SEQMINER: An R-Package to Facilitate the Functional Interpretation of Sequence-Based Associations

Cockayne syndrome B protein is implicated in transcription and associated chromatin dynamics in homeostatic and genotoxic conditions

The integrity of the actively transcribed genome against helix-distorting DNA lesions relies on a multilayered cellular response that enhances Transcription-Coupled Nucleotide Excision Repair (TC-NER). When defective, TC-NER is causatively associated with Cockayne-Syndrome (CS), a rare severe human progeroid disorder. Although the presence of unresolved transcription-blocking lesions is considered a driver of the aging process, the molecular features of the transcription-driven response to genotoxic stress in CS-B cells remain largely unknown. Here, an in-depth view of the transcriptional and associated chromatin dynamics that occur in CS-B cells illuminates the role of CSB therein. By employing high-throughput genome-wide approaches, we observed that absence of a functional CSB protein results in a delay in transcription progression, more positioned +1 nucleosomes, and less dynamic chromatin structure, compared to normal cells. We found that early after exposure to UV, CS-B cells released RNA polymerase II (RNAPII) from promoter-proximal pause sites into elongation. However, the magnitude of this response and the progression of RNAPII were reduced compared to normal counterparts. Notably, we detected increased post-UV retainment of unprocessed nascent RNA transcripts and chromatin-associated elongating RNAPII molecules. Contrary to the prevailing models, we found that transcription initiation is operational in CS-B fibroblasts early after UV and that chromatin accessibility showed a marginal increase. Our study provides robust evidence for the role of CSB in shaping the transcription and chromatin landscape both in homeostasis and in response to genotoxic insults, which is independent of its known role in TC-NER, and which may underlie major aspects of the CS phenotype.

eQTL Catalogue 2023: New datasets, X chromosome QTLs, and improved detection and visualisation of transcript-level QTLs

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.

A combinatorial approach to uncover an additional Integrator subunit

RNA polymerase II (RNAPII) controls expression of all protein-coding genes and most noncoding loci in higher eukaryotes. Calibrating RNAPII activity requires an assortment of polymerase-associated factors that are recruited at sites of active transcription. The Integrator complex is one of the most elusive transcriptional regulators in metazoans, deemed to be recruited after initiation to help establish and modulate paused RNAPII. Integrator is known to be composed of 14 subunits that assemble and operate in a modular fashion. We employed proteomics and machine-learning structure prediction (AlphaFold2) to identify an additional Integrator subunit, INTS15. We report that INTS15 assembles primarily with the INTS13/14/10 module and interfaces with the Int-PP2A module. Functional genomics analysis further reveals a role for INTS15 in modulating RNAPII pausing at a subset of genes. Our study shows that omics approaches combined with AlphaFold2-based predictions provide additional insights into the molecular architecture of large and dynamic multiprotein complexes.

Phosphorylated histone variant γH2Av is associated with chromatin insulators in Drosophila

Chromatin insulators are responsible for orchestrating long-range interactions between enhancers and promoters throughout the genome and align with the boundaries of Topologically Associating Domains (TADs). Here, we demonstrate an association between gypsy insulator proteins and the phosphorylated histone variant H2Av (γH2Av), normally a marker of DNA double strand breaks. Gypsy insulator components colocalize with γH2Av throughout the genome, in polytene chromosomes and in diploid cells in which Chromatin IP data shows it is enriched at TAD boundaries. Mutation of insulator components su(Hw) and Cp190 results in a significant reduction in γH2Av levels in chromatin and phosphatase inhibition strengthens the association between insulator components and γH2Av and rescues γH2Av localization in insulator mutants. We also show that γH2Av, but not H2Av, is a component of insulator bodies, which are protein condensates that form during osmotic stress. Phosphatase activity is required for insulator body dissolution after stress recovery. Together, our results implicate the H2A variant with a novel mechanism of insulator function and boundary formation.

