyping, high-resolution panel development, and the updating of statistical algorithms. This review highlights the necessity of integrating LRS-based platforms, pangenome-based study designs, and graph-based pangenome references in short-read mapping and LRS-based innovations to achieve precision forensic science.

Single chromatin fiber profiling and nucleosome position mapping in the human brain

We apply a single-molecule chromatin fiber sequencing (Fiber-seq) protocol designed for amplification-free cell-type-specific mapping of the regulatory architecture at nucleosome resolution along extended ∼10-kb chromatin fibers to neuronal and non-neuronal nuclei sorted from human brain tissue. Specifically, application of this method enables the resolution of cell-selective promoter and enhancer architectures on single fibers, including transcription factor footprinting and position mapping, with sequence-specific fixation of nucleosome arrays flanking transcription start sites and regulatory motifs. We uncover haplotype-specific chromatin patterns, multiple regulatory elements cis-aligned on individual fibers, and accessible chromatin at 20,000 unique sites encompassing retrotransposons and other repeat sequences hitherto "unmappable" by short-read epigenomic sequencing. Overall, we show that Fiber-seq is applicable to human brain tissue, offering sharp demarcation of nucleosome-depleted regions at sites of open chromatin in conjunction with multi-kilobase nucleosomal positioning at single-fiber resolution on a genome-wide scale.

Human genetic variation reveals FCRL3 is a lymphocyte receptor for Yersinia pestis

Yersinia pestis is the gram-negative bacterium responsible for plague, one of the deadliest and most feared diseases in human history. This bacterium is known to infect phagocytic cells, such as dendritic cells and macrophages, but interactions with non-phagocytic cells of the adaptive immune system are frequently overlooked despite the importance they likely hold for human infection. To discover human genetic determinants of Y. pestis infection, we utilized nearly a thousand genetically diverse lymphoblastoid cell lines in a cellular genome-wide association study method called Hi-HOST (High-throughput Human in-vitrO Susceptibility Testing). We identified a nonsynonymous SNP, rs2282284, in Fc receptor like 3 (FCRL3) associated with bacterial invasion of host cells (p=9×10-8). FCRL3 belongs to the immunoglobulin superfamily and is primarily expressed in lymphocytes. rs2282284 is within a tyrosine-based signaling motif, causing an asparagine-to-serine mutation (N721S) in the most common FCRL3 isoform. Overexpression of FCRL3 facilitated attachment and invasion of non-opsonized Y. pestis. Additionally, FCRL3 colocalized with Y. pestis at sites of cellular attachment, suggesting FCRL3 is a receptor for Y. pestis. These properties were variably conserved across the FCRL family, revealing molecular requirements of attachment and invasion, including an Ig-like C2 domain and a SYK interaction motif. Direct binding was confirmed with purified FCRL5 extracellular domain. Following attachment, invasion of Y. pestis was dependent on SYK and decreased with the N721S mutation. Unexpectedly, this same variant is associated with risk of chronic hepatitis C virus infection in BioBank Japan. Thus, Y. pestis hijacks FCRL proteins, possibly taking advantage of an immune receptor to create a lymphocyte niche during infection.

Reconstructing the population history of the Nicobarese

The Nicobarese are the major tribal groups in the Nicobar district, situated south of the Andaman group of Islands. Linguistic phylogeny suggests that the linguistic ancestors of the Nicobarese settled the Nicobar archipelago in the early Holocene. So far, genetic research on them is low-resolution and restricted to the haploid DNA markers. Therefore, in the present analysis, we have used the high-resolution biparental (1554 published and 5 newly genotyped Nicobarese individuals) and uniparental genetic markers and looked at the genetic association of Nicobarese with the South and Southeast Asian populations. We report a common ancestral component shared among the Austroasiatic of South and Southeast Asia. Our analyses have suggested that the Nicobarese peoples retain this ancestral Austroasiatic predominant component in their genomes in the highest proportion. On the Southeast Asian mainland, the Htin Mal, who speak an Austroasiatic language of the Khmuic branch, represent a population that has preserved their ethnic distinctness from other groups over time and consequently shown the highest drift with the Nicobarese. The analysis based on haplotypes indicated a significant level of genomic segment sharing across linguistic groups, indicating an ancient broader distribution of Austroasiatic populations in Southeast Asia. Based on the temporal analyses of haploid DNA, it is suggested that the forebears of the Nicobarese people may have arrived on the Nicobar Islands in the last 5000 YBP. Therefore, among the modern populations, the Nicobarese peoples and the Htin Mal language community represent good genetic proxies for ancient Austroasiatics.

Genome-wide association studies, Polygenic Risk Scores and Mendelian randomisation: an overview of common genetic epidemiology methods for ophthalmic clinicians

Genetic information will be increasingly integrated into clinical eye care within the current generation of ophthalmologists. For monogenic diseases such as retinoblastoma, genetic studies have been relatively straightforward as these conditions result from pathogenic variants in a single gene resulting in large physiological effects. However, most eye diseases result from the cumulative effects of multiple genetic variants and environmental factors. In such diseases, because each variant usually has an individually small effect, genetic studies for complex diseases are comparatively more challenging. This article aims to provide an overview of three genetic epidemiology methods for polygenic (or complex) diseases: genome-wide association studies (GWAS), Polygenic Risk Scores (PRS) and Mendelian randomisation (MR). A GWAS systematically conducts association analyses of a trait of interest against millions of genetic variants, usually in the form of single nucleotide polymorphisms, across the genome. GWAS findings can then be used for PRS construction and MR analyses. To construct a PRS, the cumulative effect of many genetic variants associated with a trait from a prior GWAS is calculated and taken as a quantitative representation of an individual's genetic risk of a complex disease. MR studies analyse an outcome measure against the genetic variants of an exposure, and are particularly useful in investigating causal relations between two traits where randomised controlled trials are not possible or ethical. In addition to explaining the principles of these three genetic epidemiology concepts, this article provides a minimally technical description of their basic methodology that is accessible to the non-expert reader.

