Coordination versus Solvation in Al⁺(benzene)_<i>n</i> Complexes Studied with Infrared Spectroscopy

Singly charged aluminum–benzene cation complexes are produced by laser vaporization in a pulsed supersonic expansion. The Al⁺(benzene)_<i>n</i> (<i>n</i> = 1–4) ions are mass selected and investigated with infrared laser photodissociation spectroscopy. Density functional theory (DFT) is employed to investigate the structures, energetics and vibrational spectra of these complexes. Spectra in the C–H stretching region exhibit sharp multiplet bands similar to the pattern known for the Fermi triad of the isolated benzene molecule. In the fingerprint region, strong bands are seen corresponding to the ν₁₉ C–C ring motion and the ν₁₁ out-of-plane hydrogen bend. The hydrogen bend is strongly blue-shifted compared to this vibration in benzene, whereas the ν₁₉ carbon ring distortion is only slightly shifted to the red. Computed structures and energetics, together with experimental fragmentation and vibrational patterns, indicate a primary coordination of three benzene molecules around the central Al⁺ cation. The <i>n</i> = 4 complex contains one second-sphere solvent molecule

Integrating EMR-Linked and <i>In Vivo</i> Functional Genetic Data to Identify New Genotype-Phenotype Associations

<div><p>The coupling of electronic medical records (EMR) with genetic data has created the potential for implementing reverse genetic approaches in humans, whereby the function of a gene is inferred from the shared pattern of morbidity among homozygotes of a genetic variant. We explored the feasibility of this approach to identify phenotypes associated with low frequency variants using Vanderbilt's EMR-based BioVU resource. We analyzed 1,658 low frequency non-synonymous SNPs (nsSNPs) with a minor allele frequency (MAF)<10% collected on 8,546 subjects. For each nsSNP, we identified diagnoses shared by at least 2 minor allele homozygotes and with an association p<0.05. The diagnoses were reviewed by a clinician to ascertain whether they may share a common mechanistic basis. While a number of biologically compelling clinical patterns of association were observed, the frequency of these associations was identical to that observed using genotype-permuted data sets, indicating that the associations were likely due to chance. To refine our analysis associations, we then restricted the analysis to 711 nsSNPs in genes with phenotypes in the On-line Mendelian Inheritance in Man (OMIM) or knock-out mouse phenotype databases. An initial comparison of the EMR diagnoses to the known <i>in vivo</i> functions of the gene identified 25 candidate nsSNPs, 19 of which had significant genotype-phenotype associations when tested using matched controls. Twleve of the 19 nsSNPs associations were confirmed by a detailed record review. Four of 12 nsSNP-phenotype associations were successfully replicated in an independent data set: thrombosis (<i>F5</i>,rs6031), seizures/convulsions (<i>GPR98</i>,rs13157270), macular degeneration (<i>CNGB3</i>,rs3735972), and GI bleeding (<i>HGFAC</i>,rs16844401). These analyses demonstrate the feasibility and challenges of using reverse genetics approaches to identify novel gene-phenotype associations in human subjects using low frequency variants. As increasing amounts of rare variant data are generated from modern genotyping and sequence platforms, model organism data may be an important tool to enable discovery.</p></div

Association statistics for the 12 candidate nsSNPs.

<p>For each nsSNP, clinical phenotypes were constructed using diagnosis codes that closely approximated the phenotype descriptions in the OMIM and KO mouse databases. Shown are the subject counts and results of exact logistic regression analyses comparing minor allele homozygotes to matched common allele homozygotes. The common allele homozygotes were matched for age, race, gender and data set.</p

Overview of the nsSNP selection process.

<p>There was no difference in number of diagnoses significantly associated with the 1,658 nsSNPs when compared to genotype-permuted data. Hence, a nsSNP selection strategy that compared to diagnoses to those reported in either OMIM or the KO Mouse data was used. A multi-step selection and review process identified 12 candidate nsSNPs.</p

Population characteristics.

<p>Population characteristics.</p

Characteristics of the selected nsSNPs.

<p>OMIM/KO mouse phenotypes are associated at the gene level, not the specific nsSNP. Minor allele frequencies (MAF) are based on the frequencies observed in this study population. Chromosome and position are from Human Annotation Release 104.</p

Replication analyses.

<p>Replication analyses for nsSNP-phenotype associations using an additive logistic regression model adjusting for age, gender and principal components. A (—) indicates that less than 50 cases (i.e., individuals with the given phenotype) were available for analyses.</p

Concordance rate and call rate for each imputation program.

<p>Concordance rate and call rate for each imputation program.</p

Allele frequency versus concordance rates of HLA alleles by imputation program in European Americans.

<p>Concordance rates were generated using OMNI1 and OMNI5 combined SNP-level data and posterior probability >0.50 for each imputation program.</p

Comparison of HLA allelic imputation programs

<div><p>Imputation of human leukocyte antigen (HLA) alleles from SNP-level data is attractive due to importance of HLA alleles in human disease, widespread availability of genome-wide association study (GWAS) data, and expertise required for HLA sequencing. However, comprehensive evaluations of HLA imputations programs are limited. We compared HLA imputation results of HIBAG, SNP2HLA, and HLA*IMP:02 to sequenced HLA alleles in 3,265 samples from BioVU, a de-identified electronic health record database coupled to a DNA biorepository. We performed four-digit HLA sequencing for <i>HLA-A</i>, <i>-B</i>, <i>-C</i>, <i>-DRB1</i>, <i>-DPB1</i>, and <i>-DQB1</i> using long-read 454 FLX sequencing. All samples were genotyped using both the Illumina HumanExome BeadChip platform and a GWAS platform. Call rates and concordance rates were compared by platform, frequency of allele, and race/ethnicity. Overall concordance rates were similar between programs in European Americans (EA) (0.975 [SNP2HLA]; 0.939 [HLA*IMP:02]; 0.976 [HIBAG]). SNP2HLA provided a significant advantage in terms of call rate and the number of alleles imputed. Concordance rates were lower overall for African Americans (AAs). These observations were consistent when accuracy was compared across HLA loci. All imputation programs performed similarly for low frequency HLA alleles. Higher concordance rates were observed when HLA alleles were imputed from GWAS platforms versus the HumanExome BeadChip, suggesting that high genomic coverage is preferred as input for HLA allelic imputation. These findings provide guidance on the best use of HLA imputation methods and elucidate their limitations.</p></div