Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol

Gender difference in the association of OSBPL8 polymorphisms with nephrolithiasis within a Chinese cohort

BACKGROUND

Kidney stone disease (KSD) is a common disorder of the urinary system and is closely related to genetic polymorphisms. However, the relationship between OSBPL8 polymorphisms and kidney stones has not been thoroughly investigated.

METHODS

Six OSBPL8 polymorphisms (rs17042391,rs17042409,rs4761431,rs7303892,rs4761434, and rs17042390) were analyzed in a Chinese case-control cohort containing 923 nephrolithiasis patients and 945 healthy controls.The association of these OSBPL8 gene polymorphisms with KSD susceptibility was analyzed using logistic regression, and examined by calculating the odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

The OSBPL8 polymorphisms (GG for rs17042391, rs17042409, rs4761431, rs7303892; AA for rs4761434; and G for rs17042390) were significantly associated with a decreased risk of KSD in females.The protected alleles (G allele of rs17042391, G allele of rs17042409, G allele of rs4761431, A allele of rs4761434, and G allele of rs17042390) were related to decreased BMI levels in KSD patients; female patients with these alleles also exhibited lower BMI, HDL, and LDL levels,the G allele of rs7303892 was linked to reduced serum cholesterol levels in these females.Additionally, the haplotype ACAAGA was associated with decreased KSD risk in females, but haplotype GGGGAG presented an opposing effect.

CONCLUSION

Our research shows that the OSBPL8 gene polymorphisms reduced the risk of KSD in females, and were also associated with lipid-related metabolic traits.

Assessment of the functionality and usability of open-source rare variant analysis pipelines

Sequencing of increasingly larger cohorts has revealed many rare variants, presenting an opportunity to further unravel the genetic basis of complex traits. Compared with common variants, rare variants are more complex to analyze. Specialized computational tools for these analyses should be both flexible and user-friendly. However, an overview of the available rare variant analysis pipelines and their functionalities is currently lacking. Here, we provide a systematic review of the currently available rare variant analysis pipelines. We searched MEDLINE and Google Scholar until 27 November 2023, and included open-source rare variant pipelines that accepted genotype data from cohort and case-control studies and group variants into testing units. Eligible pipelines were assessed based on functionality and usability criteria. We identified 17 rare variant pipelines that collectively support various trait types, association tests, testing units, and variant weighting schemes. Currently, no single pipeline can handle all data types in a scalable and flexible manner. We recommend different tools to meet diverse analysis needs. STAARpipeline is suitable for newcomers and common applications owing to its built-in definitions for the testing units. REGENIE is highly scalable, actively maintained, regularly updated, and well documented. Ravages is suitable for analyzing multinomial variables, and OrdinalGWAS is tailored for analyzing ordinal variables. Opportunities remain for developing a user-friendly pipeline that provides high degrees of flexibility and scalability. Such a pipeline would enable researchers to exploit the potential of rare variant analyses to uncover the genetic basis of complex traits.

Low-frequency variants in genes involved in glutamic acid metabolism and γ-glutamyl cycle and risk of coronary artery disease in type 2 diabetes

BACKGROUND

A common genetic variant at the glutamate-ammonia ligase (GLUL) locus has been previously associated with an increased risk of coronary artery disease (CAD) as well as alterations of glutamic acid metabolism and the γ-glutamyl cycle in individuals with type 2 diabetes (T2D). Here we investigated whether less frequent variants in GLUL and 15 additional genes in these pathways are associated with differences in CAD risk in T2D.

METHODS

Coding sequences and regulatory elements of these genes were sequenced in 2,394 individuals with T2D from three CAD case/control sets.

RESULTS

Ninety-six variants with minor allele frequency [MAF]< 0.05 were identified as being nominally associated with CAD status. One of these variants (rs62447457, MAF 0.025), placed in a non-coding region flanking the γ-glutamylcyclotransferase (GGCT) gene, showed nominal evidence of replication in two other cases-control sets (n = 1,132), with summary OR of 0.54 (p = 2.5 × 10-4). Another variant (rs145322388, MAF = 0.039), flanking the dipeptidase 2 (DPEP2) gene, showed association with CAD status across discovery and replications sets (summary OR 0.61, p = 2.5 × 10-4). A third variant (rs1238275622, MAF 0.004), flanking the GLUL gene, was associated with increased risk of CAD (summary OR 1.84, p-value 2.1 × 10-3). Based on their Regulome scores (2b, 2a, and 3a, respectively), all three variants are very likely to have regulatory functions.

CONCLUSIONS

In summary, we have identified low-frequency variants associated with CAD in T2D at two loci involved in glutamic acid metabolism and the γ-glutamyl cycle. These findings provide further evidence for a role of these pathways in the link between T2D and CAD.

The contributions of deleterious rare alleles in NLRP12 and inflammasome-related genes to polymyalgia rheumatica

Polymyalgia rheumatica (PMR) is a chronic inflammatory disease characterized by arthralgia and myalgia of the shoulder and hip girdles, and fever. PMR is linked to autoimmune diseases and autoinflammatory disorders. Exome sequencing has revealed the roles of rare variants in some diseases. Causative genes for monogenic autoinflammatory disorders might be candidate genes for the selective exome analysis of PMR. We investigated rare variants in the coding and boundary regions of candidate genes for PMR. Exome sequencing was performed to analyze deleterious rare variants in candidate genes, and the frequencies of the deleterious rare alleles in PMR were compared with those of Japanese population controls. Deleterious rare alleles in the NLRL12 gene were associated with PMR (P = 0.0069, Pc = 0.0415, odds ratio [OR] 4.49, 95% confidence interval [CI] 1.79-11.27). A multigene analysis demonstrated the deleterious rare allele frequency of the candidate genes for autoinflammatory disorders was also increased in PMR (P = 0.0016, OR 3.69, 95%CI 1.81-7.54). The deleterious rare allele frequencies of the candidate genes including NLRP12 were increased in PMR patients, showing links to autoinflammatory disorders in the pathogenesis of PMR.

An evolutionary perspective on complex neuropsychiatric disease

The forces of evolution-mutation, selection, migration, and genetic drift-shape the genetic architecture of human traits, including the genetic architecture of complex neuropsychiatric illnesses. Studying these illnesses in populations that are diverse in genetic ancestry, historical demography, and cultural history can reveal how evolutionary forces have guided adaptation over time and place. A fundamental truth of shared human biology is that an allele responsible for a disease in anyone, anywhere, reveals a gene critical to the normal biology underlying that condition in everyone, everywhere. Understanding the genetic causes of neuropsychiatric disease in the widest possible range of human populations thus yields the greatest possible range of insight into genes critical to human brain development. In this perspective, we explore some of the relationships between genes, adaptation, and history that can be illuminated by an evolutionary perspective on studies of complex neuropsychiatric disease in diverse populations.

