Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Leveraging the transcriptome to further our understanding of GWAS findings: eQTLs associated with genes related to LDL and LDL subclasses, in a cohort of African Americans

Background: GWAS discoveries often pose a significant challenge in terms of understanding their underlying mechanisms. Further research, such as an integration with expression quantitative trait locus (eQTL) analyses, are required to decipher the mechanisms connecting GWAS variants to phenotypes. An eQTL analysis was conducted on genes associated with low-density lipoprotein (LDL) cholesterol and its subclasses, with the aim of pinpointing genetic variants previously implicated in GWAS studies focused on lipid-related traits. Notably, the study cohort consisted of African Americans, a population characterized by a heightened prevalence of hypercholesterolemia. Methods: A comprehensive differential expression (DE) analysis was undertaken, with a dataset of 17,948 protein-coding mRNA transcripts extracted from the whole-blood transcriptomes of 416 samples to identify mRNA transcripts associated with LDL, with further granularity delineated between small LDL and large LDL subclasses. Subsequently, eQTL analysis was conducted with a subset of 242 samples for which whole-genome sequencing data were available to identify single-nucleotide polymorphisms (SNPs) associated with the LDL-related mRNA transcripts. Lastly, plausible functional connections were established between the identified eQTLs and genetic variants reported in the GWAS catalogue. Results: DE analysis revealed 1,048, 284, and 94 mRNA transcripts that exhibited differential expression in response to LDL, small LDL, and large LDL, respectively. The eQTL analysis identified a total of 9,950 significant SNP-mRNA associations involving 6,955 SNPs including a subset 101 SNPs previously documented in GWAS of LDL and LDL-related traits. Conclusion: Through comprehensive differential expression analysis, we identified numerous mRNA transcripts responsive to LDL, small LDL, and large LDL. Subsequent eQTL analysis revealed a rich landscape of eQTL-mRNA associations, including a subset of eQTL reported in GWAS studies of LDL and related traits. The study serves as a testament to the important role of integrative genomics in unraveling the enigmatic GWAS relationships between genetic variants and the complex fabric of human traits and diseases.

Integrative functional genomic analyses identify genetic variants influencing skin pigmentation in Africans

Skin color is highly variable in Africans, yet little is known about the underlying molecular mechanism. Here we applied massively parallel reporter assays to screen 1,157 candidate variants influencing skin pigmentation in Africans and identified 165 single-nucleotide polymorphisms showing differential regulatory activities between alleles. We combine Hi-C, genome editing and melanin assays to identify regulatory elements for MFSD12, HMG20B, OCA2, MITF, LEF1, TRPS1, BLOC1S6 and CYB561A3 that impact melanin levels in vitro and modulate human skin color. We found that independent mutations in an OCA2 enhancer contribute to the evolution of human skin color diversity and detect signals of local adaptation at enhancers of MITF, LEF1 and TRPS1, which may contribute to the light skin color of Khoesan-speaking populations from Southern Africa. Additionally, we identified CYB561A3 as a novel pigmentation regulator that impacts genes involved in oxidative phosphorylation and melanogenesis. These results provide insights into the mechanisms underlying human skin color diversity and adaptive evolution.

CABLES1 expression is reduced in human subcutaneous adipose tissue in obesity and type 2 diabetes but may not directly impact adipocyte glucose and lipid metabolism