Somatic Recombination Between an Ancient and a Recent NOTCH2 Gene Variant Is Associated with the NOTCH2 Gain-of-Function Phenotype in Chronic Lymphocytic Leukemia

Constitutively active NOTCH2 signaling is a hallmark in chronic lymphocytic leukemia (CLL). The precise underlying defect remains obscure. Here we show that the mRNA sequence coding for the NOTCH2 negative regulatory region (NRR) is consistently deleted in CLL cells. The most common NOTCH2ΔNRR-DEL2 deletion is associated with two intronic single nucleotide variations (SNVs) which either create (CTTAT, G>A for rs2453058) or destroy (CTCGT, A>G for rs5025718) a putative splicing branch point sequence (BPS). Phylogenetic analysis demonstrates that rs2453058 is part of an ancient NOTCH2 gene variant (*1A01) which is associated with type 2 diabetes mellitus (T2DM) and is two times more frequent in Europeans than in East Asians, resembling the differences in CLL incidence. In contrast, rs5025718 belongs to a recent NOTCH2 variant (*1a4) that dominates the world outside Africa. Nanopore sequencing indicates that somatic reciprocal crossing over between rs2453058 (*1A01) and rs5025718 (*1a4) leads to recombined NOTCH2 alleles with altered BPS patterns in NOTCH2*1A01/*1a4 CLL cases. This would explain the loss of the NRR domain by aberrant pre-mRNA splicing and consequently the NOTCH2 gain-of-function phenotype. Together, our findings suggest that somatic recombination of inherited NOTCH2 variants might be relevant to CLL etiology and may at least partly explain its geographical clustering.

High-coverage nanopore sequencing of samples from the 1000 Genomes Project to build a comprehensive catalog of human genetic variation

Fewer than half of individuals with a suspected Mendelian or monogenic condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control data sets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project (1KGP) Oxford Nanopore Technologies Sequencing Consortium aims to generate LRS data from at least 800 of the 1KGP samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37× and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.

Pharmacogenomics in Sri Lanka: a comprehensive systematic review of the research landscape and clinical implications

Aim: Pharmacogenomics is emerging in South Asia, including Sri Lanka, with potential to optimize drug therapy and reduce adverse effects. This review evaluates the state of pharmacogenomics research in Sri Lanka, emphasizing population-specific factors to guide future advancements.Materials & methods: A literature search was performed across PubMed/Web-of-Science/SciVerse-Scopus/Embase, and Sri Lanka Journals Online, along with searches for relevant theses in local health repositories/university databases. Studies were categorized into clinical correlational, descriptive or novel assay development studies.Results: Eleven published articles and eight theses were included. One study examined somatic variants (KRAS gene), while all others focused on germline variants. There were two clinical correlational studies: tamoxifen adverse effects and CYP2D6 variants and FTO gene rs9939609 variants and weight gain caused by second-generation antipsychotics. Eight descriptive studies evaluated prevalence of CYP2D6 variants, HLA-B*15:02 allele, KRAS gene mutations and variants related to statin, warfarin and anticancer drug metabolism. Additionally, nine studies developed, validated and tested novel assays for detecting key pharmacogenomically important variants.Conclusion: While pharmacogenomics research in Sri Lanka has made strides, more clinical studies and broader genomic research are needed. Overcoming challenges related to funding, public awareness and regional collaboration is essential to advance personalized medicine and improve therapeutic outcomes in Sri Lanka and South Asia.

Deep vein thrombosis in response to stress induced by earthquakes in Japan: a meta-analysis of possible exacerbating factors

The principal goal of this study was to assess factors associated with deep vein thrombosis (DVT) in the aftermath of earthquakes in Japan. We searched PubMed, Google Scholar, Web of Science, and Cochrane Library for articles published in English or Japanese regarding indicators for DVT associated with Japanese earthquakes. We calculated pooled odds ratios (OR) or mean differences (MD) with 95% confidence intervals (CIs) for patients with DVT (the DVT group) as compared with the non-DVT group for potential predictors. Ultimately, 7 articles were included in the analysis, comprising 6,637 subjects (DVT, 895; and non-DVT, 5,742). The following factors proved statistically significant: female gender (OR = 1.48, 95% CI; 1.20-1.81, p = 0.0002), greater age (MD = 4.44, 95% CI; 1.62-7.25, p = 0.002), hypertension (OR = 1.44, 95% CI; 1.18-1.75, p = 0.0003), heart disease (OR = 1.45, 95% CI; 1.07-1.97, p = 0.02), previous history of DVT (OR = 9.23, 95% CI; 2.94-28.91, p = 0.0001), sleeping pill use (OR = 1.81, 95% CI; 1.37-2.38, p = 0.0001), lower leg varix (OR = 1.63, 95% CI; 1.17-2.28, p = 0.004), and soleal vein dilatation ≥ 8 mm (OR = 2.13, 95% CI; 1.32-3.42, p = 0.002). Our findings furnish insights that can aid in making informed choices about healthcare and policy in the context of earthquake-induced stress.

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

An integrative genomic toolkit for studying the genetic, evolutionary, and molecular underpinnings of eusociality in insects

While genomic resources for social insects have vastly increased over the past two decades, we are still far from understanding the genetic and molecular basis of eusociality. Here, we briefly review three scientific advancements that, when integrated, can be highly synergistic for advancing our knowledge of the genetics and evolution of eusocial traits. Population genomics provides a natural way to quantify the strength of natural selection on coding and regulatory sequences, highlighting genes that have undergone adaptive evolution during the evolution or maintenance of eusociality. Genome-wide association studies (GWAS) can be used to characterize the complex genetic architecture underlying eusocial traits and identify candidate causal variants. Concurrently, CRISPR/Cas9 enables the precise manipulation of gene function to both validate genotype-phenotype associations and study the molecular biology underlying interesting traits. While each approach has its own advantages and disadvantages, which we discuss herein, we argue that their combination will ultimately help us better understand the genetics and evolution of eusocial behavior. Specifically, by triangulating across these three different approaches, researchers can directly identify and study loci that have a causal association with key phenotypes and have evidence of positive selection over the relevant timescales associated with the evolution and maintenance of eusociality in insects.

