Reference Allele Research Articles

3448 Macrophages, APOL1 Genotype, & Immunometabolism in CVD (MAGIC

OBJECTIVES/SPECIFIC AIMS: This study aims to understand the potential immunomudualtory effect of APOL1 variants in auto-antigen activated myeloid cells by assessing lysosomal integrity in activated cells expressing APOL1. The primary stimuli were: 1. ssRNA hY3 as a proxy for the Ro immune complex; 2. in an bulk RNA seq model, interferon-response gene, Siglec 1, as a read out of interferon activity. The primary outcomes were: 1. Myeloid cell APOL1 expression both in primary macrophage cultures and ex-vivo patient derived macrophages; 2. Lysosome integrity as measured by fluorescence intensity of lysotracker dye on light microscopy. METHODS/STUDY POPULATION: All recruited subjects provided written informed consent as per the NEW YORK UNIVERSITY Division of Rheumatology-wide Specimen and Matched Phenotype Linked Examination (SAMPLE) protocol. Subjects were African American; SLE subjects met 4 American College of Rheumatology criteria for SLE. Healthy donor monocytes representing each genotype in duplicate (reference allele: G0/G0; heterozygote variant: RV/G0; and homozygote variant RV/RV) were cultured with GM-CSF to yield macrophages which were incubated in serum free media or with hY3 ssRNA (TLR 7/8 agonist) to yield inflammatory M1 macrophages. Fold increase of APOL1 in untreated vs hY3 treated macrophages was measured using qPCR. Live cells were then cultured on glass chamber slides with DNA dye, DAPI, and Lysotracker red, a fluorescent dye that stains acidic lysosomes. As a proof of concept, interferon response gene, Signlec1, and APOL1 transcriptional activity in peripheral blood monocytes (PBMCs) were measured and correlated in 17 SLE patients by RNA seq. RESULTS/ANTICIPATED RESULTS: Regardless of genotype, hY3 increased APOL1 expression by 29 (+/−18.4) fold (P = 0.007 vs no treatment). Genotyping of the qPCR product showed concordance with the chromosomal DNA with the RV heterozygotes expressing both alleles. To examine lysosomal membrane integrity, live hY3-treated macrophages were stained with lysosotracker dye and fluorescence intensity was measured. Compared to reference allele carrying macrophages, each additional variant allele corresponded with a lesser degree of lysosome compartment staining. In SLE PBMCs, we found that APOL1 was highly expressed, and significantly correlated with Siglec1 (F=10.5; P = 0.005) supporting an association between circulating interferons and APOL1 accumulation in monocytes. DISCUSSION/SIGNIFICANCE OF IMPACT: Given that the “cytokine milieu” in SLE elicits APOL1 expression, induces inflammatory cell metabolic rewiring, and stimulates autophagy thereby exposing defects in autophagic flux, this gene-environment interaction may underpin the relationship between chronic inflammation and heightened APOL1 polymorphism-attributed cardiovascular risk. These data support further inquiry into the intersection between chronic autoimmunity and APOL1’s functional role in the vascular microenvironment. The in vitro studies herein extend our prior work by demonstrating a mechanistic link between SLE-associated inflammation, APOL1 risk variant status and CVD via a lysosomal defect which converges on common autophagic and metabolic pathways in mononuclear cells.

Impact of germinal center-associated nuclear protein polymorphisms on breast cancer risk and prognosis in a Japanese population.

Germinal center-associated nuclear protein (GANP) is a phosphoprotein involved in mRNA export and regulation of DNA recombination. Although GANP expression in human breast cancer tissue is associated with breast cancer prognosis, the association between the genetic background of GANP and susceptibility and prognosis of breast cancer is unclear. We selected 694 breast cancer cases and 1376 age- and menopausal status-matched non-cancer controls from the Hospitable-based Epidemiologic Research Program, conducted at Aichi Cancer Center between 2001 and 2005. We evaluated the impact of two polymorphisms at the GANP locus (rs2839178 and rs11702450) on the susceptibility and prognosis of breast cancer. Reference alleles were defined as the A allele for rs2839178 and G allele for rs11702450. The GG genotype of rs2839178 was statistically significantly associated with breast cancer risk (odds ratio [OR] 0.48, 95% confidence interval [CI] 0.30-0.76, P = 0.002). In prognostic analysis, compared to those with AA genotype of rs2839178, patients with AG or GG genotypes had longer disease-free survival (DFS) (hazard ratio [HR] 0.71, 95% CI 0.49-1.04 and HR 0.42, 95% CI 013-1.42, respectively, P for trend = 0.04). eQTL analysis indicated that association with rs2839178 can be explained by the effect of rs2839173 on expression of GANP/MCM3AP. The G allele of rs2839178 at the GANP locus was significantly associated with reduced breast cancer risk and longer DFS in breast cancer patients, showing a consistent direction in the association between susceptibility and clinical outcome. GANP is, therefore, important for the occurrence and progression of sporadic breast cancer.

