Progress and promise in understanding the genetic basis of common diseases

Global and Local Ancestry and its Importance: A Review

The fastest way to significantly change the composition of a population is through admixture, an evolutionary mechanism. In animal breeding history, genetic admixture has provided both short-term and long-term advantages by utilizing the phenomenon of complementarity and heterosis in several traits and genetic diversity, respectively. The traditional method of admixture analysis by pedigree records has now been replaced greatly by genome-wide marker data that enables more precise estimations. Among these markers, SNPs have been the popular choice since they are cost-effective, not so laborious, and automation of genotyping is easy. Certain markers can suggest the possibility of a population's origin from a sample of DNA where the source individual is unknown or unwilling to disclose their lineage, which are called Ancestry-Informative Markers (AIMs). Revealing admixture level at the locus-specific level is termed as local ancestry and can be exploited to identify signs of recent selective response and can account for genetic drift. Considering the importance of genetic admixture and local ancestry, in this mini-review, both concepts are illustrated, encompassing basics, their estimation/identification methods, tools/software used and their applications.

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.

Integrating External Controls by Regression Calibration for Genome-Wide Association Study

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

Evaluation of noninvasive biospecimens for transcriptome studies

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to 'pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Potential application of elastic nets for shared polygenicity detection with adapted threshold selection

Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.

Jaw size variation is associated with a novel craniofacial function for galanin receptor 2 in an adaptive radiation of pupfishes

Understanding the genetic basis of novel adaptations in new species is a fundamental question in biology. Here we demonstrate a new role for galr2 in vertebrate craniofacial development using an adaptive radiation of trophic specialist pupfishes endemic to San Salvador Island, Bahamas. We confirmed the loss of a putative Sry transcription factor binding site upstream of galr2 in scale-eating pupfish and found significant spatial differences in galr2 expression among pupfish species in Meckel's cartilage using in situ hybridization chain reaction (HCR). We then experimentally demonstrated a novel role for Galr2 in craniofacial development by exposing embryos to Garl2-inhibiting drugs. Galr2-inhibition reduced Meckel's cartilage length and increased chondrocyte density in both trophic specialists but not in the generalist genetic background. We propose a mechanism for jaw elongation in scale-eaters based on the reduced expression of galr2 due to the loss of a putative Sry binding site. Fewer Galr2 receptors in the scale-eater Meckel's cartilage may result in their enlarged jaw lengths as adults by limiting opportunities for a circulating Galr2 agonist to bind to these receptors during development. Our findings illustrate the growing utility of linking candidate adaptive SNPs in non-model systems with highly divergent phenotypes to novel vertebrate gene functions.

Jaw size variation is associated with a novel craniofacial function for galanin receptor 2 in an adaptive radiation of pupfishes

Understanding the genetic basis of novel adaptations in new species is a fundamental question in biology that also provides an opportunity to uncover new genes and regulatory networks with potential clinical relevance. Here we demonstrate a new role for galr2 in vertebrate craniofacial development using an adaptive radiation of trophic specialist pupfishes endemic to San Salvador Island in the Bahamas. We confirmed the loss of a putative Sry transcription factor binding site in the upstream region of galr2 in scale-eating pupfish and found significant spatial differences in galr2 expression among pupfish species in Meckel's cartilage and premaxilla using in situ hybridization chain reaction (HCR). We then experimentally demonstrated a novel function for Galr2 in craniofacial development and jaw elongation by exposing embryos to drugs that inhibit Galr2 activity. Galr2-inhibition reduced Meckel's cartilage length and increased chondrocyte density in both trophic specialists but not in the generalist genetic background. We propose a mechanism for jaw elongation in scale-eaters based on the reduced expression of galr2 due to the loss of a putative Sry binding site. Fewer Galr2 receptors in the scale-eater Meckel's cartilage may result in their enlarged jaw lengths as adults by limiting opportunities for a postulated Galr2 agonist to bind to these receptors during development. Our findings illustrate the growing utility of linking candidate adaptive SNPs in non-model systems with highly divergent phenotypes to novel vertebrate gene functions.

Association of Polygenic Risk Scores for Hearing Difficulty in Older Adults With Hearing Loss in Mid-Childhood and Midlife: A Population-Based Cross-sectional Study Within the Longitudinal Study of Australian Children

Importance

Although more than 200 genes have been associated with monogenic congenital hearing loss, the polygenic contribution to hearing decline across the life course remains largely unknown.

Objective

To examine the association of polygenic risk scores (PRSs) for self-reported hearing difficulty among adults (40-69 years) with measured hearing and speech reception abilities in mid-childhood and early midlife.

Design, Setting, and Participants

This was a population-based cross-sectional study nested within the Longitudinal Study of Australian Children that included 1608 children and 1642 adults. Pure tone audiometry, speech reception threshold against noise, and genetic data were evaluated. Linear and logistic regressions of PRSs were conducted for hearing outcomes. Study analysis was performed from March 1 to 31, 2022.