Identifying genetic variants associated with chromatin looping and genome function

Here we present a comprehensive HiChIP dataset on naïve CD4 T cells (nCD4) from 30 donors and identify QTLs that associate with genotype-dependent and/or allele-specific variation of HiChIP contacts defining loops between active regulatory regions (iQTLs). We observe a substantial overlap between iQTLs and previously defined eQTLs and histone QTLs, and an enrichment for fine-mapped QTLs and GWAS variants. Furthermore, we describe a distinct subset of nCD4 iQTLs, for which the significant variation of chromatin contacts in nCD4 are translated into significant eQTL trends in CD4 T cell memory subsets. Finally, we define connectivity-QTLs as iQTLs that are significantly associated with concordant genotype-dependent changes in chromatin contacts over a broad genomic region (e.g., GWAS SNP in the RNASET2 locus). Our results demonstrate the importance of chromatin contacts as a complementary modality for QTL mapping and their power in identifying previously uncharacterized QTLs linked to cell-specific gene expression and connectivity.

How could our genetics impact COVID-19 vaccine response?

INTRODUCTION

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has posed unprecedented global health challenges since its emergence in December 2019. The rapid availability of vaccines has been estimated to save millions of lives, but there is variation in how individuals respond to vaccines, influencing their effectiveness at an individual, and population level.

AREAS COVERED

This review focuses on human genetic factors influencing the immune response and effectiveness of vaccines, highlighting the importance of associations across the HLA locus. Genome-Wide Association Studies (GWAS) and other genetic association analyses have identified statistically significant associations between specific HLA alleles including HLA-DRB1*13, DBQ1*06, and A*03 impacting antibody responses and the risk of breakthrough infections post-vaccination. Relationships between these associations and potential mechanisms and links with risks of natural infection or disease are explored, and this review concludes by emphasizing how understanding the mechanisms of these genetic determinants may inform the development of tailored vaccination strategies.

EXPERT OPINION

Although complex, we believe these findings from the SARS-CoV2 pandemic offer a unique opportunity to understand the relationships between HLA and infection and vaccine response, with a goal of optimizing individual protection against COVID-19 in the ongoing pandemic, and possibly influencing wider vaccine development in the future.

Imputation strategies for low-coverage whole-genome sequencing data and their effects on genomic prediction and genome-wide association studies in pigs

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31-1.04% compared to the chip data, and by 0.7-1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

Haplotype analysis identifies functional elements in monoclonal gammopathy of unknown significance

Genome-wide association studies (GWASs) based on common single nucleotide polymorphisms (SNPs) have identified several loci associated with the risk of monoclonal gammopathy of unknown significance (MGUS), a precursor condition for multiple myeloma (MM). We hypothesized that analyzing haplotypes might be more useful than analyzing individual SNPs, as it could identify functional chromosomal units that collectively contribute to MGUS risk. To test this hypothesis, we used data from our previous GWAS on 992 MGUS cases and 2910 controls from three European populations. We identified 23 haplotypes that were associated with the risk of MGUS at the genome-wide significance level (p < 5 × 10-8) and showed consistent results among all three populations. In 10 genomic regions, strong promoter, enhancer and regulatory element-related histone marks and their connections to target genes as well as genome segmentation data supported the importance of these regions in MGUS susceptibility. Several associated haplotypes affected pathways important for MM cell survival such as ubiquitin-proteasome system (RNF186, OTUD3), PI3K/AKT/mTOR (HINT3), innate immunity (SEC14L1, ZBP1), cell death regulation (BID) and NOTCH signaling (RBPJ). These pathways are important current therapeutic targets for MM, which may highlight the advantage of the haplotype approach homing to functional units.

Causal association of type 2 diabetes with central retinal artery occlusion: a Mendelian randomization study

Background

Central retinal artery occlusion (CRAO) is a medical condition characterized by sudden blockage of the central retinal artery, which leads to a significant and often irreversible loss of vision. Observational studies have indicated that diabetes mellitus is a risk factor for CRAO; however, there is no research on the causal relationship between diabetes mellitus, particularly type 2 diabetes, and CRAO. This study aimed to perform Mendelian randomization (MR) analysis to clarify the causal relationship between type 2 diabetes and CRAO.

Methods

Genetic variants associated with type 2 diabetes were selected from two different datasets. A recent genome-wide association study of CRAO conducted using the FinnGen database was used as the outcome data. A two-sample MR was performed to evaluate the causal relationship between type 2 diabetes and CRAO. Inverse variance weighting was the primary method, and MR-Egger, maximum likelihood, and median weighting were used as complementary methods. A multivariate MR (MVMR) analysis was performed to further evaluate the robustness of the results. Cochran's Q test, MR-Egger intercept test, and MR-PRESSO global test were used for the sensitivity analyses.

Results

Genetically predicted type 2 diabetes was causally associated with CRAO(odds ratio [OR] =2.108, 95% confidence interval [CI]: 1.221-3.638, P=7.423×10-3), which was consistent with the results from the validation dataset (OR=1.398, 95%CI: 1.015-1.925, P=0.040). The MVMR analysis suggested that type 2 diabetes may be an independent risk factor for CRAO (adjusted OR=1.696; 95%CI=1.150-2.500; P=7.655×10-3), which was assumed by the validation dataset (adjusted OR=1.356; 95%CI=1.015-1.812; P=0.039).

Conclusion

Our results show that genetically predicted type 2 diabetes may be causally associated with CRAO in European populations. This suggests that preventing and controlling type 2 diabetes may reduce the risk of CRAO.

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

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

Common and rare genetic variants predisposing females to unexplained recurrent pregnancy loss

Recurrent pregnancy loss (RPL) is a major reproductive health issue with multifactorial causes, affecting 2.6% of all pregnancies worldwide. Nearly half of the RPL cases lack clinically identifiable causes (e.g., antiphospholipid syndrome, uterine anomalies, and parental chromosomal abnormalities), referred to as unexplained RPL (uRPL). Here, we perform a genome-wide association study focusing on uRPL in 1,728 cases and 24,315 female controls of Japanese ancestry. We detect significant associations in the major histocompatibility complex (MHC) region at 6p21 (lead variant=rs9263738; P = 1.4 × 10-10; odds ratio [OR] = 1.51 [95% CI: 1.33-1.72]; risk allele frequency = 0.871). The MHC associations are fine-mapped to the classical HLA alleles, HLA-C*12:02, HLA-B*52:01, and HLA-DRB1*15:02 (P = 1.1 × 10-10, 1.5 × 10-10, and 1.2 × 10-9, respectively), which constitute a population-specific common long-range haplotype with a protective effect (P = 2.8 × 10-10; OR = 0.65 [95% CI: 0.57-0.75]; haplotype frequency=0.108). Genome-wide copy-number variation (CNV) calling demonstrates rare predicted loss-of-function (pLoF) variants of the cadherin-11 gene (CDH11) conferring the risk of uRPL (P = 1.3 × 10-4; OR = 3.29 [95% CI: 1.78-5.76]). Our study highlights the importance of reproductive immunology and rare variants in the uRPL etiology.

