Damped Ly{\alpha} Absorption Systems in Semi-Analytic Models with Multiphase Gas

We investigate the properties of damped Ly{\alpha} absorption systems (DLAs) in semi-analytic models of galaxy formation, including partitioning of cold gas in galactic discs into atomic, molecular, and ionized phases with a molecular gas-based star formation recipe. We investigate two approaches for partitioning gas into these constituents: a pressure-based and a metallicity-based recipe. We identify DLAs by passing lines of sight through our simulations to compute HI column densities. We find that models with "standard" gas radial profiles - where the average specific angular momentum of the gas disc is equal to that of the host dark matter halo - fail to reproduce the observed column density distribution of DLAs. These models also fail to reproduce the distribution of velocity widths {\Delta}v, overproducing low {\Delta}v relative to high {\Delta}v systems. Models with "extended" radial gas profiles - corresponding to gas discs with higher specific angular momentum - are able to reproduce quite well the column density distribution of absorbers over the column density range 19 < log NHI < 22.5 in the redshift range 2 < z < 3.5. The model with pressure-based gas partitioning also reproduces the observed line density of DLAs, HI gas density, and {\Delta}v distribution at z < 3 remarkably well. However all of the models investigated here underproduce DLAs and the HI gas density at z > 3. If this is the case, the flatness in the number of DLAs and HI gas density over the redshift interval 0 < z < 5 may be due to a cosmic coincidence where the majority of DLAs at z > 3 arise from intergalactic gas in filaments while those at z < 3 arise predominantly in galactic discs. We further investigate the dependence of DLA metallicity on redshift and {\Delta}v, and find reasonably good agreement with the observations, particularly when including the effects of metallicity gradients (abbrv.).Comment: 27 pages, 15 figures, submitted to MNRA

A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data

Background: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results: In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions: Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants