Since the initial release of the human genome sequence 20 years ago, human chromosomes have remained unfinished due to large regions of highly identical repeats clustered within centromeres, regions of segmental duplication, and the acrocentric short arms of chromosomes. However, recent advances in long-read sequencing technologies and associated algorithms have now made it possible to systematically assemble these regions from native DNA for the first time. 

In this session, we will present the first complete sequence of a human genome and provide an in-depth look at the newly resolved regions, their variation across individuals, and the resulting impact on human health, disease, and evolution. Our first speaker is a co-lead of the Telomere-to-Telomere (T2T) Consortium (https://sites.google.com/ucsc.edu/t2tworkinggroup), and he will introduce the session by unveiling the complete human genome and explaining the efforts to sequence, assemble, and validate the genome assembly. Our second speaker will present the genetic and epigenetic maps of all human centromeric regions and discuss their evolution across the hominid phylogeny over the last 25 million years. Our third speaker will focus on the segmental duplications found within the genome and discuss their transcriptional and epigenetic status. Finally, our fourth speaker will present the human methylome, with a particular focus on the epigenetic profile of newly resolved regions. At the end of the session, we will host a panel discussion to allow for a Q&A between the audience and each of our four speakers.

Recorded session from the 2021 virtual meeting.

A resounding call for increased workforce diversity has been made in the genomics research community in recent years (Green et al. 2020; Channaoui et al. 2020). Recognizing the lack of diversity in both research participants and in the genomics workforce, workforce diversity initiatives strive to train and retain diverse members of the scientific community such that scientific fields are more inclusive and better represent racial, ethnic, sexual, gender minority, and differently abled groups. The NHGRI 2020 Strategic Vision, for instance, articulates how building a diverse genomics workforce will be a key priority “to promote workforce diversity, leadership in the field, and inclusion practices.” A broad literature demonstrates the lack of diversity among NIH funded investigators, even as research has demonstrated that researchers from underrepresented groups develop novel scientific projects at higher rates (Hofstra et al. 2020). 

This panel will examine workforce diversity initiatives and practices that aim to redress inequities that have excluded underrepresented and communities of color from the genomics leadership and workforce more broadly. Drawing on empirical cases and the experiences and perspectives of researchers and program leaders on initiatives aimed at increasing diversity and inclusion in the field, this panel will discuss how definitions of diversity, commitments to diverse experiences, distribution of resources and infrastructures, and professional networks directly impact equitable diversification of the workforce. This panel will consider how an equity framework can be brought to bear on questions of what workforce diversity efforts can and should accomplish, who should be responsible for such initiatives, and what sustainable/lasting commitment to workforce diversity means for the genomics community moving forward.

Recorded session from the 2021 virtual meeting.

Many polygenic risk scores (PRS) have been published with an eye towards clinical implementation. However, little work has been done on the social and ethical considerations of calculating and returning PRS, particularly across genetic ancestral backgrounds. 

This session reports findings from embedded ELSI studies examining social and ethical considerations of returning clinical PRS across diverse populations. The panel will advance our understanding of critical issues that must be addressed to maximize potential benefits of clinical PRS. Following an introduction to the topic, Maya Sabatello describes the views of patients, clinicians and IRB members about challenges translating PRS research into improved care and strategies to promote health equity. Broadening our understanding of variation in stakeholder views, Sabrina Suckiel highlights English- and Spanish- speaking patients’ perceptions of clinical utility of PRS, preferences regarding return of information and potential barriers to uptake. 

The format of PRS results can impact patient and provider understanding of risk and responsiveness to corresponding recommendations. Anna Lewis discusses research on stakeholder preferences regarding various formats of return and the potential impacts on use and understanding. Finally, Ellen Clayton presents data on the role of patient education to ensure researchers understand racial/ethnic minority views on clinical PRS. Following discussion, closing remarks will highlight the utility of embedded ELSI projects within large-scale PRS or genomic studies and offer recommendations for future research. These studies, embedded in the Electronic Medical Records and Genomics (eMERGE) IV Network, were designed to inform return of actionable PRS for common complex diseases to patients and their healthcare providers.

Recorded session from the 2021 virtual meeting. 

This session will focus on opportunities, challenges and ethical issues related to studying ethnically diverse populations to improve discovery and health equity in genomic medicine. Here we gather scientists from across the globe with experience in conducting genomic research in populations under-represented in human genetics research. 