Genetic diversity analysis of Inner Mongolia cashmere goats (Erlangshan subtype) based on whole genome re-sequencing

BACKGROUND

Inner Mongolia cashmere goat (IMCG), renowned for its superior cashmere quality, is a Chinese indigenous goat breed that has been developed through natural and artificial selection over a long period. However, recently, the genetic resources of IMCGs have been significantly threatened by the introduction of cosmopolitan goat breeds and the absence of adequate breed protection systems.

RESULTS

In order to assess the conservation effectiveness of IMCGs and efficiently preserve and utilize the purebred germplasm resources, this study analyzed the genetic diversity, kinship, family structure, and inbreeding of IMCGs utilizing resequencing data from 225 randomly selected individuals analyzed using the Plink (v.1.90), GCTA (v.1.94.1), and R (v.4.2.1) software. A total of 12,700,178 high-quality SNPs were selected through quality control from 34,248,064 SNP sites obtained from 225 individuals. The average minor allele frequency (MAF), polymorphic information content (PIC), and Shannon information index (SHI) were 0.253, 0.284, and 0.530, respectively. The average observed heterozygosity (Ho) and the average expected heterozygosity (He) were 0.355 and 0.351, respectively. The analysis of the identity by state distance matrix and genomic relationship matrix has shown that most individuals' genetic distance and genetic relationship are far away, and the inbreeding coefficient is low. The family structure analysis identified 10 families among the 23 rams. A total of 14,109 runs of homozygosity (ROH) were identified in the 225 individuals, with an average ROH length of 1014.547 kb. The average inbreeding coefficient, calculated from ROH, was 0.026 for the overall population and 0.027 specifically among the 23 rams, indicating a low level of inbreeding within the conserved population.

CONCLUSIONS

The IMCGs exhibited moderate polymorphism and a low level of kinship with inbreeding occurring among a limited number of individuals. Simultaneously, it is necessary to prevent the loss of bloodline to guarantee the perpetuation of the IMCGs' germplasm resources.

Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection

Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ancestral recombination graph (ARG)-based approaches to demographic inference in typical empirical analyses are susceptible to misinference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying, and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, although it could cause misinference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion, which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid misinference due to the linked effects of selection.

The great tit HapMap project: A continental-scale analysis of genomic variation in a songbird

A major aim of evolutionary biology is to understand why patterns of genomic diversity vary within taxa and space. Large-scale genomic studies of widespread species are useful for studying how environment and demography shape patterns of genomic divergence. Here, we describe one of the most geographically comprehensive surveys of genomic variation in a wild vertebrate to date; the great tit (Parus major) HapMap project. We screened ca 500,000 SNP markers across 647 individuals from 29 populations, spanning ~30 degrees of latitude and 40 degrees of longitude - almost the entire geographical range of the European subspecies. Genome-wide variation was consistent with a recent colonisation across Europe from a South-East European refugium, with bottlenecks and reduced genetic diversity in island populations. Differentiation across the genome was highly heterogeneous, with clear 'islands of differentiation', even among populations with very low levels of genome-wide differentiation. Low local recombination rates were a strong predictor of high local genomic differentiation (FST), especially in island and peripheral mainland populations, suggesting that the interplay between genetic drift and recombination causes highly heterogeneous differentiation landscapes. We also detected genomic outlier regions that were confined to one or more peripheral great tit populations, probably as a result of recent directional selection at the species' range edges. Haplotype-based measures of selection were related to recombination rate, albeit less strongly, and highlighted population-specific sweeps that likely resulted from positive selection. Our study highlights how comprehensive screens of genomic variation in wild organisms can provide unique insights into spatio-temporal evolutionary dynamics.

Genome-wide association and polygenic risk score estimation of type 2 diabetes mellitus in Kinh Vietnamese-A pilot study

A genome-wide association study (GWAS) is a powerful tool in investigating genetic contribution, which is a crucial factor in the development of complex multifactorial diseases, such as type 2 diabetes mellitus. Type 2 diabetes mellitus is a major healthcare burden in the Western Pacific region; however, there is limited availability of genetic-associated data for type 2 diabetes in Southeast Asia, especially among the Kinh Vietnamese population. This lack of information exacerbates global healthcare disparities. In this study, 997 Kinh Vietnamese individuals (503 with type 2 diabetes and 494 controls) were prospectively recruited and their clinical and paraclinical information was recorded. DNA samples were collected and whole genome genotyping was performed. Standard quality control and genetic imputation using the 1000 Genomes database were executed. A polygenic risk score for type 2 diabetes was generated in different models using East Asian, European, and mix ancestry GWAS summary statistics as training datasets. After quality control and genetic imputation, 107 polymorphisms reached suggestive statistical significance for GWAS (≤5 × 10-6) and rs11079784 was one of the potential markers strongly associated with type 2 diabetes in the studied population. The best polygenic risk score model predicting type 2 diabetes mellitus had AUC = 0.70 (95% confidence interval = 0.62-0.77) based on a mix of ancestral GWAS summary statistics. These data show promising results for genetic association with a polygenic risk score estimation in the Kinh Vietnamese population; the results also highlight the essential role of population diversity in a GWAS of type 2 diabetes mellitus.

A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk

Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

Computing linkage disequilibrium aware genome embeddings using autoencoders

MOTIVATION

The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction.

RESULTS

We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data.