Cdk5 and Abl enzyme substrate 1 (CABLES1) is a cell cycle regulator that has previously been identified as a candidate gene for obesity-related phenotypes, but little is known about its role in adipose tissue metabolism. In this study, we explore the role of CABLES1 in obesity and type 2 diabetes (T2D) in human subcutaneous adipose tissue (SAT). We performed gene expression analysis of SAT obtained from subjects with and without T2D, and from a second validation cohort consisting of subjects without T2D. We used CRISPR/Cas9 genome editing to perform CABLES1 loss-of-function studies in human primary preadipocytes and assessed them functionally after differentiation. CABLES1 gene expression in SAT was decreased in T2D by almost 25%, and inversely associated with insulin resistance markers and hyperglycaemia. mRNA levels were reduced with increasing BMI and negatively correlated with obesity markers. We found that adipocytes are likely the main CABLES1-expressing cell type in SAT, but CABLES1 depletion in adipocytes caused no phenotypical changes in regards to differentiation, glucose uptake, or expression of key genes of adipocyte function. These findings suggest that CABLES1 gene expression in SAT might be altered in obesity and T2D as a consequence of metabolic dysregulation rather than being a causal factor.

Skin Phototype and Disease: A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy Using PRS in the GCAT Cohort

Human pigmentation has largely been associated with different disease prevalence among populations, but most of these studies are observational and inconclusive. Known to be genetically determined, pigmentary traits have largely been studied by Genome-Wide Association Study (GWAS), mostly in Caucasian ancestry cohorts from North Europe, identifying robustly, several loci involved in many of the pigmentary traits. Here, we conduct a detailed analysis by GWAS and Polygenic Risk Score (PRS) of 13 pigmentary-related traits in a South European cohort of Caucasian ancestry (n = 20,000). We observed fair phototype strongly associated with non-melanoma skin cancer and other dermatoses and confirmed by PRS-approach the shared genetic basis with skin and eye diseases, such as melanoma (OR = 0.95), non-melanoma skin cancer (OR = 0.93), basal cell carcinoma (OR = 0.97) and darker phototype with vitiligo (OR = 1.02), cataracts (OR = 1.04). Detailed genetic analyses revealed 37 risk loci associated with 10 out of 13 analyzed traits, and 16 genes significantly associated with at least two pigmentary traits. Some of them have been widely reported, such as MC1R, HERC2, OCA2, TYR, TYRP1, SLC45A2, and some novel candidate genes C1QTNF3, LINC02876, and C1QTNF3-AMACR have not been reported in the GWAS Catalog, with regulatory potential. These results highlight the importance of the assess phototype as a genetic proxy of skin functionality and disease when evaluating open mixed populations.

Association of TYR SNP rs1042602 with Melanoma Risk and Prognosis

Cutaneous melanoma is the most aggressive of skin tumors. In order to discover new biomarkers that could help us improve prognostic prediction in melanoma patients, we have searched for germline DNA variants associated with melanoma progression. Thus, after exome sequencing of a set of melanoma patients and healthy control individuals, we identified rs1042602, an SNP within TYR, as a good candidate. After genotyping rs1042602 in 1025 patients and 773 healthy donors, we found that the rs1042602-A allele was significantly associated with susceptibility to melanoma (CATT test: p = 0.0035). Interestingly, we also observed significant differences between patients with good and bad prognosis (5 years of follow-up) (n = 664) (CATT test for all samples p = 0.0384 and for men alone p = 0.0054). Disease-free-survival (DFS) analyses also showed that patients with the A allele had shorter DFS periods. In men, the association remained significant even in a multivariate Cox Proportional-hazards model, which was adjusted for age at diagnosis, Breslow thickness, ulceration and melanoma subtype (HR 0.4; 95% confidence interval (CI) 0.20-0.83; p = 0.0139). Based on our results, we propose that rs1042602-A is a risk allele for melanoma, which also seems to be responsible for a poorer prognosis of the disease, particularly in men.

Investigating the genetic architecture of eye colour in a Canadian cohort

Eye color is highly variable in populations with European ancestry, ranging from low to high quantities of melanin in the iris. Polymorphisms in the HERC2/OCA2 locus have the largest effect on eye color in these populations, although other genomic regions also influence eye color. We performed genome-wide association studies of eye color in a Canadian cohort of European ancestry (N = 5,641) and investigated candidate causal variants. We uncovered several candidate causal signals in the HERC2/OCA2 region, whereas other loci likely harbor a single causal signal. We observed colocalization of eye color signals with the expression or methylation profiles of cultured primary melanocytes. Genetic correlations of eye and hair color suggest high genome-wide pleiotropy, but locus-level differences in the genetic architecture of both traits. Overall, we provide a better picture of the polymorphisms underpinning eye color variation, which may be a consequence of specific molecular processes in the iris melanocytes.