Speakers will address research on genetic risk factors for medical phenotypes of particular relevance to the study populations. We will also discuss ethical, social, and legal issues (ELSI) that arise when conducting genomic research in Indigenous communities, ways in which we can achieve more inclusive and equitable research, and ensure benefit sharing. We will have four 15-minute presentations followed by a 30 minute panel discussion. 

Our session will start with a presentation discussing studies of pharmacogenetic variation in Indigenous peoples from South America and implications for personalized medicine in these populations. Our second speaker will describe results of a multi ethnic genome wide association study (GWAS) of Differentiated Thyroid Cancer (DTC) in Melanesians from New Caledonia and Polynesians from French Polynesian, two populations with the highest incidence of DTC worldwide. The speaker will illustrate the impact of genetic studies of DTC risk on community health in Oceanian populations. The next speaker will follow on the promising future for genetic discovery that can be achieved by studying African populations that have high levels of genomic and phenotypic diversity. This speaker will also illustrate how the study of ethnically diverse African populations has shed light on the genetic basis of hearing impairment, resulting in identification of multiple novel genes influencing hearing loss. Our last speaker will discuss ethical perspectives and the challenges of conducting genomic research in Indigenous populations from North America, the potential benefit for personalized medicine, and the importance of creating a partnership with Indigenous communities. 

The panel discussion, which will include the two moderators and audience participation, will focus on how studies of ethnically diverse populations are of benefit to the global medical genetics community. We will further discuss ethical issue that arise from consequences of research that stigmatizes Indigenous communities and will touch base on principals of how to conduct research in minority and Indigenous populations in an ethical manner.

Recorded session from the 2021 virtual meeting.

The success of genome-wide association studies (GWAS) in humans have yielded a wealth of clues about the molecular basis of many common human diseases. In addition, polygenic risk scores (PRS) for a variety of traits are increasingly becoming accurate enough to be useful for clinical practice, realizing the longstanding goal of personalized medicine. However, data collection continues to be predominantly imbalanced towards individuals of European ancestry, and it is abundantly clear that methods developed in one human ancestry group do not perform well in other ancestry groups, limiting their utility and exacerbating already severe health disparities. The speakers in this session will introduce recent efforts to level ancestry imbalance in genomic research, including the formation of large collaborative efforts and the development of novel statistical methods.

Recorded session from the 2021 virtual meeting.

Platform sessions are abstract driven sessions with 6 talks per session. These talks are 10 minutes in length and are cross-topical in nature to represent the broad discipline our field of genetics and genomics represent. After each talk, there will be a 5-minute Q&A with each speaker. For information on each individual session, please view the "Details" tab. 

Recorded session from the 2021 virtual meeting.

Revisiting the out of Africa event with a deep learning approach

Anatomically modern humans evolved around 300 thousand years ago in Africa. Modern humans started to appear in the fossil record outside of Africa about 100 thousand years ago though other hominins existed throughout Eurasia much earlier. Recently, several researchers argued in favourof a single out of Africa event for modern humans based on whole-genome sequences analyses. However, the single out of Africa model is in contrast with some of the findings from fossil records, which supports two out of Africa, and uniparental data, which proposes a back to Africa movement. Here, we used a deep learning approach coupled with Approximate Bayesian Computation and Sequential Monte Carlo to revisit these hypotheses from the whole genome sequence perspective. Our results support the back to Africa model over other alternatives. We estimated that there are two successive splits between Africa and out of African populations happening around 60-90 thousand years ago and separated by 13-15 thousand years. One of the populations resulting from the more recent split has to a large extent replaced the older West African population while the other one has founded the out of Africa populations.

