GRAF-pop: A Fast Distance-Based Method To Infer Subject Ancestry from Multiple Genotype Datasets Without Principal Components Analysis

Genomic alterations in normal breast tissues preceding breast cancer diagnosis

BACKGROUND

Normal breast tissues adjacent to cancer often harbor many of the same genomic alterations as the cancer itself. However, it remains unclear whether histologically normal breast tissues carry genomic changes related to cancer development years before a cancer diagnosis.

METHODS

Whole exome sequencing was performed to examine germline and somatic alterations in histologically normal breast tissues from women who subsequently developed breast cancer (n = 79, pre-diagnosis tissues) and compared these with results from breast tissues of women who did not (n = 81). No patient had germline mutations in cancer predisposition genes.

RESULTS

The pre-diagnosis tissues had significantly more high functional impact germline variants per sample than the healthy controls (P = 0.034), 36.5% of affected genes were cancer hallmark genes, among these 62.4% were involved with evading growth suppressors and 5.7% with genome instability. The average number of somatic mutations were similar between the two cohorts. Mutation signature analysis revealed COSMIC signatures 3 (associated with impaired homologous recombination) as a dominant signature more frequent in pre-diagnosis tissues. At gene and variant level, nine common germline polymorphisms in two immune regulatory genes, FCGBP and TPSBP2, and along with three somatic mutations in F13A1, FRY and TMLHE, were significantly more frequently mutated in the pre-diagnosis samples.

CONCLUSIONS

Individuals who develop breast cancer have a higher germline variant burden in normal breast tissues leading to subtle deficiencies in DNA repair that in the context of other germline and somatic mutations could facilitate malignant transformation.

LILRB3 genetic variation is associated with kidney transplant failure in African American recipients

African American (AA) kidney transplant recipients exhibit a higher rate of graft loss compared with other racial and ethnic populations, highlighting the need to identify causative factors. Here, in the Genomics of Chronic Allograft Rejection cohort, pretransplant blood RNA sequencing revealed a cluster of four consecutive missense single-nucelotide polymorphisms (SNPs), within the leukocyte immunoglobulin-like receptor B3 (LILRB3) gene, strongly associated with death-censored graft loss. This SNP cluster (named LILRB3-4SNPs) encodes missense mutations at amino acids 617-618 proximal to a SHP1/2 phosphatase-binding immunoreceptor tyrosine-based inhibitory motif. The LILRB3-4SNPs cluster is specifically enriched within AA individuals and exhibited a strong association with death-censored graft loss and estimated glomerular filtration rate decline in the AA participants from multiple transplant cohorts. In two large Biobanks (BioMe and All-of-Us), the LILRB3-4SNPs cluster was associated with the early onset of end-stage renal disease and acted synergistically with the apolipoprotein L1 (APOL1) G1/G2 allele to accelerate disease progression. The SNPs were also linked to multiple immune-related diseases in AA individuals. Last, on multiomics analysis of blood and biopsies, recipients with LILRB3-4SNPs showed enhanced inflammation and monocyte ferroptosis. While larger and prospective studies are needed, our data provide insights on the genetic variation underlying kidney transplant outcomes.

Copy Number Variation and Haplotype Analysis of 17q21.31 Reveals Increased Risk Associated with Progressive Supranuclear Palsy and Gene Expression Changes in Neuronal Cells

BACKGROUND

The 17q21.31 region with various structural forms characterized by the H1/H2 haplotypes and three large copy number variations (CNVs) represents the strongest risk locus in progressive supranuclear palsy (PSP).

OBJECTIVE

To investigate the association between CNVs and structural forms on 17q.21.31 with the risk of PSP.

METHODS

Utilizing whole genome sequencing data from 1684 PSP cases and 2392 controls, the three large CNVs (α, β, and γ) and structural forms within 17q21.31 were identified and analyzed for their association with PSP.

RESULTS

We found that the copy number of γ was associated with increased PSP risk (odds ratio [OR] = 1.10, P = 0.0018). From H1β1γ1 (OR = 1.21) and H1β2γ1 (OR = 1.24) to H1β1γ4 (OR = 1.57), structural forms of H1 with additional copies of γ displayed a higher risk for PSP. The frequency of the risk sub-haplotype H1c rises from 1% in individuals with two γ copies to 88% in those with eight copies. Additionally, γ duplication up-regulates expression of ARL17B, LRRC37A/LRRC37A2, and NSFP1, while down-regulating KANSL1. Single-nucleus RNA-seq of the dorsolateral prefrontal cortex analysis reveals γ duplication primarily up-regulates LRRC37A/LRRC37A2 in neuronal cells.

CONCLUSIONS

The copy number of γ is associated with the risk of PSP after adjusting for H1/H2, indicating that the complex structure at 17q21.31 is an important consideration when evaluating the genetic risk of PSP. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Worldwide study of the taste of bitter medicines and their modifiers

The bitter taste of medicines hinders patient compliance, but not everyone experiences these difficulties because people worldwide differ in their bitterness perception. To better understand how people from diverse ancestries perceive medicines and taste modifiers, 338 adults, European and recent US and Canadian immigrants from Asia, South Asia, and Africa, rated the bitterness intensity of taste solutions on a 100-point generalized visual analog scale and provided a saliva sample for genotyping. The taste solutions were 5 medicines, tenofovir alafenamide (TAF), moxifloxacin, praziquantel, amodiaquine, and propylthiouracil (PROP), and 4 other solutions, TAF mixed with sucralose (sweet, reduces bitterness) or 6-methylflavone (tasteless, reduces bitterness), sucralose alone, and sodium chloride alone. Bitterness ratings differed by ancestry for 2 of the 5 drugs (amodiaquine and PROP) and for TAF mixed with sucralose. Genetic analysis showed that people with variants in 1 bitter receptor variant gene (TAS2R38) reported PROP was more bitter than did those with a different variant (P = 7.6e-19) and that people with either an RIMS2 or a THSD4 genotype found sucralose more bitter than did others (P = 2.6e-8, P = 7.9e-11, respectively). Our findings may help guide the formulation of bad-tasting medicines to meet the needs of those most sensitive to them.