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Genome-wide association studies of eye color in 5,641 participants

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Multiple independent candidate causal variants were identified across HERC2/OCA2

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Single candidate causal variants observed on or near IRF4, SLC24A4, TYR, and TYRP1

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Colocalization of eye color signals with expression and methylation profiles

Association of Novel Loci With Keratoconus Susceptibility in a Multitrait Genome-Wide Association Study of the UK Biobank Database and Canadian Longitudinal Study on Aging

Importance

Keratoconus can be a debilitating corneal ectasia in which the cornea thins, bulges, and steepens into a conical shape. Early features of keratoconus include myopia and irregular astigmatism, which affect vision and can be treated with contact lenses, collagen cross-linking, or, in advanced cases, corneal transplant. Recent estimates of the prevalence of keratoconus based on results of Scheimpflug imaging in young adults are as high as 1.2%. However, obtaining very large keratoconus data sets for a genome-wide association study (GWAS) is problematic because few population studies include Scheimpflug imaging and because severe keratoconus is relatively rare.

Objective

To identify novel keratoconus loci using corneal resistance factor (CRF) and central corneal thickness (CCT).

Design, Setting, and Participants

This multitrait GWAS used European ancestry CRF data from UK Biobank (UKB) (n = 105 427) and the Canadian Longitudinal Study on Aging (CLSA) (n = 18 307) and European ancestry CCT data from the International Glaucoma Genetics Consortium (IGGC) (n = 17 803). The CRF and CCT variants in published keratoconus data sets (4669 cases and 116 547 controls) were compared. The data set from UKB was compiled March 24, 2020; data were released from the CLSA in July 2020; and IGGC data were available from May 1, 2018.

Main Outcomes and Measures

Association of CRF and CCT variants with keratoconus risk.

Results

The GWAS included 4 cohorts: 105 427 UKB European ancestry (56 134 women [53.2%] and 49 293 men [46.7%]; mean [SD] age, 57 [8] years), 5029 UKB South Asian ancestry (2368 women [47.1%] and 2661 men [52.9%]; mean [SD] age, 54 [8] years), 902 UKB East Asian ancestry (622 women [68.9%] and 280 men [31.0%]; mean [SD] age, 53 [8] years), and 18 307 CLSA European ancestry (9260 women [50.6%] and 9047 men [49.4%]; mean [SD] age, 63 [10] years) participants. A total of 369 CRF and 233 CCT loci were identified, including 36 novel CRF loci and 114 novel CCT loci. Twenty-nine CRF loci and 24 CCT loci were associated with keratoconus. Polygenic risk scores (PRS) were constructed using CRF- and CCT-associated variants and published keratoconus variants. The PRS result showed that adding a CRF- or CCT-based PRS to the keratoconus PRS from previously published variants improved the prediction area under the receiver operating characteristic curve (from 0.705 to 0.756 for CRF and from 0.715 to 0.755 for CCT).

Conclusions and Relevance

These findings support the use of multitrait modeling of corneal parameters in a relatively large data set to identify new keratoconus risk loci and enhance polygenic risk score models.

Identification of Prognostic Markers of N6-Methylandenosine-Related Noncoding RNAs in Non-Small-Cell Lung Cancer

Background

Non-small-cell lung cancer (NSCLC) is a major type of lung carcinoma that threatens the health and life of humans worldwide. We aimed to establish an n6-methyladenosine (m6A)-relevant ncRNA model to effectively evaluate the outcome of patients.

