A PLURIPOTENT STEM CELL PLATFORM FOR IN VITRO SYSTEMS GENETICS STUDIES OF MOUSE DEVELOPMENT.

The directed differentiation of pluripotent stem cells (PSCs) from panels of genetically diverse individuals is emerging as a powerful experimental system for characterizing the impact of natural genetic variation on developing cell types and tissues. Here, we establish new PSC lines and experimental approaches for modeling embryonic development in a genetically diverse, outbred mouse stock (Diversity Outbred mice). We show that a range of inbred and outbred PSC lines can be stably maintained in the primed pluripotent state (epiblast stem cells -- EpiSCs) and establish the contribution of genetic variation to phenotypic differences in gene regulation and directed differentiation. Using pooled in vitro fertilization, we generate and characterize a genetic reference panel of Diversity Outbred PSCs (n = 230). Finally, we demonstrate the feasibility of pooled culture of Diversity Outbred EpiSCs as "cell villages", which can facilitate the differentiation of large numbers of EpiSC lines for forward genetic screens. These data can complement and inform similar efforts within the stem cell biology and human genetics communities to model the impact of natural genetic variation on phenotypic variation and disease-risk.

Evolution of diapause in the African turquoise killifish by remodeling the ancient gene regulatory landscape.

Suspended animation states allow organisms to survive extreme environments. The African turquoise killifish has evolved diapause as a form of suspended development to survive a complete drought. However, the mechanisms underlying the evolution of extreme survival states are unknown. To understand diapause evolution, we performed integrative multi-omics (gene expression, chromatin accessibility, and lipidomics) in the embryos of multiple killifish species. We find that diapause evolved by a recent remodeling of regulatory elements at very ancient gene duplicates (paralogs) present in all vertebrates. CRISPR-Cas9-based perturbations identify the transcription factors REST/NRSF and FOXOs as critical for the diapause gene expression program, including genes involved in lipid metabolism. Indeed, diapause shows a distinct lipid profile, with an increase in triglycerides with very-long-chain fatty acids. Our work suggests a mechanism for the evolution of complex adaptations and offers strategies to promote long-term survival by activating suspended animation programs in other species.

postGWAS: A web server for deciphering the causality post the genome-wide association studies.

While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks.

Background: Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks. Results: We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples. Conclusion: This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

Changes in environmental exposures over decades may influence the genetic architecture of severe spermatogenic failure.

STUDY QUESTION: Do the genetic determinants of idiopathic severe spermatogenic failure (SPGF) differ between generations? SUMMARY ANSWER: Our data support that the genetic component of idiopathic SPGF is impacted by dynamic changes in environmental exposures over decades. WHAT IS KNOWN ALREADY: The idiopathic form of SPGF has a multifactorial etiology wherein an interaction between genetic, epigenetic, and environmental factors leads to the disease onset and progression. At the genetic level, genome-wide association studies (GWASs) allow the analysis of millions of genetic variants across the genome in a hypothesis-free manner, as a valuable tool for identifying susceptibility risk loci. However, little is known about the specific role of non-genetic factors and their influence on the genetic determinants in this type of conditions. STUDY DESIGN, SIZE, DURATION: Case-control genetic association analyses were performed including a total of 912 SPGF cases and 1360 unaffected controls. PARTICIPANTS/MATERIALS, SETTING, METHODS: All participants had European ancestry (Iberian and German). SPGF cases were diagnosed during the last decade either with idiopathic non-obstructive azoospermia (n = 547) or with idiopathic non-obstructive oligozoospermia (n = 365). Case-control genetic association analyses were performed by logistic regression models considering the generation as a covariate and by in silico functional characterization of the susceptibility genomic regions. MAIN RESULTS AND THE ROLE OF CHANCE: This analysis revealed 13 novel genetic association signals with SPGF, with eight of them being independent. The observed associations were mostly explained by the interaction between each lead variant and the age-group. Additionally, we established links between these loci and diverse non-genetic factors, such as toxic or dietary habits, respiratory disorders, and autoimmune diseases, which might potentially influence the genetic architecture of idiopathic SPGF. LARGE SCALE DATA: GWAS data are available from the authors upon reasonable request. LIMITATIONS, REASONS FOR CAUTION: Additional independent studies involving large cohorts in ethnically diverse populations are warranted to confirm our findings. WIDER IMPLICATIONS OF THE FINDINGS: Overall, this study proposes an innovative strategy to achieve a more precise understanding of conditions such as SPGF by considering the interactions between a variable exposome through different generations and genetic predisposition to complex diseases. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the "Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI 2020)" (ref. PY20_00212, P20_00583), the Spanish Ministry of Economy and Competitiveness through the Spanish National Plan for Scientific and Technical Research and Innovation (ref. PID2020-120157RB-I00 funded by MCIN/ AEI/10.13039/501100011033), and the 'Proyectos I+D+i del Programa Operativo FEDER 2020' (ref. B-CTS-584-UGR20). ToxOmics-Centre for Toxicogenomics and Human Health, Genetics, Oncology and Human Toxicology, is also partially supported by the Portuguese Foundation for Science and Technology (Projects: UIDB/00009/2020; UIDP/00009/2020). The authors declare no competing interests. TRIAL REGISTRATION NUMBER: N/A.

