Search

Po-Ru Loh, Alkes Price and colleagues developed a fast algorithm for multicomponent, multi-trait variance-components analysis and use it to analyze the genetic architectures of schizophrenia and nine complex diseases from the PGC and GERA cohorts. Their analyses support a largely polygenic architecture for schizophrenia and significant genetic correlations for several pairs of GERA diseases.

Po-Ru Loh, Pier Francesco Palamara and Alkes Price develop a new long-range phasing method, Eagle, that harnesses long, shared identical-by-descent tracts and can be applied to large outbred populations. They use Eagle to phase samples from the UK Biobank and find that it is faster and has better accuracy than existing methods.

MultiSuSiE is an extension of the Sum of Single Effects fine-mapping model that allows causal effect sizes to vary across ancestries. Applying MultiSuSiE to quantitative traits in All of Us identifies fine-mapped variants not implicated by other methods.

Po-Ru Loh, Alkes Price and colleagues present Eagle2, a reference-based phasing algorithm that allows for highly accurate and efficient phasing of genotypes across a broad range of cohort sizes. They demonstrate an approximately 10% improvement in accuracy and 20% improvement in speed compared to a competing method, SHAPEIT2.

Alkes Price, Po-Ru Loh and colleagues report the BOLT-LMM method for mixed-model association. They apply their method to 9 quantitative traits in 23,294 samples and demonstrate that it provides improvements in computational efficiency as well as gains in power that increase with the size of the cohort, making it useful for the analysis of large cohorts.

Noah Zaitlen, Alkes Price and colleagues report a new approach to estimate the narrow-sense heritability of complex traits from unrelated individuals in a recently admixed population. They apply this approach to estimate the heritability for 13 quantitative or case-control phenotypes in 21,497 African-American individuals and suggest the inflation of family-based h² estimates.

Bogdan Pasaniuc, David Reich, Alkes Price and colleagues report analyses considering the potential of genome-wide association studies (GWAS) based on extremely low-coverage sequence data sets combined with imputation using data sets from the 1000 Genomes Project. They show with simulations and real exome-sequencing data that low-coverage sequencing can increase power for GWAS relative to genotyping arrays.

A transcriptome-wide association study integrating genome-wide association data with expression data from brain, blood and adipose tissues identifies new candidate susceptibility genes for schizophrenia, providing a step toward understanding the underlying biology.

Incorporating functional information has shown promise for improving polygenic risk prediction of complex traits. Here, the authors describe polygenic prediction method LDpred-funct, and demonstrate its utility across 21 heritable traits in the UK Biobank.

Hilary Finucane, Brendan Bulik-Sullivan, Benjamin Neale, Alkes Price and colleagues introduce a new method, called stratified LD score regression, for partitioning heritability by functional category using genome-wide association study summary statistics. They observe a substantial enrichment of heritability in conserved regions and illustrate how this approach can provide insights into the biological basis of disease and direction for functional follow-up.

Brendan Bulik-Sullivan, Benjamin Neale, Hilary Finucane, Alkes Price and colleagues introduce a new technique for estimating genetic correlation that requires only genome-wide association summary statistics and that is not biased by sample overlap. Using this method, they find genetic correlations between anorexia nervosa and schizophrenia, and between educational attainment and autism spectrum disorder.

Shamil Sunyaev and colleagues present exome sequencing methods and their applications in studies to identify the genetic basis of human complex traits. They include analyses of the whole-exome sequences of 438 individuals from across several studies.

The increased genetic diversity in populations with recent ancestry from more than one continent may help in the identification of genetic variants underlying disease risk. This Progress article discusses recent developments in methods to study complex traits in these admixed populations, including combining SNP and admixture association signals.

Alkes Price, Peter Visscher and colleagues provide recommendations on the application of mixed-linear-model association methods across a range of study designs.

Here the authors present a method to transform polygenic scores into disorder probabilities using only GWAS summary statistics, genotype data and a prior - no tuning sample is needed. The method enables individualized, well-calibrated predictions.

This study introduces pgBoost, a gradient boosting framework that predicts regulatory single-nucleotide polymorphism–gene links by combining existing linking scores from single-cell multiomics datasets and genomic distance.

This article compares the different approaches that have been developed for detecting confounding due to population stratification, family structure and cryptic relatedness, with an emphasis on the potential of mixed models for addressing these problems simultaneously.

The sc-linker is an analysis framework that combines genome-wide association study summary statistics, epigenomics and single-cell transcriptomes to identify disease-critical cell types and cellular processes across tissues and states.

Steven Gazal, Alkes Price and colleagues extend stratified LD score regression to continuous annotations. They analyze summary statistics from 56 complex diseases and traits and find that SNPs with low levels of linkage disequilibrium have larger per-SNP heritability, consistent with the action of negative selection on deleterious variants that affect complex traits.

Tissue–gene fine-mapping (TGFM) generalizes the SuSiE method to fine-map causal tissues and genes at disease loci using external eQTL data, offering improved calibration owing to modeling of cis-predicted expression uncertainty.

Here the authors identify hundreds of genetic loci linked to 36 traits in East Asians, revealing population-specific associations and emphasizing the importance of diverse ancestry groups in genome-wide association studies.

Timothy Frayling, Joel Hirschhorn, Peter Visscher and colleagues report a meta-analysis of genome-wide association studies for adult height in 253,288 individuals. They identify 697 variants in 423 loci significantly associated with adult height and find that these variants cluster in pathways involved in growth and together explain one-fifth of the heritability for this trait.