Functional characterization of missense variants affecting the extracellular domains of ABCA1 using a fluorescence-based assay

Excess cholesterol originating from nonhepatic tissues is transported within HDL particles to the liver for metabolism and excretion. Cholesterol efflux is initiated by lipid-free or lipid-poor apolipoprotein A1 interacting with the transmembrane protein ABCA1, a key player in cholesterol homeostasis. Defective ABCA1 results in reduced serum levels of HDL cholesterol, deposition of cholesterol in arteries, and an increased risk of early onset CVD. Over 300 genetic variants in ABCA1 have been reported, many of which are associated with reduced HDL cholesterol levels. Only a few of these have been functionally characterized. In this study, we have analyzed 51 previously unclassified missense variants affecting the extracellular domains of ABCA1 using a sensitive, easy, and low-cost fluorescence-based assay. Among these, only 12 variants showed a distinct loss-of-function phenotype, asserting their direct association with severe HDL disorders. These findings emphasize the crucial role of functional characterization of genetic variants in pathogenicity assessment and precision medicine. The functional rescue of ABCA1 loss-of-function variants through proteasomal inhibition or by the use of the chemical chaperone 4-phenylbutyric acid was genotype specific. Genotype-specific responses were also observed for the ability of apolipoprotein A1 to stabilize the different ABCA1 variants. In view of personalized medicine, this could potentially form the basis for novel therapeutic strategies.

Human Genetics of Tetralogy of Fallot and Double-Outlet Right Ventricle

Tetralogy of Fallot (TOF) and double-outlet right ventricle (DORV) are conotruncal defects resulting from disturbances of the second heart field and the neural crest, which can occur as isolated malformations or as part of multiorgan syndromes. Their etiology is multifactorial and characterized by overlapping genetic causes. In this chapter, we present the different genetic alterations underlying the two diseases, which range from chromosomal abnormalities like aneuploidies and structural mutations to rare single nucleotide variations affecting distinct genes. For example, mutations in the cardiac transcription factors NKX2-5, GATA4, and HAND2 have been identified in isolated TOF cases, while mutations of TBX5 and 22q11 deletion, leading to haploinsufficiency of TBX1, cause Holt-Oram and DiGeorge syndrome, respectively. Moreover, genes involved in signaling pathways, laterality determination, and epigenetic mechanisms have also been found mutated in TOF and/or DORV patients. Finally, genome-wide association studies identified common single nucleotide polymorphisms associated with the risk for TOF.

An exome-wide study of renal operational tolerance

Background

Renal operational tolerance is a rare and beneficial state of prolonged renal allograft function in the absence of immunosuppression. The underlying mechanisms are unknown. We hypothesized that tolerance might be driven by inherited protein coding genetic variants with large effect, at least in some patients.

Methods

We set up a European survey of over 218,000 renal transplant recipients and collected DNAs from 40 transplant recipients who maintained good allograft function without immunosuppression for at least 1 year. We performed an exome-wide association study comparing the distribution of moderate to high impact variants in 36 tolerant patients, selected for genetic homogeneity using principal component analysis, and 192 controls, using an optimal sequence-kernel association test adjusted for small samples.

Results

We identified rare variants of HOMER2 (3/36, FDR 0.0387), IQCH (5/36, FDR 0.0362), and LCN2 (3/36, FDR 0.102) in 10 tolerant patients vs. 0 controls. One patient carried a variant in both HOMER2 and LCN2. Furthermore, the three genes showed an identical variant in two patients each. The three genes are expressed at the primary cilium, a key structure in immune responses.

Conclusion

Rare protein coding variants are associated with operational tolerance in a sizable portion of patients. Our findings have important implications for a better understanding of immune tolerance in transplantation and other fields of medicine.ClinicalTrials.gov, identifier: NCT05124444.

Case-only exome variation analysis of severe alcohol dependence using a multivariate hierarchical gene clustering approach

BACKGROUND

Variation in genes involved in ethanol metabolism has been shown to influence risk for alcohol dependence (AD) including protective loss of function alleles in ethanol metabolizing genes. We therefore hypothesized that people with severe AD would exhibit different patterns of rare functional variation in genes with strong prior evidence for influencing ethanol metabolism and response when compared to genes not meeting these criteria.

OBJECTIVE

Leverage a novel case only design and Whole Exome Sequencing (WES) of severe AD cases from the island of Ireland to quantify differences in functional variation between genes associated with ethanol metabolism and/or response and their matched control genes.

METHODS

First, three sets of ethanol related genes were identified including those a) involved in alcohol metabolism in humans b) showing altered expression in mouse brain after alcohol exposure, and altering ethanol behavioral responses in invertebrate models. These genes of interest (GOI) sets were matched to control gene sets using multivariate hierarchical clustering of gene-level summary features from gnomAD. Using WES data from 190 individuals with severe AD, GOI were compared to matched control genes using logistic regression to detect aggregate differences in abundance of loss of function, missense, and synonymous variants, respectively.

RESULTS

Three non-independent sets of 10, 117, and 359 genes were queried against control gene sets of 139, 1522, and 3360 matched genes, respectively. Significant differences were not detected in the number of functional variants in the primary set of ethanol-metabolizing genes. In both the mouse expression and invertebrate sets, we observed an increased number of synonymous variants in GOI over matched control genes. Post-hoc simulations showed the estimated effects sizes observed are unlikely to be under-estimated.

CONCLUSION

The proposed method demonstrates a computationally viable and statistically appropriate approach for genetic analysis of case-only data for hypothesized gene sets supported by empirical evidence.

The Role of Neural and Genetic Processes in Learning to Read and Specific Reading Disabilities: Implications for Instruction

To learn to read, the brain must repurpose neural systems for oral language and visual processing to mediate written language. We begin with a description of computational models for how alphabetic written language is processed. Next, we explain the roles of a dorsal sublexical system in the brain that relates print and speech, a ventral lexical system that develops the visual expertise for rapid orthographic processing at the word level, and the role of cognitive control networks that regulate attentional processes as children read. We then use studies of children, adult illiterates learning to read, and studies of poor readers involved in intervention, to demonstrate the plasticity of these neural networks in development and in relation to instruction. We provide a brief overview of the rapid increase in the field's understanding and technology for assessing genetic influence on reading. Family studies of twins have shown that reading skills are heritable, and molecular genetic studies have identified numerous regions of the genome that may harbor candidate genes for the heritability of reading. In selected families, reading impairment has been associated with major genetic effects, despite individual gene contributions across the broader population that appear to be small. Neural and genetic studies do not prescribe how children should be taught to read, but these studies have underscored the critical role of early intervention and ongoing support. These studies also have highlighted how structured instruction that facilitates access to the sublexical components of words is a critical part of training the brain to read.