Main Outcomes and Measures

Genotypes were generated from saliva or blood using global single-nucleotide polymorphisms array and PRSs derived from published genome-wide association studies of self-reported hearing difficulty (PRS1) and hearing aid use (PRS2). Hearing outcomes were continuous using the high Fletcher index (mean hearing threshold, 1, 2, and 4 kHz) and speech reception threshold (SRT); and dichotomized for bilateral hearing loss of more than 15 dB HL and abnormal SRT.

Results

Included in the study were 1608 children (mean [SD] age, 11.5 [0.5] years; 812 [50.5%] male children; 1365 [84.9%] European and 243[15.1%] non-European) and 1642 adults (mean [SD] age, 43.7 [5.1] years; 1442 [87.8%] female adults; 1430 [87.1%] European and 212 [12.9%] non-European individuals). In adults, both PRS1 and PRS2 were associated with hearing thresholds. For each SD increment in PRS1 and PRS2, hearing thresholds were 0.4 (95% CI, 0-0.8) decibel hearing level (dB HL) and 0.9 (95% CI, 0.5-1.2) dB HL higher on the high Fletcher index, respectively. Each SD increment in PRS increased the odds of adult hearing loss of more than 15 dB HL by 10% to 30% (OR for PRS1, 1.1; 95% CI, 1.0-1.3; OR for PRS2, 1.3; 95% CI, 1.1-1.5). Similar but attenuated patterns were noted in children (OR for PRS1, 1.1; 95% CI, 0.8-1.2; OR for PRS2, 1.2; 95% CI, 1.0-1.5). Both PRSs showed minimal evidence of associations with speech reception thresholds or abnormal SRT in children or adults.

Conclusions and Relevance

This population-based cross-sectional study of PRSs for self-reported hearing difficulty among adults found an association with hearing ability in mid-childhood. This adds to the evidence that age-related hearing loss begins as early as the first decade of life and that polygenic inheritance may play a role together with other environmental risk factors.

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.

Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics

Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. In the present study, we introduce sc-linker, a framework for integrating single-cell RNA-sequencing, epigenomic SNP-to-gene maps and genome-wide association study summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. The inferred disease enrichments recapitulated known biology and highlighted notable cell-disease relationships, including γ-aminobutyric acid-ergic neurons in major depressive disorder, a disease-dependent M-cell program in ulcerative colitis and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease-dependent immune cell-type programs were associated, whereas only disease-dependent epithelial cell programs were prominent, suggesting a role in disease response rather than initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

We assess contributions to autoimmune disease of genes whose regulation is driven by enhancer regions (enhancer-related) and genes that regulate other genes in trans (candidate master-regulator). We link these genes to SNPs using several SNP-to-gene (S2G) strategies and apply heritability analyses to draw three conclusions about 11 autoimmune/blood-related diseases/traits. First, several characterizations of enhancer-related genes using functional genomics data are informative for autoimmune disease heritability after conditioning on a broad set of regulatory annotations. Second, candidate master-regulator genes defined using trans-eQTL in blood are also conditionally informative for autoimmune disease heritability. Third, integrating enhancer-related and master-regulator gene sets with protein-protein interaction (PPI) network information magnified their disease signal. The resulting PPI-enhancer gene score produced >2-fold stronger heritability signal and >2-fold stronger enrichment for drug targets, compared with the recently proposed enhancer domain score. In each case, functionally informed S2G strategies produced 4.1- to 13-fold stronger disease signals than conventional window-based strategies.

GPA-Tree: statistical approach for functional-annotation-tree-guided prioritization of GWAS results

MOTIVATION

In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework.

RESULTS

First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

AVAILABILITY AND IMPLEMENTATION

The GPATree software is available at https://dongjunchung.github.io/GPATree/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

Genome-wide association studies (GWAS) provide a powerful means to identify loci and genes contributing to disease, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important for identifying pathogenic processes and developing therapeutics. Here, we introduce sc-linker, a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N= 297K). Cell type, disease progression, and cellular process programs captured distinct heritability signals even within the same cell type, as we show in multiple complex diseases that affect the brain (Alzheimer’s disease, multiple sclerosis), colon (ulcerative colitis) and lung (asthma, idiopathic pulmonary fibrosis, severe COVID-19). The inferred disease enrichments recapitulated known biology and highlighted novel cell-disease relationships, including GABAergic neurons in major depressive disorder (MDD), a disease progression M cell program in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. In autoimmune disease, both healthy and disease progression immune cell type programs were associated, whereas for epithelial cells, disease progression programs were most prominent, perhaps suggesting a role in disease progression over initiation. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.

Evidence in disease and non-disease contexts that nonsense mutations cause altered splicing via motif disruption

Transcripts containing premature termination codons (PTCs) can be subject to nonsense-associated alternative splicing (NAS). Two models have been evoked to explain this, scanning and splice motif disruption. The latter postulates that exonic cis motifs, such as exonic splice enhancers (ESEs), are disrupted by nonsense mutations. We employ genome-wide transcriptomic and k-mer enrichment methods to scrutinize this model. First, we show that ESEs are prone to disruptive nonsense mutations owing to their purine richness and paucity of TGA, TAA and TAG. The motif model correctly predicts that NAS rates should be low (we estimate 5–30%) and approximately in line with estimates for the rate at which random point mutations disrupt splicing (8–20%). Further, we find that, as expected, NAS-associated PTCs are predictable from nucleotide-based machine learning approaches to predict splice disruption and, at least for pathogenic variants, are enriched in ESEs. Finally, we find that both in and out of frame mutations to TAA, TGA or TAG are associated with exon skipping. While a higher relative frequency of such skip-inducing mutations in-frame than out of frame lends some credence to the scanning model, these results reinforce the importance of considering splice motif modulation to understand the etiology of PTC-associated disease.

Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing

Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.

A transcriptome-wide association study identifies novel susceptibility genes for psoriasis

Although more than 80 psoriasis genetic risk loci have been reported through genome-wide association studies (GWASs), the genetic mechanism of psoriasis remains unclear. To identify novel candidate genes associated with psoriasis and reveal the potential effects of genetic factors in the development of psoriasis, we conducted a transcriptome-wide association study (TWAS) based on summary statistics from GWAS of psoriasis (5175 cases and 447 089 controls) and gene expression levels from six tissues datasets (blood and skin). We identified 11 conditionally independent genes for psoriasis after Bonferroni corrections, such as the most significant genes UBLCP1 (PYFS = 2.98 × 10-16), and LCE3C (PSNSE = 9.72 × 10-12, PSSE = 6.24 × 10-12). The omnibus test identified additional 5 genes associated with psoriasis via the joint association model from multiple reference tissues. Among the 16 identified genes, 5 genes (CTSW, E1F1AD, KLRC3, FIBP, and EFEMP2) were regarded as novel genes for psoriasis. We evaluated the 16 candidate genes by querying public databases and identified 11 differentially expressed genes and 8 genes proved by the knockout mice models. Through GO enrichment analyses, we found that TWAS genes were enriched in the known GO terms associated with skin development, such as cornified envelope (P = 4.80 × 10-8) and peptide cross-linking (P = 1.50 × 10-7). Taken together, our results detected multiple novel candidate genes for psoriasis, providing clues for understanding the genetic mechanism of psoriasis.

Sclerostin Downregulation Globally by Naturally Occurring Genetic Variants, or Locally in Atherosclerotic Plaques, Does Not Associate With Cardiovascular Events in Humans

Inhibition of sclerostin increases bone formation and decreases bone resorption, leading to increased bone mass, bone mineral density, and bone strength and reduced fracture risk. In a clinical study of the sclerostin antibody romosozumab versus alendronate in postmenopausal women (ARCH), an imbalance in adjudicated serious cardiovascular (CV) adverse events driven by an increase in myocardial infarction (MI) and stroke was observed. To explore whether there was a potential mechanistic plausibility that sclerostin expression, or its inhibition, in atherosclerotic (AS) plaques may have contributed to this imbalance, sclerostin was immunostained in human plaques to determine whether it was detected in regions relevant to plaque stability in 94 carotid and 50 femoral AS plaques surgically collected from older female patients (mean age 69.6 ± 10.4 years). Sclerostin staining was absent in most plaques (67%), and when detected, it was of reduced intensity compared with normal aorta and was located in deeper regions of the plaque/wall but was not observed in areas considered relevant to plaque stability (fibrous cap and endothelium). Additionally, genetic variants associated with lifelong reduced sclerostin expression were explored for associations with phenotypes including those related to bone physiology and CV risk factors/events in a population-based phenomewide association study (PheWAS). Natural genetic modulation of sclerostin by variants with a significant positive effect on bone physiology showed no association with lifetime risk of MI or stroke. These data do not support a causal association between the presence of sclerostin, or its inhibition, in the vasculature and increased risk of serious cardiovascular events. © 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges

Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.

Transcriptome-wide association study identifies multiple genes associated with childhood body mass index

BACKGROUND

Childhood obesity is one of the most common and costly nutritional problems with high heritability. The genetic mechanism of childhood obesity remains unclear. Here, we conducted a transcriptome-wide association study (TWAS) to identify novel genes for childhood obesity.

METHODS

By integrating the GWAS summary of childhood body mass index (BMI), we conducted TWAS analyses with pre-computed gene expression weights in 39 obesity priority tissues. The GWAS summary statistics of childhood BMI were derived from the early growth genetics consortium with 35,668 children from 20 studies.

RESULTS

We identified 15 candidate genes for childhood BMI after Bonferroni corrections. The most significant gene, ADCY3, was identified in 13 tissues, including adipose, brain, and blood. Interestingly, eight genes were only identified in the specific tissue, such as FAIM2 in the brain (P = 2.04 × 10^-7) and fat mass and obesity-associated gene (FTO) in the muscle (P = 1.93 × 10^-8). Compared with the TWAS results of adult BMI, we found that one gene TUBA1B with predominant influence only on childhood BMI in the muscle (P = 1.12 × 10^-7). We evaluated the candidate genes by querying public databases and identified 12 genes functionally related to obesity phenotypes, including nine differentially expressed genes during the differentiation of human preadipocyte cells. The remaining genes (FAM150B, KNOP1, and LMBR1L) were regarded as novel candidate genes for childhood BMI.

CONCLUSIONS

Our study identified multiple candidate genes for childhood BMI, providing novel clues for understanding the genetic mechanism of childhood obesity.