The evolution of dbSNP: 25 years of impact in genomic research

The Single Nucleotide Polymorphism Database (dbSNP), established in 1998 by the National Center for Biotechnology Information (NCBI), has been a critical resource in genomics for cataloging small genetic variations. Originally focused on single nucleotide polymorphisms (SNPs), dbSNP has since expanded to include a variety of genetic variants, playing a key role in genome-wide association studies (GWAS), population genetics, pharmacogenomics, and cancer research. Over 25 years, dbSNP has grown to include more than 4.4 billion submitted SNPs and 1.1 billion unique reference SNPs, providing essential data for identifying disease-related genetic variants and studying human diversity. Integrating large-scale projects like 1000 Genomes, gnomAD, TOPMed, and ALFA has expanded dbSNP's catalog of human genetic variation, increasing its usefulness for research and clinical applications. Keeping up with advancements such as next-generation sequencing and cloud-based infrastructure, dbSNP remains a cornerstone of genetic research supporting continued discoveries in precision medicine and population genomics. DATABASE URL: https://www.ncbi.nlm.nih.gov/snp.

Alzheimer's Disease Sequencing Project Release 4 Whole Genome Sequencing Dataset

The Alzheimer's Disease Sequencing Project (ADSP) is a national initiative to understand the genetic architecture of Alzheimer's Disease and Related Dementias (AD/ADRD) by sequencing whole genomes of affected participants and age-matched cognitive controls from diverse populations. The Genome Center for Alzheimer's Disease (GCAD) processed whole-genome sequencing data from 36,361 ADSP participants, including 35,014 genetically unique participants of which 45% are from non-European ancestry, across 17 cohorts in 14 countries in this fourth release (R4). This sequencing effort identified 387 million bi-allelic variants, 42 million short insertions/deletions, and 2.2 million structural variants. Annotations and quality control data are available for all variants and samples. Additionally, detailed phenotypes from 15,927 participants across 10 domains are also provided. A linkage disequilibrium panel was created using unrelated AD cases and controls. Researchers can access and analyze the genetic data via NIAGADS Data Sharing Service, the VariXam tool, or NIAGADS GenomicsDB.

Association of common and rare variants with Alzheimer's disease in more than 13,000 diverse individuals with whole-genome sequencing from the Alzheimer's Disease Sequencing Project

INTRODUCTION

Alzheimer's disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous.

METHODS

We investigated the association of AD with both common variants and aggregates of rare coding and non-coding variants in 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data.

RESULTS

Pooled-population analyses of all individuals identified genetic variants at apolipoprotein E (APOE) and BIN1 associated with AD (p < 5 × 10-8). Subgroup-specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and non-coding variants in the region. Common variants in LINC00320 were observed associated with AD in Black individuals (p = 1.9 × 10-9). Finally, we observed rare non-coding variants in the promoter of TOMM40 distinct of APOE in pooled-population analyses (p = 7.2 × 10-8).

DISCUSSION

We observed that complementary pooled-population and subgroup-specific analyses offered unique insights into the genetic architecture of AD.

HIGHLIGHTS

We determine the association of genetic variants with Alzheimer's disease (AD) using 13,371 individuals of diverse ancestry with whole genome sequencing (WGS) data. We identified genetic variants at apolipoprotein E (APOE), BIN1, PSEN1, and LINC00320 associated with AD. We observed rare non-coding variants in the promoter of TOMM40 distinct of APOE.

Known pathogenic gene variants and new candidates detected in sudden unexpected infant death using whole genome sequencing

The purpose of this study is to gain insights into potential genetic factors contributing to the infant's vulnerability to Sudden Unexpected Infant Death (SUID). Whole Genome Sequencing (WGS) was performed on 144 infants that succumbed to SUID, and 573 healthy adults. Variants were filtered by gnomAD allele frequencies and predictions of functional consequences. Variants of interest were identified in 88 genes, in 64.6% of our cohort. Seventy-three of these have been previously associated with SIDS/SUID/SUDP. Forty-three can be characterized as cardiac genes and are related to cardiomyopathies, arrhythmias, and other conditions. Variants in 22 genes were associated with neurologic functions. Variants were also found in 13 genes reported to be pathogenic for various systemic disorders and in two genes associated with immunological function. Variants in eight genes are implicated in the response to hypoxia and the regulation of reactive oxygen species (ROS) and have not been previously described in SIDS/SUID/SUDP. Seventy-two infants met the triple risk hypothesis criteria. Our study confirms and further expands the list of genetic variants associated with SUID. The abundance of genes associated with heart disease and the discovery of variants associated with the redox metabolism have important mechanistic implications for the pathophysiology of SUID.

Social, Behavioral, and Clinical Risk Factors Are Associated with Clonal Hematopoiesis

BACKGROUND

Risk factors including smoking, alcohol intake, physical activity (PA), and sleep patterns have been associated with cancer risk. Clonal hematopoiesis (CH), including mosaic chromosomal alterations and clonal hematopoiesis of indeterminate potential, is linked to increased hematopoietic cancer risk and could be used as common preclinical intermediates for the better understanding of associations of risk factors with rare hematologic malignancies.

METHODS

We analyzed cross-sectional data from 478,513 UK Biobank participants without hematologic malignancies using multivariable-adjusted analyses to assess the associations between lifestyle factors and CH types.

RESULTS

Smoking was reinforced as a potent modifiable risk factor for multiple CH types, with dose-dependent relationships persisting after cessation. Males in socially deprived areas of England had a lower risk of mosaic loss of chromosome Y (mLOY), females with moderate/high alcohol consumption (2-3 drinks/day) had increased mosaic loss of the X chromosome risk [OR = 1.17; 95% confidence interval (CI), 1.09-1.25; P = 8.31 × 10-6] compared with light drinkers, active males (moderate-high PA) had elevated risks of mLOY (PA category 3: OR = 1.06; 95% CI, 1.03-1.08; P = 7.57 × 10-6), and men with high body mass index (≥40) had reduced risk of mLOY (OR = 0.57; 95% CI, 0.51-0.65; P = 3.30 × 10-20). Sensitivity analyses with body mass index adjustment attenuated the effect in the mLOY-PA associations (IPAQ2: OR = 1.03; 95% CI, 1.00-1.06; P = 2.13 × 10-2 and IPAQ3: OR = 1.03; 95% CI, 1.01-1.06; P = 7.77 × 10-3).