AVAILABILITY AND IMPLEMENTATION

Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Genetic variants in C1GALT1 are associated with gastric cancer risk by influencing immune infiltration

Core 1 synthase glycoprotein-N-acetylgalactosamine 3-β-galactosyltransferase 1 (C1GALT1) is known to play a critical role in the development of gastric cancer, but few studies have elucidated associations between genetic variants in C1GALT1 and gastric cancer risk. By using the genome-wide association study data from the database of Genotype and Phenotype (dbGAP), we evaluated such associations with a multivariable logistic regression model and identified that the rs35999583 G>C in C1GALT1 was associated with gastric cancer risk (odds ratio, 0.83; 95% confidence interval [CI], 0.75-0.92; P = 3.95 × 10 -4). C1GALT1 mRNA expression levels were significantly higher in gastric tumor tissues than in normal tissues, and gastric cancer patients with higher C1GALT1 mRNA levels had worse overall survival rates (hazards ratio, 1.33; 95% CI, 1.05-1.68; P log-rank = 1.90 × 10 -2). Furthermore, we found that C1GALT1 copy number differed in various immune cells and that C1GALT1 mRNA expression levels were positively correlated with the infiltrating levels of CD4 + T cells and macrophages. These results suggest that genetic variants of C1GALT1 may play an important role in gastric cancer risk and provide a new insight for C1GALT1 into a promising predictor of gastric cancer susceptibility and immune status.

Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review

BACKGROUND

Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation.

OBJECTIVE

This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets.

METHODS

We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success.

RESULTS

From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants.

CONCLUSIONS

On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.

Recent advances in the genetics of familial and sporadic ALS

ALS shows complex genetic inheritance patterns. In about 5% to 10% of cases, there is a family history of ALS or a related condition such as frontotemporal dementia in a first or second degree relative, and for about 80% of such people a pathogenic gene variant can be identified. Such variants are also seen in people with no family history because of factor influencing the expression of genes, such as age. Genetic susceptibility factors also contribute to risk, and the heritability of ALS is between 40% and 60%. The genetic variants influencing ALS risk include single base changes, repeat expansions, copy number variants, and others. Here we review what is known of the genetic landscape and architecture of ALS.

A resampling-based approach to share reference panels

For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, here we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves data attributes, such as linkage disequilibrium patterns and, to some degree, identity-by-descent sharing, allowing for genotype imputation. Our experiments on gold-standard datasets show that simulated descendants up to eight generations can serve as reference panels without substantially reducing genotype imputation accuracy.

An Investigation in the Comparability of the Exposure and Recommended Dose of Selected Pfizer Drugs in East Asian Countries: Is Mutual Usage of Clinical Data Among East Asian Countries Feasible?

The current regulatory path for new drug registration in East Asian countries has led to significant delay of the new medicines in these countries. A unified regulatory path and allowance of mutual usage of clinical data in East Asian countries would lead to cost saving in drug development and expedite the new drug registration in these countries. The objectives of the present analysis are to compare the approval dates of a selection of products developed by Pfizer in the United States and East Asian countries (China, Japan, Korea) and compare the pharmacokinetics and recommended doses of these products in East Asian countries. Eighteen products (20 drugs, 2 products with 2 combination drugs) with exposure data available in at least 2 of the 3 East Asian countries across different therapeutic areas were included in the analyses. The results showed that most products had delayed approval in East Asian countries (up to 8 years) after US or EU approval. No distinct differences were observed in the drug exposure and recommended doses for the selected products in East Asian countries. These results together with literature data of genetic similarity of the East Asian populations support the mutual usage of the clinical data in the East Asian countries for expedited regulatory submission and approval.

Low-frequency and rare genetic variants associated with rheumatoid arthritis risk

Rheumatoid arthritis (RA) has an estimated heritability of nearly 50%, which is particularly high in seropositive RA. HLA alleles account for a large proportion of this heritability, in addition to many common single-nucleotide polymorphisms with smaller individual effects. Low-frequency and rare variants, such as those captured by next-generation sequencing, can also have a large role in heritability in some individuals. Rare variant discovery has informed the development of drugs such as inhibitors of PCSK9 and Janus kinases. Some 34 low-frequency and rare variants are currently associated with RA risk. One variant (19:10352442G>C in TYK2) was identified in five separate studies, and might therefore represent a promising therapeutic target. Following a set of best practices in future studies, including studying diverse populations, using large sample sizes, validating RA and serostatus, replicating findings, adjusting for other variants and performing functional assessment, could help to ensure the relevance of identified variants. Exciting opportunities are now on the horizon for genetics in RA, including larger datasets and consortia, whole-genome sequencing and direct applications of findings in the management, and especially treatment, of RA.

Comparative assessment of the effective population size and linkage disequilibrium of Karan Fries cattle revealed viable population dynamics

OBJECTIVE

Karan Fries (KF), a high-producing composite cattle was developed through crossing indicine Tharparkar cows with taurine bulls (Holstein Friesian, Brown Swiss, and Jersey), to increase the milk yield across India. This composite cattle population must maintain sufficient genetic diversity for long-term development and breed improvement in the coming years. The level of linkage disequilibrium (LD) measures the influence of population genetic forces on the genomic structure and provides insights into the evolutionary history of populations, while the decay of LD is important in understanding the limits of genome-wide association studies for a population. Effective population size (Ne) which is genomically based on LD accumulated over the course of previous generations, is a valuable tool for e valuation of the genetic diversity and level of inbreeding. The present study was undertaken to understand KF population dynamics through the estimation of Ne and LD for the longterm sustainability of these breeds.

METHODS

The present study included 96 KF samples genotyped using Illumina HDBovine array to estimate the effective population and examine the LD pattern. The genotype data were also obtained for other crossbreds (Santa Gertrudis, Brangus, and Beefmaster) and Holstein Friesian cattle for comparison purposes.

RESULTS

The average LD between single nucleotide polymorphisms (SNPs) was r2 = 0.13 in the present study. LD decay (r2 = 0.2) was observed at 40 kb inter-marker distance, indicating a panel with 62,765 SNPs was sufficient for genomic breeding value estimation in KF cattle. The pedigree-based Ne of KF was determined to be 78, while the Ne estimates obtained using LD-based methods were 52 (SNeP) and 219 (genetic optimization for Ne estimation), respectively.

CONCLUSION

KF cattle have an Ne exceeding the FAO's minimum recommended level of 50, which was desirable. The study also revealed significant population dynamics of KF cattle and increased our understanding of devising suitable breeding strategies for longterm sustainable development.