Neurotensin Gene rs2234762 C>G Variant Associates with Reduced Circulating Pro-NT Levels and Predicts Lower Insulin Resistance in Overweight/Obese Children

Neurotensin (NT) is a small protein implicated in the regulation of energy balance which acts as both a neurotransmitter in the central nervous system and as a gastrointestinal peptide. In the gut, NT is secreted after fat ingestion and promotes the absorption of fatty acids. The circulating levels of its precursor, pro-NT, predicts the presence and development of metabolic and cardiovascular diseases. Despite the extensive knowledge on the dynamic changes that occur to pro-NT = after fat load, the determinants of fasting pro-NT are unknown. The aim of this study was to determine the possible genetic regulation of plasma pro-NT. The NT gene (NTS) was sequenced for potential functional variants, evaluating its entire genomic and potentially regulatory regions, in DNA from 28 individuals, stratified by low and high pro-NT levels. The identified variant differently distributed in the two pro-NT subgroups was genotyped in a cohort of nine hundred and thirty-two overweight/obese children and adolescents. A total of seven sequence variations across the NTS gene, none of them located in coding regions, were identified. The rs2234762 polymorphism, sited in the NTS gene promoter, was statistically more frequent in the lowest pro-NTS level group. Carriers of the rs2234762 variant showed lower pro-NT levels, after adjusting for sex, age, BMI, triglycerides and the Tanner stage. Having NTS rs2234762 predicted less pronounced insulin resistance at the 6.5-year follow-up with OR: 0.46 (0.216–0.983), at the logistic regression analysis adjusted for age, sex and BMI. In conclusion, the NTS rs2234762 gene variant is a determinant of reduced circulating pro-NT levels in overweight and obese children, which predisposes this group to a more favorable metabolic profile and a reduced insulin resistance later in life, independently from metabolic confounders.

Cryo-EM structures of human ABCA7 provide insights into its phospholipid translocation mechanisms

Phospholipid extrusion by ABC subfamily A (ABCA) exporters is central to cellular physiology, although the specifics of the underlying substrate interactions and transport mechanisms remain poorly resolved at the molecular level. Here we report cryo-EM structures of lipid-embedded human ABCA7 in an open state and in a nucleotide-bound, closed state at resolutions between 3.6 and 4.0 Å. The former reveals an ordered patch of bilayer lipids traversing the transmembrane domain (TMD), while the latter reveals a lipid-free, closed TMD with a small extracellular opening. These structures offer a structural framework for both substrate entry and exit from the ABCA7 TMD and highlight conserved rigid-body motions that underlie the associated conformational transitions. Combined with functional analysis and molecular dynamics (MD) simulations, our data also shed light on lipid partitioning into the ABCA7 TMD and localized membrane perturbations that underlie ABCA7 function and have broader implications for other ABCA family transporters.

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.

Interactive effects of the low-carbohydrate diet score and genetic risk score on Hypo-HDL-cholesterolemia among Korean adults: A cross-sectional analysis from the Ansan and Ansung Study of the Korean Genome and Epidemiology Study

This cross-sectional study investigated the interaction between the genetic risk score (GRS) and abnormal high-density lipoprotein (HDL) cholesterol lipid levels, which are modified by low-carbohydrate diets (LCDs) and their effects on the prevalence of hypo-HDL-cholesterolemia (hypo-HDL-C) in Korean adults. Baseline data were obtained from the Ansan and Ansung study of the Korean Genome and Epidemiology Study (KoGES), conducted from 2001 to 2002, that targeted 8,314 Korean adults aged 40-69 years, including old men (47.6%) and women (52.4%), and whole genomic single nucleotide polymorphism (SNP) genotyping was performed. We identified 18 SNPs significantly associated with hypo-HDL-C in the proximity of several genes, including LPL, APOA5, LIPC, and CETP, and calculated the GRS. The low-carbohydrate diet score (LCDS) was calculated on the basis of energy intake information from food frequency questionnaires. Furthermore, we performed multivariable-adjusted logistic modeling to examine the odds ratio (OR) for hypo-HDL-C across tertiles of LCDS and GRS, adjusted for several covariates. Among participants in the highest GRS tertile, those in the highest tertile of the LCDS had a significantly lower risk of hypo-HDL-C (OR: 0.759, 95% CI (confidence interval): 0.625-0.923) than those in the lowest tertile of the LCDS. In the joint effect model, the group with the lowest GRS and highest LCDS was found to have the lowest risk of hypo-HDL-C prevalence. This study suggests that individuals with a high genetic risk for low HDL concentrations may have a beneficial effect on a lower intake of carbohydrates.

Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling

In genetic association studies, rare variants with extremely low allele frequencies play a crucial role in complex traits. Therefore, set-based testing methods that jointly assess the effects of groups of single nucleotide polymorphisms (SNPs) were developed to increase the powers of the association tests. However, these powers are still insufficient, and precise estimations of the effect sizes of individual SNPs are largely impossible. In this article, we provide an efficient set-based statistical inference framework that addresses both of these important issues simultaneously using an empirical Bayes method with semiparametric multilevel mixture modeling. We propose to utilize the hierarchical model that incorporates variations in set-specific effects and to apply the optimal discovery procedure (ODP) that achieves the largest overall power in multiple significance testing. In addition, we provide an optimal "set-based" estimator of the empirical distribution of effect sizes. The efficiency of the proposed methods is demonstrated through application to a genome-wide association study of coronary artery disease and through simulation studies. The results demonstrated numerous rare variants with large effect sizes for coronary artery disease, and the number of significant sets detected by the ODP was much greater than those identified by existing methods.

The Contribution of Deleterious Rare Alleles in ENPP1 and Osteomalacia Causative Genes to Atypical Femoral Fracture

CONTEXT

Atypical femoral fractures (AFFs) are very rare atraumatic or mild trauma fractures in the subtrochanteric region or femoral shaft. Some unique genetic variants in Asian populations might confer susceptibility to AFF, since the incidence of AFFs is higher in Asian populations.

OBJECTIVE

Because rare variants have been found to be causative in some diseases and the roles of osteomalacia causative genes have not been reported, we investigated rare variants in genes causing abnormal mineralization.

