Biology subjects

Ganna, A.

Publications and source records attributed to Ganna, A..

At least 19 recordsLinked to original sources

bioRxiv2024

50,000 years of Evolutionary History of India: Insights from ~2,700 Whole Genome Sequences

India has been underrepresented in whole genome sequencing studies. We generated 2,762 high coverage genomes from India--including individuals from most geographic regions, speakers of all major languages, and tribal and caste groups--providing a comprehensive survey of genetic variation in India. With these data, we reconstruct the evolutionary history of India through space and time at fine scales. We show that most Indians derive ancestry from three ancestral groups related to ancient Iranian farmers, Eurasian Steppe pastoralists and South Asian hunter-gatherers. We uncover a common source of Iranian-related ancestry from early Neolithic cultures of Central Asia into the ancestors of Ancestral South Indians (ASI), Ancestral North Indians (ANI), Austro-asiatic-related and East Asian-related groups in India. Following these admixtures, India experienced a major demographic shift towards endogamy, resulting in extensive homozygosity and identity-by-descent sharing among individuals. At deep time scales, Indians derive around 1-2% of their ancestry through gene flow from archaic hominins, Neanderthals and Denisovans. By assembling the surviving fragments of archaic ancestry in modern Indians, we recover [~]1.5 Gb (or 50%) of the introgressing Neanderthal and [~]0.6 Gb (or 20%) of the introgressing Denisovan genomes, more than any other previous archaic ancestry study. Moreover, Indians have the largest variation in Neanderthal ancestry, as well as the highest amount of population-specific Neanderthal segments among worldwide groups. Finally, we demonstrate that most of the genetic variation in Indians stems from a single major migration out of Africa that occurred around 50,000 years ago, with minimal contribution from earlier migration waves. Together, these analyses provide a detailed view of the population history of India and underscore the value of expanding genomic surveys to diverse groups outside Europe.

evolutionary biology↗

bioRxiv2023

Characterization of cortical neurodevelopment in vitro using gene expression and morphology profiles from single cells

AbstractDifferentiation of induced pluripotent stem cells (iPSC) towards neuronal lineages has enabled diverse cellular models of human neurodevelopment and related disorders. However, in vitro differentiation is a variable process that frequently leads to heterogeneous cell populations that may confound disease-relevant phenotypes. Here, we report a thorough characterization of cortical neuron differentiation from iPSCs using joint profiling of neuronal morphology and transcriptomics in single cells. We assayed 60,000 developing neurons across three timepoints with CellPainting, a high-content imaging method, and single-cell RNA-sequencing (scRNA-seq). By modeling the relationship between morphological features and gene expression within our differentiation system, we annotated image-based features with biological functions and found that while phenotypes related to cell morphology capture broader neuronal classes than scRNA-seq, they enhance our ability to quantify the biological processes that drive neuronal differentiation over time, such as mitochondrial function and cell cycle. Further, we found that while over 60% of the cells match those seen in the fetal brain, 28% represented metabolically abnormal cell states and broader neuronal classes specific to in vitro cells. Finally, we show that specific subtypes of iPSC-derived cortical neurons are a relevant model for a range of brain-related complex traits, including schizophrenia and bipolar disorder, highlighting the potential of multi-modal single cell phenotyping for disease modeling.

genomics↗

bioRxiv2023

Polygenic risk score prediction accuracy convergence

Polygenic risk scores (PRS) trained from genome-wide association study (GWAS) results are set to play a pivotal role in biomedical research addressing multifactorial human diseases. The prospect of using these risk scores in clinical care and public health is generating both enthusiasm and controversy, with varying opinions about strengths and limitations across experts1. The performances of existing polygenic scores are still limited, and although it is expected to improve with increasing sample size of GWAS and the development of new powerful methods, it remains unclear how much prediction can be ultimately achieved. Here, we conducted a retrospective analysis to assess the progress in PRS prediction accuracy since the publication of the first large-scale GWASs using six common human diseases with sufficient GWAS data. We show that while PRS accuracy has grown rapidly for years, the improvement pace from recent GWAS has decreased substantially, suggesting that further increasing GWAS sample size may translate into very modest risk discrimination improvement. We next investigated the factors influencing the maximum achievable prediction using recently released whole genome-sequencing data from 125K UK Biobank participants, and state-of-the-art modeling of polygenic outcomes. Our analyses point toward increasing the variant coverage of PRS, using either more imputed variants or sequencing data, as a key component for future improvement in prediction accuracy.