Are some children genetically predisposed to poor sleep? A polygenic risk study in the general population

BACKGROUND

Twin studies show moderate heritability of sleep traits: 40% for insomnia symptoms and 46% for sleep duration. Genome-wide association studies (GWAS) have identified genetic variants involved in insomnia and sleep duration in adults, but it is unknown whether these variants affect sleep during early development. We assessed whether polygenic risk scores for insomnia (PRS-I) and sleep duration (PRS-SD) affect sleep throughout early childhood to adolescence.

METHODS

We included 2,458 children of European ancestry (51% girls). Insomnia-related items of the Child Behavior Checklist were reported by mothers at child's age 1.5, 3, and 6 years. At 10-15 years, the Sleep Disturbance Scale for Children and actigraphy were assessed in a subsample (N = 975). Standardized PRS-I and PRS-SD (higher scores indicate genetic susceptibility for insomnia and longer sleep duration, respectively) were computed at multiple p-value thresholds based on largest GWAS to date.

RESULTS

Children with higher PRS-I had more insomnia-related sleep problems between 1.5 and 15 years (BPRS-I < 0.001 = .09, 95% CI: 0.05; 0.14). PRS-SD was not associated with mother-reported sleep problems. A higher PRS-SD was in turn associated with longer actigraphically estimated sleep duration (BPRS-SD < 5e08 = .05, 95% CI: 0.001; 0.09) and more wake after sleep onset (BPRS-SD < 0.005 = .25, 95% CI: 0.04; 0.47) at 10-15 years, but these associations did not survive multiple testing correction.

CONCLUSIONS

Children who are genetically predisposed to insomnia have more insomnia-like sleep problems, whereas those who are genetically predisposed to longer sleep have longer sleep duration, but are also more awake during the night in adolescence. This indicates that polygenic risk for sleep traits, based on GWAS in adults, affects sleep already in children.

Phased nanopore assembly with Shasta and modular graph phasing with GFAse

Reference-free genome phasing is vital for understanding allele inheritance and the impact of single-molecule DNA variation on phenotypes. To achieve thorough phasing across homozygous or repetitive regions of the genome, long-read sequencing technologies are often used to perform phased de novo assembly. As a step toward reducing the cost and complexity of this type of analysis, we describe new methods for accurately phasing Oxford Nanopore Technologies (ONT) sequence data with the Shasta genome assembler and a modular tool for extending phasing to the chromosome scale called GFAse. We test using new variants of ONT PromethION sequencing, including those using proximity ligation, and show that newer, higher accuracy ONT reads substantially improve assembly quality.

Unravelling the Genetic Landscape of Hemiplegic Migraine: Exploring Innovative Strategies and Emerging Approaches

Migraine is a severe, debilitating neurovascular disorder. Hemiplegic migraine (HM) is a rare and debilitating neurological condition with a strong genetic basis. Sequencing technologies have improved the diagnosis and our understanding of the molecular pathophysiology of HM. Linkage analysis and sequencing studies in HM families have identified pathogenic variants in ion channels and related genes, including CACNA1A, ATP1A2, and SCN1A, that cause HM. However, approximately 75% of HM patients are negative for these mutations, indicating there are other genes involved in disease causation. In this review, we explored our current understanding of the genetics of HM. The evidence presented herein summarises the current knowledge of the genetics of HM, which can be expanded further to explain the remaining heritability of this debilitating condition. Innovative bioinformatics and computational strategies to cover the entire genetic spectrum of HM are also discussed in this review.

Nanopore sequencing of 1000 Genomes Project samples to build a comprehensive catalog of human genetic variation

Less than half of individuals with a suspected Mendelian condition receive a precise molecular diagnosis after comprehensive clinical genetic testing. Improvements in data quality and costs have heightened interest in using long-read sequencing (LRS) to streamline clinical genomic testing, but the absence of control datasets for variant filtering and prioritization has made tertiary analysis of LRS data challenging. To address this, the 1000 Genomes Project ONT Sequencing Consortium aims to generate LRS data from at least 800 of the 1000 Genomes Project samples. Our goal is to use LRS to identify a broader spectrum of variation so we may improve our understanding of normal patterns of human variation. Here, we present data from analysis of the first 100 samples, representing all 5 superpopulations and 19 subpopulations. These samples, sequenced to an average depth of coverage of 37x and sequence read N50 of 54 kbp, have high concordance with previous studies for identifying single nucleotide and indel variants outside of homopolymer regions. Using multiple structural variant (SV) callers, we identify an average of 24,543 high-confidence SVs per genome, including shared and private SVs likely to disrupt gene function as well as pathogenic expansions within disease-associated repeats that were not detected using short reads. Evaluation of methylation signatures revealed expected patterns at known imprinted loci, samples with skewed X-inactivation patterns, and novel differentially methylated regions. All raw sequencing data, processed data, and summary statistics are publicly available, providing a valuable resource for the clinical genetics community to discover pathogenic SVs.

Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction

Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available.

Effects of genetics on odor perception: Can a quick smell test effectively screen everyone?

SCENTinel, a rapid smell test designed to screen for olfactory disorders, including anosmia (no ability to smell an odor) and parosmia (distorted sense of smell), measures 4 components of olfactory function: detection, intensity, identification, and pleasantness. Each test card contains one of 9 odorant mixtures. Some people born with genetic insensitivities to specific odorants (i.e. specific anosmia) may fail the test if they cannot smell an odorant but otherwise have a normal sense of smell. However, using odorant mixtures has largely been found to prevent this from happening. To better understand whether genetic differences affect SCENTinel test results, we asked genetically informative adult participants (twins or triplets, N = 630; singletons, N = 370) to complete the SCENTinel test. A subset of twins (n = 304) also provided a saliva sample for genotyping. We examined data for differences between the 9 possible SCENTinel odors; effects of age, sex, and race on SCENTinel performance, test-retest variability; and heritability using both structured equation modeling and SNP-based statistical methods. None of these strategies provided evidence for specific anosmia for any of the odors, but ratings of pleasantness were, in part, genetically determined (h2 = 0.40) and were nominally associated with alleles of odorant receptors (e.g. OR2T33 and OR1G1; P < 0.001). These results provide evidence that using odorant mixtures protected against effects of specific anosmia for ratings of intensity but that ratings of pleasantness showed effects of inheritance, possibly informed by olfactory receptor genotypes.