A compendium of uniformly processed human gene expression and splicing quantitative trait loci

Many gene expression quantitative trait locus (eQTL) studies have published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization. However, technical differences between these datasets are a barrier to their widespread use. Consequently, target genes for most genome-wide association study (GWAS) signals have still not been identified. In the present study, we present the eQTL Catalogue ( https://www.ebi.ac.uk/eqtl ), a resource of quality-controlled, uniformly re-computed gene expression and splicing QTLs from 21 studies. We find that, for matching cell types and tissues, the eQTL effect sizes are highly reproducible between studies. Although most QTLs were shared between most bulk tissues, we identified a greater diversity of cell-type-specific QTLs from purified cell types, a subset of which also manifested as new disease co-localizations. Our summary statistics are freely available to enable the systematic interpretation of human GWAS associations across many cell types and tissues.

Androgen and glucocorticoid receptor direct distinct transcriptional programs by receptor-specific and shared DNA binding sites

The glucocorticoid (GR) and androgen (AR) receptors execute unique functions in vivo, yet have nearly identical DNA binding specificities. To identify mechanisms that facilitate functional diversification among these transcription factor paralogs, we studied them in an equivalent cellular context. Analysis of chromatin and sequence suggest that divergent binding, and corresponding gene regulation, are driven by different abilities of AR and GR to interact with relatively inaccessible chromatin. Divergent genomic binding patterns can also be the result of subtle differences in DNA binding preference between AR and GR. Furthermore, the sequence composition of large regions (>10 kb) surrounding selectively occupied binding sites differs significantly, indicating a role for the sequence environment in guiding AR and GR to distinct binding sites. The comparison of binding sites that are shared shows that the specificity paradox can also be resolved by differences in the events that occur downstream of receptor binding. Specifically, shared binding sites display receptor-specific enhancer activity, cofactor recruitment and changes in histone modifications. Genomic deletion of shared binding sites demonstrates their contribution to directing receptor-specific gene regulation. Together, these data suggest that differences in genomic occupancy as well as divergence in the events that occur downstream of receptor binding direct functional diversification among transcription factor paralogs.

EGR1 is a gatekeeper of inflammatory enhancers in human macrophages

Monocytes and monocyte-derived macrophages originate through a multistep differentiation process. First, hematopoietic stem cells generate lineage-restricted progenitors that eventually develop into peripheral, postmitotic monocytes. Second, blood-circulating monocytes undergo differentiation into macrophages, which are specialized phagocytic cells capable of tissue infiltration. While monocytes mediate some level of inflammation and cell toxicity, macrophages boast the widest set of defense mechanisms against pathogens and elicit robust inflammatory responses. Here, we analyze the molecular determinants of monocytic and macrophagic commitment by profiling the EGR1 transcription factor. EGR1 is essential for monopoiesis and binds enhancers that regulate monocytic developmental genes such as CSF1R However, differentiating macrophages present a very different EGR1 binding pattern. We identify novel binding sites of EGR1 at a large set of inflammatory enhancers, even in the absence of its binding motif. We show that EGR1 repressive activity results in suppression of inflammatory genes and is mediated by the NuRD corepressor complex.

Rapid and Scalable Profiling of Nascent RNA with fastGRO

Genome-wide profiling of nascent RNA has become a fundamental tool to study transcription regulation. Unlike steady-state RNA-sequencing (RNA-seq), nascent RNA profiling mirrors real-time activity of RNA polymerases and provides an accurate readout of transcriptome-wide variations. Some species of nuclear RNAs (i.e., large intergenic noncoding RNAs [lincRNAs] and eRNAs) have a short half-life and can only be accurately gauged by nascent RNA techniques. Furthermore, nascent RNA-seq detects post-cleavage RNA at termination sites and promoter-associated antisense RNAs, providing insights into RNA polymerase II (RNAPII) dynamics and processivity. Here, we present a run-on assay with 4-thio ribonucleotide (4-S-UTP) labeling, followed by reversible biotinylation and affinity purification via streptavidin. Our protocol allows streamlined sample preparation within less than 3 days. We named the technique fastGRO (fast Global Run-On). We show that fastGRO is highly reproducible and yields a more complete and extensive coverage of nascent RNA than comparable techniques can. Importantly, we demonstrate that fastGRO is scalable and can be performed with as few as 0.5 × 10⁶ cells.