Methods

m6A-Related ncRNAs (lncRNA/miRNA) were acquired from the UCSC Xena database. Pearson's correlation analysis among 21 m6A regulatory factors and ncRNAs were implemented to explore m6A-relevant ncRNAs. Weighted gene co-expression network analysis (WGCNA) identified hub modules of gene associated with prognosis of NSCLC patients. Univariate Cox regression analysis identified 80 m6A-related ncRNAs. Least absolute shrinkage and selector operation (LASSO) filtered out redundant factors and established a risk score model (m6A-NSCLC) in the TCGA training data set. Validation of prognostic ability was performed using testing data sets from the TCGA database. We also conducted a correlation analysis among the risk score and different clinical traits. Both univariate and multivariate Cox analyses were combined to verify prognostic factors which have independent value, and a nomogram on the basis of m6A-NSCLC risk scores and clinical traits was constructed to assess the prognosis of patients. In addition, we screened differentially expressed genes (DEGs) based on different risk scores and performed enrichment analysis. Finally, 21 m6A regulators were detected to be differentially expressed between two risk groups.

Results

An m6A-NSCLC risk model with 18 ncRNAs was constructed. By comparison with low-risk patients, high-risk score patients had poor prognosis. The distribution of risk score in the tumor size and extent (T), number of near lymph nodes (N), clinical stage, sex, and tumor types was significantly different. The risk score could act as an independent prognostic factor with the nomogram assessing overall survival in NSCLC. DEGs inherent to cell movement and immune regulation were involved in NSCLC development. Furthermore, 18 of 21 m6A regulators were differentially expressed, implying their correlation to survival prognosis.

Conclusion

The m6A-NSCLC could be effectively utilized for evaluation of prognosis of patients.

GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing

The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies.

Genome-Wide Association Study for Body Length, Body Height, and Total Teat Number in Large White Pigs

Body length, body height, and total teat number are economically important traits in pig breeding, as these traits are usually associated with the growth, reproductivity, and longevity potential of piglets. Here, we report a genetic analysis of these traits using a population comprising 2,068 Large White pigs. A genotyping-by-sequencing (GBS) approach was used to provide high-density genome-wide SNP discovery and genotyping. Univariate and bivariate animal models were used to estimate heritability and genetic correlations. The results showed that heritability estimates for body length, body height, and total teat number were 0.25 ± 0.04, 0.11 ± 0.03, and 0.22 ± 0.04, respectively. The genetic correlation between body length and body height exhibited a strongly positive correlation (0.63 ± 0.15), while a positive but low genetic correlation was observed between total teat number and body length. Furthermore, we used two different genome-wide association study (GWAS) approaches: single-locus GWAS and weighted single-step GWAS (WssGWAS), to identify candidate genes for these traits. Single-locus GWAS detected 76, 13, and 29 significant single-nucleotide polymorphisms (SNPs) associated with body length, body height, and total teat number. Notably, the most significant SNP (S17_15781294), which is located 20 kb downstream of the BMP2 gene, explained 9.09% of the genetic variance for body length traits, and it also explained 9.57% of the genetic variance for body height traits. In addition, another significant SNP (S7_97595973), which is located in the ABCD4 gene, explained 8.92% of the genetic variance for total teat number traits. GWAS results for these traits identified some candidate genomic regions, such as SSC6: 14.96–15.02 Mb, SSC7: 97.18–98.18 Mb, SSC14: 128.29–131.15 Mb, SSC17: 15.39–17.27 Mb, and SSC17: 22.04–24.15 Mb, providing a starting point for further inheritance research. Most quantitative trait loci were detected by single-locus GWAS and WssGWAS. These findings reveal the complexity of the genetic mechanism of the three traits and provide guidance for subsequent genetic improvement through genome selection.