METHODS

Exome sequencing was performed to detect variants in gene coding and boundary regions, and the frequencies of deleterious rare alleles were compared between Japanese patients with AFF (n = 42) and controls of the 4.7KJPN panel of Tohoku Medical Megabank by whole genome sequencing (n = 4773).

RESULTS

The frequency of the deleterious rare allele of ENPP1 was significantly increased in AFF (P = .0012, corrected P [Pc] = .0155, OR 4.73, 95% CI 2.15-10.40). In multigene panel analysis, the frequencies of deleterious rare alleles of candidate genes were increased in AFF (P = .0025, OR 2.72, 95% CI 1.49-4.93). Principal component analysis of bone metabolism markers identified a subgroup of patients with AFF with higher frequencies of deleterious rare alleles in ENPP1 (P = 4.69 × 10-5, Pc = .0006, OR 8.47, 95% CI 3.76-19.09) and the candidate genes (P = 1.08 × 10-5, OR 5.21, 95% CI 2.76-9.86).

CONCLUSION

AFF is associated with genes including ENPP1 that cause abnormal mineralization, suggesting that osteomalacia is an underlying condition predisposing to AFF and that higher incident rates of AFFs in Asian populations might be explained by the genetic risk factors including ENPP1.

Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank

Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.

Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa

Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.

Deep Resequencing of 9 Candidate Genes Identifies a Role for ARAP1 and IGF2BP2 in Modulating Insulin Secretion Adjusted for Insulin Resistance in Obese Southern Europeans

Type 2 diabetes is characterized by impairment in insulin secretion, with an established genetic contribution. We aimed to evaluate common and low-frequency (1–5%) variants in nine genes strongly associated with insulin secretion by targeted sequencing in subjects selected from the extremes of insulin release measured by the disposition index. Collapsing data by gene and/or function, the association between disposition index and nonsense variants were significant, also after adjustment for confounding factors (OR = 0.25, 95% CI = 0.11–0.59, p = 0.001). Evaluating variants individually, three novel variants in ARAP1, IGF2BP2 and GCK, out of eight reaching significance singularly, remained associated after adjustment. Constructing a genetic risk model combining the effects of the three variants, only carriers of the ARAP1 and IGF2BP2 variants were significantly associated with a reduced probability to be in the lower, worst, extreme of insulin secretion (OR = 0.223, 95% CI = 0.105–0.473, p < 0.001). Observing a high number of normal glucose tolerance between carriers, a regression posthoc analysis was performed. Carriers of genetic risk model variants had higher probability to be normoglycemic, also after adjustment (OR = 2.411, 95% CI = 1.136–5.116, p = 0.022). Thus, in our southern European cohort, nonsense variants in all nine candidate genes showed association with better insulin secretion adjusted for insulin resistance, and we established the role of ARAP1 and IGF2BP2 in modulating insulin secretion.

Long-term Benefits and Harms Associated With Genetic Cholesteryl Ester Transfer Protein Deficiency in the General Population

Importance

The balance between the potential long-term clinical benefits and harms associated with genetic cholesteryl ester transfer protein (CETP) deficiency, mimicking pharmacologic CETP inhibition, is unknown.

Objective

To assess the relative benefits and harms associated with genetic CETP deficiency.

Design, Setting, and Participants

This study examined 2 similar prospective cohorts of the Danish general population, with data on a total of 102 607 participants collected from October 10, 1991, through December 7, 2018.

Exposures

Weighted CETP allele scores.

Main Outcomes and Measures

Incident cardiovascular mortality, ischemic heart disease, myocardial infarction, ischemic stroke, peripheral arterial disease, vascular dementia, Alzheimer disease, all-cause mortality, and age-related macular degeneration (AMD). The study first tested whether a CETP allele score was associated with morbidity and mortality, when scaled to genetically lower levels of non-high-density lipoprotein (HDL) cholesterol (ie, 17 mg/dL), corresponding to the reduction observed for anacetrapib vs placebo in the Randomized Evaluation of the Effects of Anacetrapib Through Lipid-Modification (REVEAL) trial. Second, the study assessed how much of the change in morbidity and mortality was associated with genetically lower levels of non-HDL cholesterol. Finally, the balance between the potential long-term clinical benefits and harms associated with genetic CETP deficiency was quantified. For AMD, the analyses also included higher levels of HDL cholesterol associated with genetic CETP deficiency.

Results

Of 102 607 individuals in the study, 56 559 (55%) were women (median age, 58 years [IQR, 47-67 years]). Multivariable adjusted hazard ratios showed that a genetically lower level of non-HDL cholesterol (ie, 17 mg/dL) was associated with a lower risk of cardiovascular mortality (hazard ratio [HR], 0.77 [95% CI, 0.62-0.95]), ischemic heart disease (HR, 0.80 [95% CI, 0.68-0.95]), myocardial infarction (HR, 0.72 [95% CI, 0.55-0.93]), peripheral arterial disease (HR, 0.80 [95% CI, 0.63-1.02]), and vascular dementia (HR, 0.38 [95% CI, 0.18-0.80]) and an increased risk of AMD (HR, 2.33 [95% CI, 1.63-3.30]) but was not associated with all-cause mortality (HR, 0.91 [95% CI, 0.81-1.02]), ischemic stroke (HR, 1.05 [95% CI, 0.81-1.36]), or Alzheimer disease (HR, 1.25 [95% CI, 0.89-1.76]). When scaled to a higher level of HDL cholesterol, the increased risk of AMD was even larger. A considerable fraction of the lower risk of cardiovascular end points was associated with genetically lower levels of non-HDL cholesterol, while the higher risk of AMD was associated with genetically higher levels of HDL cholesterol. Per 1 million person-years, the projected 1916 more AMD events associated with genetically higher levels of HDL cholesterol was similar to the 1962 fewer events of cardiovascular mortality and myocardial infarction combined associated with genetically lower levels of non-HDL cholesterol.

Conclusions and Relevance

This study suggests that genetic CETP deficiency, mimicking pharmacologic CETP inhibition, was associated with a lower risk of cardiovascular morbidity and mortality, but with a markedly higher risk of AMD.

Deep learning for cancer type classification and driver gene identification

Background

Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction.

Results

We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p < 0.001). Strikingly, using DeepCues, the top 20 breast cancer relevant genes we have identified, had a 40% overlap with the top 20 known breast cancer driver genes.

Conclusion

Our results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04400-4.