CONCLUSIONS

Our study reveals associations between social deprivation, smoking, and alcohol consumption and CH risk, suggesting that these exposures could contribute to common types of CH and potentially rare hematologic cancers.

IMPACT

This study underscores the impact of lifestyle factors on CH frequency, emphasizing social, behavioral, and clinical influences and the importance of sociobehavioral contexts when investigating CH risk factors.

Interpretable GWAS by linking clinical phenotypes to quantifiable immune repertoire components

Bridging the gap between genotype and phenotype in GWAS studies is challenging. A multitude of genetic variants have been associated with immune-related diseases, including cancer, yet the interpretability of most variants remains low. Here, we investigate the quantitative components in the T cell receptor (TCR) repertoire, the frequency of clusters of TCR sequences predicted to have common antigen specificity, to interpret the genetic associations of diverse human diseases. We first developed a statistical model to predict the TCR components using variants in the TRB and HLA loci. Applying this model to over 300,000 individuals in the UK Biobank data, we identified 2309 associations between TCR abundances and various immune diseases. TCR clusters predicted to be pathogenic for autoimmune diseases were significantly enriched for predicted autoantigen-specificity. Moreover, four TCR clusters were associated with better outcomes in distinct cancers, where conventional GWAS cannot identify any significant locus. Collectively, our results highlight the integral role of adaptive immune responses in explaining the associations between genotype and phenotype.

Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement

BACKGROUND

Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets.

METHODS

We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups.

RESULTS

Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations.

CONCLUSIONS

This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.

Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions

Here, in a multi-ancestry genome-wide association study meta-analysis of kidney cancer (29,020 cases and 835,670 controls), we identified 63 susceptibility regions (50 novel) containing 108 independent risk loci. In analyses stratified by subtype, 52 regions (78 loci) were associated with clear cell renal cell carcinoma (RCC) and 6 regions (7 loci) with papillary RCC. Notably, we report a variant common in African ancestry individuals ( rs7629500 ) in the 3' untranslated region of VHL, nearly tripling clear cell RCC risk (odds ratio 2.72, 95% confidence interval 2.23-3.30). In cis-expression quantitative trait locus analyses, 48 variants from 34 regions point toward 83 candidate genes. Enrichment of hypoxia-inducible factor-binding sites underscores the importance of hypoxia-related mechanisms in kidney cancer. Our results advance understanding of the genetic architecture of kidney cancer, provide clues for functional investigation and enable generation of a validated polygenic risk score with an estimated area under the curve of 0.65 (0.74 including risk factors) among European ancestry individuals.

Exploring the role of HLA variants in neuroblastoma susceptibility through whole exome sequencing

Although a number of susceptibility loci for neuroblastoma (NB) have been identified by genome-wide association studies, it is still unclear whether variants in the HLA region contribute to NB susceptibility. In this study, we conducted a comprehensive genetic analysis of variants in the HLA region among 724 NB patients and 2863 matched controls from different cohorts. We exploited whole-exome sequencing data to accurately type HLA alleles with an ensemble approach on the results from three different typing tools, and carried out rigorous sample quality control to ensure a fine-scale ancestry matching. The frequencies of common HLA alleles were compared between cases and controls by logistic regression under additive and non-additive models. Population stratification was taken into account adjusting for ancestry-informative principal components. We detected significant HLA associations with NB. In particular, HLA-DQB1*05:02 (OR = 1.61; padj = 5.4 × 10-3) and HLA-DRB1*16:01 (OR = 1.60; padj = 2.3 × 10-2) alleles were associated to higher risk of developing NB. Conditional analysis highlighted the HLA-DQB1*05:02 allele and its residue Ser57 as key to this association. DQB1*05:02 allele was not associated to clinical features worse outcomes in the NB cohort. Nevertheless, a risk score derived from the allelic combinations of five HLA variants showed a substantial predictive value for patient survival (HR = 1.53; p = 0.032) that was independent from established NB prognostic factors. Our study leveraged powerful computational methods to explore WES data and HLA variants and to reveal complex genetic associations. Further studies are needed to validate the mechanisms of these interactions that contribute to the multifaceted pattern of factors underlying the disease initiation and progression.

Understanding the determinants of sweet taste liking in the African and East Asian ancestry groups in the U.S.-A study protocol

BACKGROUND

The liking for sweet taste is a powerful driver for consuming added sugars, and therefore, understanding how sweet liking is formed is a critical step in devising strategies to lower added sugars consumption. However, current research on the influence of genetic and environmental factors on sweet liking is mostly based on research conducted with individuals of European ancestry. Whether these results can be generalized to people of other ancestry groups warrants investigation.

METHODS

We will determine the differences in allele frequencies in sweet-related genetic variants and their effects on sweet liking in 426 adults of either African or East Asian ancestry, who have the highest and lowest average added sugars intake, respectively, among ancestry groups in the U.S. We will collect information on participants' sweet-liking phenotype, added sugars intake (sweetness exposure), anthropometric measures, place-of-birth, and for immigrants, duration of time living in the U.S. and age when immigrated. Ancestry-specific polygenic scores of sweet liking will be computed based on the effect sizes of the sweet-related genetic variants on the sweet-liking phenotype for each ancestry group. The predictive validity of the polygenic scores will be tested using individuals of African and East Asian ancestry from the UK Biobank. We will also compare sweet liking between U.S.-born individuals and immigrants within each ancestry group to test whether differences in environmental sweetness exposure during childhood affect sweet liking in adulthood.

DISCUSSION

Expanding genetic research on taste to individuals from ancestry groups traditionally underrepresented in such research is consistent with equity goals in sensory and nutrition science. Findings from this study will help in the development of a more personalized nutrition approach for diverse populations.

TRIAL REGISTRATION

This protocol has been preregistered with the Center for Open Science (https://doi.org/10.17605/OSF.IO/WPR9E).

