Imputation methods for missing data for polygenic models

Methods to handle missing data have been an area of statistical research for many years. Little has been done within the context of pedigree analysis. In this paper we present two methods for imputing missing data for polygenic models using family data. The imputation schemes take into account familial relationships and use the observed familial information for the imputation. A traditional multiple imputation approach and multiple imputation or data augmentation approach within a Gibbs sampler for the handling of missing data for a polygenic model are presented. We used both the Genetic Analysis Workshop 13 simulated missing phenotype and the complete phenotype data sets as the means to illustrate the two methods. We looked at the phenotypic trait systolic blood pressure and the covariate gender at time point 11 (1970) for Cohort 1 and time point 1 (1971) for Cohort 2. Comparing the results for three replicates of complete and missing data incorporating multiple imputation, we find that multiple imputation via a Gibbs sampler produces more accurate results. Thus, we recommend the Gibbs sampler for imputation purposes because of the ease with which it can be extended to more complicated models, the consistency of the results, and the accountability of the variation due to imputation

Adjusting Learning Parameters to Increase Cognitive Resource Allocation in Persons with Alcoholism Risk

Parental history of alcoholism is associated with increased alcoholism risk in their children. One factor increasing alcoholism risk is the presence of attention and information encoding disruptions in adult children of alcoholics (ACOA) compared to persons who are not ACOAs (NACOA). Alcohol ingestion reduces these disruptions in ACOAs. This study examined whether alterations of information processing parameters can function like alcohol and reduce processing disruptions experienced by the ACOA. Participants were 80 ACOAs and 80 NACOAs, partitioned into four groups of 20 participants. During learning, subjects studied presentations of stimulus items followed by the presentation of associated response items. The task was to learn which stimulus was associated with which response item. Based on information processing parameters, the study used a 2.5 second learning response period and either a short (3.0 second) or a long (5.0 second) period for evaluating whether the response was or was not correct. Within each group, one-half of the subjects received a short and one-half received a long response evaluation period. In addition to learning performance, information processing was evaluated using psychophysiological-indices of resource allocation in the central nervous system. Whereas the learning performance of the ACOAs during the short review periods was significantly below the performance of the NACOAs, the groups did not significantly differ during long review period conditions. The findings support the implementation of “tuning” information processing parameters to compensate for processing disruptions related to ACOA-status. This outcome could allow development of focused preventive strategies for persons at higher risk for alcoholism

Comparison of variable and model selection methods for genetic association studies using the GAW15 simulated data

We compared and evaluated several variable and model selection methods using Bayesian and non-Bayesian approaches for three replicates of the Genetic Analysis Workshop 15 (GAW15) simulated data. In doing so, two phenotypes were utilized: rheumatoid arthritis (RA) affection status as a binary trait and IgM as a continuous measure. The analyses were performed adjusting for sex, age, and smoking status. For both outcomes, all the methods were comparable in finding the single-nucleotide polymorphisms (SNPs) generated to have a genetic signal. We successfully identified the susceptibility SNPs for RA in the HLA region (chromosome 6), and chromosome 18, and the susceptibility SNP for IgM on chromosome 11; however, many of the methods produced false-positive results

Control of zootechnology leads to improved Cuttlefish (Sepia officinalis, L.) reproduction performance up to pre-industrial levels

Cephalopods are gaining momentum as an alternate group for aquaculture species diversification, not only because they are a good food source (highly appreciated in some worldwide markets) but they also have the potential to quickly reach a market size. However, there are some bottlenecks impeding the transition of culture technology from the laboratory to industry. One is related to control over reproduction in captivity. The objective of the present experiment was to verify the effects of tanks with different bottom areas/volumes on the reproduction performance of S. officinalis breeding stocks, when sex ratios were controlled a priori; and the food cost associated with such performance when individuals are fed a natural frozen diet. One hundred and ninety two juvenile cuttlefish were used to compare three different round-shaped tanks: one type with 3000L volume and two types with 9000L volume (with differences in bottom areas and water column). Individuals had their sex and maturity stage determined to establish a sexual ratio of 2 female:1 male per tank and assure that cuttlefish were still immature. Biological data was collected during both growth and reproduction stages and until the death of all females in each tank. The experiment lasted nearly 300 days. Temperature differences between tank types were registered during both stages. The optimizing of rearing conditions has allowed for higher growth and a higher amount of cuttlefish available for breeding purposes. A total of 123,751 eggs (in 85 batches) was obtained during this experiment, which is a number that may meet a small scale cuttlefish commercial hatchery facility requirements. The present conditions contributed to a better and predictable reproduction performance in specific 9000L tanks, with values reaching pre-industrial numbers (approximate to 24,000 eggs/tank). Moreover, both the amount of eggs per batch and the overall quality of eggs has increased. Three of these 9000L tanks have an overall consumption of approximate to 38.64 Kg tank(-1), which translates in an investment in feed of approximate to 193 (sic) tank(-1), 8.40 (sic) per cuttlefish and an overall daily tank expense of 1.76 (sic) d(-1).info:eu-repo/semantics/publishedVersio