genetics↗

bioRxiv2022

HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes

Existing methods for simulating synthetic genotype and phenotype datasets have limited scalability, constraining their usability for large-scale analyses. Moreover, a systematic approach for evaluating synthetic data quality and a benchmark synthetic dataset for developing and evaluating methods for polygenic risk scores are lacking. We present HAPNEST, a novel approach for efficiently generating diverse individual-level genotypic and phenotypic data. In comparison to alternative methods, HAPNEST shows faster computational speed and a lower degree of relatedness with reference panels, while generating datasets that preserve key statistical properties of real data. These desirable synthetic data properties enabled us to generate 6.8 million common variants and nine phenotypes with varying degrees of heritability and polygenicity across 1 million individuals. We demonstrate how HAPNEST can facilitate biobank-scale analyses through the comparison of seven methods to generate polygenic risk scoring across multiple ancestry groups and different genetic architectures.

bioinformatics↗

bioRxiv2022

The lingering effects of Neanderthal introgression on human complex traits

The mutations introduced into the ancestors of modern humans from interbreeding with Neanderthals have been suggested to contribute an unexpected extent to complex human traits. However, testing this hypothesis has been challenging due to the idiosyncratic population genetic properties of introgressed mutations. We developed rigorous methods to assess the contribution of introgressed Neanderthal mutations to heritable trait variation relative to that of modern human variants. We applied these methods to analyze 235,592 introgressed Neanderthal mutations and 96 distinct phenotypes measured in about 300,000 unrelated white British individuals in the UK Biobank. Introgressed Neanderthal mutations have a significant contribution to trait variation consistent with the polygenic architecture of complex phenotypes (contributing 0.1% of heritable variation averaged across phenotypes; p = 9.59x10-9). However, the contribution of introgressed mutations tends to be significantly depleted relative to modern human mutations matched for allele frequency and linkage disequilibrium (about 57% depletion on average), consistent with purifying selection on introgressed mutations. Different from previous studies (McArthur 2021), we find no evidence for elevated heritability across the phenotypes examined. We identified 348 independent significant associations of introgressed Neanderthal mutations with 64 phenotypes (p < 1 x10-10). Previous work (Skov 2021) has suggested that a majority of such associations are likely driven by statistical association with nearby modern human variants that are the true causal variants. We therefore developed a customized statistical fine-mapping methodology for introgressed mutations that led us to identify 112 regions (at a false discovery proportion of 16%) across 47 phenotypes containing 4,303 unique genetic variants where introgressed mutations are highly likely to have a phenotypic effect. Examination of these mutations reveal their substantial impact on genes that are important for the immune system, development, and metabolism. Our results provide the first rigorous basis for understanding how Neanderthal introgression modulates complex trait variation in present-day humans.

genetics↗

bioRxiv2022

Patterns of item nonresponse behavior to survey questionnaires are systematic and have a genetic basis

Response to survey questionnaires is vital for social and behavioral research, and most analyses assume full and accurate response by survey participants. However, nonresponse is common and impedes proper interpretation and generalizability of results. We examined item nonresponse behavior across 109 questionnaire items from the UK Biobank (UKB) (N=360,628). Phenotypic factor scores for two participant-selected nonresponse answers, "Prefer not to answer" (PNA) and "I dont know" (IDK), each predicted participant nonresponse in follow-up surveys, controlling for education and self-reported general health. We performed genome-wide association studies on these factors and identified 39 genome-wide significant loci, and further validated these effects with polygenic scores in an independent study (N=3,414), gaining information that we could not have had from phenotypic data alone. PNA and IDK were highly genetically correlated with one another and with education, health, and income, although unique genetic effects were also observed for both PNA and IDK. We discuss how these effects may bias studies of traits correlated with nonresponse and how genetic analyses can further enhance our understanding of nonresponse behaviors in survey research, for instance by helping to correct for nonresponse bias.