Mayukh Mondal
Institute of Genomics, University of Tartu

An analysis of population copy number variation in sub-Saharan African genomes

Introduction Copy number variation (CNV) is responsible for a large component of normal human variation and has been implicated in the cause/genetic aetiology of several rare diseases. Population reference databases containing CNV information from all global populations is critical in disease genetics research, but current resources lack diversity, especially from the African continent. This makes such databases of limited use in studies looking at genetic diseases in African individuals. This study therefore aims to address this knowledge gap by producing a map of CNV using whole-genome data from several, previously unstudied African populations
Methods 1027 high coverage whole genome sequences obtained from individuals across west, central, southern and east Africa, were analysed using Manta and Graphtyper2. Additionally, 919 of the samples were also analysed using Genome STRiP to detect multi-allelic CNV. Quality control specific to each tool was performed in order to achieve high quality variant call sets.
Results 56 816 CNVs were detected by the Manta pipeline, consisting of 44 671 deletions and 12 145 duplications. Due the ability of Manta to detect small variants (<100bp), we are able to describe this previously less studied class of variants in an African cohort. 25% of the variants detected by Manta were <100 bp and 40% of these were common variants at >5% allele frequency. 50% of these variants are novel compared to 27% of the remaining variants >100bp. Overall, 32% of the variants identified were novel. A comparison between central, west, east and southern African regions yielded a number of variants unique to each region. We find deletions tend to have lower allele frequencies compared to duplications. The majority of variants were found in the non-coding genome, with only 8% of variants overlapping coding transcripts. An additional 5% of variants overlapped regulatory features. Genome STRiP detected 3991 multi-allelic variants with 99% having a copy number between 3-20. There were also variants with copy numbers greater than 20, some of which appear to be incidences of excessive runaway duplications not previously described.
Conclusion The amount of novel variation found demonstrates the importance of including African individuals from multiple African regions when producing reference databases and the rich genomic diversity of African genomes. Work is currently being performed to combine the full Genome STRiP and Manta call sets to produce a robust combined dataset. The variant database produced in this study will provide a valuable resource as a reference of normal CNV for the study of diseases in African populations.Emma Wiener

Platform sessions are abstract driven sessions with 6 talks per session. These talks are 10 minutes in length and are cross-topical in nature to represent the broad discipline our field of genetics and genomics represent. After each talk, there will be a 5-minute Q&A with each speaker. For information on each individual session, please view the "Details" tab. 

Recorded session from the 2021 virtual meeting.

Long-Term systemic expression and cross-correction ability of HMI-203: Investigational gene therapy candidate for mucopolysaccharidosis type II or Hunter Syndrome

Mucopolysaccharidosis type II (MPS II), or Hunter syndrome, is a rare X-linked lysosomal storage disorder caused by mutations in the iduronate-2-sulfatase (IDS) gene, resulting in loss of I2S activity leading to systemic (peripheral organs and central nervous system (CNS)) toxic lysosomal accumulation of glycosaminoglycans (GAGs). GAGs are large polysaccharides made of repeating disaccharide units responsible for providing structure and hydration to the cell. The disease results in skeletal dysplasia, joint stiffness, organomegaly, airway obstruction and, in severe cases, neurocognitive deficits. Hunter syndrome occurs in approximately 1 in 100,000 to 1 in 170,000 males, and causes significantly reduced lifespan, with the severe form leading to life expectancy of 10 to 20 years The proposed therapeutic mechanism of gene therapy candidate HMI‑203 is based on both intracellular expression and synthesis of active I2S, as well as high levels of expression and secretion of active I2S enzyme to support cross correction. Herein, we report preclinical data where a single intravenous dose of HMI-203 delivering human IDS via a rAAVHSC vector in the MPS II murine model resulted in dose-dependent and long-term transduction, IDS expression and I2S enzymatic activity in the evaluated tissues, e.g., liver, brain and serum through 52 weeks post-dose. A significant correlation was observed between liver and serum I2S activity, suggesting that the liver was likely the major contributor to the elevated levels of active I2S in the serum. The circulating I2S protein in the serum was functionally active (i.e., 90 kDa form) and cross-correction activity via a mannose-6-phosphate receptor dependent pathway was demonstrated using an in vitro competition assay. The robust and broad IDS tissue expression, along with demonstrated cross-correction significantly reduced GAG heparan sulfate (GAG-HS) to wild type (WT) levels in all evaluated organs associated with the disease, cerebrospinal fluid (CSF) and urine. In addition, lysosomal-associated membrane protein-1 (LAMP1) levels were significantly reduced to WT-like levels in the peripheral organs and CNS tissues. Of note, positive and significant correlations were observed between reduction in GAG-HS and LAMP1 levels in the CNS and brain and CSF GAG-HS levels, suggesting that CSF GAG-HS levels could be indicative of overall brain GAG and lysosomal burden levels in the clinic. Taken together, we have demonstrated that HMI-203 combines transduction and expression with the potential for cross-correction. These HMI-203 IND-enabling studies support HMI-203 as a gene therapy candidate for the treatment of MPS II. 

Kruti Patel
Homology Medicines, Inc.