A neutral comparison of algorithms to minimize L0 penalties for high-dimensional variable selection

Variable selection methods based on L0 penalties have excellent theoretical properties to select sparse models in a high-dimensional setting. There exist modifications of the Bayesian Information Criterion (BIC) which either control the familywise error rate (mBIC) or the false discovery rate (mBIC2) in terms of which regressors are selected to enter a model. However, the minimization of L0 penalties comprises a mixed-integer problem which is known to be NP-hard and therefore becomes computationally challenging with increasing numbers of regressor variables. This is one reason why alternatives like the LASSO have become so popular, which involve convex optimization problems that are easier to solve. The last few years have seen some real progress in developing new algorithms to minimize L0 penalties. The aim of this article is to compare the performance of these algorithms in terms of minimizing L0 -based selection criteria. Simulation studies covering a wide range of scenarios that are inspired by genetic association studies are used to compare the values of selection criteria obtained with different algorithms. In addition, some statistical characteristics of the selected models and the runtime of algorithms are compared. Finally, the performance of the algorithms is illustrated in a real data example concerned with expression quantitative trait loci (eQTL) mapping.

The complete diploid reference genome of RPE-1 identifies human phased epigenetic landscapes

Comparative analysis of recent human genome assemblies highlights profound sequence divergence that peaks within polymorphic loci such as centromeres. This raises the question about the adequacy of relying on human reference genomes to accurately analyze sequencing data derived from experimental cell lines. Here, we generated the complete diploid genome assembly for the human retinal epithelial cells (RPE-1), a widely used non-cancer laboratory cell line with a stable karyotype, to use as matched reference for multi-omics sequencing data analysis. Our RPE1v1.0 assembly presents completely phased haplotypes and chromosome-level scaffolds that span centromeres with ultra-high base accuracy (>QV60). We mapped the haplotype-specific genomic variation specific to this cell line including t(Xq;10q), a stable 73.18 Mb duplication of chromosome 10 translocated onto the microdeleted chromosome X telomere t(Xq;10q). Polymorphisms between haplotypes of the same genome reveals genetic and epigenetic variation for all chromosomes, especially at centromeres. The RPE-1 assembly as matched reference genome improves mapping quality of multi-omics reads originating from RPE-1 cells with drastic reduction in alignments mismatches compared to using the most complete human reference to date (CHM13). Leveraging the accuracy achieved using a matched reference, we were able to identify the kinetochore sites at base pair resolution and show unprecedented variation between haplotypes. This work showcases the use of matched reference genomes for multiomics analyses and serves as the foundation for a call to comprehensively assemble experimentally relevant cell lines for widespread application.

Genetic association studies in critically ill patients: protocol for a systematic review

INTRODUCTION

Patients in the intensive care unit (ICU) are highly heterogeneous in characteristics, their clinical course, and outcomes. Genetic variability may partly explain the variability and similarity in disease courses observed among critically ill patients and may identify clusters of subgroups. The aim of this study is to conduct a systematic review of all genetic association studies of critically ill patients with their outcomes.

METHODS AND ANALYSIS

This systematic review will be conducted and reported according to the HuGE Review Handbook V1.0. We will search PubMed, Embase, and the Cochrane Library for relevant studies. All types of genetic association studies that included acutely admitted medical and surgical adult ICU patients will be considered for this review. All studies will be selected according to predefined selection criteria, evaluated and assessed for risk of bias independently by two reviewers. Risk of bias will be assessed according to the HuGE Review Handbook V1.0 with some modifications reflecting recent insights. We will provide an overview of all included studies by reporting the characteristics of the study designs, the patients included in the studies, the genetic variables, and the outcomes evaluated.

ETHICS AND DISSEMINATION

We will use data from peer-reviewed published articles, and hence, there is no requirement for ethics approval. The results of this systematic review will be disseminated through publication in a peer-reviewed scientific journal.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021209744.

The roles, challenges, and merits of the p value

Since the 18th century, the p value has been an important part of hypothesis-based scientific investigation. As statistical and data science engines accelerate, questions emerge: to what extent are scientific discoveries based on p values reliable and reproducible? Should one adjust the significance level or find alternatives for the p value? Inspired by these questions and everlasting attempts to address them, here, we provide a systematic examination of the p value from its roles and merits to its misuses and misinterpretations. For the latter, we summarize modest recommendations to handle them. In parallel, we present the Bayesian alternatives for seeking evidence and discuss the pooling of p values from multiple studies and datasets. Overall, we argue that the p value and hypothesis testing form a useful probabilistic decision-making mechanism, facilitating causal inference, feature selection, and predictive modeling, but that the interpretation of the p value must be contextual, considering the scientific question, experimental design, and statistical principles.

Bi-level structured functional analysis for genome-wide association studies

Genome-wide association studies (GWAS) have led to great successes in identifying genotype-phenotype associations for complex human diseases. In such studies, the high dimensionality of single nucleotide polymorphisms (SNPs) often makes analysis difficult. Functional analysis, which interprets SNPs densely distributed in a chromosomal region as a continuous process rather than discrete observations, has emerged as a promising avenue for overcoming the high dimensionality challenges. However, the majority of the existing functional studies continue to be individual SNP based and are unable to sufficiently account for the intricate underpinning structures of SNP data. SNPs are often found in groups (e.g., genes or pathways) and have a natural group structure. Additionally, these SNP groups can be highly correlated with coordinated biological functions and interact in a network. Motivated by these unique characteristics of SNP data, we develop a novel bi-level structured functional analysis method and investigate disease-associated genetic variants at the SNP level and SNP group level simultaneously. The penalization technique is adopted for bi-level selection and also to accommodate the group-level network structure. Both the estimation and selection consistency properties are rigorously established. The superiority of the proposed method over alternatives is shown through extensive simulation studies. A type 2 diabetes SNP data application yields some biologically intriguing results.

A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study

Precision medicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10 000 samples with medium-depth WGS to construct a reference panel that we named the CKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases.