Common Susceptibility Loci for Male Breast Cancer

BACKGROUND

The etiology of male breast cancer (MBC) is poorly understood. In particular, the extent to which the genetic basis of MBC differs from female breast cancer (FBC) is unknown. A previous genome-wide association study of MBC identified 2 predisposition loci for the disease, both of which were also associated with risk of FBC.

METHODS

We performed genome-wide single nucleotide polymorphism genotyping of European ancestry MBC case subjects and controls in 3 stages. Associations between directly genotyped and imputed single nucleotide polymorphisms with MBC were assessed using fixed-effects meta-analysis of 1380 cases and 3620 controls. Replication genotyping of 810 cases and 1026 controls was used to validate variants with P values less than 1 × 10-06. Genetic correlation with FBC was evaluated using linkage disequilibrium score regression, by comprehensively examining the associations of published FBC risk loci with risk of MBC and by assessing associations between a FBC polygenic risk score and MBC. All statistical tests were 2-sided.

RESULTS

The genome-wide association study identified 3 novel MBC susceptibility loci that attained genome-wide statistical significance (P < 5 × 10-08). Genetic correlation analysis revealed a strong shared genetic basis with estrogen receptor-positive FBC. Men in the top quintile of genetic risk had a fourfold increased risk of breast cancer relative to those in the bottom quintile (odds ratio = 3.86, 95% confidence interval = 3.07 to 4.87, P = 2.08 × 10-30).

CONCLUSIONS

These findings advance our understanding of the genetic basis of MBC, providing support for an overlapping genetic etiology with FBC and identifying a fourfold high-risk group of susceptible men.

A catalog of GWAS fine-mapping efforts in autoimmune disease

Genome-wide association studies (GWASs) have enabled unbiased identification of genetic loci contributing to common complex diseases. Because GWAS loci often harbor many variants and genes, it remains a major challenge to move from GWASs’ statistical associations to the identification of causal variants and genes that underlie these association signals. Researchers have applied many statistical and functional fine-mapping strategies to prioritize genetic variants and genes as potential candidates. There is no gold standard in fine-mapping approaches, but consistent results across different approaches can improve confidence in the fine-mapping findings. Here, we combined text mining with a systematic review and formed a catalog of 85 studies with evidence of fine mapping for at least one autoimmune GWAS locus. Across all fine-mapping studies, we compiled 230 GWAS loci with allelic heterogeneity estimates and predictions of causal variants and trait-relevant genes. These 230 loci included 455 combinations of locus-by-disease association signals with 15 autoimmune diseases. Using these estimates, we assessed the probability of mediating disease risk associations across genes in GWAS loci and identified robust signals of causal disease biology. We predict that this comprehensive catalog of GWAS fine-mapping efforts in autoimmune disease will greatly help distill the plethora of information in the field and inform therapeutic strategies.

Serine biosynthesis defect due to haploinsufficiency of PHGDH causes retinal disease

Macular telangiectasia type 2 (MacTel) is a progressive, late-onset retinal degenerative disease linked to decreased serum levels of serine that elevate circulating levels of a toxic ceramide species, deoxysphingolipids (deoxySLs); however, causal genetic variants that reduce serine levels in patients have not been identified. Here we identify rare, functional variants in the gene encoding the rate-limiting serine biosynthetic enzyme, phosphoglycerate dehydrogenase (PHGDH), as the single locus accounting for a significant fraction of MacTel. Under a dominant collapsing analysis model of a genome-wide enrichment analysis of rare variants predicted to impact protein function in 793 MacTel cases and 17,610 matched controls, the PHGDH gene achieves genome-wide significance (P = 1.2 × 10^-13) with variants explaining ~3.2% of affected individuals. We further show that the resulting functional defects in PHGDH cause decreased serine biosynthesis and accumulation of deoxySLs in retinal pigmented epithelial cells. PHGDH is a significant locus for MacTel that explains the typical disease phenotype and suggests a number of potential treatment options.

Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation

OBJECTIVE

To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population.

DESIGN

Retrospective, population based diagnostic evaluation.

PARTICIPANTS

49 908 people recruited to the UK Biobank with SNP chip and next generation sequencing data, and an additional 21 people who purchased consumer genetic tests and shared their data online via the Personal Genome Project.

MAIN OUTCOME MEASURES

Genotyping (that is, identification of the correct DNA base at a specific genomic location) using SNP chips versus sequencing, with results split by frequency of that genotype in the population. Rare pathogenic variants in the BRCA1 and BRCA2 genes were selected as an exemplar for detailed analysis of clinically actionable variants in the UK Biobank, and BRCA related cancers (breast, ovarian, prostate, and pancreatic) were assessed in participants through use of cancer registry data.