genetics↗

bioRxiv2021

The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia

To study how the 22q11.2 deletion predisposes to psychiatric disease, we generated induced pluripotent stem cells from deletion carriers and controls, as well as utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Upon differentiation into neural progenitor cells, we found the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including Autism Spectrum Disorder. In more differentiated excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common (per-SNP heritability p ({tau}c)= 4.2 x 10-6) and rare, loss of function variants (p = 1.29x10-12). These findings suggest a potential relationship between cellular states, developmental windows and susceptibility to psychiatric conditions with different ages of onset. To understand how the deletion contributed to these observed changes in gene expression, we developed and applied PPItools, which identifies the minimal protein-protein interaction network that best explains an observed set of gene expression alterations. We found that many of the genes in the 22q11.2 interval interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways that underlie the broader alterations in psychiatric risk gene expression we identified. Our findings suggest that the 22q11.2 deletion impacts genes and pathways that may converge with risk loci implicated by psychiatric genetic studies to influence disease manifestation in each deletion carrier.

neuroscience↗

bioRxiv2021

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.

genomics↗

bioRxiv2021

Genetically independent phenotype analysis identifies LPA and VCAM1 as drug targets for human ageing

The length and quality of life is important to us all, yet identification of promising drug targets for human ageing using genetics has had limited success. Here, we combine six large European-ancestry genome-wide association studies (GWAS) of human ageing traits--healthspan, father and mother lifespan, exceptional longevity, frailty index, and self-rated health--in a principal component framework that maximises their shared genetic architecture. The first principal component (GIP1) is more heritable than the original studies and shows strong genetic correlations with length of life as well as multiple indices of mental and physical wellbeing. We identify 27 genomic regions associated with GIP1, and provide additional, independent evidence for an effect on human ageing for loci near HTT and MAML3 using a study of Finnish and Japanese subject survival. Across the genome, GIP1 associations are enriched in genes involved in haem metabolism and pathways related to transcription, neurogenesis, homeostasis, proteolysis, intracellular signalling, immunity, and the muscle system. Finally, using proteome-wide two-sample Mendelian randomisation and colocalisation, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) (LPA) and vascular cell adhesion molecule 1 (VCAM1) on GIP1. Together, our results demonstrate that combining multiple ageing traits using genetic principal components enhances power to detect biological targets for human ageing.

genomics↗

bioRxiv2020

Genome-wide association study of school grades identifies a genetic overlap between language ability, psychopathology and creativity

Individuals with psychiatric disorders perform differently in school compared to the general population. Genetic factors contribute substantially to such differences. It is however unclear if differential performance is seen across all cognitive domains such as math and language. Here we report a genome-wide association study (GWAS) of school grades in 30,982 individuals (18,495 with and 12,487 without one or more of six major psychiatric disorders) and a replication study in 4,547 individuals. GWAS of overall school performance yielded results that were highly similar to the results of a previous GWAS of educational attainment. Analyzing subject specific grades, we observed that math performance was severely affected whereas language performance (Danish and English) was relatively unaffected or enhanced in those with psychiatric disorders compared to controls. We found that the genetic variants associated with poor math performance, but better language performance were also associated with increased risk for multiple psychiatric disorders. The same variants were also associated with creativity, which we show through a polygenic score analysis of 2953 creative professionals and 164,622 controls. The results overall suggest that risk for psychiatric disorders, language ability and creativity might have overlapping genetic roots.

