Steven Buyske

Background/Objectives: Autism spectrum disorder (ASD) is a heritable neurodevelopmental condition that displays heterogeneity in both presentation and etiology, and often presents with concomitant communication difficulties. The hypothesis behind the New Jersey Language and Autism Genetic Study is that genetic heterogeneity for component phenotypes of ASD may be reduced relative to the disorder as a whole. We previously published an initial phase of this study with family recruitment that used very restricted inclusion/exclusion criteria for both autism and language deficits. Here, we present an expanded sample that includes a wider range of phenotypic presentations in the autism and language domains. Methods: Bioinformatics tools focusing on variant prioritization were used to identify candidate risk genes. Results: Our previous findings on 15q and 16q, connecting ASD and oral/written communication, are only relevant to the narrow ASD and language impairment phenotypes, though addition of families did reduce both critical regions. After variant and gene prioritization, we determined a set of ten and six top candidate risk genes with a strong association with language impairment and reading impairment, respectively. The top candidate genes include both genes previously implicated in neurodevelopmental disorders (e.g., ZNF774 and DNAH3) and genes not previously reported but with strong evidence of being involved in neurodevelopmental phenotypes. Conclusions: Our analysis elucidates the genetic architecture and interaction of ASD and language-related phenotypes. In addition, we reported a number of high-confidence candidate genes within the top linkage regions. These genes will provide insights into the genetic etiology of neurodevelopmental disorders.

The Functional Annotation of Variants Online Resource (FAVOR), http://favor.genohub.org, is a whole genome variant annotation database and portal that provides comprehensive variant functional annotations of all possible variants across the genome. It can facilitate the analysis of whole-genome sequencing studies, support the interpretation of variant functional impacts, and help prioritize causal variants of diseases or traits. To support the growing popularity and expand the scope of FAVOR, we present here a substantial platform update. The new release features dramatically expanded annotations, a completely redesigned infrastructure powered by a newly implemented application programming interface (FAVOR-API), and a revamped web interface with advanced data-visualization capabilities and enhanced query performance. Key expansions include much more comprehensive variant annotations, including global, tissue- and cell-type–specific variant annotations; gene and protein annotations; support for both hg38 and hg19 reference genomes; and an interactive genome-browser for visualization of multi-faceted variant annotations. The updated platform also includes FAVOR-GPT, a large language model-powered interface for navigating the FAVOR database and interpreting results. FAVOR continues to evolve to keep pace with advances in research on interpreting the functional and phenotypic impact of genomic variation.

Polygenic risk scores hold prognostic value for identifying individuals at higher risk of type 2 diabetes. However, further characterization is needed to understand the generalizability of type 2 diabetes polygenic risk scores in diverse populations across various contexts. We systematically characterize a multi-ancestry type 2 diabetes polygenic risk score among 244,637 cases and 637,891 controls across diverse populations from the Population Architecture Genomics and Epidemiology Study and 13 additional biobanks and cohorts. Polygenic risk score performance is context dependent, with better performance in those who are younger, male, without hypertension, and not obese or overweight. Additionally, the polygenic risk score is associated with various diabetes-related cardiometabolic traits and type 2 diabetes complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between type 2 diabetes and other diseases. These findings highlight the need to account for context when evaluating polygenic risk score as a tool for type 2 diabetes risk prognostication and the potentially generalizable associations of type 2 diabetes polygenic risk score with diabetes-related traits, despite differential performance in type 2 diabetes prediction across diverse populations. Our study provides a comprehensive resource to characterize a type 2 diabetes polygenic risk score.

Polygenic scores (PGS) have promising clinical applications for risk stratification, disease screening, and personalized medicine. However, most PGS are trained on predominantly European ancestry cohorts and have limited portability to external populations. While cross-population PGS methods have demonstrated greater generalizability than single-ancestry PGS, they fail to properly account for individuals with recent admixture between continental ancestry groups. GAUDI is a recently proposed PGS method which overcomes this gap by leveraging local ancestry to estimate ancestry-specific effects, penalizing but allowing ancestry-differential effects. However, the modified fused LASSO approach used by GAUDI is computationally expensive and does not readily accommodate more than two-way admixture. To address these limitations, we introduce HAUDI, an efficient LASSO framework for admixed PGS construction. HAUDI re-parameterizes the GAUDI model as a standard LASSO problem, allowing for extension to multi-way admixture settings and far superior computational speed than GAUDI. In extensive simulations, HAUDI compares favorably to GAUDI while dramatically reducing computation time. In real data applications, HAUDI uniformly out-performs GAUDI across 18 clinical phenotypes, including total triglycerides (TG), C-reactive protein (CRP), and mean corpuscular hemoglobin concentration (MCHC), and shows substantial benefits over an ancestry-agnostic PGS for white blood cell count (WBC) and chronic kidney disease (CKD).