A phenome-wide scan reveals convergence of common and rare variant associations.

BACKGROUND: Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences. METHODS: Leveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies. RESULTS: We found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e - 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis. CONCLUSIONS: The presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies.

Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits.

Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.

Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits.

Assessment of genomic conservation between humans and pigs at the functional level can improve the potential of pigs as a human biomedical model. To address this, we developed a deep learning-based approach to learn the genomic conservation at the functional level (DeepGCF) between species by integrating 386 and 374 functional profiles from humans and pigs, respectively. DeepGCF demonstrated better prediction performance compared with the previous method. In addition, the resulting DeepGCF score captures the functional conservation between humans and pigs by examining chromatin states, sequence ontologies, and regulatory variants. We identified a core set of genomic regions as functionally conserved that plays key roles in gene regulation and is enriched for the heritability of complex traits and diseases in humans. Our results highlight the importance of cross-species functional comparison in illustrating the genetic and evolutionary basis of complex phenotypes.

Identification of novel genetic risk factors of dilated cardiomyopathy: from canine to human.

BACKGROUND: Dilated cardiomyopathy (DCM) is a life-threatening heart disease and a common cause of heart failure due to systolic dysfunction and subsequent left or biventricular dilatation. A significant number of cases have a genetic etiology; however, as a complex disease, the exact genetic risk factors are largely unknown, and many patients remain without a molecular diagnosis. METHODS: We performed GWAS followed by whole-genome, transcriptome, and immunohistochemical analyses in a spontaneously occurring canine model of DCM. Canine gene discovery was followed up in three human DCM cohorts. RESULTS: Our results revealed two independent additive loci associated with the typical DCM phenotype comprising left ventricular systolic dysfunction and dilatation. We highlight two novel candidate genes, RNF207 and PRKAA2, known for their involvement in cardiac action potentials, energy homeostasis, and morphology. We further illustrate the distinct genetic etiologies underlying the typical DCM phenotype and ventricular premature contractions. Finally, we followed up on the canine discoveries in human DCM patients and discovered candidate variants in our two novel genes. CONCLUSIONS: Collectively, our study yields insight into the molecular pathophysiology of DCM and provides a large animal model for preclinical studies.

Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits.

BACKGROUND: Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits. RESULTS: We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension (p-value = 2.35E-08) and decreasing heel bone mineral density (p-value = 1.92E-19). CONCLUSIONS: These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits.

A pleiotropic-epistatic entangelement model of drug response.

Because drug response is multifactorial, graph models are uniquely powerful for comprehending its genetic architecture. We deconstruct drug response into many different and interdependent sub-traits, with each sub-trait controlled by multiple genes that act and interact in a complicated manner. The outcome of drug response is the consequence of multileveled intertwined interactions between pleiotropic effects and epistatic effects. Here, we propose a general statistical physics framework to chart the 3D geometric network that codes how epistasis pleiotropically influences a complete set of sub-traits to shape body-drug interactions. This model can dissect the topological architecture of epistatically induced pleiotropic networks (EiPN) and pleiotropically influenced epistatic networks (PiEN). We analyze and interpret the practical implications of the pleiotropic-epistatic entanglement model for pharmacogenomic studies.

A Bayesian method for estimating gene-level polygenicity under the framework of transcriptome-wide association study.