Worldwide study of the taste of bitter medicines and their modifiers

The bitter taste of medicines hinders patient compliance, but not everyone experiences these difficulties because people worldwide differ in their bitterness perception. To better understand how people from diverse ancestries perceive medicines and taste modifiers, 338 adults, European and recent US and Canada immigrants from Asia, South Asia, and Africa, rated the bitterness intensity of taste solutions on a 100-point generalized visual analog scale and provided a saliva sample for genotyping. The taste solutions were five medicines, tenofovir alafenamide (TAF), moxifloxacin, praziquantel, amodiaquine, and propylthiouracil (PROP), and four other solutions, TAF mixed with sucralose (sweet, reduces bitterness) or 6-methylflavone (tasteless, reduces bitterness), sucralose alone, and sodium chloride alone. Bitterness ratings differed by ancestry for two of the five drugs (amodiaquine and PROP) and for TAF mixed with sucralose. Genetic analysis showed that people with variants in one bitter receptor variant gene (TAS2R38) reported PROP was more bitter than did those with a different variant (p= 7.6e-19) and that people with either an RIMS2 or a THSD4 genotype found sucralose more bitter than did others (p=2.6e-8, p=7.9e-11, resp.). Our findings may help guide the formulation of bad-tasting medicines to meet the needs of those most sensitive to them.

Canadian COVID-19 host genetics cohort replicates known severity associations

The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic contributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospitalized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and constructed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation.

Association of Structural Forms of 17q21.31 with the Risk of Progressive Supranuclear Palsy and MAPT Sub-haplotypes

Importance

The chromosome 17q21.31 region, containing a 900 Kb inversion that defines H1 and H2 haplotypes, represents the strongest genetic risk locus in progressive supranuclear palsy (PSP). In addition to H1 and H2, various structural forms of 17q21.31, characterized by the copy number of α, β, and γ duplications, have been identified. However, the specific effect of each structural form on the risk of PSP has never been evaluated in a large cohort study.

Objective

To assess the association of different structural forms of 17q.21.31, defined by the copy numbers of α, β, and γ duplications, with the risk of PSP and MAPT sub-haplotypes.

Design setting and participants

Utilizing whole genome sequencing data of 1,684 (1,386 autopsy confirmed) individuals with PSP and 2,392 control subjects, a case-control study was conducted to investigate the association of copy numbers of α, β, and γ duplications and structural forms of 17q21.31 with the risk of PSP. All study subjects were selected from the Alzheimer's Disease Sequencing Project (ADSP) Umbrella NG00067.v7. Data were analyzed between March 2022 and November 2023.

Main outcomes and measures

The main outcomes were the risk (odds ratios [ORs]) for PSP with 95% CIs. Risks for PSP were evaluated by logistic regression models.

Results

The copy numbers of α and β were associated with the risk of PSP only due to their correlation with H1 and H2, while the copy number of γ was independently associated with the increased risk of PSP. Each additional duplication of γ was associated with 1.10 (95% CI, 1.04-1.17; P = 0.0018) fold of increased risk of PSP when conditioning H1 and H2. For the H1 haplotype, addition γ duplications displayed a higher odds ratio for PSP: the odds ratio increases from 1.21 (95%CI 1.10-1.33, P = 5.47 × 10-5) for H1β1γ1 to 1.29 (95%CI 1.16-1.43, P = 1.35 × 10-6) for H1β1γ2, 1.45 (95%CI 1.27-1.65, P = 3.94 × 10-8) for H1β1γ3, and 1.57 (95%CI 1.10-2.26, P = 1.35 × 10-2) for H1β1γ4. Moreover, H1β1γ3 is in linkage disequilibrium with H1c (R2 = 0.31), a widely recognized MAPT sub-haplotype associated with increased risk of PSP. The proportion of MAPT sub-haplotypes associated with increased risk of PSP (i.e., H1c, H1d, H1g, H1o, and H1h) increased from 34% in H1β1γ1 to 77% in H1β1γ4.

Conclusions and relevance

This study revealed that the copy number of γ was associated with the risk of PSP independently from H1 and H2. The H1 haplotype with more γ duplications showed a higher odds ratio for PSP and were associated with MAPT sub-haplotypes with increased risk of PSP. These findings expand our understanding of how the complex structure at 17q21.31 affect the risk of PSP.

SNVstory: inferring genetic ancestry from genome sequencing data

BACKGROUND

Genetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level.

RESULTS

To address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy.

CONCLUSIONS

SNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory .

Sex-dependent placental methylation quantitative trait loci provide insight into the prenatal origins of childhood onset traits and conditions

Molecular quantitative trait loci (QTLs) allow us to understand the biology captured in genome-wide association studies (GWASs). The placenta regulates fetal development and shows sex differences in DNA methylation. We therefore hypothesized that placental methylation QTL (mQTL) explain variation in genetic risk for childhood onset traits, and that effects differ by sex. We analyzed 411 term placentas from two studies and found 49,252 methylation (CpG) sites with mQTL and 2,489 CpG sites with sex-dependent mQTL. All mQTL were enriched in regions that typically affect gene expression in prenatal tissues. All mQTL were also enriched in GWAS results for growth- and immune-related traits, but male- and female-specific mQTL were more enriched than cross-sex mQTL. mQTL colocalized with trait loci at 777 CpG sites, with 216 (28%) specific to males or females. Overall, mQTL specific to male and female placenta capture otherwise overlooked variation in childhood traits.

Risk and protection of different rare protein-coding variants of complement component C4A in age-related macular degeneration

Introduction: Age-related macular degeneration (AMD) is the leading cause of central vision loss in the elderly. One-third of the genetic contribution to this disease remains unexplained. Methods: We analyzed targeted sequencing data from two independent cohorts (4,245 cases, 1,668 controls) which included genomic regions of known AMD loci in 49 genes. Results: At a false discovery rate of <0.01, we identified 11 low-frequency AMD variants (minor allele frequency <0.05). Two of those variants were present in the complement C4A gene, including the replacement of the residues that contribute to the Rodgers-1/Chido-1 blood group antigens: [VDLL1207-1210ADLR (V1207A)] with discovery odds ratio (OR) = 1.7 (p = 3.2 × 10-5) which was replicated in the UK Biobank dataset (3,294 cases, 200,086 controls, OR = 1.52, p = 0.037). A novel variant associated with reduced risk for AMD in our discovery cohort was P1120T, one of the four C4A-isotypic residues. Gene-based tests yielded aggregate effects of nonsynonymous variants in 10 genes including C4A, which were associated with increased risk of AMD. In human eye tissues, immunostaining demonstrated C4A protein accumulation in and around endothelial cells of retinal and choroidal vasculature, and total C4 in soft drusen. Conclusion: Our results indicate that C4A protein in the complement activation pathways may play a role in the pathogenesis of AMD.