RESULTS

Overall, genotyping using SNP chips performed well compared with sequencing; sensitivity, specificity, positive predictive value, and negative predictive value were all above 99% for 108 574 common variants directly genotyped on the SNP chips and sequenced in the UK Biobank. However, the likelihood of a true positive result decreased dramatically with decreasing variant frequency; for variants that are very rare in the population, with a frequency below 0.001% in UK Biobank, the positive predictive value was very low and only 16% of 4757 heterozygous genotypes from the SNP chips were confirmed with sequencing data. Results were similar for SNP chip data from the Personal Genome Project, and 20/21 individuals analysed had at least one false positive rare pathogenic variant that had been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, which are individually very rare, the overall performance metrics for the SNP chips versus sequencing in the UK Biobank were: sensitivity 34.6%, specificity 98.3%, positive predictive value 4.2%, and negative predictive value 99.9%. Rates of BRCA related cancers in UK Biobank participants with a positive SNP chip result were similar to those for age matched controls (odds ratio 1.31, 95% confidence interval 0.99 to 1.71) because the vast majority of variants were false positives, whereas sequence positive participants had a significantly increased risk (odds ratio 4.05, 2.72 to 6.03).

CONCLUSIONS

SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.

Integration of genetic and clinical information to improve imputation of data missing from electronic health records

OBJECTIVE

Clinical data of patients' measurements and treatment history stored in electronic health record (EHR) systems are starting to be mined for better treatment options and disease associations. A primary challenge associated with utilizing EHR data is the considerable amount of missing data. Failure to address this issue can introduce significant bias in EHR-based research. Currently, imputation methods rely on correlations among the structured phenotype variables in the EHR. However, genetic studies have shown that many EHR-based phenotypes have a heritable component, suggesting that measured genetic variants might be useful for imputing missing data. In this article, we developed a computational model that incorporates patients' genetic information to perform EHR data imputation.

MATERIALS AND METHODS

We used the individual single nucleotide polymorphism's association with phenotype variables in the EHR as input to construct a genetic risk score that quantifies the genetic contribution to the phenotype. Multiple approaches to constructing the genetic risk score were evaluated for optimal performance. The genetic score, along with phenotype correlation, is then used as a predictor to impute the missing values.

RESULTS

To demonstrate the method performance, we applied our model to impute missing cardiovascular related measurements including low-density lipoprotein, heart failure, and aortic aneurysm disease in the electronic Medical Records and Genomics data. The integration method improved imputation's area-under-the-curve for binary phenotypes and decreased root-mean-square error for continuous phenotypes.

CONCLUSION

Compared with standard imputation approaches, incorporating genetic information offers a novel approach that can utilize more of the EHR data for better performance in missing data imputation.

Genomics and bioinformatics capacity in Africa: no continent is left behind

Despite the poor genomics research capacity in Africa, efforts have been made to empower African scientists to get involved in genomics research, particularly that involving African populations. As part of the Human Heredity and Health in Africa (H3Africa) Consortium, an initiative was set to make genomics research in Africa an African endeavor and was developed through funding from the United States' National Institutes of Health Common Fund and the Wellcome Trust. H3Africa is intended to encourage a contemporary research approach by African investigators and to stimulate the study of genomic and environmental determinants of common diseases. The goal of these endeavors is to improve the health of African populations. To build capacity for bioinformatics and genomics research, organizations such as the African Society for Bioinformatics and Computational Biology have been established. In this article, we discuss the current status of the bioinformatics infrastructure in Africa as well as the training challenges and opportunities.

The Future of Childhood Cancer Survivorship: Challenges and Opportunities for Continued Progress

As treatment evolves and the population who survive childhood cancer ages and increases in number, researchers must use novel approaches to prevent, identify and mitigate adverse effects of treatment. Future priorities include collaborative efforts to pool large cohort data to improve detection of late effects, identify late effects of novel therapies, and determine the contribution of genetic factors along with physiologic and accelerated aging among survivors. This knowledge should translate to individual risk prediction and prevention strategies. Finally, we must utilize health services research and implementation science to improve adoption of survivorship care recommendations outside of specialized pediatric oncology centers.

Validation of prostate cancer risk variants rs10993994 and rs7098889 by CRISPR/Cas9 mediated genome editing

GWAS have identified numerous SNPs associated with prostate cancer risk. One such SNP is rs10993994. It is located in the β-microseminoprotein (MSMB) promoter region, mediates MSMB prostate secretion levels, and is linked to mRNA expression changes in both MSMB and the adjacent gene NCOA4. In addition, our previous work showed a second SNP, rs7098889, is in positive linkage disequilibrium with rs10993994 and associated with MSMB expression independent of rs10993994. Here, we generate a series of clones with single alleles removed by double guide RNA (gRNA) mediated CRISPR/Cas9 deletions, through which we demonstrate that each of these SNPs independently and greatly alters MSMB expression in an allele-specific manner. We further show that these SNPs have no substantial effect on the expression of NCOA4. These data demonstrate that a single SNP can have a large effect on gene expression and illustrate the importance of functional validation studies to deconvolute observed correlations. The method we have developed is generally applicable to test any SNP for which a relevant heterozygous cell line is available. AUTHOR SUMMARY: In pursuing the underlying biological mechanism of prostate cancer pathogenesis, scientists utilized the existence of common single nucleotide polymorphisms (SNPs) in the human genome as genetic markers to perform large scale genome wide association studies (GWAS) and have so far identified more than a hundred prostate cancer risk variants. Such variants provide an unbiased and systematic new venue to study the disease mechanism, and the next big challenge is to translate these genetic associations to the causal role of altered gene function in oncogenesis. The majority of these variants are waiting to be studied and lots of them may act in oncogenesis through gene expression regulation. To prove the concept, we took rs10993994 and its linked rs7098889 as an example and engineered single cell clones by allelic-specific CRISPR/Cas9 deletion to separate the effect of each allele. We observed that a single nucleotide difference would lead to surprisingly high level of MSMB gene expression change in a gene specific and cell-type specific manner. Our study strongly supports the notion that differential level of gene expression caused by risk variants and their associated genetic locus play a major role in oncogenesis and also highlights the importance of studying the function of MSMB encoded β-MSP in prostate cancer pathogenesis.