A custom capture sequence approach for oculocutaneous albinism identifies structural variant alleles at the OCA2 locus

Oculocutaneous albinism (OCA) is a heritable disorder of pigment production that manifests as hypopigmentation and altered eye development. Exon sequencing of known OCA genes is unsuccessful in producing a complete molecular diagnosis for a significant number of affected individuals. We sequenced the DNA of individuals with OCA using short-read custom capture sequencing that targeted coding, intronic, and noncoding regulatory regions of known OCA genes, and genome-wide association study-associated pigmentation loci. We identified an OCA2 complex structural variant (CxSV), defined by a 143 kb inverted segment reintroduced in intron 1, upstream of the native location. The corresponding CxSV junctions were observed in 11/390 probands screened. The 143 kb CxSV presents in one family as a copy number variant duplication for the 143 kb region. In the remaining 10/11 families, the 143 kb CxSV acquired an additional 184 kb deletion across the same region, restoring exons 3-19 of OCA2 to a copy-number neutral state. Allele-associated haplotype analysis found rare SNVs rs374519281 and rs139696407 are linked with the 143 kb CxSV in both OCA2 alleles. For individuals in which customary molecular evaluation does not reveal a biallelic OCA diagnosis, we recommend preliminary screening for these haplotype-associated rare variants, followed by junction-specific validation for the OCA2 143 kb CxSV.

Evolutionary genetics of skin pigmentation in African populations

Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.

Trans-ethnic meta-analysis identifies new loci associated with longitudinal blood pressure traits

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with cross-sectional blood pressure (BP) traits; however, GWAS based on longitudinal BP have been underexplored. We performed ethnic-specific and trans-ethnic GWAS meta-analysis using longitudinal and cross-sectional BP data of 33,720 individuals from five cohorts in the US and one in Brazil. In addition to identifying several known loci, we identified thirteen novel loci with nine based on longitudinal and four on cross-sectional BP traits. Most of the novel loci were ethnic- or study-specific, with the majority identified in African Americans (AA). Four of these discoveries showed additional evidence of association in independent datasets, including an intergenic variant (rs4060030, p = 7.3 × 10^–9) with reported regulatory function. We observed a high correlation between the meta-analysis results for baseline and longitudinal average BP (rho = 0.48). BP trajectory results were more correlated with those of average BP (rho = 0.35) than baseline BP(rho = 0.18). Heritability estimates trended higher for longitudinal traits than for cross-sectional traits, providing evidence for different genetic architectures. Furthermore, the longitudinal data identified up to 20% more BP known associations than did cross-sectional data. Our analyses of longitudinal BP data in diverse ethnic groups identified novel BP loci associated with BP trajectory, indicating a need for further longitudinal GWAS on BP and other age-related traits.

A likelihood ratio approach for identifying three-quarter siblings in genetic databases

The detection of family relationships in genetic databases is of interest in various scientific disciplines such as genetic epidemiology, population and conservation genetics, forensic science, and genealogical research. Nowadays, screening genetic databases for related individuals forms an important aspect of standard quality control procedures. Relatedness research is usually based on an allele sharing analysis of identity by state (IBS) or identity by descent (IBD) alleles. Existing IBS/IBD methods mainly aim to identify first-degree relationships (parent-offspring or full siblings) and second degree (half-siblings, avuncular, or grandparent-grandchild) pairs. Little attention has been paid to the detection of in-between first and second-degree relationships such as three-quarter siblings (3/4S) who share fewer alleles than first-degree relationships but more alleles than second-degree relationships. With the progressively increasing sample sizes used in genetic research, it becomes more likely that such relationships are present in the database under study. In this paper, we extend existing likelihood ratio (LR) methodology to accurately infer the existence of 3/4S, distinguishing them from full siblings and second-degree relatives. We use bootstrap confidence intervals to express uncertainty in the LRs. Our proposal accounts for linkage disequilibrium (LD) by using marker pruning, and we validate our methodology with a pedigree-based simulation study accounting for both LD and recombination. An empirical genome-wide array data set from the GCAT Genomes for Life cohort project is used to illustrate the method.