Calling known variants and identifying new variants while rapidly aligning sequence data

Whole-genome sequencing studies can identify causative mutations for subsequent use in genomic evaluations. Speed and accuracy of sequence alignment can be improved by accounting for known variant locations during alignment instead of calling the variants after alignment as in previous programs. The new programs Findmap and Findvar were compared with alignment using Burrows-Wheeler alignment (BWA) or SNAP and variant identification using Genome Analysis ToolKit (GATK) or SAMtools. Findmap stores the reference map and any known variant locations while aligning reads and counting reference and alternate alleles for each DNA source. Findmap also outputs potential new single nucleotide variant, insertion, and deletion alleles. Findvar separates likely true variants from read errors and outputs genotype probabilities. Strategies were tested using cattle, human, and a completely random reference map and simulated or actual data. Most tests simulated 10 bulls, each with 10× simulated sequence reads containing 39 million variants from the 1000 Bull Genomes Project. With 10 processors, clock times for processing 100× data were 105 h for BWA, 25 h for GATK, and 11 h for SAMtools but only about 4 h for SNAP, 3 h for Findmap, and 1 h for Findvar. Alignment programs required about the same total memory; BWA used 46 GB (4.6 GB/processor), whereas >10 processors can share the same memory in SNAP and Findmap, which used 40 and 46 GB, respectively. Findmap correctly mapped 92.9% of reads (compared with 92.6% from SNAP and 90.5% from BWA) and had high accuracy of calling alleles for known variants. For new variants, Findvar found 99.8% of single nucleotide variants, 79% of insertions, and 67% of deletions; GATK found 99.4, 95, and 90%, respectively; and SAMtools found 99.8, 12, and 16%, respectively. False positives (as percentages of true variants) were 11% of single nucleotide variants, 0.4% of insertions, and 0.3% of deletions from Findvar; 12, 8.4, and 2.9%, respectively, from GATK; and 37, 1.3, and 0.4%, respectively, from SAMtools. Advantages of Findmap and Findvar are fast processing, precise alignment, more useful data summaries, more compact output, and fewer steps. Calling known variants during alignment allows more efficient and accurate sequence-based genotyping.

Fully Bayesian analysis of allele-specific RNA-seq data.

Diploid organisms have two copies of each gene, called alleles, that can be separately transcribed. The RNA abundance associated to any particular allele is known as allele-specific expression (ASE). When two alleles have polymorphisms in transcribed regions, ASE can be studied using RNA-seq read count data. ASE has characteristics different from the regular RNA-seq expression: ASE cannot be assessed for every gene, measures of ASE can be biased towards one of the alleles (reference allele), and ASE provides two measures of expression for a single gene for each biological samples with leads to additional complications for single-gene models. We present statistical methods for modeling ASE and detecting genes with differential allelic expression. We propose a hierarchical, overdispersed, count regression model to deal with ASE counts. The model accommodates gene-specific overdispersion, has an internal measure of the reference allele bias, and uses random effects to model the gene-specific regression parameters. Fully Bayesian inference is obtained using the fbseq package that implements a parallel strategy to make the computational times reasonable. Simulation and real data analysis suggest the proposed model is a practical and powerful tool for the study of differential ASE.