Platform sessions are abstract driven sessions with 6 talks per session. These talks are 10 minutes in length and are cross-topical in nature to represent the broad discipline our field of genetics and genomics represent. After each talk, there will be a 5-minute Q&A with each speaker. For information on each individual session, please view the "Details" tab. 

Recorded session from the 2021 virtual meeting.

Local ancestry allows for improved genomic prediction in underrepresented and admixed populations

Due to the paucity of methodological and computational approaches that account for their genomic complexity, admixed populations are systematically excluded from statistical genomic studies. Admixed populations make up more than a third of the US populace but are severely underrepresented in biomedical research which may contribute to health disparities. To reap the full benefits from the ongoing efforts to collect samples from underrepresented populations and from existing mixed ancestry cohorts, tools facilitating the well-calibrated research of admixed peoples are urgently needed.
We recently developed a local ancestry aware GWAS model, Tractor, which corrects for fine-scale population structure at the genotype level, often boosts locus discovery power, and produces ancestry-specific effect size estimates and p values. Using Tractor summary statistics from African ancestry (AFR) tracts in ~4500 admixed UK Biobank (UKB) individuals, we built polygenic risk scores (PRS) and predicted blood panel phenotypes on homogenous African ancestry UKB individuals. We benchmarked these PRS against scores created from traditional GWAS runs on 1) the same admixed cohort, 2) a large European UKB sample, and 3) a large multi-ancestry meta-analysis of continental ancestry groups from the pan-UKB project (https://pan.ukbb.broadinstitute.org/). We also tested the accuracy of several PRS models including pruning and thresholding and PRS-CSx. We find that incorporating diverse samples and ancestry-specific estimates from admixed populations results in higher prediction accuracy for homogeneous AFR individuals. The bulk of African-descent GWAS participants are currently admixed individuals of the Americas, and some underrepresented ancestries are rarely found outside of the admixed context. Thus, building models based on ancestry-specific estimates generated from the deconvolved local ancestry tracts of admixed genomes allows for better PRS performance on many diverse populations from making better use of existing collections.
We additionally highlight several loci which we find to have well-demonstrated effect size differences across ancestries, a phenomenon for which there are few prior examples in the literature. As our models are constructed off of local ancestry components from the same admixed individuals, these results hint at genetic differences rather than environmental factors, which are often tricky to disentangle. Ultimately, our work highlights how Tractor and local ancestry allow for improved population characterization and can be leveraged to advance the understanding of complex diseases across diverse cohorts. 

Elizabeth G Atkinson
Baylor College of MedicinePolygenic risk prediction of obesity across the life course and in diverse populations

Platform sessions are abstract driven sessions with 6 talks per session. These talks are 10 minutes in length and are cross-topical in nature to represent the broad discipline our field of genetics and genomics represent. After each talk, there will be a 5-minute Q&A with each speaker. For information on each individual session, please view the "Details" tab. 

Recorded session from the 2021 virtual meeting.

Trans-ancestry imputation and exome sequencing of more than 1 million individuals identifies genetic variation protecting against SARS-CoV-2 infection and predicts individuals at risk for severe COVID-19 outcomes

COVID-19 symptoms vary widely, ranging from asymptomatic in some patients to fatal in others. Elucidating the host genetics of COVID-19 holds the potential for understanding both susceptibility to SARS-CoV-2 infection as well as heterogeneity in patient presentation and outcome. Prior work focused on identifying common variants associated with COVID-19 susceptibility and severity, but little has been done to explore the entire allele frequency spectrum of genetic variation, from common to rare exonic variants. Here, we present the largest trans-ancestry exome sequencing study of COVID-19 to date in 586,713 individuals, with a larger set of 1,012,636 individuals with imputed data across 7 studies and 5 continental ancestries.
Through exome sequencing of 21,820 COVID-19 cases and 564,893 controls, we did not identify any rare variants after Bonferroni correction (P<9.6e-10). Burden tests identified three genes tentatively associated with COVID-19: DISP3 (P=2e-8; OR=1.8±0.3), MARK1 (P=3e-9; OR=38.4±16.9), and TLR7 (P=4e-8; OR=4.5±2.2). Despite having a 100x larger sample size, we could not replicate a previous reported role for rare variants in the interferon pathway (P=0.59).
Our larger GWAS of 56,841 cases and 955,795 controls found 11 loci (P<5e-8). Most notably, we identified a strong protective association amongst SARS-CoV-2 infected cases for rs190509934 located 60bp upstream of ACE2, the primary cell receptor for the SARS-CoV-2 spike protein (P=4.5e-13; OR=0.6±0.08; EUR MAF=0.003). Using RNA-seq, rs190509934 reduced ACE2 expression by 39% (P=3e-8), supporting the hypothesis that reduced ACE2 expression protects against SARS-CoV-2 infection.
Lastly, we developed a polygenic risk score (PRS) to predict hospitalization and severity of COVID-19. Among those of European ancestry, individuals with the top 10% PRSs are 1.8-fold more likely to be hospitalized (P=6e-11) and 1.58-fold more likely to be placed on a ventilator or die from COVID-19 (P=7e-10). These associations hold in other non-European populations (albeit with decreased power) and after accounting for known clinical risk factors.
Our data represents the most comprehensive survey of common and rare exonic variation associated with COVID-19 identifying new loci and polygenic risk scores that predict severity of COVID-19. 