Inferring a directed acyclic graph of phenotypes from GWAS summary statistics

Estimating phenotype networks is a growing field in computational biology. It deepens the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package sumdag implementing the proposed method, all relevant code, and a Shiny application are available at https://github.com/chunlinli/sumdag.

xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs

Genome-wide association studies (GWAS) have identified thousands of disease-associated non-coding variants, posing urgent needs for functional interpretation. Molecular Quantitative Trait Loci (xQTLs) such as eQTLs serve as an essential intermediate link between these non-coding variants and disease phenotypes and have been widely used to discover disease-risk genes from many population-scale studies. However, mining and analyzing the xQTLs data presents several significant bioinformatics challenges, particularly when it comes to integration with GWAS data. Here, we developed xQTLbiolinks as the first comprehensive and scalable tool for bulk and single-cell xQTLs data retrieval, quality control and pre-processing from public repositories and our integrated resource. In addition, xQTLbiolinks provided a robust colocalization module through integration with GWAS summary statistics. The result generated by xQTLbiolinks can be flexibly visualized or stored in standard R objects that can easily be integrated with other R packages and custom pipelines. We applied xQTLbiolinks to cancer GWAS summary statistics as case studies and demonstrated its robust utility and reproducibility. xQTLbiolinks will profoundly accelerate the interpretation of disease-associated variants, thus promoting a better understanding of disease etiologies. xQTLbiolinks is available at https://github.com/lilab-bioinfo/xQTLbiolinks.

Bayesian multivariate genetic analysis improves translational insights

While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.

A fine mapping of single nucleotide variants and haplotype analysis of IL13 gene in patients with Leishmania guyanensis-cutaneous leishmaniasis and plasma cytokines IL-4, IL-5, and IL-13

Introduction

Leishmaniasis continues to pose a substantial health burden in 97 countries worldwide. The progression and outcome of Leishmania infection are influenced by various factors, including the cytokine milieu, the skin microbiota at the infection site, the specific Leishmania species involved, the genetic background of the host, and the parasite load. In endemic regions to leishmaniasis, only a fraction of individuals infected actually develops the disease. Overexpression of IL-13 in naturally resistant C57BL/6 mice renders them susceptible to L. major infection. Haplotypes constructed from several single nucleotide variant (SNV) along a chromosome fragment may provide insight into any SNV near the fragment that may be genuinely associated with a phenotype in genetic association studies.

Methods

We investigated nine SNVs (SNV1rs1881457A>C, SNV2rs1295687C>G, SNV3rs2069744C>T, SNV4rs2069747C>T, SNV5rs20541A>G, SNV6rs1295685A>G, SNV7rs848A>C, SNV8rs2069750G >C, and SNV9rs847T>C) spanning the entire IL13 gene in patients with L. guyanensis cutaneous leishmaniasis (Lg-CL).

Results

Our analysis did not reveal any significant association between the SNVs and susceptibility/protection against Lg-CL development. However, haplotype analysis, excluding SNV4rs2069747 and SNV8rs2069750 due to low minor allele frequency, revealed that carriers of the haplotype CCCTAAC had a 93% reduced likelihood developing Lg-CL. Similarly, the haplotypes ACCCGCT (ORadj=0.02 [95% CI 0.00-0.07]; p-value, 6.0×10-19) and AGCTAAC (ORadj=0.00[95% CI 0.00-0.00]; p-value 2.7×10-12) appeared to provide protection against the development of Lg-CL. Conversely, carriers of haplotype ACCTGCC have 190% increased likelihood of developing Lg-CL (ORadj=2.9 [95%CI 1.68-5.2]; p-value, 2.5×10-6). Similarly, haplotype ACCCAAT (ORadj=2.7 [95%CI 1.5-4.7]; p-value, 3.2×10-5) and haplotype AGCCGCC are associated with susceptibility to the development of Lg-CL (ORadj=1.7[95%CI 1.04-2.8]; p-value, 0.01). In our investigation, we also found a correlation between the genotypes of rs2069744, rs20541, rs1295685, rs847, and rs848 and plasma IL-5 levels among Lg-Cl patients. Furthermore, rs20541 showed a correlation with plasma IL-13 levels among Lg-Cl patients, while rs2069744 and rs848 showed a correlation with plasma IL-4 levels among the same group.

Conclusions

Overall, our study identifies three haplotypes of IL13 associated with resistance to disease development and three haplotypes linked to susceptibility. These findings suggest the possibility of a variant outside the gene region that may contribute, in conjunction with other genes, to differences in susceptibility and partially to the pathology.

Outer dynein arm docking complex subunit 2 polymorphism rs7893462 modulates hepatocellular carcinoma susceptibility and can serve as an overall survival biomarker for hepatitis B virus-related hepatocellular carcinoma after hepatectomy: a cohort study with a long-term follow-up

BACKGROUND

Genetic variants of outer dynein arm docking complex subunit 2 (ODAD2) have been reported to be closely associated with primary ciliary dyskinesia and colorectal cancer in previous studies, but the association of genetic variants of ODAD2 with hepatocellular carcinoma (HCC) has not been reported.

METHODS

We enrolled 80 healthy subjects and 468 Guangxi hepatitis B virus (HBV)-related HCC patients in this study. A case-control study method was used to explore the association of different ODAD2-rs7893462 genotypes with hepatocarcinogenesis. A comprehensive survival analysis was used to explore the association of rs7893462 with the prognosis of HBV-related HCC in Guangxi.

RESULTS

Through a case-control study, we observed that patients carrying the G allele of rs7893462 had a markedly increased susceptibility to hepatocarcinogenesis (odds ratio = 1.712, 95% confidence interval = 1.032-2.839, P = 0.037). We found that there were significant prognosis differences among three different genotypes of rs7893462. Nomogram analysis suggested that the contribution of rs7893462 polymorphisms to the prognosis of HBV-related HCC was second only to the BCLC stage. Stratified survival analysis suggested that the AG genotype of rs7893462 was an independent prognostic risk factor for HBV-related HCC. Joint effect survival analysis also observed that the AG genotype of rs7893462 combined with clinical parameters could significantly identify HBV-related HCC patients with different prognostic outcomes more accurately, and the AG genotype was also observed to be independent of clinical factors in HBV-related HCC survival.

CONCLUSION

The ODAD2-rs7893462 polymorphisms can be used as an independent prognostic indicator of HBV-related HCC overall survival and are significantly associated with susceptibility to hepatocarcinogenesis.