SU69 - DECIPHERING THE ASSOCIATION BETWEEN POLYGENIC RISK FOR SCHIZOPHRENIA AND HIPPOCAMPAL FUNCTION

Background In recent years, Polygenic Risk Profile Scores (PRS's) are being increasingly used to indirectly measure the aggregate genetic effect of many weakly associated markers on the risk for a neuropsychiatric disorder. While the use of PRS's has supported the polygenic architecture of schizophrenia, a link between PRS's and the pathophysiology of this disorder is still missing. In this study, we analyze the association between PRS's for schizophrenia and hippocampal function during a simple declarative memory task in healthy volunteers. We then used regularized regression, a machine learning method, to further investigate the genetic structure that drives the association between schizophrenia PRS's and altered hippocampal engagement. Methods Two hundred and five healthy volunteers underwent BOLD fMRI (3 T) during a Simple Declarative Memory Task (SDMT), which included incidental encoding and retrieval of visual scenes. For both the encoding and retrieval sessions, the scenes were presented in a blocked fashion, with 4 blocks of neutral scenes and 4 blocks of visual scenes alternating with 9 blocks of resting state (fixation cross hair). In the current study, we focused on the encoding phase for neutral scenes only (Rasetti et al. 2014). PRS's were calculated for each individual as the weighted sum of the number of reference alleles on preselected markers based on PGC GWAS study (PGC 2014), with p-value threshold at 0.05 (24,670 SNPs). Imaging space association study was conducted using multiple regressions in SPM8. Region of interest (ROI) was defined as averaged signal within a 6 mm sphere around the peak of association between PRS and right hippocampus. Regularized regression was run using ROI in R using glmnet package (Zou et al. 2005). Results The PRS constructed with 24,670 SNPs (with association with schizophrenia at scoring threshold of p=0.05) is significantly associated with hippocampal activation (z=4.48, p=0.004, FWE corrected in bilateral hippocampal ROI), so that higher PRS corresponds to blunted hippocampal activation. Using the regularized regression with shrinkage factor lambda=0.001, we identified 255 SNPs driving this association, so that the association between the PRS based on these 255 SNPs (PRS255) and hippocampal activation increases dramatically (z=7.14, p Discussion Our results show that PRS's for schizophrenia are significantly associated with altered hippocampal function during a simple declarative memory task even in healthy volunteers. Using regularized regression, we are able to find a small subset of SNPs that drive the association and account for high percentage of variance in hippocampal activation. The use of machine learning techniques for dimension reduction allows disentangling the cumulative effect of risk genes on brain function.

AtSNP Search: a web resource for statistically evaluating influence of human genetic variation on transcription factor binding.

Understanding the regulatory roles of non-coding genetic variants has become a central goal for interpreting results of genome-wide association studies. The regulatory significance of the variants may be interrogated by assessing their influence on transcription factor binding. We have developed atSNP Search, a comprehensive web database for evaluating motif matches to the human genome with both reference and variant alleles and assessing the overall significance of the variant alterations on the motif matches. Convenient search features, comprehensive search outputs and a useful help menu are key components of atSNP Search. atSNP Search enables convenient interpretation of regulatory variants by statistical significance testing and composite logo plots, which are graphical representations of motif matches with the reference and variant alleles. Existing motif-based regulatory variant discovery tools only consider a limited pool of variants due to storage or other limitations. In contrast, atSNP Search users can test more than 37 billion variant-motif pairs with marginal significance in motif matches or match alteration. Computational evidence from atSNP Search, when combined with experimental validation, may help with the discovery of underlying disease mechanisms. atSNP Search is freely available at http://atsnp.biostat.wisc.edu. Supplementary data are available at Bioinformatics online.

Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

BackgroundInherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing.ResultsWe used a dataset of 26 pigs sequenced both at 2× with multiplexing and at 30× without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, which is a default and desired step of some variant callers for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage sequence data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points.ConclusionsWe propose a simple pipeline to correct the preferential bias towards the reference allele that can occur during variant discovery and we recommend that users of low-coverage sequence data be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-coverage sequence data.

Identification of the Genetic Variant Responsible for Variable Platelet CD36 Expression By Massively Parallel Reporter Assay