flexsdm: An r package for supporting a comprehensive and flexible species distribution modelling workflow

Species distribution models (SDM) are widely used in diverse research areas because of their simple data requirements and application versatility. However, SDM outcomes are sensitive to data input and methodological choices. Such sensitivity and diverse applications mean that flexibility is necessary to create SDMs with tailored protocols for a given set of data and model use. We introduce the r package flexsdm for supporting flexible species distribution modelling workflows. flexsdm functions and their arguments serve as building blocks to construct a specific modelling protocol for user's needs. The main flexsdm features are modelling flexibility, integration with other modelling tools, simplicity of the objects returned and function speed. As an illustration, we used flexsdm to define a complete workflow for California red fir Abies magnifica. This package provides modelling flexibility by incorporating comprehensive tools structured in three steps: (a) The Pre-modelling functions that prepare input, for example, sampling bias correction, sampling pseudo-absences and background points, data partitioning, and reducing collinearity in predictors. (b) The Modelling functions allow fitting and evaluating different modelling approaches, including individual algorithms, tuned models, ensembles of small models and ensemble models. (c) The Post-modelling functions include tools related to models' predictions, interpolation and overprediction correction. Because flexsdm comprises a large part of the SDM process, from outlier detection to overprediction correction, flexsdm users can delineate partial or complete workflows based on the combination functions to meet specific modelling needs.Fil: Velazco, Santiago José Elías. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. University of California; Estados Unidos. Universidade Federal da Integração Latino-Americana; BrasilFil: Rose, Miranda Brooke. University of California; Estados UnidosFil: de Andrade, André Felipe Alves. Universidade Federal de Goiás; BrasilFil: Minoli, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Franklin, Janet. University of California; Estados Unido

Comparison of tagging single-nucleotide polymorphism methods in association analyses

Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tagging SNP selection methods to detect known associations with quantitative expression phenotypes. Using data from HapMap release 21 on unrelated Utah residents with ancestors from northern and western Europe (CEPH-Utah, CEU), we selected tagging SNPs in five chromosomal regions using ldSelect, Tagger, and TagSNPs. We found that SNP subsets did not substantially overlap, and that the use of trio data did not greatly impact SNP selection. We then tested associations between HapMap genotypes and expression phenotypes on 28 CEU individuals as part of Genetic Analysis Workshop 15. Relative to the use of all SNPs (n = 210 SNPs across all regions), most subset methods were able to detect single-SNP and haplotype associations. Generally, pairwise selection approaches worked extremely well, relative to use of all SNPs, with marked reductions in the number of SNPs required. Haplotype-based approaches, which had identified smaller SNP subsets, missed associations in some regions. We conclude that the optimal tagging SNP method depends on the true model of the genetic association (i.e., whether a SNP or haplotype is responsible); unfortunately, this is often unknown at the time of SNP selection. Additional evaluations using empirical and simulated data are needed

Assessment of genotype imputation methods

Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset to compare the performance of four of these imputation methods: IMPUTE, MACH, PLINK, and fastPHASE. We compared the methods' imputation error rates and performance of association tests using the imputed data, in the context of imputing completely untyped markers as well as imputing missing genotypes to combine two datasets genotyped at different sets of markers. As expected, all methods performed better for single-nucleotide polymorphisms (SNPs) in high linkage disequilibrium with genotyped SNPs. However, MACH and IMPUTE generated lower imputation error rates than fastPHASE and PLINK. Association tests based on allele "dosage" from MACH and tests based on the posterior probabilities from IMPUTE provided results closest to those based on complete data. However, in both situations, none of the imputation-based tests provide the same level of evidence of association as the complete data at SNPs strongly associated with disease