Known pathogenic gene variants and new candidates detected in Sudden Unexpected Infant Death using Whole Genome Sequencing

Purpose

To gain insights into potential genetic factors contributing to the infant's vulnerability to Sudden Unexpected Infant Death (SUID).

Methods

Whole Genome Sequencing (WGS) was performed on 145 infants that succumbed to SUID, and 576 healthy adults. Variants were filtered by gnomAD allele frequencies and predictions of functional consequences.

Results

Variants of interest were identified in 86 genes, 63.4% of our cohort. Seventy-one of these have been previously associated with SIDS/SUID/SUDP. Forty-three can be characterized as cardiac genes and are related to cardiomyopathies, arrhythmias, and other conditions. Variants in 22 genes were associated with neurologic functions. Variants were also found in 13 genes reported to be pathogenic for various systemic disorders. Variants in eight genes are implicated in the response to hypoxia and the regulation of reactive oxygen species (ROS) and have not been previously described in SIDS/SUID/SUDP. Seventy-two infants met the triple risk hypothesis criteria (Figure 1).

Conclusion

Our study confirms and further expands the list of genetic variants associated with SUID. The abundance of genes associated with heart disease and the discovery of variants associated with the redox metabolism have important mechanistic implications for the pathophysiology of SUID.

Understanding the Determinants of Sweet Liking in the African and East Asian Ancestry Groups in the U.S. - A Study Protocol

Background

The liking for sweet taste is a powerful driver for consuming added sugars, and therefore, understanding how sweet liking is formed is a critical step in devising strategies to lower added sugars consumption. However, current research on the influence of genetic and environmental factors on sweet liking is mostly based on research conducted with individuals of European ancestry. Whether these results can be generalized to people of other ancestry groups warrants investigation.

Methods

We will determine the differences in allele frequencies in sweet-related genetic variants and their effects on sweet liking in 426 adults of either African or East Asian ancestry, who have the highest and lowest average added sugars intake, respectively, among ancestry groups in the U.S. We will collect information on participants' sweet-liking phenotype, added sugars intake (sweetness exposure), anthropometric measures, place-of-birth, and for immigrants, duration of time living in the U.S. and age when immigrated. Ancestry-specific polygenic scores of sweet liking will be computed based on the effect sizes of the sweet-related genetic variants on the sweet-liking phenotype for each ancestry group. The predictive validity of the polygenic scores will be tested using individuals of African and East Asian ancestry from the UK Biobank. We will also compare sweet liking between U.S.-born individuals and immigrants within each ancestry group to test whether differences in environmental sweetness exposure during childhood affect sweet liking in adulthood.

Discussion

Expanding genetic research on taste to individuals from ancestry groups traditionally underrepresented in such research is consistent with equity goals in sensory and nutrition science. Findings from this study will help in the development of a more personalized nutrition approach for diverse populations.

Trial registration

This protocol has been preregistered with the Center for Open Science (https://doi.org/10.17605/OSF.IO/WPR9E) and is approved by the City University of New York Human Research Protection Program (IRB#: 2023-0064-Brooklyn).

Serum biomarkers are altered in UK Biobank participants with mosaic chromosomal alterations

Age-related clonal expansion of cells harbouring mosaic chromosomal alterations (mCAs) is one manifestation of clonal haematopoiesis. Identifying factors that influence the generation and promotion of clonal expansion of mCAs are key to investigate the role of mCAs in health and disease. Herein, we report on widely measured serum biomarkers and their possible association with mCAs, which could provide new insights into molecular alterations that promote acquisition and clonal expansion. We performed a cross-sectional investigation of the association of 32 widely measured serum biomarkers with autosomal mCAs, mosaic loss of the Y chromosome, and mosaic loss of the X chromosome in 436 784 cancer-free participants from the UK Biobank. mCAs were associated with a range of commonly measured serum biomarkers such as lipid levels, circulating sex hormones, blood sugar homeostasis, inflammation and immune function, vitamins and minerals, kidney function, and liver function. Biomarker levels in participants with mCAs were estimated to differ by up to 5% relative to mCA-free participants, and individuals with higher cell fraction mCAs had greater deviation in mean biomarker values. Polygenic scores associated with sex hormone binding globulin, vitamin D, and total cholesterol were also associated with mCAs. Overall, we observed commonly used clinical serum biomarkers related to disease risk are associated with mCAs, suggesting mechanisms involved in these diseases could be related to mCA proliferation and clonal expansion.

Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer's Diseases Sequencing Project Subjects

Structural variations (SVs) are important contributors to the genetics of human diseases. However, their role in Alzheimer's disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. We analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (N = 16,905) and identified 400,234 (168,223 high-quality) SVs. Laboratory validation yielded a sensitivity of 82% (85% for high-quality). We found a significant burden of deletions and duplications in AD cases, particularly for singletons and homozygous events. On AD genes, we observed the ultra-rare SVs associated with the disease, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1. Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, exemplified by a 5k deletion in complete LD with rs143080277 in NCK2. We also identified 16 SVs associated with AD and 13 SVs linked to AD-related pathological/cognitive endophenotypes. This study highlights the pivotal role of SVs in shaping our understanding of AD genetics.

Forensic biogeographical ancestry inference: recent insights and current trends

BACKGROUND

As a powerful complement to the paradigmatic DNA profiling strategy, biogeographical ancestry inference (BGAI) plays a significant part in human forensic investigation especially when a database hit or eyewitness testimony are not available. It indicates one's biogeographical profile based on known population-specific genetic variations, and thus is crucial for guiding authority investigations to find unknown individuals. Forensic biogeographical ancestry testing exploits much of the recent advances in the understanding of human genomic variation and improving of molecular biology.

OBJECTIVE

In this review, recent development of prospective ancestry informative markers (AIMs) and the statistical approaches of inferring biogeographic ancestry from AIMs are elucidated and discussed.

METHODS

We highlight the research progress of three potential AIMs (i.e., single nucleotide polymorphisms, microhaplotypes, and Y or mtDNA uniparental markers) and discuss the prospects and challenges of two methods that are commonly used in BGAI.