CD36 is a platelet membrane glycoprotein whose engagement with oxidized low density lipoprotein (oxLDL) results in platelet activation. Deletion of CD36 in mice fed a high fat diet results in attenuation of the pro-thrombotic state and platelet hyper-activity. The CD36 gene has been associated with platelet count, platelet volume, as well as lipid levels and CVD risk. Platelet CD36 expression is highly variable with 3-8% of non-white populations displaying a complete lack of expression. Platelet CD36 expression levels have been shown to be associated with both the platelet oxLDL response and an elevated risk of thrombo-embolism. Several genomic variants have been identified as associated with platelet CD36 levels, however none have been conclusively demonstrated to be causative.We have previously reported platelet expression Quantitative Trait Loci (eQTLs) that associate single nucleotide polymorphisms (SNPs) with platelet RNA levels, indicating genetic influence on gene expression. CD36 is one of the 612 platelet-expressed RNAs whose abundance has significant genotypic associations, suggesting a mechanism by which CD36 level is regulated. 86 eQTL SNPs located within a +/- 100kb window of the CD36 gene are associated with platelet CD36 mRNA levels at a significance of P<1x10-6, spanning a range of 125kb. Rather than prioritizing these 86 variants for functional testing based on genomic annotation, we opted to use an approach termed a Massively Parallel Reporter Array (MPRA), in which all 86 SNPs were tested simultaneously. A library of plasmids was generated in which both alleles of the variants including 150bp of surrounding genomic context were cloned 5' to a minimal promoter and unique barcode. For statistical robustness, 40 barcodes were constructed per allele, resulting in a library containing a total of ~5000 plasmids that were generated and transfected into K562 erythroleukemia cells. RNA-Seq was then conducted on the transfected cells and the abundance of individual barcodes was quantified, normalized to the DNA plasmid library, and then compared across variant alleles.The MPRA analysis led to identification of 5 SNPs located within a 35kb region 5‘ to the CD36 transcription start site that had significantly different barcode production between alleles. These 5 SNPs are in linkage disequilibrium with one another, with variants reported to be associated with CD36 level, and with variants reported to be associated with platelet count and volume by GWAS. To verify the functionality of these variants, we conducted luciferase assays in which the genomic sequence surrounding the variants were inserted 5‘ to the reporter gene. The genomic fragment containing the alternate alleles of two of these SNPs, rs2366739 and rs1194196, located within 29bp of each other, demonstrated 25-40% lower transcriptional activity than the reference alleles. Next, to determine if these two variants altered the interaction of DNA-binding proteins with the locus, we conducted electromobility-shift assays (EMSA) using probes derived from both alleles of the genomic sequence. As with the MPRA and luciferase assays, the alternative alleles of rs2366739 and rs1194196 resulted in lower levels of DNA:protein complexes, indicating an altered affinity for regulatory complexes. Finally, to further confirm the role of this genomic locus in regulating CD36 expression levels, we deleted a 596bp region containing these two SNPs from the K562 erythroleukemia cell genome using CRISPR/Cas. Deletion of this region resulted in an approximately 10-fold increase in CD36 RNA in these cells, confirming a role for this loci in the regulation of CD36 expression.These data indicate that we have identified a regulatory locus for CD36 expression, which has important implications for platelet function in cardiovascular disease. These variants are located within a retrotransposon long-terminal repeat and alter a putative GATA1 binding site, suggesting that these variants may alter binding of that transcription factor to the locus. Platelet CD36 expression level has been linked to the platelet response to oxLDL and may modulate the thrombotic response in hyperlipidemic conditions. Therefore, the mechanism described here, in which a genetic variant alters platelet CD36 levels, and thus the platelet response to hyperlipidemic conditions, may serve as an important biomarker for increased thrombotic potential. DisclosuresNo relevant conflicts of interest to declare.

Data Access and Interactive Visualization of Whole Genome Sequence of Sickle Cell Patients within the St. Jude Cloud