Evaluating the informativeness of deep learning annotations for human complex diseases

Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations.

Deep learning models have shown great promise in predicting regulatory effects from DNA sequence. Here the authors evaluate sequence-based epigenomic deep learning models and conclude that these models are not yet ready to inform our knowledge of human disease.

Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits

BACKGROUND

We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci.

METHODS

We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test.

RESULTS

We identified 6 novels (CD36, PITX2, EMB, ZNF592, YPEL2, and BC043580) and 87 known loci (adaptive sum of powered score test P<5×10-9). Lead single-nucleotide polymorphism rs3211938 at CD36 was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci.

CONCLUSIONS

Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability

Regulatory variation plays a major role in complex disease and that cell type-specific binding of transcription factors (TF) is critical to gene regulation. However, assessing the contribution of genetic variation in TF-binding sites to disease heritability is challenging, as binding is often cell type-specific and annotations from directly measured TF binding are not currently available for most cell type-TF pairs. We investigate approaches to annotate TF binding, including directly measured chromatin data and sequence-based predictions. We find that TF-binding annotations constructed by intersecting sequence-based TF-binding predictions with cell type-specific chromatin data explain a large fraction of heritability across a broad set of diseases and corresponding cell types; this strategy of constructing annotations addresses both the limitation that identical sequences may be bound or unbound depending on surrounding chromatin context and the limitation that sequence-based predictions are generally not cell type-specific. We partitioned the heritability of 49 diseases and complex traits using stratified linkage disequilibrium (LD) score regression with the baseline-LD model (which is not cell type-specific) plus the new annotations. We determined that 100 bp windows around MotifMap sequenced-based TF-binding predictions intersected with a union of six cell type-specific chromatin marks (imputed using ChromImpute) performed best, with an 58% increase in heritability enrichment compared to the chromatin marks alone (11.6× vs. 7.3×, P = 9 × 10-14 for difference) and a 20% increase in cell type-specific signal conditional on annotations from the baseline-LD model (P = 8 × 10-11 for difference). Our results show that TF-binding annotations explain substantial disease heritability and can help refine genome-wide association signals.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

A polygenic biomarker to identify patients with severe hypercholesterolemia of polygenic origin

Background

Severe hypercholesterolemia (HC, LDL‐C > 4.9 mmol/L) affects over 30 million people worldwide. In this study, we validated a new polygenic risk score (PRS) for LDL‐C.

Methods

Summary statistics from the Global Lipid Genome Consortium and genotype data from two large populations were used.

Results

A 36‐SNP PRS was generated using data for 2,197 white Americans. In a replication cohort of 4,787 Finns, the PRS was strongly associated with the LDL‐C trait and explained 8% of its variability (p = 10^–41). After risk categorization, the risk of having HC was higher in the high‐ versus low‐risk group (RR = 4.17, p < 1 × 10⁻⁷). Compared to a 12‐SNP LDL‐C raising score (currently used in the United Kingdom), the PRS explained more LDL‐C variability (8% vs. 6%). Among Finns with severe HC, 53% (66/124) versus 44% (55/124) were classified as high risk by the PRS and LDL‐C raising score, respectively. Moreover, 54% of individuals with severe HC defined as low risk by the LDL‐C raising score were reclassified to intermediate or high risk by the new PRS.

Conclusion

The new PRS has a better predictive role in identifying HC of polygenic origin compared to the currently available method and can better stratify patients into diagnostic and therapeutic algorithms.

Combining genome-wide association study and FST -based approaches to identify targets of Borrelia-mediated selection in natural rodent hosts

Recent advances in high-throughput sequencing technologies provide opportunities to gain novel insights into the genetic basis of phenotypic trait variation. Yet to date, progress in our understanding of genotype-phenotype associations in nonmodel organisms in general and natural vertebrate populations in particular has been hampered by small sample sizes typically available for wildlife populations and a resulting lack of statistical power, as well as a limited ability to control for false-positive signals. Here we propose to combine a genome-wide association study (GWAS) and F_ST -based approach with population-level replication to partly overcome these limitations. We present a case study in which we used this approach in combination with genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) data to identify genomic regions associated with Borrelia afzelii resistance or susceptibility in the natural rodent host of this Lyme disease-causing spirochete, the bank vole (Myodes glareolus). Using this combined approach we identified four consensus SNPs located in exonic regions of the genes Slc26a4, Tns3, Wscd1 and Espnl, which were significantly associated with the voles' Borrelia infectious status within and across populations. Functional links between host responses to bacterial infections and most of these genes have previously been demonstrated in other rodent systems, making them promising new candidates for the study of evolutionary host responses to Borrelia emergence. Our approach is applicable to other systems and may facilitate the identification of genetic variants underlying disease resistance or susceptibility, as well as other ecologically relevant traits, in wildlife populations.