Genetics and regulation of HDL metabolism

The inverse association between plasma HDL cholesterol (HDL-C) levels and risk for cardiovascular disease (CVD) has been demonstrated by numerous epidemiological studies. However, efforts to reduce CVD risk by pharmaceutically manipulating HDL-C levels failed and refused the HDL hypothesis. HDL-C levels in the general population are highly heterogeneous and are determined by a combination of genetic and environmental factors. Insights into the causes of HDL-C heterogeneity came from the study of monogenic HDL deficiency syndromes but also from genome wide association and Μendelian randomization studies which revealed the contribution of a large number of loci to low or high HDL-C cases in the general or in restricted ethnic populations. Furthermore, HDL-C levels in the plasma are under the control of transcription factor families acting primarily in the liver including members of the hormone nuclear receptors (PPARs, LXRs, HNF-4) and forkhead box proteins (FOXO1-4) and activating transcription factors (ATFs). The effects of certain lipid lowering drugs used today are based on the modulation of the activity of specific members of these transcription factors. During the past decade, the roles of small or long non-coding RNAs acting post-transcriptionally on the expression of HDL genes have emerged and provided novel insights into HDL regulation and new opportunities for therapeutic interventions. In the present review we summarize recent progress made in the genetics and the regulation (transcriptional and post-transcriptional) of HDL metabolism.

LCAT deficiency: a systematic review with the clinical and genetic description of Mexican kindred

BACKGROUND

LCAT (lecithin-cholesterol acyltransferase) deficiency is characterized by two distinct phenotypes, familial LCAT deficiency (FLD) and Fish Eye disease (FED). This is the first systematic review evaluating the ethnic distribution of LCAT deficiency, with particular emphasis on Latin America and the discussion of three Mexican-Mestizo probands.

METHODS

A systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic review and Meta-Analysis) Statement in Pubmed and SciELO. Articles which described subjects with LCAT deficiency syndromes and an assessment of the ethnic group to which the subject pertained, were included.

RESULTS

The systematic review revealed 215 cases (154 FLD, 41 FED and 20 unclassified) pertaining to 33 ethnic/racial groups. There was no association between genetic alteration and ethnicity. The mean age of diagnosis was 42 ± 16.5 years, with fish eye disease identified later than familial LCAT deficiency (55 ± 13.8 vs. 41 ± 14.7 years respectively). The prevalence of premature coronary heart disease was significantly greater in FED vs. FLD. In Latin America, 48 cases of LCAT deficiency have been published from six countries (Argentina (1 unclassified), Brazil (38 FLD), Chile (1 FLD), Columbia (1 FLD), Ecuador (1 FLD) and Mexico (4 FLD, 1 FED and 1 unclassified). Of the Mexican probands, one showed a novel LCAT mutation.

CONCLUSIONS

The systematic review shows that LCAT deficiency syndromes are clinically and genetically heterogeneous. No association was confirmed between ethnicity and LCAT mutation. There was a significantly greater risk of premature coronary artery disease in fish eye disease compared to familial LCAT deficiency. In FLD, the emphasis should be in preventing both cardiovascular disease and the progression of renal disease, while in FED, cardiovascular risk management should be the priority. The LCAT mutations discussed in this article are the only ones reported in the Mexican- Amerindian population.

Biomarkers for interstitial lung disease and acute-onset diffuse interstitial lung disease in rheumatoid arthritis

Interstitial lung disease (ILD) is frequently a complication of rheumatoid arthritis (RA) as an extra-articular manifestation which has a poor prognosis. Acute-onset diffuse ILD (AoDILD) occasionally occurs in RA and includes acute exacerbation of ILD, drug-induced ILD, and Pneumocystis pneumonia. AoDILD also confers a poor prognosis in RA. Previously-established biomarkers for ILD include Krebs von den lungen-6 and surfactant protein-D originally defined in patients with idiopathic pulmonary fibrosis; the sensitivity of these markers for RA-associated ILD (RA-ILD) is low. Although many studies on ILD markers have been performed in idiopathic pulmonary fibrosis, only a few validation studies in RA-ILD or AoDILD have been reported. Biomarkers for RA-ILD and AoDILD are thus still required. Recently, genomic, cytokine, antibody, and metabolomic profiles of RA-ILD or AoDILD have been investigated with the aim of improving biomarkers. In this review, we summarize current preliminary data on these potential biomarkers for RA-ILD or AoDILD. The development of biomarkers on RA-ILD has only just begun. When validated, such candidate biomarkers will provide valuable information on pathogenesis, prognosis, and drug responses in RA-ILD in future.

Translating genetic association of lipid levels for biological and clinical application

PURPOSE OF REVIEW

This review focuses on the foundational evidence from the last two decades of lipid genetics research and describes the current status of data-driven approaches for transethnic GWAS, fine-mapping, transcriptome informed fine-mapping, and disease prediction.

RECENT FINDINGS

Current lipid genetics research aims to understand the association mechanisms and clinical relevance of lipid loci as well as to capture population specific associations found in global ancestries. Recent genome-wide trans-ethnic association meta-analyses have identified 118 novel lipid loci reaching genome-wide significance. Gene-based burden tests of whole exome sequencing data have identified three genes-PCSK9, LDLR, and APOB-with significant rare variant burden associated with familial dyslipidemia. Transcriptome-wide association studies discovered five previously unreported lipid-associated loci. Additionally, the predictive power of genome-wide genetic risk scores amalgamating the polygenic determinants of lipid levels can potentially be used to increase the accuracy of coronary artery disease prediction.

CONCLUSIONS

Lipids are one of the most successful group of traits in the era of genome-wide genetic discovery for identification of novel loci and plausible drug targets. However, a substantial fraction of lipid trait heritability remains unexplained. Further analysis of diverse ancestries and state of the art methods for association locus refinement could potentially reveal some of this missing heritability and increase the clinical application of the genomic association results.

Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus

Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.

Heterozygosity for a Pathogenic Variant in SLC12A3 That Causes Autosomal Recessive Gitelman Syndrome Is Associated with Lower Serum Potassium

BACKGROUND

Potassium levels regulate multiple physiologic processes. The heritability of serum potassium level is moderate, with published estimates varying from 17% to 60%, suggesting genetic influences. However, the genetic determinants of potassium levels are not generally known.

METHODS

A whole-exome sequencing association study of serum potassium levels in 5812 subjects of the Old Order Amish was performed. A dietary salt intervention in 533 Amish subjects estimated interaction between p.R642G and sodium intake.

RESULTS

A cluster of variants, spanning approximately 537 kb on chromosome 16q13, was significantly associated with serum potassium levels. Among the associated variants, a known pathogenic variant of autosomal recessive Gitelman syndrome (p.R642G SLC12A3) was most likely causal; there were no homozygotes in our sample. Heterozygosity for p.R642G was also associated with lower chloride levels, but not with sodium levels. Notably, p.R642G showed a novel association with lower serum BUN levels. Heterozygotes for p.R642G had a two-fold higher rate of self-reported bone fractures and had higher resting heart rates on a low-salt diet compared with noncarriers.