Combination of Genome-Wide Polymorphisms and Copy Number Variations of Pharmacogenes in Koreans

For predicting phenotypes and executing precision medicine, combination analysis of single nucleotide variants (SNVs) genotyping with copy number variations (CNVs) is required. The aim of this study was to discover SNVs or common copy CNVs and examine the combined frequencies of SNVs and CNVs in pharmacogenes using the Korean genome and epidemiology study (KoGES), a consortium project. The genotypes (N = 72,299) and CNV data (N = 1000) were provided by the Korean National Institute of Health, Korea Centers for Disease Control and Prevention. The allele frequencies of SNVs, CNVs, and combined SNVs with CNVs were calculated and haplotype analysis was performed. CYP2D6 rs1065852 (c.100C>T, p.P34S) was the most common variant allele (48.23%). A total of 8454 haplotype blocks in 18 pharmacogenes were estimated. DMD ranked the highest in frequency for gene gain (64.52%), while TPMT ranked the highest in frequency for gene loss (51.80%). Copy number gain of CYP4F2 was observed in 22 subjects; 13 of those subjects were carriers with CYP4F2*3 gain. In the case of TPMT, approximately one-half of the participants (N = 308) had loss of the TPMT*1*1 diplotype. The frequencies of SNVs and CNVs in pharmacogenes were determined using the Korean cohort-based genome-wide association study.

Genomewide Association Study of Severe Covid-19 with Respiratory Failure

BACKGROUND

There is considerable variation in disease behavior among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (Covid-19). Genomewide association analysis may allow for the identification of potential genetic factors involved in the development of Covid-19.

METHODS

We conducted a genomewide association study involving 1980 patients with Covid-19 and severe disease (defined as respiratory failure) at seven hospitals in the Italian and Spanish epicenters of the SARS-CoV-2 pandemic in Europe. After quality control and the exclusion of population outliers, 835 patients and 1255 control participants from Italy and 775 patients and 950 control participants from Spain were included in the final analysis. In total, we analyzed 8,582,968 single-nucleotide polymorphisms and conducted a meta-analysis of the two case-control panels.

RESULTS

We detected cross-replicating associations with rs11385942 at locus 3p21.31 and with rs657152 at locus 9q34.2, which were significant at the genomewide level (P<5×10-8) in the meta-analysis of the two case-control panels (odds ratio, 1.77; 95% confidence interval [CI], 1.48 to 2.11; P = 1.15×10-10; and odds ratio, 1.32; 95% CI, 1.20 to 1.47; P = 4.95×10-8, respectively). At locus 3p21.31, the association signal spanned the genes SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6 and XCR1. The association signal at locus 9q34.2 coincided with the ABO blood group locus; in this cohort, a blood-group-specific analysis showed a higher risk in blood group A than in other blood groups (odds ratio, 1.45; 95% CI, 1.20 to 1.75; P = 1.48×10-4) and a protective effect in blood group O as compared with other blood groups (odds ratio, 0.65; 95% CI, 0.53 to 0.79; P = 1.06×10-5).

CONCLUSIONS

We identified a 3p21.31 gene cluster as a genetic susceptibility locus in patients with Covid-19 with respiratory failure and confirmed a potential involvement of the ABO blood-group system. (Funded by Stein Erik Hagen and others.).

Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease

BACKGROUND

Advanced age-related macular degeneration (AMD) is a leading cause of blindness. While around half of the genetic contribution to advanced AMD has been uncovered, little is known about the genetic architecture of early AMD.

METHODS

To identify genetic factors for early AMD, we conducted a genome-wide association study (GWAS) meta-analysis (14,034 cases, 91,214 controls, 11 sources of data including the International AMD Genomics Consortium, IAMDGC, and UK Biobank, UKBB). We ascertained early AMD via color fundus photographs by manual grading for 10 sources and via an automated machine learning approach for > 170,000 photographs from UKBB. We searched for early AMD loci via GWAS and via a candidate approach based on 14 previously suggested early AMD variants.