Age-dependent topic modeling provides a low-rank representation of longitudinal disease records and identifies diseases with heterogeneous comorbidity profiles, defining subtypes that exhibit distinct genome-wide and locus-specific association patterns.

The data from genome-wide association studies can be applied to genotype data to predict the phenotype of a complex trait. Here the authors discuss the potential pitfalls of such analyses and the inherent limitations of the method.

Here, the analysis of 'HapMap 3' is reported — a public data set of genomic variants in human populations. The resource integrates common and rare single nucleotide polymorphisms (SNPs) and copy number polymorphisms (CNPs) from 11 global populations, providing insights into population-specific differences among variants. It also demonstrates the feasibility of imputing newly discovered rare SNPs and CNPs.

Genome-wide analysis of human variation in 25 diverse groups from India reveals two ancient populations, genetically divergent, that are ancestral to most Indians today. Traditionally upper caste and Indo-European speakers tend to be descended from a group that is genetically close to Middle Easterners, Central Asians and Europeans. The other group, the 'Ancestral South Indians', does not appear to be close to any group outside the subcontinent.

Analysis of genotyping data for more than 150,000 individuals from the UK Biobank using long-range phase information sheds light on mechanisms of clonal haematopoiesis.

Similarities in cancers can be studied to interrogate their etiology. Here, the authors use genome-wide association study summary statistics from six cancer types based on 296,215 cases and 301,319 controls of European ancestry, showing that solid tumours arising from different tissues share a degree of common germline genetic basis.

This study analyzed data from human cells assayed using single-cell technologies, together with data associating genetic variants to disease, to identify fetal and brain cell types whose biologically critically influences the etiology of disease.

SCENT is a nonparametric method that models association between chromatin accessibility and gene expression in single-cell multimodal datasets, enabling construction of cell-type-specific enhancer–gene maps to aid mapping of candidate causal variants and genes for common diseases.

Tissue co-regulation score regression (TCSC) infers causal tissues and partitions trait heritability into tissue-specific components using a transcriptome-wide association study framework. Applying TCSC to 78 complex traits and diseases identifies biologically plausible tissue–trait relationships.

Stratified medicine promises to tailor treatment for individual patients, however it remains a major challenge to leverage genetic risk data to aid patient stratification. Here the authors introduce an approach to stratify individuals based on the aggregated impact of their genetic risk factor profiles on tissue-specific gene expression levels, and highlight its ability to identify biologically meaningful and clinically actionable patient subgroups, supporting the notion of different patient ‘biotypes’ characterized by partially distinct disease mechanisms.

Analysis of single-nucleus RNA sequencing and single-nucleus assay for transposase-accessible chromatin with sequencing data derived from synovium of patients with rheumatoid arthritis identifies regions with dynamic accessibility that correlate with cell states. Dynamic peaks are more strongly enriched for autoimmune disease heritability.

A resource of cell-type-specific IMPACT regulatory annotations improves the trans-ancestry portability of polygenic risk scores by prioritizing variants enriched for trait heritability.

Pathogenicity scores are instrumental in prioritizing variants for Mendelian disease, yet their application to common disease is largely unexplored. Here, the authors assess the utility of pathogenicity scores for 41 complex traits and develop a framework to improve their informativeness for common disease.

The CNV analysis group of the Psychiatric Genomic Consortium analyzes a large schizophrenia cohort to examine genomic copy number variants (CNVs) and disease risk. They find an enrichment of CNV burden in cases versus controls and identify 8 genome-wide significant loci as well as novel suggestive loci conferring either risk or protection to schizophrenia.

Kumarasamy Thangaraj and colleagues describe the association of a 25-bp deletion in MYBPC3 with heritable cardiomyopathies in Indian populations. They find a high prevalence (4–8%) of the deletion in surveyed Indian populations and an absence of the deletion in surveyed populations outside of Southeast Asia.

Mediated expression score regression (MESC) is a new method that estimates disease heritability mediated by the cis genetic component of gene expression levels by using summary statistics from GWAS and eQTL studies.

PolyPred and PolyPred⁺ methods that leverage fine-mapping and non-European training data significantly improve cross-population polygenic prediction accuracy when applied to diseases and complex traits in UK Biobank populations.

John Perry and colleagues report the results of a large genome-wide association study meta-analysis to identify variants influencing age at natural menopause. They identify 54 independent signals and find enrichment near genes involved in delayed puberty and DNA damage response.

Transposable elements (TE) make up a large component of the human genome and have been shown to contribute to human diseases. Here, Hormozdiari et al. estimate the contribution of TEs to the heritability of 41 complex traits and diseases and find enrichment of SINEs in blood traits.

A single-cell Poisson model is used to analyse eQTLs in memory T cells across continuous, dynamic cell states, revealing that the cell context is critical to understanding variation in eQTLs and their association with disease.

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.

Identification of the genetic differences between two different disorders has been hampered by a need for individual-level data from cases of both disorders. CC-GWAS enables the comparison of allele frequencies among cases of two disorders using case–control GWAS summary statistics.

Principal component analysis (PCA) has been a useful tool for analysis of genetic data, particularly in studies of human migration. A new study finds evidence that the observed geographic gradients, traditionally thought to represent major historical migrations, may in fact have other interpretations.