Barbieri et al. developed fastGRO, a nascent RNA-sequencing technique based on nuclear run-on. Using a streamlined, under-3-days protocol, fastGRO tracks the activity of RNA polymerase for differential gene expression analysis, polymerase kinetic studies, and profiling of lowly expressed and unstable RNA species. A low-input fastGRO protocol profiles nascent RNA in as little as 0.5 × 10⁶ cells.

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.

An adaptive test for meta-analysis of rare variant association studies

Single genome-wide studies may be underpowered to detect trait-associated rare variants with moderate or weak effect sizes. As a viable alternative, meta-analysis is widely used to increase power by combining different studies. The power of meta-analysis critically depends on the underlying association patterns and heterogeneity levels, which are unknown and vary from locus to locus. However, existing methods mainly focus on one or only a few combinations of the association pattern and heterogeneity level, thus may lose power in many situations. To address this issue, we propose a general and unified framework by combining a class of tests including and beyond some existing ones, leading to high power across a wide range of scenarios. We demonstrate that the proposed test is more powerful than some existing methods in simulation studies, then show their performance with the NHLBI Exome-Sequencing Project (ESP) data. One gene (B4GALNT2) was found by our proposed test, but not by others, to be statistically significantly associated with plasma triglyceride. The signal was driven by African-ancestry subjects but it was previously reported to be associated with coronary artery disease among European-ancestry subjects. We implemented our method in an R package aSPUmeta, publicly available at https://github.com/ytzhong/metaRV and will be on CRAN soon.

The Tumor Suppressor ARID1A Controls Global Transcription via Pausing of RNA Polymerase II

AT-rich interactive domain-containing proteins 1A and 1B (ARID1A and ARID1B) are mutually exclusive subunits of the chromatin remodeler SWI/SNF. ARID1A is the most frequently mutated chromatin regulator across all cancers, and ovarian clear cell carcinoma (OCCC) carries the highest prevalence of ARID1A mutations (∼57%). Despite evidence implicating ARID1A in tumorigenesis, the mechanism remains elusive. Here, we demonstrate that ARID1A binds active regulatory elements in OCCC. Depletion of ARID1A represses RNA polymerase II (RNAPII) transcription but results in modest changes to accessibility. Specifically, pausing of RNAPII is severely impaired after loss of ARID1A. Compromised pausing results in transcriptional dysregulation of active genes, which is compensated by upregulation of ARID1B. However, a subset of ARID1A-dependent genes is not rescued by ARID1B, including many p53 and estrogen receptor (ESR1) targets. Our results provide insight into ARID1A-mediated tumorigenesis and unveil functions of SWI/SNF in modulating RNAPII dynamics.

Early chromatin shaping predetermines multipotent vagal neural crest into neural, neuronal and mesenchymal lineages

The enteric nervous system (ENS) predominantly originates from vagal neural crest cells (VNC) that emerge from the caudal hindbrain, invade the foregut and populate the gastrointestinal tract. However, the gene regulatory network (GRN) orchestrating the early specification of VNC remains unknown. Using an EdnrB enhancer, we generated a comprehensive temporal map of the chromatin and transcriptional landscape of VNC in the avian model, revealing three VNC cell clusters (neural, neurogenic and mesenchymal), each predetermined epigenetically prior to neural tube delamination. We identify and functionally validate regulatory cores (Sox10/Tfap2B/SoxB/Hbox) mediating each programme and elucidate their combinatorial activities with other spatiotemporally-specific transcription factors (bHLH/NR). Our global deconstruction of the VNC-GRN in vivo sheds light on critical early regulatory mechanisms that may influence the divergent neural phenotypes in enteric neuropathies.