genomics↗

bioRxiv2020

Genetic analyses identify widespread sex-differential participation bias

Genetic association results are often interpreted with the assumption that study participation does not affect downstream analyses. Understanding the genetic basis of this participation bias is challenging as it requires the genotypes of unseen individuals. However, we demonstrate that it is possible to estimate comparative biases by performing GWAS contrasting one subgroup versus another. For example, we show that sex exhibits autosomal heritability in the presence of sex-differential participation bias. By performing a GWAS of sex in ~3.3 million males and females, we identify over 158 autosomal loci significantly associated with sex and highlight complex traits underpinning differences in study participation between sexes. For example, the body mass index (BMI) increasing allele at the FTO locus was observed at higher frequency in males compared to females (OR 1.02 [1.02-1.03], P=4.4x10-36). Finally, we demonstrate how these biases can potentially lead to incorrect inferences in downstream analyses and propose a conceptual framework for addressing such biases. Our findings highlight a new challenge that genetic studies may face as sample sizes continue to grow.

genetics↗

bioRxiv2019

Prediction performance of a cardiovascular risk assessment tool using Stanford EHR data repository

BackgroundStratification of individuals for their risk to develop cardiovascular diseases can be used for effective prevention and intervention. A significant amount of information for risk assessment can be obtained through repurposing electronic health records (EHR). The objective of this study is to derive and assess the performance of prediction models for cardiovascular outcomes by using EHR-derived data.\n\nMethodsWe used the Stanford Medicine Research Data Repository (STARR) data from 2000-2017, containing over 2.1 million patients. A subset of 762,372 individuals with complete International Classification of Diseases (ICD) data was used to fit Cox proportional hazard models for prediction of six cardiovascular-related diseases and type 2 diabetes.\n\nResultsThe derived prediction models indicated consistent high discrimination performance (C-index) for all diseases examined: coronary artery disease (0.85), hypertension (0.82), type 2 diabetes (0.77), stroke (0.76), atrial fibrillation (0.82) and abdominal aortic aneurysm (0.77). Lower prediction abilities were observed for deep vein thrombosis (0.67). These results were consistent across age groups and maintained good prediction abilities among individuals with pre-existing diabetes or hypertension. Assessment of model calibration is ongoing.\n\nConclusionsWe proposed new prediction models for the seven diseases using ICD codes derived from EHR data. EHR data can be used for health risk assessment, but challenges related to data quality and model generalizability and calibration remain to be solved.

bioinformatics↗

bioRxiv2019

Development and validation of a next-gen health stratification engine to determine risk for multiple cardiovascular diseases

Cardiometabolic diseases (CMD) impose greater impact on every aspect of health care than any other disease group. Accurate and in-time risk assessment of individuals for their propensity to develop CMD events is one of the most critical paths in preventing these conditions. The principal objective of the present study is to report the development, and validation of a next generation risk engine to predict CMD. UK Biobank population data was used to derive predictive models for six CMD. Missing data were imputed using imputation algorithms. Cox proportional hazard models were used to estimate annual absolute risk and relative risk of different risk factors for these conditions. In addition to conventional risk factors, the applied model included socioeconomic data, lifestyle factors and comorbidities as predictors of outcomes. In total, 416,936 individuals were included in the analysis. The derived prediction models achieved consistent and moderate-to-high discrimination performance (C-index) for all diseases: coronary artery disease (0.79), hypertension (0.82), type 2 diabetes mellitus (0.87), stroke (0.79), deep vein thrombosis (0.75), and abdominal aortic aneurysm (0.90). These results were consistent across age groups (37-73 years) and showed similar predictive abilities amongst those with pre-existing diabetes or hypertension. Calibration of risk scores showed that there was moderate overestimation of CMD-related conditions only in the highest decile of risk scores for all models. In summary, the newly developed algorithms, based on Cox proportional models, resulted in high disclination and good calibration for several CMD. The integrations of these algorithms on a single platform may have direct clinical impact.

bioinformatics↗

bioRxiv2019

Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organisms function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

genomics↗

bioRxiv2018

Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies

Genetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of subsignificant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.

evolutionary biology↗

bioRxiv2018

Low-frequency variant functional architectures reveal strength of negative selection across coding and non-coding annotations

