NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations

Genome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson's Genetics Program (GP2) and the Center for Alzheimer's and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson's (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson's disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale.

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources

OBJECTIVE

The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings.

MATERIALS AND METHODS

Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy.

RESULTS

Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address.

DISCUSSION

Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Genetic analyses of diverse populations improves discovery for complex traits

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry^1-3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific^4-10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations^11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States-where minority populations have a disproportionately higher burden of chronic conditions¹³-the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities.

SOS2 and ACP1 Loci Identified through Large-Scale Exome Chip Analysis Regulate Kidney Development and Function

Genome-wide association studies have identified >50 common variants associated with kidney function, but these variants do not fully explain the variation in eGFR. We performed a two-stage meta-analysis of associations between genotypes from the Illumina exome array and eGFR on the basis of serum creatinine (eGFRcrea) among participants of European ancestry from the CKDGen Consortium (n_Stage1: 111,666; n_Stage2: 48,343). In single-variant analyses, we identified single nucleotide polymorphisms at seven new loci associated with eGFRcrea (PPM1J, EDEM3, ACP1, SPEG, EYA4, CYP1A1, and ATXN2L; P_Stage1<3.7×10^-7), of which most were common and annotated as nonsynonymous variants. Gene-based analysis identified associations of functional rare variants in three genes with eGFRcrea, including a novel association with the SOS Ras/Rho guanine nucleotide exchange factor 2 gene, SOS2 (P=5.4×10^-8 by sequence kernel association test). Experimental follow-up in zebrafish embryos revealed changes in glomerular gene expression and renal tubule morphology in the embryonic kidney of acp1- and sos2-knockdowns. These developmental abnormalities associated with altered blood clearance rate and heightened prevalence of edema. This study expands the number of loci associated with kidney function and identifies novel genes with potential roles in kidney formation.

Genome-Wide Association of CKD Progression: The Chronic Renal Insufficiency Cohort Study

The rate of decline of renal function varies significantly among individuals with CKD. To understand better the contribution of genetics to CKD progression, we performed a genome-wide association study among participants in the Chronic Renal Insufficiency Cohort Study. Our outcome of interest was CKD progression measured as change in eGFR over time among 1331 blacks and 1476 whites with CKD. We stratified all analyses by race and subsequently, diabetes status. Single-nucleotide polymorphisms (SNPs) that surpassed a significance threshold of P<1×10^-6 for association with eGFR slope were selected as candidates for follow-up and secondarily tested for association with proteinuria and time to ESRD. We identified 12 such SNPs among black patients and six such SNPs among white patients. We were able to conduct follow-up analyses of three candidate SNPs in similar (replication) cohorts and eight candidate SNPs in phenotype-related (validation) cohorts. Among blacks without diabetes, rs653747 in LINC00923 replicated in the African American Study of Kidney Disease and Hypertension cohort (discovery P=5.42×10^-7; replication P=0.039; combined P=7.42×10^-9). This SNP also associated with ESRD (hazard ratio, 2.0 (95% confidence interval, 1.5 to 2.7); P=4.90×10^-6). Similarly, rs931891 in LINC00923 associated with eGFR decline (P=1.44×10^-4) in white patients without diabetes. In summary, SNPs in LINC00923, an RNA gene expressed in the kidney, significantly associated with CKD progression in individuals with nondiabetic CKD. However, the lack of equivalent cohorts hampered replication for most discovery loci. Further replication of our findings in comparable study populations is warranted.

Analysis of ABCG2 and other urate transporters in uric acid homeostasis in chronic kidney disease: potential role of remote sensing and signaling

Background

In the setting of chronic kidney disease (CKD), altered extra-renal urate handling may be necessary to regulate plasma uric acid. The Remote Sensing and Signaling Hypothesis (Nigam S. What do drug transporters really do? Nat Rev Drug Discov 2015; 14: 29–44) suggests that multispecific solute carrier (SLC) and ATP-binding cassette (ABC) drug transporters in different tissues are part of an inter-organ communication system that maintains levels of urate and other metabolites after organ injury.

Methods

Data from the Chronic Renal Insufficiency Cohort (CRIC; n = 3598) were used to study associations between serum uric acid and single nucleotide polymorphisms (SNPs) on the following uric acid transporters: ABCG2 (BRCP), SLC22A6 (OAT1), SLC22A8 (OAT3), SLC22A10 (OAT5), SLC22A11 (OAT4), SLC22A12 (URAT1), SLC22A13 (OAT10), SLC17A1-A3 (NPTs), SLC2A9 (GLUT9), ABCC2 (MRP2) and ABCC4 (MRP4). Regression models, controlling for principal components age, gender and renal function, were run separately for those of European (EA) and African ancestry (AA), and P-values corrected for multiple comparisons. A twin cohort with participants of EA and normal renal function was used for comparison.

Results

Among those of EA in CRIC, statistically significant signals were observed for SNPs in ABCG2 (rs4148157; beta-coefficient = 0.68; P = 4.78E-13) and SNPs in SLC2A9 (rs13125646; beta-coefficient = −0.30; P = 1.06E-5). Among those of AA, the strongest (but not statistically significant) signals were observed for SNPs in SLC2A9, followed by SNPs in ABCG2. In the twin study (normal renal function), only SNPs in SLC2A9 were significant (rs4481233; beta-coefficient=−0.45; P = 7.0E-6). In CRIC, weaker associations were also found for SLC17A3 (NPT4) and gender-specific associations found for SLC22A8 (OAT3), SLC22A11 (OAT4), and ABCC4 (MRP4).

Conclusions

In patients of EA with CKD (CRIC cohort), we found striking associations between uric acid and SNPs on ABCG2, a key transporter of uric acid by intestine. Compared with ABCG2, SLC2A9 played a much less significant role in this subset of patients with CKD. SNPs in other SLC (e.g. SLC22A8 or OAT3) and ABC (e.g. ABCC4 or MRP4) genes appear to make a weak gender-dependent contribution to uric acid homeostasis in CKD. As renal urate transport is affected in the setting of declining kidney function, extra-renal ABCG2 appears to play a compensatory role—a notion consistent with animal studies and the Remote Sensing and Signaling Hypothesis. Overall, the data indicate how different urate transporters become more or less important depending on renal function, ethnicity and gender. Therapies focused on enhancing ABCG2 urate handling may be helpful in the setting of CKD and hyperuricemia.

Defining the role of common variation in the genomic and biological architecture of adult human height

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.