With the increase in availability of high depth whole genome sequencing (WGS) data of individuals with sickle cell disease (SCD), easy access to the raw sequencing data remains an issue due to technical and regulatory challenges. A compliant system that can provide facile data access would accelerate scientific discovery of genetic variants associated with clinical phenotypes. Cloud storage and computing provide an ultimate solution to this data access, which we have shown through the St. Jude Cloud (https://stjude.cloud) where over 5000 whole genome sequences for pediatric cancer patients are being shared in collaboration with DNANexus and Microsoft. Here we expand the St. Jude Cloud to sickle cell disease data through the Sickle Genome Project (SGP) Data Portal (https://pecan.stjude.org/permalink/sgp) to allow instantaneous raw data access (following data access committee approval), as well as visualization of genotype calls at individual level in a novel genome. The SGP WGS data was generated from 871 patients from St. Jude Children's Research Hospital (St. Jude) through the Sickle Cell Clinical Research and Intervention Program (SCCRIP, Pediatr Blood Cancer. 2018 May 24: e27228) and from Baylor College of Medicine (BCM). All study participants provided informed consent for genomic study and data sharing on IRB-approved research protocols.The SGP data portal will have multi-tiered access. All users will have access to a general heat map view which shows anonymized patient clinical values (e.g., fetal hemoglobin (HbF), mean corpuscular volume (MCV), hemoglobin concentration (Hb)) and relevant SCD modifying variants (e.g., Beta-globin locus, MYB, BCL11A, HBA). The GenomePaint bowser allows for viewing coding and noncoding variants. Displayed with each variant will be a visual indication of the median fetal hemoglobin values for patients homozygous for the reference allele, heterozygous, or homozygous for the alternative allele. The browser also displays erythroid specific DNA-accessibility and epigenetic marks and indicates variant that may disrupt erythroid specific transcription factor binding sites (GATA1 and BCL11A). For anonymization purposes, within the genome browser and heat map views, clinical values and the patients age will be binned into ranges when displayed as single or low count values. Lastly, the ProteinPaint tool (Zhang and Zhou, Nature Gen, Dec 29, 2015) will enable visualization and filtering of variants with reference to protein domain and amino acid sequence.To access processed data such as BAM and VCF files for downstream analyses, a user will be required to apply for access which will be adjudicated by a data access committee. Verified researchers will be granted access to clinical data in a manner consistent with the protocol specific informed consent documentation and protocol under which the sequencing was performed. This may include coded clinical and demographic data when specified by the research protocol and informed consentThe SGP data set will be one of the first WGS datasets from primarily African American Sickle cell patients to be made available to clinicians and researchers worldwide. In addition, no SCD-centric data portal exists that contains controlled access to data and provides graphical tools for visual analysis. The combination of the visual tools and ability to download tools provides the scientific community an invaluable resource for studying sickle cell disease. DisclosuresEstepp:Daiichi Sankyo: Consultancy; NHLBI: Research Funding; Global Blood Therapeutics: Consultancy, Research Funding; ASH Scholar: Research Funding. Hankins:Global Blood Therapeutics: Research Funding; bluebird bio: Consultancy; NCQA: Consultancy; Novartis: Research Funding.

Adiponectin receptor 1 gene polymorphisms (rs1342387 & rs12733285) are not associated directly with type 2 diabetes mellitus in Iraqi population

BackgroundThe incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide. Many studies carried out in several populations exhibited an association of adiponectin receptor 1 gene (AdipoR1) polymorphisms with type 2 diabetes mellitus. MethodsA case-control study was conducted to verify the association of AdipoR1 polymorphisms (rs1342387 G>A and rs12733285C>T) with T2DM in Iraqi population. The study included 400 T2DM patients as well as 400 healthy control individuals. DNA was extracted from whole blood and genotyped for the analyzed genes by PCR-RFLP. The odds ratio for the risk of developing T2DM was calculated after adjustment for age, sex, and BMI. Multinomial logistic regression analysis was applied to compare the results. ResultsThe genotyping of rs1342387 G>A SNP in AdipoR1 highlighted an insignificant (OR = 1.53, CI 95% = 0.88–6.32 & P = 0.08) difference of the AA genotype in T2DM patients when compared with the control group. Similarly, the AG genotype was insignificantly (OR = 1.27, CI 95% = 0.93–1.74 & P = 0.14) changed among the two groups after sex and age adjustment. The minor allele frequency A was insignificantly (P = 0.13) differed in T2DM patients (29.88%) with respect to the control group (26.25%). The assessment of characteristics-genotype distribution relationship in T2DM patient group revealed significant elevations of BMI (P = 0.03) and HOMA-IR (P = 0.02) values in subgroups carried the A allele when compared with those of the reference alleles. The evaluation of the results of the genotype distribution, minor allele frequency and characteristics-genotype distribution relationship for the rs12733285C>T SNP failed to point out significant alteration. ConclusionAdipoR1polymorphisms (rs1342387 G>A & rs12733285C>T) do not associate directly with T2DM in Iraqi population. However, there may be an indirect impact via the directing of insulin resistance and BMI.

Alloimmunization risk associated with amino acid 223 substitution in the RhD protein: analysis in the light of molecular modeling.

Partial D status is a major concern for transfusion and pregnancy, due to the possibility of carriers becoming immunized. When known carriers of a D variant have never been exposed to complete D, they are assumed to have D partial status based on the position of the amino acid substituted. New approaches for predicting immunization risk are required. We built a three-dimensional (3D) structural model to investigate the consequences of substitutions of Amino Acid 223 involved in a large number of D variants. Homology modeling was performed with multiple templates. The model was evaluated by comparing the interactions of the known p.Phe223Val variant (RHD*08.01) and a new p.Phe223Ser variant (RHD*52) to RhD reference allele (p.Phe223). The consequences predicted by modeling the variants were compared with serologic data. The 3D structural model was generated from two related protein structures and assessed with state-of-the-art approaches. An analysis of the interactions of the variant Residue 223 in the proposed 3D model highlighted the importance of this position. Modeling predictions were consistent with the serologic and clinical data obtained for the D antigen with a substitution of Amino Acid 223. We used a 3D structural model to evaluate the effect of the p.Phe223 substitution on the conformation of the RhD protein. This model shed light on the influence of substitutions on the structure of the RhD protein and the associated alloimmunization risk. These initial findings indicate that the p.Phe223Ser variant can be considered partial.