FTO Variant rs1421085 Associates With Increased Body Weight, Soft Lean Mass, and Total Body Water Through Interaction With Ghrelin and Apolipoproteins in Arab Population

Association studies have implicated single nucleotide polymorphisms (SNPs), particularly rs1421085, from the fat mass and obesity-associated (FTO) gene with body composition phenotypes, obesity, dietary intake, and physical activity in European, East Asian, and African populations. However, the impact of the rs1421085 variant has not been sufficiently tested in ethnic populations (such as Arabs) with high levels of obesity. Further, there is a lack of studies identifying biomarkers that interact with FTO. Therefore, we investigated the association of rs1421085 with obesity and body composition traits and metabolic biomarkers in Arab population. We genotyped rs1421085 SNP in 278 Arab individuals, where multiple biomarkers relating to obesity, inflammation, and other metabolic pathways were quantified. We performed genetic association tests under additive mode of inheritance using linear regression models and found association of rs1421085_C allele with higher levels of body weight, soft lean mass (SLM), and total body water. Examination (using linear regression models under dominant mode of inheritance) of correlation among biomarkers and interaction with genotypes at the variant revealed that measures of these three body composition traits were found mediated by interaction between carrier genotypes (TC+CC) and measures of ghrelin, ApoA1, and ApoB48. Lean body mass (LBM), to which SLM contributes, is an important determinant of physical strength and is a focal point in studies on sarcopenia. Low LBM is known to be associated with higher risk of cardiometabolic disorders. Thus, the finding on the FTO variant as a genetic determinant of SLM via interaction with ghrelin, ApoA1, and ApoB48 is important.

A super enhancer controls expression and chromatin architecture within the MHC class II locus

Super enhancers (SEs) play critical roles in cell type-specific gene regulation. The mechanisms by which such elements work are largely unknown. Two SEs termed DR/DQ-SE and XL9-SE are situated within the human MHC class II locus between the HLA-DRB1 and HLA-DQA1 genes and are highly enriched for disease-causing SNPs. To test the function of these elements, we used CRISPR/Cas9 to generate a series of mutants that deleted the SE. Deletion of DR/DQ-SE resulted in reduced expression of HLA-DRB1 and HLA-DQA1 genes. The SEs were found to interact with each other and the promoters of HLA-DRB1 and HLA-DQA1. DR/DQ-SE also interacted with neighboring CTCF binding sites. Importantly, deletion of DR/DQ-SE reduced the local chromatin interactions, implying that it functions as the organizer for the local three-dimensional architecture. These data provide direct mechanisms by which an MHC-II SE contributes to expression of the locus and suggest how variation in these SEs may contribute to human disease and altered immunity.

Informing disease modelling with brain-relevant functional genomic annotations

The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.

Bayesian multivariate reanalysis of large genetic studies identifies many new associations

Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.

Genome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

Translating Immunology into Therapeutic Concepts for Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) defines a spectrum of complex disorders. Understanding how environmental risk factors, alterations of the intestinal microbiota, and polygenetic and epigenetic susceptibility impact on immune pathways is key for developing targeted therapies. Mechanistic understanding of polygenic IBD is complemented by Mendelian disorders that present with IBD, pharmacological interventions that cause colitis, autoimmunity, and multiple animal models. Collectively, this multifactorial pathogenesis supports a concept of immune checkpoints that control microbial-host interactions in the gut by modulating innate and adaptive immunity, as well as epithelial and mesenchymal cell responses. In addition to classical immunosuppressive strategies, we discuss how resetting the microbiota and restoring innate immune responses, in particular autophagy and epithelial barrier function, might be key for maintaining remission or preventing IBD. Targeting checkpoints in genetically stratified subgroups of patients with Mendelian disorder-associated IBD increasingly directs treatment strategies as part of personalized medicine.

Bayesian estimation of genetic regulatory effects in high-throughput reporter assays

MOTIVATION

High-throughput reporter assays dramatically improve our ability to assign function to noncoding genetic variants, by measuring allelic effects on gene expression in the controlled setting of a reporter gene. Unlike genetic association tests, such assays are not confounded by linkage disequilibrium when loci are independently assayed. These methods can thus improve the identification of causal disease mutations. While work continues on improving experimental aspects of these assays, less effort has gone into developing methods for assessing the statistical significance of assay results, particularly in the case of rare variants captured from patient DNA.

RESULTS

We describe a Bayesian hierarchical model, called Bayesian Inference of Regulatory Differences, which integrates prior information and explicitly accounts for variability between experimental replicates. The model produces substantially more accurate predictions than existing methods when allele frequencies are low, which is of clear advantage in the search for disease-causing variants in DNA captured from patient cohorts. Using the model, we demonstrate a clear tradeoff between variant sequencing coverage and numbers of biological replicates, and we show that the use of additional biological replicates decreases variance in estimates of effect size, due to the properties of the Poisson-binomial distribution. We also provide a power and sample size calculator, which facilitates decision making in experimental design parameters.