Polygenicity refers to the phenomenon that multiple genetic variants have a nonzero effect on a complex trait. It is defined as the proportion of genetic variants with a nonzero effect on the trait. Evaluation of polygenicity can provide valuable insights into the genetic architecture of the trait. Several recent works have attempted to estimate polygenicity at the single nucleotide polymorphism level. However, evaluating polygenicity at the gene level can be biologically more meaningful. We propose the notion of gene-level polygenicity, defined as the proportion of genes having a nonzero effect on the trait under the framework of a transcriptome-wide association study. We introduce a Bayesian approach genepoly to estimate this quantity for a trait. The method is based on spike and slab prior and simultaneously estimates the subset of non-null genes. Our simulation study shows that genepoly efficiently estimates gene-level polygenicity. The method produces a downward bias for small choices of trait heritability due to a non-null gene, which diminishes rapidly with an increase in the genome-wide association study (GWAS) sample size. While identifying the subset of non-null genes, genepoly offers a high level of specificity and an overall good level of sensitivity-the sensitivity increases as the sample size of the reference panel expression and GWAS data increase. We applied the method to seven phenotypes in the UK Biobank, integrating expression data. We find height to be the most polygenic and asthma to be the least polygenic.

Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes.

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n = 20,833-766,345) and xQTL data from seven omics layers (n = 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.

Variability of longitudinal triglyceride phenotype in patients heterozygous for pathogenic APOA5 variants.

BACKGROUND: Biallelic pathogenic variants in APOA5 are an infrequent cause of familial chylomicronemia syndrome characterized by severe, refractory hypertriglyceridemia (HTG), and fasting plasma triglyceride (TG) >10 mmol/L (>875 mg/dL). The TG phenotype of heterozygous individuals with one copy of a pathogenic APOA5 variant is less familiar. We evaluated the longitudinal TG phenotype of individuals with a single pathogenic APOA5 variant allele. METHODS: Medically stable outpatients from Ontario, Canada were selected for study based on having: 1) a rare pathogenic APOA5 variant in a single allele; and 2) at least three serial fasting TG measurements obtained over >1.5 years of follow-up. RESULTS: Seven patients were followed for a mean of 5.3 ± 3.7 years. Fasting TG levels varied widely both within and between patients. Three patients displayed at least one normal TG measurement (<2.0 mmol/L or <175 mg/dL). All patients displayed mild-to-moderate HTG (2 to 9.9 mmol/L or 175 to 875 mg/dL) at multiple time points. Five patients displayed at least one severe HTG measurement. 10%, 54%, and 36% of all TG measurements were in normal, mild-to-moderate, and severe HTG ranges, respectively. CONCLUSIONS: Heterozygosity for pathogenic variants in APOA5 is associated with highly variable TG phenotypes both within and between patients. Heterozygosity confers susceptibility to elevated TG levels, with secondary factors likely modulating the phenotypic severity.

Genetic pathways regulating the longitudinal acquisition of cocaine self-administration in a panel of inbred and recombinant inbred mice.

To identify addiction genes, we evaluate intravenous self-administration of cocaine or saline in 84 inbred and recombinant inbred mouse strains over 10 days. We integrate the behavior data with brain RNA-seq data from 41 strains. The self-administration of cocaine and that of saline are genetically distinct. We maximize power to map loci for cocaine intake by using a linear mixed model to account for this longitudinal phenotype while correcting for population structure. A total of 15 unique significant loci are identified in the genome-wide association study. A transcriptome-wide association study highlights the Trpv2 ion channel as a key locus for cocaine self-administration as well as identifying 17 additional genes, including Arhgef26, Slc18b1, and Slco5a1. We find numerous instances where alternate splice site selection or RNA editing altered transcript abundance. Our work emphasizes the importance of Trpv2, an ionotropic cannabinoid receptor, for the response to cocaine.

Impact of cross-ancestry genetic architecture on GWASs in admixed populations.

Genome-wide association studies (GWASs) have identified thousands of variants for disease risk. These studies have predominantly been conducted in individuals of European ancestries, which raises questions about their transferability to individuals of other ancestries. Of particular interest are admixed populations, usually defined as populations with recent ancestry from two or more continental sources. Admixed genomes contain segments of distinct ancestries that vary in composition across individuals in the population, allowing for the same allele to induce risk for disease on different ancestral backgrounds. This mosaicism raises unique challenges for GWASs in admixed populations, such as the need to correctly adjust for population stratification. In this work we quantify the impact of differences in estimated allelic effect sizes for risk variants between ancestry backgrounds on association statistics. Specifically, while the possibility of estimated allelic effect-size heterogeneity by ancestry (HetLanc) can be modeled when performing a GWAS in admixed populations, the extent of HetLanc needed to overcome the penalty from an additional degree of freedom in the association statistic has not been thoroughly quantified. Using extensive simulations of admixed genotypes and phenotypes, we find that controlling for and conditioning effect sizes on local ancestry can reduce statistical power by up to 72%. This finding is especially pronounced in the presence of allele frequency differentiation. We replicate simulation results using 4,327 African-European admixed genomes from the UK Biobank for 12 traits to find that for most significant SNPs, HetLanc is not large enough for GWASs to benefit from modeling heterogeneity in this way.