Functional expression of human arylamine N-acetyltransferase NAT1*10 and NAT1*11 alleles: a mini review.

The arylamine N-acetyltransferase (NAT) nomenclature committee assigns functional phenotypes for human arylamine N-acetyltransferase 1 (NAT1) alleles in those instances in which the committee determined a consensus has been achieved in the scientific literature. In the most recent nomenclature update, the committee announced that functional phenotypes for NAT1*10 and NAT1*11 alleles were not provided owing to a lack of consensus. Phenotypic inconsistencies observed among various studies for NAT1*10 and NAT1*11 may be owing to variable allelic expression among different tissues, the limitations of the genotyping assays (which mostly relied on techniques not involving direct DNA sequencing), the differences in recombinant protein expression systems used (bacteria, yeast, and mammalian cell lines) and/or the known inherent instability of human NAT1 protein, which requires very careful handling of native and recombinant cell lysates. Three recent studies provide consistent evidence of the mechanistic basis underlying the functional phenotype of NAT1*10 and NAT1*11 as 'increased-activity' alleles. Some NAT1 variants (e.g. NAT1*14, NAT1*17, and NAT1*22) may be designated as 'decreased-activity' alleles and other NAT1 variants (e.g. NAT1*15 and NAT1*19) may be designated as 'no-activity' alleles compared with the NAT1*4 reference allele. We propose that phenotypic designations as 'rapid' and 'slow' acetylator should be discontinued for NAT1 alleles, although these designations remain very appropriate for NAT2 alleles.

P068 The novel recombinant HLA-A*11:271 story: Serology to the rescue of the misnomenclature

Aim The wide spread use of NGS for HLA typing has resulted in exponential growth of novel alleles reported to the IMGT. However, the majority of these novel alleles have not been serologically characterized which has significant clinical implications in solid organ and HCT, particularly pertaining to assignment of donor specific HLA antibodies. Methods The identified novel allele had been originally and unsuccessfully tested by rSSOP, SSP, Sanger SBT and Real-Time PCR. Subsequently, the sample was tested by NGS using Mia Fora reagents and analysis software (Immucor). The subject was redrawn for serologic typing using Terasaki trays (One Lambda). A family study analysis was conducted to determine the segregation of the novel allele carrying haplotype by descent. Results With the exception of NGS, none of the molecular methods produced a result of the A locus. NGS revealed a novel HLA-A allele that appears to result from a recombination between A*11:01:01:01 (ex 1 and partial ex 2) & A:01:01:01 (starting from ex 2) as depicted in the figure of the nucleotide and amino acid partial sequence alignments and the NGS coverage plot. The Mia Fora software assigned the HLA A locus typing as A*33:03:01 and the second allele as novel with the closest reference allele match as A*01:01:01:01. The haplotype assignment function of Mia Fora identified the 2 haplotypes of the subject carrying the novel allele which was confirmed by a family study. The novel allele was confirmed by NGS using TruSight HLA typing kit (Illumina) and submitted to IMGT and assigned the name A*11:271. Serological typing was performed and showed positive reactions with 2 operationally monospecific A1 antisera and one polyspecific (A1 & A36) sera, but with neither of the 2 operationally monospecific A11 antisera on the typing tray. Download : Download high-res image (465KB) Download : Download full-size image Conclusions This report presents a cautionary note that in the absence of serological characterization of HLA alleles, the current IMGT nomenclature may not reflect the clinically relevant antigen specificity of these alleles.