CONCLUSION

While BGAI for forensic purposes has been thriving in recent years, important challenges, such as ethics and responsibilities, data completeness, and ununified standards for evaluation, remain for the use of biogeographical ancestry information in human forensic investigations. To address these issues and fully realize the value of BGAI in forensic investigation, efforts should be made not only by labs/institutions around the world independently, but also by inter-lab/institution collaborations.

Population Pharmacogenomics for Health Equity

Health equity means the opportunity for all people and populations to attain optimal health, and it requires intentional efforts to promote fairness in patient treatments and outcomes. Pharmacogenomic variants are genetic differences associated with how patients respond to medications, and their presence can inform treatment decisions. In this perspective, we contend that the study of pharmacogenomic variation within and between human populations-population pharmacogenomics-can and should be leveraged in support of health equity. The key observation in support of this contention is that racial and ethnic groups exhibit pronounced differences in the frequencies of numerous pharmacogenomic variants, with direct implications for clinical practice. The use of race and ethnicity to stratify pharmacogenomic risk provides a means to avoid potential harm caused by biases introduced when treatment regimens do not consider genetic differences between population groups, particularly when majority group genetic profiles are assumed to hold for minority groups. We focus on the mitigation of adverse drug reactions as an area where population pharmacogenomics can have a direct and immediate impact on public health.

Structural Variation Detection and Association Analysis of Whole-Genome-Sequence Data from 16,905 Alzheimer's Diseases Sequencing Project Subjects

Structural variations (SVs) are important contributors to the genetics of numerous human diseases. However, their role in Alzheimer's disease (AD) remains largely unstudied due to challenges in accurately detecting SVs. Here, we analyzed whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP, N=16,905 subjects) and identified 400,234 (168,223 high-quality) SVs. We found a significant burden of deletions and duplications in AD cases (OR=1.05, P=0.03), particularly for singletons (OR=1.12, P=0.0002) and homozygous events (OR=1.10, P<0.0004). On AD genes, the ultra-rare SVs, including protein-altering SVs in ABCA7, APP, PLCG2, and SORL1, were associated with AD (SKAT-O P=0.004). Twenty-one SVs are in linkage disequilibrium (LD) with known AD-risk variants, e.g., a deletion (chr2:105731359-105736864) in complete LD (R2=0.99) with rs143080277 (chr2:105749599) in NCK2. We also identified 16 SVs associated with AD and 13 SVs associated with AD-related pathological/cognitive endophenotypes. Our findings demonstrate the broad impact of SVs on AD genetics.

Association of Common and Rare Variants with Alzheimer's Disease in over 13,000 Diverse Individuals with Whole-Genome Sequencing from the Alzheimer's Disease Sequencing Project

Alzheimer's Disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous. Here, we investigated the association between AD and both common variants and aggregates of rare coding and noncoding variants in 13,371 individuals of diverse ancestry with whole genome sequence (WGS) data. Pooled-population analyses identified genetic variants in or near APOE, BIN1, and LINC00320 significantly associated with AD (p < 5×10-8). Population-specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and noncoding variants in this region. Finally, we observed suggestive associations (p < 5×10-5) of aggregates of rare coding rare variants in ABCA7 among non-Hispanic Whites (p=5.4×10-6), and rare noncoding variants in the promoter of TOMM40 distinct of APOE in pooled-population analyses (p=7.2×10-8). Complementary pooled-population and population-specific analyses offered unique insights into the genetic architecture of AD.

Polygenic risk scores point toward potential genetic mechanisms of type 2 myocardial infarction in people with HIV

BACKGROUND

People with human immunodeficiency virus (HIV) infection (PWH) are at higher risk of myocardial infarction (MI) than those without HIV. About half of MIs in PWH are type 2 (T2MI), resulting from mismatch between myocardial oxygen supply and demand, in contrast to type 1 MI (T1MI), which is due to primary plaque rupture or coronary thrombosis. Despite worse survival and rising incidence in the general population, evidence-based treatment recommendations for T2MI are lacking. We used polygenic risk scores (PRS) to explore genetic mechanisms of T2MI compared to T1MI in PWH.

METHODS

We derived 115 PRS for MI-related traits in 9541 PWH enrolled in the Centers for AIDS Research Network of Integrated Clinical Systems cohort with adjudicated T1MI and T2MI. We applied multivariate logistic regression analyses to determine the association with T1MI and T2MI. Based on initial findings, we performed gene set enrichment analysis of the top variants composing PRS associated with T2MI.

RESULTS

We found that T1MI was strongly associated with PRS for cardiovascular disease, lipid profiles, and metabolic traits. In contrast, PRS for alcohol dependence and cholecystitis, significantly enriched in energy metabolism pathways, were predictive of T2MI risk. The association remained after the adjustment for actual alcohol consumption.

CONCLUSIONS

We demonstrate distinct genetic traits associated with T1MI and T2MI among PWH further highlighting their etiological differences and supporting the role of energy regulation in T2MI pathogenesis.

Race, Ethnicity, and Pharmacogenomic Variation in the United States and the United Kingdom

The relevance of race and ethnicity to genetics and medicine has long been a matter of debate. An emerging consensus holds that race and ethnicity are social constructs and thus poor proxies for genetic diversity. The goal of this study was to evaluate the relationship between race, ethnicity, and clinically relevant pharmacogenomic variation in cosmopolitan populations. We studied racially and ethnically diverse cohorts of 65,120 participants from the United States All of Us Research Program (All of Us) and 31,396 participants from the United Kingdom Biobank (UKB). Genome-wide patterns of pharmacogenomic variation-6311 drug response-associated variants for All of Us and 5966 variants for UKB-were analyzed with machine learning classifiers to predict participants' self-identified race and ethnicity. Pharmacogenomic variation predicts race/ethnicity with averages of 92.1% accuracy for All of Us and 94.3% accuracy for UKB. Group-specific prediction accuracies range from 99.0% for the White group in UKB to 92.9% for the Hispanic group in All of Us. Prediction accuracies are substantially lower for individuals who identified with more than one group in All of Us (16.7%) or as Mixed in UKB (70.7%). There are numerous individual pharmacogenomic variants with large allele frequency differences between race/ethnicity groups in both cohorts. Frequency differences for toxicity-associated variants predict hundreds of adverse drug reactions per 1000 treated participants for minority groups in All of Us. Our results indicate that race and ethnicity can be used to stratify pharmacogenomic risk in the US and UK populations and should not be discounted when making treatment decisions. We resolve the contradiction between the results reported here and the orthodoxy of race and ethnicity as non-genetic, social constructs by emphasizing the distinction between global and local patterns of human genetic diversity, and we stress the current and future limitations of race and ethnicity as proxies for pharmacogenomic variation.

