Canadian COVID-19 host genetics cohort replicates known severity associations.

The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic contributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospitalized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and constructed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation.

Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes.

INTRODUCTION: Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. Sparseness leads to maximum likelihood estimators (MLEs) of log-OR parameters that are biased away from their null value of zero and tests with inflated type 1 errors. Different penalized-likelihood methods have been developed to mitigate sparse-data bias. We study penalized logistic regression using a class of log-F priors indexed by a shrinkage parameter m to shrink the biased MLE towards zero. For a given m, log-F-penalized logistic regression may be easily implemented using data augmentation and standard software. METHOD: We propose a two-step approach to the analysis of a genetic association study: first, a set of variants that show evidence of association with the trait is used to estimate m; and second, the estimated m is used for log-F-penalized logistic regression analyses of all variants using data augmentation with standard software. Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. RESULTS: We evaluate the statistical properties of our proposed two-step method and compared its performance to other shrinkage methods by a simulation study. Our simulation studies suggest that the proposed log-F-penalized approach has lower bias and mean squared error than other methods considered. We also illustrate the approach on data from a study of genetic associations with "super senior" cases and middle aged controls. DISCUSSION/CONCLUSION: We have proposed a method for single rare variant analysis with binary phenotypes by logistic regression penalized by log-F priors. Our method has the advantage of being easily extended to correct for confounding due to population structure and genetic relatedness through a data augmentation approach.

Physiological phenotypes have optimal values relevant to healthy aging: sweet spots deduced from the Canadian Longitudinal Study on Aging.

Previous observations on a group of exceptionally healthy "Super-Seniors" showed a lower variance of multiple physiological measures relevant for health than did a less healthy group of the same age. The finding was interpreted as the healthier individuals having physiological measurement values closer to an optimal level, or "sweet spot." Here, we tested the generalizability of the sweet-spot hypothesis in a larger community sample, comparing differences in the variance between healthier and less healthy groups. We apply this method to the Canadian Longitudinal Study on Aging (CLSA) comprehensive cohort of 30,097 participants aged 45 to 85 years with deep phenotype data. Data from both sexes and four age ranges were analyzed. Five instruments were used to represent different aspects of health, physical, and cognitive functioning. We tested 231 phenotypic measures for lower variance in the most healthy vs. least healthy quartile of each sex and age group, as classified by the five instruments. Segmented regression was used to determine sex-specific optimal values. One hundred forty-two physiological measures (61%) showed lower variance in the healthiest than in the least healthy group, in at least one sex and age group. The difference in variance was most significant for hemoglobin A1c and was also significant for many body composition measurements, but not for bone mineral density. Ninety-four phenotypes showed a nonmonotonic relationship with health, consistent with the idea of a sweet spot; for these, we determined optimal values and 95% confidence intervals that were generally narrower than the ranges of current clinical reference intervals. These findings for sweet spot discovery validate the proposed approach for identifying traits important for healthy aging.

Kazakh national dog breed Tazy: What do we know?

The Tazy or Kazakh National sighthound has been officially recognized as the national heritage of Kazakhstan. Comprehensive genetic studies of genetic diversity and population structure that could be used for selection and conservation of this unique dog breed have not been conducted so far. The aim of this study was to determine the genetic structure of the Tazy using microsatellite and SNP markers and to place the breed in the context of the world sighthound breeds. Our results showed that all 19 microsatellite loci examined were polymorphic. The observed number of alleles in the Tazy population varied from 6 (INU030 locus) to 12 (AHT137, REN169D01, AHTh260, AHT121, and FH2054 loci) with a mean of 9.778 alleles per locus. The mean number of effective alleles was 4.869 and ranged from 3.349 f to 4.841. All markers were highly informative (PIC values greater than 0.5) and ranged from 0.543 (REN247M23 locus) to 0.865 (AHT121 locus). The observed and expected heterozygosities in a total population were 0.748 and 0.769 and ranged from 0.746 to 0.750 and 0.656 to 0.769, respectively. Overall, the results confirmed that the Tazy breed has a high level of genetic diversity, no significant inbreeding, and a specific genetic structure. Three gene pools underlie the genetic diversity of the Tazy breed. SNP analysis using the CanineHD SNP array, which contains more than 170,000 SNP markers, showed that the Tazy breed is distinct from other sighthound breeds and genetically related to ancient eastern sighthound breeds sharing the same branch with the Afghan Hound and the Saluki. The results, together with archeological findings, confirm the ancient origin of the breed. The findings can be used for the conservation and international registration of the Tazy dog breed.

Homozygosity mapping in the Kazakh national dog breed Tazy.

The Tazy is a breed of sighthound common in Kazakhstan. The identification of runs of homozygosity (ROH) is an informative approach to assessing the history and possible patterns of directional selection pressure. To our knowledge, the present study is the first to provide an overview of the ROH pattern in the Tazy dogs from a genome-wide perspective. The ROH of the Tazy was found to be mainly composed of shorter segments (1-2 Mb), accounting for approximately 67% of the total ROH. The estimated ROH-based inbreeding coefficients (FROH) ranged from 0.028 to 0.058 with a mean of 0.057. Five genomic regions under positive selection were identified on chromosomes 18, 22, and 25. The regions on chromosomes 18 and 22 may be breed specific, while the region on chromosome 22 overlaps with regions of hunting traits in other hunting dog breeds. Among the 12 candidate genes located in these regions, the gene CAB39L may be a candidate that affects running speed and endurance of the Tazy dog. Eight genes could belong to an evolutionarily conserved complex as they were clustered in a large protein network with strong linkages. The results may enable effective interventions when incorporated into conservation planning and selection of the Tazy breed.