CONCLUSIONS

This study provides evidence that heterozygosity for a pathogenic variant in SLC12A3 causing Gitelman syndrome, a canonically recessive disorder, contributes to serum potassium concentration. The findings provide insights into SLC12A3 biology and the effects of heterozygosity on electrolyte homeostasis and related subclinical phenotypes that may have implications for personalized medicine and nutrition.

Low HDL concentration in rs2048327-G carriers can predispose men to develop coronary heart disease: Tehran Cardiometabolic genetic study (TCGS)

Recent genome-wide association studies (GWAS) highlighted the importance of genetic variations on SLC22A3 and MIA3 genes in developing coronary heart disease (CHD) among different ethnicities. However, the influence of these variations is not recognized within the Iranian population. Hence, in the present study, we aim to investigate two key single nucleotide polymorphisms (SNPs) on CHD incidence in this population. For this purpose, from Tehran Cardiometabolic Genetic Study (TCGS), 453 individuals with CHD were selected as a case and 453 individuals as a control that matched their age and gender. After quality control of two selected SNPs, rs2048327 (SLC22A3) and rs17465637 (MIA3), we used genotyps resulted from chip-typing technology and conducted the logistic regression analysis adjusted for non-genetic risk factors to detect the possible association of these SNPs with the CHD development. Our findings demonstrated the rs2048327-G and rs17465637-C can significantly increase the risk of CHD development about two times in only males and females, respectively. Interestingly, in the male carriers of the risk allele (G) of rs2048327, the low high-density lipoprotein (HDL) level can significantly predispose them to develop coronary heart disease in the future. According to our results, paying more attention to gender and genetic markers can help more efficient coronary heart disease screening and diagnosis.

Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans

INTRODUCTION

Rare genetic functional variants can contribute to 30-40% of functional variability in genes relevant to drug action. Therefore, we investigated the role of rare functional variants in antidepressant response.

METHOD

Mexican-American individuals meeting the Diagnostic and Statistical Manual-IV criteria for major depressive disorder (MDD) participated in a prospective randomized, double-blind study with desipramine or fluoxetine. The rare variant analysis was performed using whole-exome genotyping data. Network and pathway analyses were carried out with the list of significant genes.

RESULTS

The Kernel-Based Adaptive Cluster method identified functional rare variants in 35 genes significantly associated with treatment remission (False discovery rate, FDR <0.01). Pathway analysis of these genes supports the involvement of the following gene ontology processes: olfactory/sensory transduction, regulation of response to cytokine stimulus, and meiotic cell cycleprocess.

LIMITATIONS

Our study did not have a placebo arm. We were not able to use antidepressant blood level as a covariate. Our study is based on a small sample size of only 65 Mexican-American individuals. Further studies using larger cohorts are warranted.

CONCLUSION

Our data identified several rare functional variants in antidepressant drug response in MDD patients. These have the potential to serve as genetic markers for predicting drug response.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00265291.

Searching Far and Genome-Wide: The Relevance of Association Studies in Amyotrophic Lateral Sclerosis

Genome-wide association studies (GWAS) and rare variant association studies (RVAS) are applied across many areas of complex disease to analyze variation in whole genomes of thousands of unrelated patients. These approaches are able to identify variants and/or biological pathways which are associated with disease status and, in contrast to traditional linkage studies or candidate gene approaches, do so without requiring multigenerational affected families, prior hypotheses, or known genes of interest. However, the novel associations identified by these methods typically have lower effect sizes than those found in classical family studies. In the motor neuron disease amyotrophic lateral sclerosis (ALS), GWAS, and RVAS have been used to identify multiple disease-associated genes but have not yet resulted in novel therapeutic interventions. There is significant urgency within the ALS community to identify additional genetic markers of disease to uncover novel biological mechanisms, stratify genetic subgroups of disease, and drive drug development. Given the widespread and increasing application of genetic association studies of complex disease, it is important to recognize the strengths and limitations of these approaches. Here, we review ALS gene discovery via GWAS and RVAS.

Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities

State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer's disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

Hepatic lipase (LIPC) sequencing in individuals with extremely high and low high-density lipoprotein cholesterol levels

Common variants in the hepatic lipase (LIPC) gene have been shown to be associated with plasma lipid levels; however, the distribution and functional features of rare and regulatory LIPC variants contributing to the extreme lipid phenotypes are not well known. This study was aimed to catalogue LIPC variants by resequencing the entire LIPC gene in 95 non-Hispanic Whites (NHWs) and 95 African blacks (ABs) with extreme HDL-C levels followed by in silico functional analyses. A total of 412 variants, including 43 novel variants were identified; 56 were unique to NHWs and 234 were unique to ABs. Seventy-eight variants in NHWs and 89 variants in ABs were present either in high HDL-C group or low HDL-C group. Two non-synonymous variants (p.S289F, p.T405M), found in NHWs with high HDL-C group were predicted to have damaging effect on LIPC protein by SIFT, MT2 and PP2. We also found several non-coding variants that possibly reside in the circRNA and lncRNA binding sites and may have regulatory potential, as identified in rSNPbase and RegulomeDB databases. Our results shed light on the regulatory nature of rare and non-coding LIPC variants as well as suggest their important contributions in affecting the extreme HDL-C phenotypes.

Identification and characterization of a rare variant in apolipoprotein A-IV, p.(V336M), and evaluation of HDL functionality in a Greek cohort with extreme HDL cholesterol levels

High-Density Lipoprotein cholesterol (HDL-C) levels do not correlate well with Coronary Artery Disease (CAD) risk, while HDL functionality affects atherogenesis and is a better prognostic marker for CAD. Often, the extreme HDL-C levels have a multigenic origin. Here, we searched for single-nucleotide polymorphisms (SNPs) in ten genes of HDL metabolism in a Greek cohort with very low (<10th percentile, n = 13) or very high (>90th percentile, n = 21) HDL-C. We also evaluated the association between HDL-C levels, HDL functionality (anti-oxidant capacity) and CAD in the subjects of this cohort. Individuals with low HDL-C levels had higher triglyceride levels, lower apoA-I levels, decreased HDL anti-oxidant capacity and higher incidence of CAD compared with individuals with control or high HDL-C levels. With next generation sequencing we identified 18 exonic SNPs in 6 genes of HDL metabolism and for selected amino acid changes we performed computer-aided structural analysis and modeling. A previously uncharacterized rare apolipoprotein A-IV variant, apoA-IV [V336M], present in a subject with low HDL-C (14 mg/dL) and CAD, was expressed in recombinant form and structurally and functionally characterized. ApoA-IV [V336M] had similar α-helical content to WT apoA-IV but displayed a small thermodynamic stabilization by chemical unfolding analysis. ApoA-IV [V336M] was able to associate with phospholipids but presented reduced kinetics compared to WT apoA-IV. Overall, we identified a rare apoA-IV variant in a subject with low HDL levels and CAD with altered biophysical and phospholipid binding properties and showed that subjects with very low HDL-C presented with HDL dysfunction and higher incidence of CAD in a Greek cohort.