Common variant heritability is known to be concentrated in variants within cell-type-specific non-coding functional annotations, with a limited role for common coding variants. However, little is known about the functional distribution of low-frequency variant heritability. Here, we partitioned the heritability of both low-frequency (0.5% [≤] MAF < 5%) and common (MAF [≥] 5%) variants in 40 UK Biobank traits (average N = 363K) across a broad set of coding and non-coding functional annotations, employing an extension of stratified LD score regression to low-frequency variants that produces robust results in simulations. We determined that non-synonymous coding variants explain 17{+/-}1% of low-frequency variant heritability [Formula] versus only 2.1{+/-}0.2% of common variant heritability [Formula], and that regions conserved in primates explain nearly half of [Formula] (43{+/-}2%). Other annotations previously linked to negative selection, including non-synonymous variants with high PolyPhen-2 scores, non-synonymous variants in genes under strong selection, and low-LD variants, were also significantly more enriched for [Formula] as compared to [Formula]. Cell-type-specific non-coding annotations that were significantly enriched for [Formula] of corresponding traits tended to be similarly enriched for [Formula] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain DPFC explain 57{+/-}12% of [Formula] vs. 12{+/-}2% of [Formula] for neuroticism, implicating the action of negative selection on low-frequency variants affecting gene regulation in the brain. Forward simulations confirmed that the ratio of low-frequency variant enrichment vs. common variant enrichment primarily depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict the effect size variance of causal rare variants (MAF < 0.5%) in the annotation, informing their prioritization in whole-genome sequencing studies. Our results provide a deeper understanding of low-frequency variant functional architectures and guidelines for the design of association studies targeting functional classes of low-frequency and rare variants.

genetics↗

bioRxiv2017

Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle where apolipoprotein(a) (protein product of the LPA gene) is covalently attached to apolipoprotein B. Lp(a) is a highly heritable, causal risk factor for cardiovascular diseases and varies in concentrations across ancestries. To comprehensively delineate the inherited basis for plasma Lp(a), we performed deep-coverage whole genome sequencing in 8,392 individuals of European and African American ancestries. Through whole genome variant discovery and direct genotyping of all structural variants overlapping LPA, we quantified the 5.5kb kringle IV-2 copy number (KIV2-CN), a known LPA structural polymorphism, and developed a model for its imputation. Through common variant analysis, we discovered a novel locus (SORT1) associated with Lp(a)-cholesterol, and also genetic modifiers of KIV2-CN. Furthermore, in contrast to previous GWAS studies, we explain most of the heritability of Lp(a), observing Lp(a) to be 85% heritable among African Americans and 75% among Europeans, yet with notable inter-ethnic heterogeneity. Through analyses of aggregates of rare coding and non-coding variants with Lp(a)-cholesterol, we found the only genome-wide significant signal to be at a non-coding SLC22A3 intronic window also previously described to be associated with Lp(a); however, this association was mitigated by adjustment with KIV2-CN. Finally, using an additional imputation dataset (N=27,344), we performed Mendelian randomization of LPA variant classes, finding that genetically regulated Lp(a) is more strongly associated with incident cardiovascular diseases than directly measured Lp(a), and is significantly associated with measures of subclinical atherosclerosis in African Americans.

genomics↗

bioRxiv2017

Deep-coverage whole genome sequences and blood lipids among 16,324 individuals

Deep-coverage whole genome sequencing at the population level is now feasible and offers potential advantages for locus discovery, particularly in the analysis rare mutations in non-coding regions. Here, we performed whole genome sequencing in 16,324 participants from four ancestries at mean depth >29X and analyzed correlations of genotypes with four quantitative traits - plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. We conducted a discovery analysis including common or rare variants in coding as well as non-coding regions and developed a framework to interpret genome sequence for dyslipidemia risk. Common variant association yielded loci previously described with the exception of a few variants not captured earlier by arrays or imputation. In coding sequence, rare variant association yielded known Mendelian dyslipidemia genes and, in non-coding sequence, we detected no rare variant association signals after application of four approaches to aggregate variants in non-coding regions. We developed a new, genome-wide polygenic score for LDL-C and observed that a high polygenic score conferred similar effect size to a monogenic mutation (~30 mg/dl higher LDL-C for each); however, among those with extremely high LDL-C, a high polygenic score was considerably more prevalent than a monogenic mutation (23% versus 2% of participants, respectively).

genomics↗