Ethnic disparities in ambient air and traffic-related pollution exposure and ethnic-specific impacts on clinical biomarker levels

BACKGROUND

Although characterizing the inequality in pollution exposure burden across ethnic groups and the ethnic-specific exposure associations is of great social and public health importance, it has not been systematically investigated in large population studies.

METHODS

The UK Biobank data (N = 485, 806) of individual-level air ambient and traffic-related pollution exposure, biomarkers routinely used in clinical practice, genotype, life-style factors, and socioeconomic status were analyzed. Air pollution exposure estimates were compared among six genetically inferred ethnic groups. We also quantified the association between exposure and biomarkers within and across ethnicities.

RESULTS

Non-European participants (defined by genetics) disproportionately bear a higher burden of exposure than their European counterparts even after adjusting for covariables including socioeconomic status. For example, exposure to NO₂ in people with African ancestry was 30.7 % higher (p = 1.5E-786) than European subjects. Within the genetically defined African group, larger African genetic ancestry proportion (AGAP) was linked to higher ambient air pollutant exposure. Trans-Ethnic analysis identified 32 clinical biomarkers associated with environmental exposure. For 13 biomarkers, the association with exposure was significantly different or even in opposing directions across ethnic groups.

CONCLUSIONS

Substantial disparities in air pollution exposure was observed among genetically-defined ethnic groups. Most importantly, we show that the impact of exposure on biomarkers varies by ethnicity. Reducing the disproportionally high exposure burden on non-European populations and alleviating the adverse consequences in an ethnic-specific manner are of great urgency and significance.

HostSeq: a Canadian whole genome sequencing and clinical data resource

HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND

The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.

METHODS

We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.

RESULTS

The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.

CONCLUSION

Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.

The approach to patient clustering based on the microchip data confined to distinct loci using the combinations of variants

Fuchs' endothelial corneal dystrophy is a socially significant hereditary disease. More than a half of cases in the European population are caused by the increased number of trinucleotude repeats in the TCF4 gene. The study was aimed to develop and test the approach of dividing patients into groups based on the chip-based genotyping and genome-wide association study (GWAS) results. The analysis was conducted using FECD Genetics Multi-center Study and AREDs project datasets containing the data of 1721 clinical cases and 2408 control patients. When analyzing the GWAS results, the patients and the control group were divided into two groups by means of hierarchical clustering suggesting that patients with the increased number of repeats in the TCF4 gene are carriers of specific combinations of genomic variants (haplotypes). It was shown that individual variants cannot be used for the molecular genetic stratification of patients with the increased number of repeats in TCF4 due to inconsistent results obtained for the variants. Furthermore, the haplotype-based approach outperformed the SNPs in terms of odds ratio. The paper proposes a method that enables further search for the biologically relevant combinations of genomic variants.

Relative impact of genetic ancestry and neighborhood socioeconomic status on all-cause mortality in self-identified African Americans

Self-identified race/ethnicity is a correlate of both genetic ancestry and socioeconomic factors, both of which may contribute to racial disparities in mortality. Investigators often hold a priori assumptions, rarely made explicit, regarding the relative importance of these factors. We studied 2,239 self-identified African Americans (SIAA) from the Prostate, Lung, Colorectal and Ovarian screening trial enrolled from 1993–1998 and followed prospectively until 2019 or until death, whichever came first. Percent African genetic ancestry was estimated using the GRAF-Pop distance-based method. A neighborhood socioeconomic status (nSES) index was estimated using census tract measures of income, housing, and employment and linked to participant residence in 2012. We used Directed Acyclic Graphs (DAGs) to represent causal models favoring (1) biomedical and (2) social causes of mortality. Hazard ratios were estimated using Cox models adjusted for sociodemographic, behavioral, and neighborhood covariates guided by each DAG. 901 deaths occurred over 40,767 person-years of follow-up. In unadjusted (biomedical) models, a 10% increase in percent African ancestry was associated with a 7% higher rate of all-cause mortality (HR: 1.07, 95% CI: 1.02, 1.12). This effect was attenuated in covariate adjusted (social) models (aHR: 1.01, 95% CI: 0.96, 1.06). Mortality was lower comparing participants in the highest to lowest nSES quintile following adjustment for covariates and ancestry (aHR: 0.74, 95% CI: 0.57, 0.98, Ptrend = 0.017). Higher African ancestry and lower nSES were associated with higher mortality, but African ancestry was not associated with mortality following covariate adjustment. Socioeconomic factors may be more important drivers of mortality in African Americans.

Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort

The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.

Association between ABO and Duffy blood types and circulating chemokines and cytokines

Blood group antigens are inherited traits that may play a role in immune and inflammatory processes. We investigated associations between blood groups and circulating inflammation-related molecules in 3537 non-Hispanic white participants selected from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Whole-genome scans were used to infer blood types for 12 common antigen systems based on well-characterized single-nucleotide polymorphisms. Serum levels of 96 biomarkers were measured on multiplex fluorescent bead-based panels. We estimated marker associations with blood type using weighted linear or logistic regression models adjusted for age, sex, smoking status, and principal components of population substructure. Bonferroni correction was used to control for multiple comparisons, with two-sided p values < 0.05 considered statistically significant. Among the 1152 associations tested, 10 were statistically significant. Duffy blood type was associated with levels of CXCL6/GCP2, CXCL5/ENA78, CCL11/EOTAXIN, CXCL1/GRO, CCL2/MCP1, CCL13/MCP4, and CCL17/TARC, whereas ABO blood type was associated with levels of sVEGFR2, sVEGFR3, and sGP130. Post hoc pairwise t-tests showed that individuals with type Fy(a+b−) had the lowest mean levels of all Duffy-associated markers, while individuals with type A blood had the lowest mean levels of all ABO-associated markers. Additional work is warranted to explore potential clinical implications of these differences.