The Relationship Between Cognitive Status and Known Single Nucleotide Polymorphisms in Age-Related Macular Degeneration

Recent literature has reported a higher occurrence of cognitive impairment among individuals with Age-related Macular Degeneration (AMD) compared to older adults with normal vision. This pilot study explored potential links between single nucleotide polymorphisms (SNPs) in AMD and cognitive status. Individuals with AMD (N = 21) and controls (N = 18) were genotyped for the SNPs CFHY402H, ARMS2A69S and FADS1 rs174547. Cognitive status was evaluated using the Montreal Cognitive Assessment. The two groups differed significantly on which subscales were most difficult. The control group had difficulty with delayed recall while those with AMD had difficulty on delayed recall in addition to abstraction and orientation. Homozygous carriers of the FADS1 rs174547 SNP had significantly lower scores than heterozygotes or non-carriers on the MoCA. The results suggest that the FADS1 SNP may play a role in visual impairment/cognitive impairment comorbidity as reflected in the poorer cognitive scores among homozygotes with AMD compared to those carrying only one, or no copies of the SNP.

Detecting X-linked common and rare variant effects in family-based sequencing studies

The breakthroughs in next generation sequencing have allowed us to access data consisting of both common and rare variants, and in particular to investigate the impact of rare genetic variation on complex diseases. Although rare genetic variants are thought to be important components in explaining genetic mechanisms of many diseases, discovering these variants remains challenging, and most studies are restricted to population-based designs. Further, despite the shift in the field of genome-wide association studies (GWAS) towards studying rare variants due to the "missing heritability" phenomenon, little is known about rare X-linked variants associated with complex diseases. For instance, there is evidence that X-linked genes are highly involved in brain development and cognition when compared with autosomal genes; however, like most GWAS for other complex traits, previous GWAS for mental diseases have provided poor resources to deal with identification of rare variant associations on X-chromosome. In this paper, we address the two issues described above by proposing a method that can be used to test X-linked variants using sequencing data on families. Our method is much more general than existing methods, as it can be applied to detect both common and rare variants, and is applicable to autosomes as well. Our simulation study shows that the method is efficient, and exhibits good operational characteristics. An application to the University of Miami Study on Genetics of Autism and Related Disorders also yielded encouraging results.

Delivery of microRNA-33 Antagomirs by Mesoporous Silica Nanoparticles to Ameliorate Lipid Metabolic Disorders

Lipid metabolic disorders have become a major global public health concern. Fatty liver and dyslipidemia are major manifestations of these disorders. Recently, MicroRNA-33 (miR-33), a post-transcriptional regulator of genes involved in cholesterol efflux and fatty acid oxidation, has been considered as a good therapeutic target for these disorders. However, the traditional methods of gene therapy impede their further clinical transformation into a mature treatment system. To counter this problem, in this study we used mesoporous silica nanoparticles (MSNs) as nanocarriers to deliver miR-33 antagomirs developing nanocomposites miR-MSNs. We observed that the hepatocellular uptake of miR-33 antagomirs increased by ∼5 times when they were delivered using miR-MSNs. The regulation effects of miR-MSNs on miR-33 and several genes involved in lipid metabolism were confirmed in L02 cells. In a high-fat diet fed mice, miR-33 intervention via miR-MSNs lowered the serum triglyceride levels remarkably by 18.9% and reduced hepatic steatosis. Thus, our results provide a proof-of-concept for a potential strategy to ameliorate lipid metabolic disorders.

Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants

The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.

The contribution of rare genetic variants to the pathogenesis of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a highly heritable disorder, but only a small proportion of the heritability can be accounted for by common genetic risk variants identified to date. It is possible that variants with lower allele frequencies that cannot be detected using genome-wide association study arrays contribute to PCOS. Here, we discuss the challenges inherent to studying rare genetic variants in complex disease and review several recent studies that have used DNA sequencing techniques to investigate whether rare variants play a role in PCOS pathogenesis. We evaluate these findings in the context of the latest literature in PCOS and complex disease genetics.

Genomic Diversity Evaluation of Populus trichocarpa Germplasm for Rare Variant Genetic Association Studies

Genome-wide association studies are powerful tools to elucidate the genome-to-phenome relationship. In order to explain most of the observed heritability of a phenotypic trait, a sufficient number of individuals and a large set of genetic variants must be examined. The development of high-throughput technologies and cost-efficient resequencing of complete genomes have enabled the genome-wide identification of genetic variation at large scale. As such, almost all existing genetic variation becomes available, and it is now possible to identify rare genetic variants in a population sample. Rare genetic variants that were usually filtered out in most genetic association studies are the most numerous genetic variations across genomes and hold great potential to explain a significant part of the missing heritability observed in association studies. Rare genetic variants must be identified with high confidence, as they can easily be confounded with sequencing errors. In this study, we used a pre-filtered data set of 1,014 pure Populus trichocarpa entire genomes to identify rare and common small genetic variants across individual genomes. We compared variant calls between Platypus and HaplotypeCaller pipelines, and we further applied strict quality filters for improved genetic variant identification. Finally, we only retained genetic variants that were identified by both variant callers increasing calling confidence. Based on these shared variants and after stringent quality filtering, we found high genomic diversity in P. trichocarpa germplasm, with 7.4 million small genetic variants. Importantly, 377k non-synonymous variants (5% of the total) were uncovered. We highlight the importance of genomic diversity and the potential of rare defective genetic variants in explaining a significant portion of P. trichocarpa's phenotypic variability in association genetics. The ultimate goal is to associate both rare and common alleles with poplar's wood quality traits to support selective breeding for an improved bioenergy feedstock.