Jack Kosmicki
Regeneron Genetics Center

Platform sessions are abstract-driven sessions with 6 talks per session. These talks are 10 minutes in length and are cross-topical in nature to represent the broad discipline our field of genetics and genomics represent. After each talk, there will be a 5-minute Q&A with each speaker. For more information, see the Details tab.

Recorded session from the 2021 virtual meeting.

A global biobank study of asthma identifies novel associations, illuminates shared genetic architecture, and improves polygenic prediction across diverse ancestry groups

Asthma is a complex and multifactorial disease that affects millions of people worldwide and varies in prevalence by an order of magnitude across geographic regions and diverse populations. However, the extent to which genetic variation contributes to these disparities is unclear, as studies probing the genetics of asthma have been primarily limited to populations of European descent. To expand our understanding of the genetic factors underlying asthma risk in different ancestral populations, we conducted the largest genome-wide association study of asthma to date (N cases=153,763 and N controls=1,647,022) via meta-analysis across 18 biobanks with harmonized phenotype definitions and spanning multiple countries and genetic ancestries, collectively called the Global Biobank Meta-analysis Initiative (GBMI). This meta-analysis discovered 180 independent genome-wide significant loci (p < 5e-8) associated with asthma, 69 of which are novel. We replicate well-known associations such as TNFRSF8 and IL1RL1, and find that the novel associations tend to have smaller effects than previously-discovered loci, highlighting our substantial increase in effective sample size and statistical power. Despite the considerable range in prevalence among biobanks, from 3% to 24%, the genetic effects of associated loci are largely consistent across biobanks, ancestries, biobank ascertainment-types, and asthma definitions. This offers insight into the potential shared biological pathways that may be differentially affected by environmental factors and contribute to variation in prevalence. To further probe the polygenic architecture of asthma, we are constructing polygenic risk scores (PRS) using multi-ancestry approaches to establish a baseline understanding of PRS performance for asthma in different populations. The vast increase in the scale and diversity of GBMI yields higher predictive power for asthma across the board; for example, with the multi-ancestry GBMI cohort, we achieve .03 phenotypic variance explained in East Asian populations compared to the highest previously reported variance explained in an East Asian population, .0075. The availability of additional phenotypic information on asthma subtypes and asthma-related diseases like COPD in GBMI-participating biobanks will allow us to further tease apart the genetics underlying various aspects of the disease. In summary, we have identified novel loci associated with asthma, found remarkable consistency of genetic effects despite enormous heterogeneity in prevalence, and have quantified the relative contribution of polygenic components to asthma risk.