Reconstruction of the Global Neural Crest Gene Regulatory Network In Vivo

Precise control of developmental processes is encoded in the genome in the form of gene regulatory networks (GRNs). Such multi-factorial systems are difficult to decode in vertebrates owing to their complex gene hierarchies and dynamic molecular interactions. Here we present a genome-wide in vivo reconstruction of the GRN underlying development of the multipotent neural crest (NC) embryonic cell population. By coupling NC-specific epigenomic and transcriptional profiling at population and single-cell levels with genome/epigenome engineering in vivo, we identify multiple regulatory layers governing NC ontogeny, including NC-specific enhancers and super-enhancers, novel trans-factors, and cis-signatures allowing reverse engineering of the NC-GRN at unprecedented resolution. Furthermore, identification and dissection of divergent upstream combinatorial regulatory codes has afforded new insights into opposing gene circuits that define canonical and neural NC fates early during NC ontogeny. Our integrated approach, allowing dissection of cell-type-specific regulatory circuits in vivo, has broad implications for GRN discovery and investigation.

Computational Analysis of HLA-presentation of Non-synonymous Recipient Mismatches Indicates Effect on the Risk of Chronic Graft-vs.-Host Disease After Allogeneic HSCT

Genetic mismatches in protein coding genes between allogeneic hematopoietic stem cell transplantation (allo-HSCT) recipient and donor can elicit an alloimmunity response via peptides presented by the recipient HLA receptors as minor histocompatibility antigens (mHAs). While the impact of individual mHAs on allo-HSCT outcome such as graft-vs.-host and graft-vs.-leukemia effects has been demonstrated, it is likely that established mHAs constitute only a small fraction of all immunogenic non-synonymous variants. In the present study, we have analyzed the genetic mismatching in 157 exome-sequenced sibling allo-HSCT pairs to evaluate the significance of polymorphic HLA class I associated peptides on clinical outcome. We applied computational mismatch estimation approaches based on experimentally verified HLA ligands available in public repositories, published mHAs, and predicted HLA-peptide affinites, and analyzed their associations with chronic graft-vs.-host disease (cGvHD) grades. We found that higher estimated recipient mismatching consistently increased the risk of severe cGvHD, suggesting that HLA-presented mismatching influences the likelihood of long-term complications in the patient. Furthermore, computational approaches focusing on estimation of HLA-presentation instead of all non-synonymous mismatches indiscriminately may be beneficial for analysis sensitivity and could help identify novel mHAs.

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

BACKGROUND

Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.

METHODS

We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.

RESULTS

Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.

CONCLUSIONS

Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.

Making Sense of the Epigenome Using Data Integration Approaches

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

The Mega2R package: R tools for accessing and processing genetic data in common formats

The standalone C++ Mega2 program has been facilitating data-reformatting for linkage and association analysis programs since 2000. Support for more analysis programs has been added over time. Currently, Mega2 converts data from several different genetic data formats (including PLINK, VCF, BCF, and IMPUTE2) into the specific data requirements for over 40 commonly-used linkage and association analysis programs (including Mendel, Merlin, Morgan, SHAPEIT, ROADTRIPS, MaCH/minimac3). Recently, Mega2 has been enhanced to use a SQLite database as an intermediate data representation. Additionally, Mega2 now stores bialleleic genotype data in a highly compressed form, like that of the GenABEL R package and the PLINK binary format. Our new Mega2R package now makes it easy to load Mega2 SQLite databases directly into R as data frames. In addition, Mega2R is memory efficient, keeping its genotype data in a compressed format, portions of which are only expanded when needed. Mega2R has functions that ease the process of applying gene-based tests by looping over genes, efficiently pulling out genotypes for variants within the desired boundaries. We have also created several more functions that illustrate how to use the data frames: these permit one to run the pedgene package to carry out gene-based association tests on family data, to run the SKAT package to carry out gene-based association tests, to output the Mega2R data as a VCF file and related files (for phenotype and family data), and to convert the data frames into GenABEL format. The Mega2R package enhances GenABEL since it supports additional input data formats (such as PLINK, VCF, and IMPUTE2) not currently supported by GenABEL. The Mega2 program and the Mega2R R package are both open source and are freely available, along with extensive documentation, from https://watson.hgen.pitt.edu/register for Mega2 and https://CRAN.R-project.org/package=Mega2R for Mega2R.