The lingering effects of Neanderthal introgression on human complex traits.

The genetic variants introduced into the ancestors of modern humans from interbreeding with Neanderthals have been suggested to contribute an unexpected extent to complex human traits. However, testing this hypothesis has been challenging due to the idiosyncratic population genetic properties of introgressed variants. We developed rigorous methods to assess the contribution of introgressed Neanderthal variants to heritable trait variation and applied these methods to analyze 235,592 introgressed Neanderthal variants and 96 distinct phenotypes measured in about 300,000 unrelated white British individuals in the UK Biobank. Introgressed Neanderthal variants make a significant contribution to trait variation (explaining 0.12% of trait variation on average). However, the contribution of introgressed variants tends to be significantly depleted relative to modern human variants matched for allele frequency and linkage disequilibrium (about 59% depletion on average), consistent with purifying selection on introgressed variants. Different from previous studies (McArthur et al., 2021), we find no evidence for elevated heritability across the phenotypes examined. We identified 348 independent significant associations of introgressed Neanderthal variants with 64 phenotypes. Previous work (Skov et al., 2020) has suggested that a majority of such associations are likely driven by statistical association with nearby modern human variants that are the true causal variants. Applying a customized fine-mapping led us to identify 112 regions across 47 phenotypes containing 4303 unique genetic variants where introgressed variants are highly likely to have a phenotypic effect. Examination of these variants reveals their substantial impact on genes that are important for the immune system, development, and metabolism.

The Genetics of Primary Biliary Cholangitis: A GWAS and Post-GWAS Update.

Primary biliary cholangitis (PBC) is a chronic, progressive cholestatic liver disease in which the small intrahepatic bile ducts are destroyed by autoimmune reactions. Among autoimmune diseases, which are polygenic complex traits caused by the combined contribution of genetic and environmental factors, PBC exhibits the strongest involvement of genetic heritability in disease development. As at December 2022, genome-wide association studies (GWASs) and associated meta-analyses identified approximately 70 PBC susceptibility gene loci in various populations, including those of European and East Asian descent. However, the molecular mechanisms through which these susceptibility loci affect the pathogenesis of PBC are not fully understood. This study provides an overview of current data regarding the genetic factors of PBC as well as post-GWAS approaches to identifying primary functional variants and effector genes in disease-susceptibility loci. Possible mechanisms of these genetic factors in the development of PBC are also discussed, focusing on four major disease pathways identified by in silico gene set analyses, namely, (1) antigen presentation by human leukocyte antigens, (2) interleukin-12-related pathways, (3) cellular responses to tumor necrosis factor, and (4) B cell activation, maturation, and differentiation pathways.

The longitudinal triglyceride phenotype in heterozygotes with LPL pathogenic variants.

BACKGROUND: Biallelic pathogenic variants in lipoprotein lipase (LPL) cause familial chylomicronemia syndrome with severe hypertriglyceridemia (HTG), defined as plasma triglycerides (TG) > 10 mmol/L (> 885 mg/dL). TG levels in individuals with one copy of a pathogenic LPL gene variant is less familiar; some assume that the phenotype is intermediate between homozygotes and controls. OBJECTIVE: We undertook an evaluation of the longitudinal TG phenotype of individuals heterozygous for pathogenic LPL variants. METHODS: Medically stable outpatients were evaluated based on having: (1) a single copy of a rare pathogenic LPL variant; and (2) serial fasting TG measurements obtained over > 1.5 years of follow-up. RESULTS: Fifteen patients with a single pathogenic LPL variant were followed for a mean of 10.3 years (range 1.5 to 30.3 years). TG levels varied widely both within and between patients. One patient had normal TG levels < 2.0 mmol/L (< 175 mg/dL) continuously, while four patients had at least one normal TG level. Most patients fluctuated between mild-to-moderate and severe HTG: five patients had only mild-to-moderate HTG, with TG levels ranging from 2.0 to 9.9 mmol/L (175 to 885 mg/dL), while 6 patients had at least one instance of severe HTG. Of the 203 total TG measurements from these patients, 14.8%, 67.0% and 18.2% were in the normal, mild-to-moderate and severe HTG ranges, respectively. CONCLUSION: The heterozygous LPL deficient phenotype is highly variable both within and between patients. Heterozygosity confers susceptibility to a wide range of TG phenotypes, with severity likely depending on secondary factors.