Robust, flexible, and scalable tests for Hardy-Weinberg equilibrium across diverse ancestries

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

Radiation-related genomic profile of papillary thyroid carcinoma after the Chernobyl accident

The 1986 Chernobyl nuclear power plant accident increased papillary thyroid carcinoma (PTC) incidence in surrounding regions, particularly for radioactive iodine (131I)-exposed children. We analyzed genomic, transcriptomic, and epigenomic characteristics of 440 PTCs from Ukraine (from 359 individuals with estimated childhood 131I exposure and 81 unexposed children born after 1986). PTCs displayed radiation dose-dependent enrichment of fusion drivers, nearly all in the mitogen-activated protein kinase pathway, and increases in small deletions and simple/balanced structural variants that were clonal and bore hallmarks of nonhomologous end-joining repair. Radiation-related genomic alterations were more pronounced for individuals who were younger at exposure. Transcriptomic and epigenomic features were strongly associated with driver events but not radiation dose. Our results point to DNA double-strand breaks as early carcinogenic events that subsequently enable PTC growth after environmental radiation exposure.

Genome-wide epistasis analysis for Alzheimer's disease and implications for genetic risk prediction

BACKGROUND

Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer's disease (AD). Epistasis has been considered as one of the main causes of "missing heritability" in AD.

METHODS

We performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction.

RESULTS

We identified 2 candidate genetic interactions with PFDR <  0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR <  0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD.

CONCLUSIONS

In summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.

Genetic architecture of cardiometabolic risks in people living with HIV

BACKGROUND

Advances in antiretroviral therapies have greatly improved the survival of people living with human immunodeficiency virus (HIV) infection (PLWH); yet, PLWH have a higher risk of cardiovascular disease than those without HIV. While numerous genetic loci have been linked to cardiometabolic risk in the general population, genetic predictors of the excessive risk in PLWH are largely unknown.

METHODS

We screened for common and HIV-specific genetic variants associated with variation in lipid levels in 6284 PLWH (3095 European Americans [EA] and 3189 African Americans [AA]), from the Centers for AIDS Research Network of Integrated Clinical Systems cohort. Genetic hits found exclusively in the PLWH cohort were tested for association with other traits. We then assessed the predictive value of a series of polygenic risk scores (PRS) recapitulating the genetic burden for lipid levels, type 2 diabetes (T2D), and myocardial infarction (MI) in EA and AA PLWH.

RESULTS

We confirmed the impact of previously reported lipid-related susceptibility loci in PLWH. Furthermore, we identified PLWH-specific variants in genes involved in immune cell regulation and previously linked to HIV control, body composition, smoking, and alcohol consumption. Moreover, PLWH at the top of European-based PRS for T2D distribution demonstrated a > 2-fold increased risk of T2D compared to the remaining 95% in EA PLWH but to a much lesser degree in AA. Importantly, while PRS for MI was not predictive of MI risk in AA PLWH, multiethnic PRS significantly improved risk stratification for T2D and MI.

CONCLUSIONS

Our findings suggest that genetic loci involved in the regulation of the immune system and predisposition to risky behaviors contribute to dyslipidemia in the presence of HIV infection. Moreover, we demonstrate the utility of the European-based and multiethnic PRS for stratification of PLWH at a high risk of cardiometabolic diseases who may benefit from preventive therapies.

Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations

Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.

Higher prevalence of homologous recombination deficiency in tumors from African Americans versus European Americans

To improve our understanding of longstanding disparities in incidence and mortality in lung cancer across ancestry, we performed a systematic comparative analysis of molecular features in tumors from African Americans (AAs) and European Americans (EAs). We find that lung squamous cell carcinoma tumors from AAs exhibit higher genomic instability-the proportion of non-diploid genome-aggressive molecular features such as chromothripsis and higher homologous recombination deficiency (HRD). In The Cancer Genome Atlas, we demonstrate that high genomic instability, HRD and chromothripsis among tumors from AAs is found across many cancer types. The prevalence of germline HRD (that is, the total number of pathogenic variants in homologous recombination genes) is higher in tumors from AAs, suggesting that the somatic differences observed have genetic ancestry origins. We also identify AA-specific copy-number-based arm-, focal- and gene-level recurrent features in lung cancer, including higher frequencies of PTEN deletion and KRAS amplification. These results highlight the importance of including under-represented populations in genomics research.

Mitochondrial and Nuclear Mitochondrial Variants in Allergic Diseases

The mitochondrial genome encodes core catalytic peptides that affect major metabolic processes within a cell. Here, we investigated the association between mitochondrial DNA (mtDNA) variants and allergic diseases, including atopic dermatitis (AD) and asthma, alongside heteroplasmy within the mtDNA in subjects with allergic sensitization. We collected genotype data from 973 subjects with allergic sensitization, consisting of 632 children with AD, 498 children with asthma, and 481 healthy controls by extracting DNA from their blood samples. Fisher's exact test was used to investigate mtDNA and nuclear DNA variants related to mitochondrial function (MT-nDNA) to identify their association with allergic diseases. Among the 69 mtDNA variants, rs28357671 located on the MT-ND6 gene displayed statistically significant associations with allergic diseases (Bonferroni-corrected P < 7.25E-4), while 6, 4, and 2 genes were associated with allergic sensitization, AD, and asthma, respectively (P < 0.0002), including NLRX1, OCA2, and CHCHD3 among the MT-nDNA genes. Heteroplasmy of mitochondrial DNA associated with allergic sensitization was evaluated in a separate cohort of patients consisting of 59 subjects with allergic sensitization and 52 controls. Heteroplasmy was verified when a patient carried both alleles of a mitochondrial single-nucleotide polymorphism (SNP) when clustered. One of the 134 mitochondrial SNPs showed heteroplasmy at a level of 0.4313 when clustering was applied. The probe sequence located at mitochondrial position 16217 and within the D-loop, which acts as a major control site for mtDNA expression. This is the first study to evaluate the association between mitochondrial DNA variants and allergic diseases. A harmonized effect of genes related to mitochondrial function may contribute to the risk of allergic diseases.