AVAILABILITY AND IMPLEMENTATION

The software is freely available from www.geneprediction.org/bird. The experimental design web tool can be accessed at http://67.159.92.22:8080.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of intermediate-sized deletions and inference of their impact on gene expression in a human population

Background

Next-generation sequencing has allowed for the identification of different genetic variations, which are known to contribute to diseases. Of these, insertions and deletions are the second most abundant type of variations in the genome, but their biological importance or disease association is not well-studied, especially for deletions of intermediate sizes.

Methods

We identified intermediate-sized deletions from whole-genome sequencing (WGS) data of Japanese samples (n = 174) with a novel deletion calling method which considered multiple samples. These deletions were used to construct a reference panel for use in imputation. Imputation was then conducted using the reference panel and data from 82 publically available Japanese samples with gene expression data. The accuracy of the deletion calling and imputation was examined with Nanopore long-read sequencing technology. We also conducted an expression quantitative trait loci (eQTL) association analysis using the deletions to infer their functional impacts on genes, before characterizing the deletions causal for gene expression level changes.

Results

We obtained a set of polymorphic 4378 high-confidence deletions and constructed a reference panel. The deletions were successfully imputed into the Japanese samples with high accuracy (97.3%). The eQTL analysis identified 181 deletions (4.1%) suggested as causal for gene expression level changes. The causal deletion candidates were significantly enriched in promoters, super-enhancers, and transcription elongation chromatin states. Generation of deletions in a cell line with the CRISPR-Cas9 system confirmed that they were indeed causative variants for gene expression change. Furthermore, one of the deletions was observed to affect the gene expression levels of a gene it was not located in.

Conclusions

This paper reports an accurate deletion calling method for genotype imputation at the whole genome level and shows the importance of intermediate-sized deletions in the human population.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0656-4) contains supplementary material, which is available to authorized users.

Evaluation of the Association of Omentin 1 rs2274907 A>T and rs2274908 G>A Gene Polymorphisms with Coronary Artery Disease in Indian Population: A Case Control Study

Coronary artery disease (CAD) is a major cause of death all over the world. CAD is caused by atherosclerosis which is induced by the interaction of genetic factors and environmental factors. Traditional environmental risk factors include hyperlipidemia, diabetes mellitus, lack of exercise, obesity, poor diet and others. Genome-wide association studies have revealed the association of certain gene polymorphisms with susceptibility to CAD. Omentin 1 is an adipokine secreted by the visceral adipose tissues and has been reported to have anti-inflammatory, cardioprotective, and enhances insulin sensitivity. In this study, we examined the role of omentin-1 common single nucleotide polymorphisms (SNPs) (rs2274907 A>T and rs2274908 G>A) in CAD. We genotyped 100 CAD patients and 100 matched healthy controls from the south Indian population using an amplification refractory mutation system (ARMS-PCR) and allele-specific PCR (AS-PCR). Our result indicated the rs2274908 G>A is not associated with CAD. Results showed that there was a significant difference in rs2274907 A>T genotype distribution between controls and CAD cases (P-value < 0.05). Results indicated that the AT genotype of the rs2274907 is associated with CAD with OR = 3.0 (95% confidence interval (CI), 1.64 to 5.49), 1.65 (1.27 to 2.163), P = 0.002. The T allele of the rs2274907 was also associated with CAD with OR = 1.82 (95% CI, 1.193 to 2.80), 1.37 (1.08 to 1.74), P = 0.005. Rs2274907 genotype distribution was also correlated with serum total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), hypertension and diabetes. We conclude that the AT genotype and the T allele of the rs2274907 A>T is associated with Cad in the south Indian population. Further studies on the effect of the rs2274907 A>T on omentin-1 function are recommended, and future well-designed studies with larger sample sizes and in different populations are required to validate our findings.

Progress in Polygenic Composite Scores in Alzheimer's and Other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies.

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians - A Machine Learning Approach

Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied. Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians. Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations. Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions. Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.

Enriched power of disease-concordant twin-case-only design in detecting interactions in genome-wide association studies

Genetic interaction is a crucial issue in the understanding of functional pathways underlying complex diseases. However, detecting such interaction effects is challenging in terms of both methodology and statistical power. We address this issue by introducing a disease-concordant twin-case-only design, which applies to both monozygotic and dizygotic twins. To investigate the power, we conducted a computer simulation study by setting a series of parameter schemes with different minor allele frequencies and relative risks. Results from the simulation study reveals that the disease-concordant twin-case-only design largely reduces sample size required for sufficient power compared to the ordinary case-only design for detecting gene-gene interaction using unrelated individuals. Sample sizes for dizygotic and monozygotic twins were roughly 1/2 and 1/4 of sample sizes in the ordinary case-only design. Since dizygotic twins are genetically similar as siblings, the enriched power for dizygotic twins also applies to affected siblings, which could help to largely extend the application of the powerful twin-case-only design. In summary, our simulation reveals high value of disease-concordant twins and siblings in efficiently detecting gene-by-gene interactions.

Bayesian meta-analysis across genome-wide association studies of diverse phenotypes

Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for a range of possible true patterns of association across studies in a computationally efficient framework.