Population genetic simulation study of power in association testing across genetic architectures and study designs

While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

Recent Advances in Basic Research for Brain Arteriovenous Malformation

Arteriovenous malformations (AVMs) are abnormal connections of vessels that shunt blood directly from arteries into veins. Rupture of brain AVMs (bAVMs) can cause life-threatening intracranial bleeding. Even though the majority of bAVM cases are sporadic without a family history, some cases are familial. Most of the familial cases of bAVMs are associated with a genetic disorder called hereditary hemorrhagic telangiectasia (HHT). The mechanism of bAVM formation is not fully understood. The most important advances in bAVM basic science research is the identification of somatic mutations of genes in RAS-MAPK pathways. However, the mechanisms by which mutations of these genes lead to AVM formation are largely unknown. In this review, we summarized the latest advance in bAVM studies and discussed some pathways that play important roles in bAVM pathogenesis. We also discussed the therapeutic implications of these pathways.

Role of Deleterious Rare Alleles for Acute-Onset Diffuse Interstitial Lung Disease in Collagen Diseases

Objective

Acute-onset diffuse interstitial lung disease (AoDILD) includes acute exacerbation of interstitial lung disease (ILD), drug-induced ILD, and Pneumocystis pneumonia in collagen diseases patients. As AoDILD causes a poor prognosis in collagen disease patients, the pathogenesis of AoDILD should be investigated. Exome sequencing studies revealed that rare variants were detected to be causative in some diseases. Recently reported upregulated genes in acute exacerbation of idiopathic pulmonary fibrosis could provide candidate genes for restricted exome analysis of AoDILD in collagen disease. Here, we investigated rare variants in the coding and boundary regions of these candidate genes in AoDILD.

Methods

Deleterious rare variants in the coding and boundary regions of the candidate genes were analyzed by exome sequencing and the deleterious rare allele frequencies in AoDILD were compared with those of controls.

Results

A significant association was detected for deleterious rare alleles in NPL (P = .0044, P _c = .0399, odds ratio [OR] = 10.05, 95% confidence interval [CI] = 3.01-33.55). A deleterious rare allele frequency in the 9 candidate genes (P = .0011, OR = 7.17, 95% CI = 2.80-18.33) was also increased in AoDILD in multigene panel analysis. The Krebs von den Lungen-6 (KL-6) levels in AoDILD patients with deleterious rare alleles were tended to be lower than those without (P = .0168, P _c = .1509).

Conclusions

The deleterious rare alleles in NPL were associated with AoDILD. In addition, the deleterious rare allele frequency in the 9 candidate genes was also increased in AoDILD. The deleterious rare alleles might contribute to the pathogenesis of AoDILD.

Pathway analysis of rare variants for the clustered phenotypes by using hierarchical structured components analysis

BACKGROUNDS

Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants.

RESULTS

Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method.

CONCLUSION

In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/ .

Analysis of 51 proposed hypertrophic cardiomyopathy genes from genome sequencing data in sarcomere negative cases has negligible diagnostic yield

PURPOSE

Increasing numbers of genes are being implicated in Mendelian disorders and incorporated into clinical test panels. However, lack of evidence supporting the gene-disease relationship can hinder interpretation. We explored the utility of testing 51 additional genes for hypertrophic cardiomyopathy (HCM), one of the most commonly tested Mendelian disorders.

METHODS

Using genome sequencing data from 240 sarcomere gene negative HCM cases and 6229 controls, we undertook case-control and individual variant analyses to assess 51 genes that have been proposed for HCM testing.

RESULTS

We found no evidence to suggest that rare variants in these genes are prevalent causes of HCM. One variant, in a single case, was categorized as likely to be pathogenic. Over 99% of variants were classified as a variant of uncertain significance (VUS) and 54% of cases had one or more VUS.

CONCLUSION

For almost all genes, the gene-disease relationship could not be validated and lack of evidence precluded variant interpretation. Thus, the incremental diagnostic yield of extending testing was negligible, and would, we propose, be outweighed by problems that arise with a high rate of uninterpretable findings. These findings highlight the need for rigorous, evidence-based selection of genes for clinical test panels.

Genetics, Dyslipidemia, and Cardiovascular Disease: New Insights

PURPOSE OF REVIEW

The cardiovascular (CV) risk related to lipid disorders is well established and is based on a robust body of evidence from well-designed randomized clinical trials, as well as prospective observational studies. In the last two decades, significant advances have been made in understanding the genetic basis of dyslipidemias. The present review is intended as a comprehensive discussion of current knowledge about the genetics and pathophysiology of disorders that predispose to dyslipidemia. We also focus on issues related to statins and the proprotein convertase subtilisin/kexin type 9 (PCSK9) and some of its polymorphisms, as well as new cholesterol-lowering medications, including PCSK9 inhibitors.

RECENT FINDING

Cholesterol is essential for the proper functioning of several body systems. However, dyslipidemia-especially elevated low-density lipoprotein (LDL-c) and triglyceride levels, as well as reduced lipoprotein lipase activity-is associated with an increased risk of coronary artery disease (CAD). High-density lipoprotein (HDL-c), however, seems to play a role as a risk marker rather than as a causal factor of the disease, as suggested by Mendelian randomization studies. Several polymorphisms in the lipoprotein lipase locus have been described and are associated with variations in the activity of this enzyme, producing high concentrations of triglycerides and increased risk of CAD. Dyslipidemia, especially increased LDL-c and triglyceride levels, continues to play a significant role in CV risk. The combination of genetic testing and counseling is important in the management of patients with dyslipidemia of genetic etiology. Strategies focused on primary prevention can offer an opportunity to reduce CV events.

Multiple rare and common variants in APOB gene locus associated with oxidatively modified low-density lipoprotein levels

Oxidatively modified low-density lipoproteins (oxLDL) play an important role in the occurrence and progression of atherosclerosis. To identify the genetic factors influencing the oxLDL levels, we have genotyped 776 DNA samples of Russian individuals for 196,725 single-nucleotide polymorphisms (SNPs) using the Cardio-MetaboChip (Illumina, USA) and conducted genome-wide association study (GWAS). Fourteen common variants in the locus including APOB gene were significantly associated with the oxLDL levels (P < 2.18 × 10⁻⁷). These variants explained only 6% of the variation in the oxLDL levels. Then, we assessed the contribution of rare coding variants of APOB gene to the oxLDL levels. Individuals with the extreme oxLDL levels (48 with the lowest and 48 with the highest values) were selected for targeted sequencing of the region including APOB gene. To evaluate the contribution of the SNPs to the oxLDL levels we used various statistical methods for the association analysis of rare variants: WST, SKAT, and SKAT-O. We revealed that both synonymous and nonsynonymous SNPs affected the oxLDL levels. For the joint analysis of the rare and common variants, we conducted the SKAT-C testing and found a group of 15 SNPs significantly associated with the oxLDL levels (P = 2.14 × 10⁻⁹). Our results indicate that the oxLDL levels depend on both common and rare variants of the APOB gene.