Kristin Tsuo
Department of Genetics, Harvard Medical School

Genome-wide polygenic risk score of prostate cancer in African and European ancestry men
Genome-wide polygenic risk scores (PRS) are reported to have higher performance than standard genome-wide significant PRS across numerous traits. We evaluated the ability of genome-wide PRS to evaluate prostate cancer risk compared to our recently developed and highly predictive multi-ancestry PRS of 269 established prostate cancer risk variants. Genome-wide PRS approaches included LDpred2, PRS-CSx, and EB-PRS. Models were trained using the largest and most diverse prostate cancer GWAS to date of 107,247 cases and 127,006 controls, which was previously used to develop the multi-ancestry PRS of 269 variants. Resulting models were tested in independent samples of 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,045 cases and 191,835 controls of European ancestry from the UK Biobank. Among the genome-wide PRS approaches, LDpred2 had the best performance, with AUCs of 0.649 (95% CI=0.627-0.670) in African and 0.819 (95% CI=0.815-0.823) in European ancestry men. African and European ancestry men in the top PRS decile relative to men in the median 40-60% PRS category had odds of prostate cancer of 3.29 (95% CI=2.47-4.40) and 2.99 (95% CI=2.78-3.23), respectively. However, the PRS constructed using 269 variants had significantly larger AUCs in both African (0.679, 95% CI=0.659-0.700) and European ancestry men (0.845, 95% CI=0.841-0.849), with African and European ancestry men in the top PRS decile having larger odds of prostate cancer (3.53, 95% CI=2.66-4.69 and 4.20, 95% CI=3.89-4.53, respectively). We are currently further validating these findings in diverse men from Million Veteran’s Program. This investigation suggests that genome-wide PRS may not improve the ability to distinguish prostate cancer compared to a genome-wide significant PRS.

Burcu F Darst
University of Southern California

Genetic association of phenotypes derived by self-supervised deep learning of retina fundus images reveals new genes for eye development
Although genome-wide association studies (GWAS) have achieved great success and identified thousands of genetic associations, phenotypes of most existing GWAS studies are predefined. While these phenotypes encode valuable biomedical knowledge, they are also biased by current clinical practice and epidemiological studies. Also as phenotype code is greatly simplified, it is often not sufficient to capture the complexity of human physiology and pathology in their entirety. Fortunately, with the medical record becoming increasingly digitized, there are new opportunities to derive phenotypes beyond expert-curation, which would avoid human bias and discover new phenotypes that are previously missed. Here, leveraging breakthroughs in self-supervised deep representation learning, we propose a new approach for phenotype discovery from medical images. We use a contrastive loss function over an Inception V3 architecture to learn a representation that captures the inherent image features of individuals. Using vessel segmentation masks generated from retina fundus images as inputs, we designed a phenotyper neural network model that generates 128 phenotypes representing retinal vasculature. After training on 40,000 images from EyePACS, our model generated phenotypes from 130,967 images of 65,629 British White participants in the UK Biobank. A GWAS of these vasculature phenotypes identified 34 independent loci, at least 5 are associated with vessel features. Mouse knockout experiments verified the role of the WNT7B gene, a newly found locus, in retinal vessel development. Our results establish a new framework of unsupervised image-based genome-wide genotype phenotype association studies (iGWAS). Our framework would expand the repertoire of GWAS phenotypes and enable discovery of new biology.

Ziqian Xie
Baylor College of Medicine

Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases
Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds of significant genetic variants. However, these loci may only contain one or a handful of causal variants. Statistical fine-mapping refines a GWAS locus to a smaller set of likely causal variants (credible set). Since non-causal variants have marginally different effects across populations where LD differs, capitalizing on the genomic diversity across ancestries holds the promise to further improve the resolution of fine-mapping. However, to date, cross-population fine-mapping efforts have been limited, partly due to the lack of statistical methods that can appropriately integrate data from multiple ancestries. Building on Sum of Single Effects (SuSiE), a single-population fine-mapping model, we have developed SuSiEx, an accurate and computationally efficient method for trans-ancestry fine-mapping. SuSiEx assumes that causal variants are largely shared across populations while allowing for varying variant effect sizes across populations. Our model can integrate data from an arbitrary number of ancestries, explicitly models population-specific LD patterns, accounts for multiple causal variants in a genomic region, and can be applied to GWAS summary statistics without access to individual-level data. We showed, via simulation studies, that compared with fine-mapping 100K European samples, integrating 50K European and 50K African samples using SuSiEx enabled fine-mapping of more association signals, and dramatically increased the resolution of credible sets. Comparing with PAINTOR, SuSiEx had a 37% reduction in the median size of credible sets and a 54% increase in the number of high Posterior Inclusion Probability (PIP) variants. We applied SuSiEx to 25 quantitative traits that are available from both the Taiwan Biobank (TWB, n = 92,615) and UK Biobank (UKBB, n = 361,194) to fine-map genetic loci reaching genome-wide significance. Compared with single-population fine-mapping in UKBB, cross-ancestry fine-mapping significantly reduced the size of credible sets and increased the PIP of the most probable variant. We additionally applied our method to schizophrenia GWAS summary statistics of East Asian and European ancestries. Compared with the published fine-mapping results from PGC using FINEMAP on the same data, SuSiEx reduced the size of credible sets in 70% of the fine-mapped loci. Manual inspection confirmed that SuSiEx provided more sensible results in many loci. As the accumulation of GWAS results from different ancestries, the application of our method will be much promising.