Targeted Enhancer Activation by a Subunit of the Integrator Complex

The control of cell fate is an epigenetic process initiated by transcription factors (TFs) that recognize DNA motifs and recruit activator complexes and transcriptional machineries to chromatin. Lineage specificity is thought to be provided solely by TF-motif pairing, while the recruited activators are passive. Here, we show that INTS13, a subunit of the Integrator complex, operates as monocytic/macrophagic differentiation factor. Integrator is a general activator of transcription at coding genes and is required for eRNA maturation. Here, we show that INTS13 functions as an independent sub-module and targets enhancers through Early Growth Response (EGR1/2) TFs and their co-factor NAB2. INTS13 binds poised monocytic enhancers eliciting chromatin looping and activation. Independent depletion of INTS13, EGR1, or NAB2 impairs monocytic differentiation of cell lines and primary human progenitors. Our data demonstrate that Integrator is not functionally homogeneous and has TF-specific regulatory potential, revealing a new enhancer regulatory axis that controls myeloid differentiation.

Hidden genomic MHC disparity between HLA-matched sibling pairs in hematopoietic stem cell transplantation

Matching classical HLA alleles between donor and recipient is an important factor in avoiding adverse immunological effects in HSCT. Siblings with no differences in HLA alleles, either due to identical-by-state or identical-by-descent status, are considered to be optimal donors. We carried out a retrospective genomic sequence and SNP analysis of 336 fully HLA-A, -B, -DRB1 matched and 14 partially HLA-matched sibling HSCT pairs to determine the level of undetected mismatching within the MHC segment as well as to map their recombination sites. The genomic sequence of 34 genes locating in the MHC region revealed allelic mismatching at 1 to 8 additional genes in partially HLA-matched pairs. Also, fully matched pairs were found to have mismatching either at HLA-DPB1 or at non-HLA region within the MHC segment. Altogether, 3.9% of fully HLA-matched HSCT pairs had large genomic mismatching in the MHC segment. Recombination sites mapped to certain restricted locations. The number of mismatched nucleotides correlated with the risk of GvHD supporting the central role of full HLA matching in HSCT. High-density genome analysis revealed that fully HLA-matched siblings may not have identical MHC segments and even single allelic mismatching at any classical HLA gene often implies larger genomic differences along MHC.

Exome-wide association study of plasma lipids in >300,000 individuals

We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.

Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association

To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as "winner's curse." We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p < 2.2 × 10^-6) and, consequently, substantially improves mean squared error and variant prioritization/ranking. The method is particularly helpful in adjustment for winner's curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winner's curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures.

RVTESTS: an efficient and comprehensive tool for rare variant association analysis using sequence data

MOTIVATION

Next-generation sequencing technologies have enabled the large-scale assessment of the impact of rare and low-frequency genetic variants for complex human diseases. Gene-level association tests are often performed to analyze rare variants, where multiple rare variants in a gene region are analyzed jointly. Applying gene-level association tests to analyze sequence data often requires integrating multiple heterogeneous sources of information (e.g. annotations, functional prediction scores, allele frequencies, genotypes and phenotypes) to determine the optimal analysis unit and prioritize causal variants. Given the complexity and scale of current sequence datasets and bioinformatics databases, there is a compelling need for more efficient software tools to facilitate these analyses. To answer this challenge, we developed RVTESTS, which implements a broad set of rare variant association statistics and supports the analysis of autosomal and X-linked variants for both unrelated and related individuals. RVTESTS also provides useful companion features for annotating sequence variants, integrating bioinformatics databases, performing data quality control and sample selection. We illustrate the advantages of RVTESTS in functionality and efficiency using the 1000 Genomes Project data.

AVAILABILITY AND IMPLEMENTATION

RVTESTS is available on Linux, MacOS and Windows. Source code and executable files can be obtained at https://github.com/zhanxw/rvtests

CONTACT

zhanxw@gmail.com; goncalo@umich.edu; dajiang.liu@outlook.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FREGAT: an R package for region-based association analysis

Several approaches to the region-based association analysis of quantitative traits have recently been developed and successively applied. However, no software package has been developed that implements all of these approaches for either independent or structured samples. Here we introduce FREGAT (Family REGional Association Tests), an R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis.

AVAILABILITY AND IMPLEMENTATION

https://cran.r-project.org/web/packages/FREGAT/index.html

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Online.

CONTACT

belon@bionet.nsc.ru.