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Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS. Author summary: Identity-by-descent (IBD) segments shared from a common ancestor can be used in association mapping to identify genomic regions associated with complex traits such as height, weight and body fat distribution. In this work, we developed FiMAP, an efficient IBD mapping test that can be applied to hundreds of thousands of individuals from biobank-scale cohorts, by leveraging IBD segments from recent efficient IBD detection software programs. Built upon the classical variance component model which has its deep root in linkage analysis, FiMAP utilizes sparse linear algebra and random matrix algorithms to speed up the computation in large samples. We demonstrated accuracy, type I error control and statistical power of the FiMAP algorithm, and illustrated how IBD mapping using FiMAP could identify associations complementary to genome-wide association study findings. With the availability of an abundance of genetic data from large biobank-scale cohorts, FiMAP provides a unique angle to leverage such rich resources and better understand the genetic architecture of complex traits. [ABSTRACT FROM AUTHOR]

Motivation The positional Burrows–Wheeler transform (PBWT) has led to tremendous strides in haplotype matching on biobank-scale data. For genetic genealogical search, PBWT-based methods have optimized the asymptotic runtime of finding long matches between a query haplotype and a predefined panel of haplotypes. However, to enable fast query searches, the full-sized panel and PBWT data structures must be kept in memory, preventing existing algorithms from scaling up to modern biobank panels consisting of millions of haplotypes. In this work, we propose a space-efficient variation of PBWT named Syllable-PBWT, which divides every haplotype into syllables, builds the PBWT positional prefix arrays on the compressed syllabic panel, and leverages the polynomial rolling hash function for positional substring comparison. With the Syllable-PBWT data structures, we then present a long match query algorithm named Syllable-Query. Results Compared to the most time- and space-efficient previously published solution to the long match query problem, Syllable-Query reduced the memory use by a factor of over 100 on both the UK Biobank genotype data and the 1000 Genomes Project sequence data. Surprisingly, the smaller size of our syllabic data structures allows for more efficient iteration and CPU cache usage, granting Syllable-Query even faster runtime than existing solutions. Availability and implementation https://github.com/ZhiGroup/Syllable-PBWT Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The present study was intended to develop suitable methods for unambiguous identification and determination of ethyl (1-(diethylamino)ethylidene) phosphoramidofluoridate (known as A234-Novichok) biomarkers in urine and plasma samples. Multiple biomarkers were investigated for the first time, to verify intoxication by the A234-Novichok agent, using sensitive and accurate techniques including gas and liquid chromatography-tandem mass spectrometry (GC–MS/MS and LC–MS/MS). Like other nerve agents, in biological matrices, the A234-Novichok agent reacts with several proteins to form related adducts. Considering this, two different protein adduct biomarkers in blood samples were analyzed, and the regenerated A234 was determined. Two-dimensional chromatography and solid-phase extraction techniques were employed for blood sample preparation. Limits of detection for butyrylcholinesterase (BChE) adduct, the regenerated A234, and albumin covalent adduct were determined and reported as 1, 1, and 10 ng mL−1, while the related calibration curves were linear within the range of 2–100, 2–100, and 15–100 ng mL−1, respectively. The detection limit and linear range for the intact agent in the urine sample were determined as 0.1 and 1–100 ng mL−1, respectively. Since A234 and some other Novichok chemicals have been added to the Schedule 1 of the Chemical Weapons Convention (CWC), Annex on Chemicals, after UK incidents, the analytical methods developed in this work might be used for verification purposes, as well as OPCW Biomedical Proficiency Tests. [ABSTRACT FROM AUTHOR]

Background: The genealogical histories of individuals within populations are of interest to studies aiming both to uncover detailed pedigree information and overall quantitative population demographic histories. However, the analysis of quantitative details of individual genealogical histories has faced challenges from incomplete available pedigree records and an absence of objective and quantitative details in pedigree information. Although complete pedigree information for most individuals is difficult to track beyond a few generations, it is possible to describe a person's genealogical history using their genetic relatives revealed by identity by descent (IBD) segments—long genomic segments shared by two individuals within a population, which are identical due to inheritance from common ancestors. When modern biobanks collect genotype information for a significant fraction of a population, dense genetic connections of a person can be traced using such IBD segments, offering opportunities to characterize individuals in the context of the underlying populations. Here, we conducted an individual-centric analysis of IBD segments among the UK Biobank participants that represent 0.7% of the UK population. Results: We made a high-quality call set of IBD segments over 5 cM among all 500,000 UK Biobank participants. On average, one UK individual shares IBD segments with 14,000 UK Biobank participants, which we refer to as "relatives." Using these segments, approximately 80% of a person's genome can be imputed. We subsequently propose genealogical descriptors based on the genetic connections of relative cohorts of individuals sharing at least one IBD segment and show that such descriptors offer important information about one's genetic makeup, personal genealogical history, and social behavior. Through analysis of relative counts sharing segments at different lengths, we identified a group, potentially British Jews, who has a distinct pattern of familial expansion history. Finally, using the enrichment of relatives in one's neighborhood, we identified regional variations of personal preference favoring living closer to one's extended families. Conclusions: Our analysis revealed genetic makeup, personal genealogical history, and social behaviors at the population scale, opening possibilities for further studies of individual's genetic connections in biobank data. [ABSTRACT FROM AUTHOR]

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts. Author summary: Inferring familial relationships has a wide range of applications. Family-based genome-wide association studies and population-based GWAS both require genetic relationships. Inferring relationship is essential for unknown familial structures and can be used to correct pedigree information due to false paternity, sample switches, or unregistered adoption. Current approaches for inferring relationships are not scalable for large cohorts comprising millions of individuals. Here, we present a fast and flexible method, called RAFFI, using Identical by Descent (IBD) segments. IBD segments are uninterrupted DNA segments inherited from a common ancestor. Relationships are usually inferred by computing the kinship coefficients and the genome-wide probability of zero IBD sharing among all pairs of individuals. In the first step, we search for IBD segments using RaPID which avoids a pairwise comparison of all individuals in a haplotype panel. In the second step, we compute the kinship coefficients to infer the relationships. To make our method robust against genotyping and phasing error, the thresholds of kinship coefficients for different degrees of relatedness are adjusted. As a result, the lower detection power of IBD segments due to phasing errors or misspecification of the genotyping error rate will not comprise the inference of relationships. [ABSTRACT FROM AUTHOR]

Runs-of-homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for the efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE (runs-of-homozygous diplotype cluster enumerator), to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 single nucleotide polymorphisms (SNPs) and are shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended human leukocyte antigen (HLA) region and autoimmune disorders. We found an association between a diplotype covering the homeostatic iron regulator (HFE) gene and hemochromatosis, even though the well-known causal SNP was not directly genotyped or imputed. Using a genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase in mortality among COVID-19 patients (p-value = 1.82 × 10 -11 ). In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at a population scale., Competing Interests: AN, DZ, SZ No competing interests declared, (© 2024, Naseri et al.)