Kai Yuan
Analytic and Translational Genetics Unit, Massachusetts General Hospital

Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits
Genome-wide association studies (GWAS) have identified tens of thousands of genetic loci associated with human complex traits and diseases. However, the majority of GWAS were conducted in individuals of European (EUR) ancestry. Failure to capture global genetic diversity has limited biological discovery and impeded equitable delivery of genomic knowledge to diverse populations. Here we performed genome-wide analysis on 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia (EAS). We identified 1,907 independent genome-wide significant loci (P-value < 5x10-8) across the 36 traits, among which 1,287 loci survived Bonferroni correction for the number of traits tested (P-value < 5x10-8/36). The number of genome-wide significant loci per trait ranged from 1 for forced expiratory volume in one second (FEV1) and FEV1 to forced vital capacity (FVC) ratio (FEV1R), to 211 for height (HT). We estimated the SNP-based heritability (h2g) for each trait, which ranged from 0.009 (FEV1R) to 0.384 (HT), and pairwise genetic correlations between these traits, which identified clusters of highly genetically correlated traits. Of the 1,907 genome-wide significant loci, 1,615 were fine-mapped to a total of 1,972 credible sets, each representing an independent association signal. Out of the 1,972 credible sets, 232 were mapped to a single variant with posterior inclusion probability (PIP) > 95%, among which 24 were missense variants. Leveraging GWAS summary statistics from Biobank Japan (BBJ) and UK Biobank (UKBB), we found that the genetic architecture of the quantitative traits examined was largely consistent within EAS and between EAS and EUR populations. Integrating TWB and BBJ GWAS identified a total of 2,975 genetic loci, among which 979 had not been reported in previous biobank studies. We also examined whether polygenic risk scores (PRS) of biomarkers can be used to predict the risk of common complex disease, and demonstrated the potential utility of biomarker GWAS in predicting disease risk (e.g., type 2 diabetes) and the promise of multi-trait cross-population polygenic prediction. Our novel findings represent a major advance in diversifying GWAS samples and the characterization of the genetic architecture of human complex traits in EAS populations. Future endeavors on increasing the sample size and phenotype coverage in TWB, and improving cross-biobank data harmonization will further facilitate genomic discovery.

Yen-Feng Lin
National Health Research Institutes

The effects of demographic-based selection bias on GWAS results in the UK Biobank
Genome-wide association studies (GWASs) are almost always based on a non-random sample of the underlying population, as obtaining very large sample sizes, rather than ensuring such samples are representative, has been key to their success. Selection bias in estimated genetic associations, including how it varies across traits, is poorly understood. A sample of particular interest is the widely used UK Biobank (UKB). Because of the need for very large samples, the UKB is included in almost all large GWASs as one of the largest cohorts. In addition, UKB's subsample of genotyped siblings (UKBSIB) has become a crucial resource for estimating genetic effects free of environmental confounding. Using nationally representative UK Census microdata as a reference, we document substantial non-random selection into the UKB, and even stronger for UKBSIB: individuals in the UKB and UKBSIB are more likely to be female, higher educated, and older, compared to the underlying population that received an invitation. We also show that this non-random selection leads to significant selection bias in associations between various demographic and health-related traits estimated in the UKB. We then estimate probabilities of UKB participation for each UKB participant to estimate selection-corrected GWASs for multiple traits using inverse probability weighting. Based on preliminary analyses for the top 5,000 SNPs associated with BMI, education, and height, respectively, we show that the extent to which selection-corrected GWAS results differ from those of regular GWASs is trait-specific. Genetic associations for educational attainment and BMI are the most altered after correcting for volunteer bias, whereas associations for height remain relatively unaffected. For educational attainment, 12.6% of our estimated SNP effects flip sign after correcting for selection bias, suggesting that current GWAS methods are not sufficiently robust. We will extend these analyses by investigating more phenotypes, conducting regular and inverse probability weighted GWASs in the UKB that incorporate all available SNPs, and comparing results. Our findings will be useful for understanding the extent to which a particular phenotype is prone to selection bias in GWAS, and our correction method provides an alternative when population-representative cohorts are not available.

Sjoerd van Alten
Vrije Universiteit Amsterdam