Autism spectrum disorder is a heritable neurodevelopmental condition that displays heterogeneity in both presentation and etiology and it often presents with concomitant communication difficulties. The hypothesis behind the New Jersey Language and Autism Genetic Study is that genetic heterogeneity for component phenotypes of autism spectrum disorder may be reduced relative to the disorder as a whole. We previously published an initial phase of this study with family recruitment that used very restricted inclusion/exclusion criteria for both autism and language deficits in other family members. Here we present an expanded sample that includes a wider range of phenotypic presentations in the autism and language domains. We found that our previous findings on 15q and 16q, connecting autism spectrum disorder and oral/written communication, are only relevant to the narrow autism spectrum disorder and language impairment phenotypes (with posterior probability of linkage of 57% and 33%, respectively) though addition of families did reduce both critical regions. After variant prioritization and additional filtration based on segregation patterns and functional annotations, we determined a set of 10 and 6 top candidate risk genes with strong association to language impairment and reading impairment, respectively. The top candidate genes include both genes previously implicated in neurodevelopmental disorders (e.g., ZNF774 and DNAH3) and genes not previously reported but with strong evidence of being involved in neurodevelopmental phenotypes.

Key Points: We aimed to elucidate potential methylation, proteomic, and metabolomic mechanisms by which APOL1 variants may be linked to kidney disease. We report distinct methylation profiling between APOL1 risk allele carriers and noncarriers, many near APOL gene family. We report higher APOL1 protein and lower C18:1 cholesteryl ester in two risk allele carriers. Background: The APOL1 high-risk haplotype has been associated with CKD and the deterioration of kidney function, particularly in populations with West African ancestry. However, the mechanisms by which APOL1 risk variants increase the risk for kidney disease and its progression have not been fully elucidated. Methods: We compared methylation ( N =3191; 715 [22%] carriers), proteomic ( N =1240; 169 [14%] carriers), and metabolomic ( N =6309; 674 [11%] carriers) profiles in African and Hispanic/Latino carriers of two APOL1 high-risk alleles (G1/G1, G2/G2, G1/G2) and noncarriers (G0/G0), excluding heterozygotes (G0/G1, G0/G2), from the Population Architecture using Genomics and Epidemiology Consortium and UK Biobank. In each study, the associations between the APOL1 high-risk haplotype and up to 722,719 cytosine-phosphate-guanine (CpG) sites, 2923 proteins, or 836 metabolites were estimated using covariate-adjusted linear regression models, followed by fixed-effects sample size–weighted meta-analyses. Results: Significant associations were observed between APOL1 high-risk haplotype and methylation at 52 CpG sites, with 48 located on chromosome 22 and 18 in the vicinity of APOL1–4 and MYH9 . All significant CpG sites near APOL2 were hypomethylated, whereas those near APOL3 and APOL4 were hypermethylated. APOL1 -associated CpG sites were also identified in genes involved in ion transport and mitochondrial stress pathways. Sensitivity analyses indicated consistent yet attenuated effects among heterozygotes, supporting an additive effect of APOL1 risk alleles. Further analyses of the 52 CpG sites identified two near APOL4 exhibiting G1-specific effects, eight associated with CKD but none with eGFR, and three showing heterogeneity by CKD status. In addition, carrying two APOL1 risk alleles was associated with higher plasma APOL1 protein ( β =1.12, P _FDR = 2.26e-70) and lower C18:1 cholesteryl ester metabolite (Z=−4.50, P _FDR = 4.83e-3). Conclusions: Our results demonstrate differential methylation, proteomic, and metabolomic profiles associated with APOL1 high-risk haplotypes.

Fear extinction leads to a decrease of originally acquired fear responses after the threat is no longer present. Fear extinction is adaptive and critical for organism’s survival, but deficits in extinction may lead to exaggerated fear in animals or post-traumatic stress disorder (PTSD) in humans. Dopamine has recently emerged as essential for fear extinction and PTSD, however the neural circuits serving this dopamine function are only beginning to be investigated, and the dopamine intracellular signaling pathways are unknown. We generated gastrin-releasing peptide gene knockout (Grp ^-/-) mice and found that they exhibit enhanced fear memory in a stress-enhanced fear learning (SEFL) paradigm, which combines stress exposure and fear extinction, two features critical for developing PTSD. Using in vivo fiber photometry to record dopamine signals, we found that the susceptibility of Grp ^-/- mice to SEFL is paralleled by an increase in basolateral amygdala (BLA) dopaminergic binding during fear conditioning and early extinction. Combined optogenetics and ex vivo electrophysiology showed an increase in presynaptic ventral tegmental area (VTA)-BLA connectivity in Grp ^-/- mice, demonstrating a role of dysregulated input from the VTA on BLA function in the absence of the GRP. When examining gene transcription using RNA-seq and qPCR, we discovered concerted down-regulation in dopamine-related genes in the BLA of Grp ^-/- mice following long-term SEFL memory recall that was not observed in naïve conditions. These experiments demonstrate that the GRP regulates dopamine function in stress-enhanced fear processing and identify the Grp as the first gene known to regulate dopaminergic control of fear extinction.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women’s Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity-related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In the investigated regions with genome-wide significant associations for obesity-related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine-mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggests generalizability of genetic mechanisms underpinning obesity-related traits across populations.