RESULTS

Altogether, we identified 10 independent loci with statistical significance for early AMD: (i) 8 from our GWAS with genome-wide significance (P < 5 × 10- 8), (ii) one previously suggested locus with experiment-wise significance (P < 0.05/14) in our non-overlapping data and with genome-wide significance when combining the reported and our non-overlapping data (together 17,539 cases, 105,395 controls), and (iii) one further previously suggested locus with experiment-wise significance in our non-overlapping data. Of these 10 identified loci, 8 were novel and 2 known for early AMD. Most of the 10 loci overlapped with known advanced AMD loci (near ARMS2/HTRA1, CFH, C2, C3, CETP, TNFRSF10A, VEGFA, APOE), except two that have not yet been identified with statistical significance for any AMD. Among the 17 genes within these two loci, in-silico functional annotation suggested CD46 and TYR as the most likely responsible genes. Presence or absence of an early AMD effect distinguished the known pathways of advanced AMD genetics (complement/lipid pathways versus extracellular matrix metabolism).

CONCLUSIONS

Our GWAS on early AMD identified novel loci, highlighted shared and distinct genetics between early and advanced AMD and provides insights into AMD etiology. Our data provide a resource comparable in size to the existing IAMDGC data on advanced AMD genetics enabling a joint view. The biological relevance of this joint view is underscored by the ability of early AMD effects to differentiate the major pathways for advanced AMD.

A Log-Ratio Biplot Approach for Exploring Genetic Relatedness Based on Identity by State

The detection of cryptic relatedness in large population-based cohorts is of great importance in genome research. The usual approach for detecting closely related individuals is to plot allele sharing statistics, based on identity-by-state or identity-by-descent, in a two-dimensional scatterplot. This approach ignores that allele sharing data across individuals has in reality a higher dimensionality, and neither regards the compositional nature of the underlying counts of shared genotypes. In this paper we develop biplot methodology based on log-ratio principal component analysis that overcomes these restrictions. This leads to entirely new graphics that are essentially useful for exploring relatedness in genetic databases from homogeneous populations. The proposed method can be applied in an iterative manner, acting as a looking glass for more remote relationships that are harder to classify. Datasets from the 1,000 Genomes Project and the Genomes For Life-GCAT Project are used to illustrate the proposed method. The discriminatory power of the log-ratio biplot approach is compared with the classical plots in a simulation study. In a non-inbred homogeneous population the classification rate of the log-ratio principal component approach outperforms the classical graphics across the whole allele frequency spectrum, using only identity by state. In these circumstances, simulations show that with 35,000 independent bi-allelic variants, log-ratio principal component analysis, combined with discriminant analysis, can correctly classify relationships up to and including the fourth degree.

Disease networks identify specific conditions and pleiotropy influencing multimorbidity in the general population

Multimorbidity is an emerging topic in public health policy because of its increasing prevalence and socio-economic impact. However, the age- and gender-dependent trends of disease associations at fine resolution, and the underlying genetic factors, remain incompletely understood. Here, by analyzing disease networks from electronic medical records of primary health care, we identify key conditions and shared genetic factors influencing multimorbidity. Three types of diseases are outlined: "central", which include chronic and non-chronic conditions, have higher cumulative risks of disease associations; "community roots" have lower cumulative risks, but inform on continuing clustered disease associations with age; and "seeds of bursts", which most are chronic, reveal outbreaks of disease associations leading to multimorbidity. The diseases with a major impact on multimorbidity are caused by genes that occupy central positions in the network of human disease genes. Alteration of lipid metabolism connects breast cancer, diabetic neuropathy and nutritional anemia. Evaluation of key disease associations by a genome-wide association study identifies shared genetic factors and further supports causal commonalities between nervous system diseases and nutritional anemias. This study also reveals many shared genetic signals with other diseases. Collectively, our results depict novel population-based multimorbidity patterns, identify key diseases within them, and highlight pleiotropy influencing multimorbidity.