Abstract 1221: Chronic lymphocytic leukemia risk variants are associated with alteration of epigenetic states in regulatory regions

Abstract Chronic lymphocytic leukemia risk variants are associated with alteration of epigenetic state in regulatory regions The vast majority of disease-associated single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWASs) reside in noncoding genomic regions. Functional impact of noncoding casual variants on target gene expression often involves regulatory mechanisms, typically through altering local chromatin accessibility, histone modifications, transcription factor (TF) binding affinity and chromatin interactions. In chronic lymphocytic leukemia (CLL), we and others have identified 41 GWAS risk variants, whose mechanism of action in CLL etiology remains largely unknown. In this study, we aim to decipher the regulatory potential of these variants and to better understand the mechanisms contributing to dysregulation of genes implicated in CLL pathogenesis. We performed ChIP-seq for H3K4me3, H3K4me1, H3K27ac and H3K27me3 in CLL. In addition, we also analyzed over 250 public ChIP-seq, ATAC-seq, DNase-seq, FAIRE-seq, and chromatin interaction data in CLL, B cells and/or lymphoblastoid cell lines. Comparing the 41 index SNPs and the nearby SNPs they associated with in linkage disequilibrium (R2 at least 0.5 in the EUR ethnic group in 1000 Genomes Project) to the above epigenetic data sets revealed overlaps with regulatory regions in 38 of the risk loci, with 28 of the loci overlapping super-enhancers. We used two approaches to elucidate the mechanisms of potential SNP causality. First, we used GATK UnifiedGenotyper to infer genotype for the SNPs that overlapped open chromatin regions and ChIP-seq peaks, by which evidence of imbalance between reference and alternative allele was identified at heterozygous sites. Second, for SNPs located within open chromatin regions or ChIP-seq peaks, we used FIMO program to scan the 100bp sequences centered on the SNPs for significant matches (p=1e-05) to the position weight matrices in five TF binding motif databases; SNPs that had TF motifs altered by the alternative alleles were retained. Together, these two approaches revealed evidence for allele imbalance or TF binding motif alterations at 36 of the 38 CLL risk loci, suggesting the generality of epigenetic regulation in CLL pathogenesis. Further analysis of chromatin interaction, gene expression and expression quantitative trait loci data will likely link the casual SNPs to their target genes. Citation Format: Huihuang Yan, Shulan Tian, Geffen Kleinstern, Susan Slager. Chronic lymphocytic leukemia risk variants are associated with alteration of epigenetic states in regulatory regions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1221.

Assessment of genetic factor and depression interactions for asthma symptom severity in cohorts of childhood and elderly asthmatics

It is well known that depression is associated with asthma symptoms. We assessed the combined effects of genetic factors and depression on asthma symptom severity using Bayesian network (BN) analysis. The common 100 top-ranked single-nucleotide polymorphisms (SNPs) were obtained from two genome-wide association studies of symptom severity in two childhood asthmatics trials (CAMP (Childhood Asthma Management Program) and CARE (Childhood Asthma Research and Education)). Using SNPs plus five discretized variables (depression, anxiety, age, sex, and race), we performed BN analysis in 529 CAMP subjects. We identified two nodes (depression and rs4672619 mapping to ERBB4 (Erb-B2 receptor tyrosine kinase 4)) that were within the Markov neighborhood of the symptom node in the network and then evaluated the interactive effects of depressive status and rs4672619 genotypes on asthma symptom severity. In childhood asthmatics with homozygous reference alleles, severe depression was related to less severe symptoms. However, in childhood asthmatics with heterozygous alleles and homozygous variant alleles, depression and symptom severity showed a positive correlation (interaction permutation P value = 0.019). We then tried to evaluate whether the interactive effects that we found were sustained in another independent cohort of elderly asthmatics. Contrary to the findings from childhood asthmatics, elderly asthmatics with homozygous reference alleles showed a positive correlation between depression and symptom severity, and elderly asthmatics with heterozygous alleles and homozygous variant alleles showed a negative correlation (interaction permutation P value = 0.003). In conclusion, we have identified a novel SNP, rs4672619, that shows interactive effects with depression on asthma symptom severity in childhood and elderly asthmatics in opposite directions.

Base-specific mutational intolerance near splice sites clarifies the role of nonessential splice nucleotides

Variation in RNA splicing (i.e., alternative splicing) plays an important role in many diseases. Variants near 5′ and 3′ splice sites often affect splicing, but the effects of these variants on splicing and disease have not been fully characterized beyond the two “essential” splice nucleotides flanking each exon. Here we provide quantitative measurements of tolerance to mutational disruptions by position and reference allele–alternative allele combinations. We show that certain reference alleles are particularly sensitive to mutations, regardless of the alternative alleles into which they are mutated. Using public RNA-seq data, we demonstrate that individuals carrying such variants have significantly lower levels of the correctly spliced transcript, compared to individuals without them, and confirm that these specific substitutions are highly enriched for known Mendelian mutations. Our results propose a more refined definition of the “splice region” and offer a new way to prioritize and provide functional interpretation of variants identified in diagnostic sequencing and association studies.

Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future.

Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.

Genomic Model with Correlation Between Additive and Dominance Effects.

Dominance genetic effects are rarely included in pedigree-based genetic evaluation. With the availability of single nucleotide polymorphism markers and the development of genomic evaluation, estimates of dominance genetic effects have become feasible using genomic best linear unbiased prediction (GBLUP). Usually, studies involving additive and dominance genetic effects ignore possible relationships between them. It has been often suggested that the magnitude of functional additive and dominance effects at the quantitative trait loci are related, but there is no existing GBLUP-like approach accounting for such correlation. Wellmann and Bennewitz (2012) showed two ways of considering directional relationships between additive and dominance effects, which they estimated in a Bayesian framework. However, these relationships cannot be fitted at the level of individuals instead of loci in a mixed model, and are not compatible with standard animal or plant breeding software. This comes from a fundamental ambiguity in assigning the reference allele at a given locus. We show that, if there has been selection, assigning the most frequent as the reference allele orients the correlation between functional additive and dominance effects. As a consequence, the most frequent reference allele is expected to have a positive value. We also demonstrate that selection creates negative covariance between genotypic additive and dominance genetic values. For parameter estimation, it is possible to use a combined additive and dominance relationship matrix computed from marker genotypes, and to use standard restricted maximum likelihood algorithms based on an equivalent model. Through a simulation study, we show that such correlations can easily be estimated by mixed model software and that the accuracy of prediction for genetic values is slightly improved if such correlations are used in GBLUP. However, a model assuming uncorrelated effects and fitting orthogonal breeding values and dominant deviations performed similarly for prediction.

A Comprehensive cis-eQTL Analysis Revealed Target Genes in Breast Cancer Susceptibility Loci Identified in Genome-wide Association Studies

Genome-wide association studies (GWASs) have identified more than 150 common genetic loci for breast cancer risk. However, the target genes and underlying mechanisms remain largely unknown. We conducted a cis-expression quantitative trait loci (cis-eQTL) analysis using normal or tumor breast transcriptome data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and the Genotype-Tissue Expression (GTEx) project. We identified a total of 101 genes for 51 lead variants after combing the results of a meta-analysis of METABRIC and TCGA, and the results from GTEx at a Benjamini-Hochberg (BH)-adjusted p < 0.05. Using luciferase reporter assays in both estrogen-receptor positive (ER+) and negative (ER-) cell lines, we showed that alternative alleles of potential functional single-nucleotide polymorphisms (SNPs), rs11552449 (DCLRE1B), rs7257932 (SSBP4), rs3747479 (MRPS30), rs2236007 (PAX9), and rs73134739 (ATG10), could significantly change promoter activities of their target genes compared to reference alleles. Furthermore, we performed invitro assays in breast cancer cell lines, and our results indicated that DCLRE1B, MRPS30, and ATG10 played a vital role in breast tumorigenesis via certain disruption of cell behaviors. Our findings revealed potential target genes for associations of genetic susceptibility risk loci and provided underlying mechanisms for a better understanding of the pathogenesis of breast cancer.

Interlaboratory comparison of fig (Ficus carica L.) microsatellite genotyping data and determination of reference alleles

Microsatellites have been identified as the marker of choice in plant genotyping projects. However, due to length discrepancies obtained between different laboratories for the same allele, interlaboratory comparison of fingerprinting results is often a difficult task. The objectives of this study were to compare genotyping results of two laboratories, to evaluate genetic parameters of microsatellite markers and to determine reference allele sizes for fig cultivars from the Istrian peninsula.Genotyping results of ninety fig (Ficus carica L.) accessions were comparable between the laboratories despite differences observed when comparing electropherograms of different capillary electrophoresis systems. Differences in lengths of the same alleles were detected due to different PCR methods and laboratory equipment, but the distances between alleles of the same locus were preserved. However, locus FSYC01 exhibited one allele dropout which led to misidentification of 28 heterozygotes as homozygote individuals suggesting this locus as unreliable. Allele dropout was assigned to the tail PCR technology or to a touchdown PCR protocol.Genotypes of twenty-four reference cultivars from the Istrian peninsula were confirmed by both laboratories. These results will contribute to the usage of markers with greater reliability, discrimination power and consequently, to more reliable standardization with other fig genotyping projects.