Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16

Recently, gene set analysis (GSA) has been extended from use on gene expression data to use on single-nucleotide polymorphism (SNP) data in genome-wide association studies. When GSA has been demonstrated on SNP data, two popular statistics from gene expression data analysis (gene set enrichment analysis [GSEA] and Fisher's exact test [FET]) have been used. However, GSEA and FET have shown a lack of power and robustness in the analysis of gene expression data. The purpose of this work is to investigate whether the same issues are also true for the analysis of SNP data. Ultimately, we conclude that GSEA and FET are not optimal for the analysis of SNP data when compared with the SUMSTAT method. In analysis of real SNP data from the Framingham Heart Study, we find that SUMSTAT finds many more gene sets to be significant when compared with other methods. In an analysis of simulated data, SUMSTAT demonstrates high power and better control of the type I error rate. GSA is a promising approach to the analysis of SNP data in GWAS and use of the SUMSTAT statistic instead of GSEA or FET may increase power and robustness

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Risk management strategies for Indiana producers in the presence of fertilizer price risk

There has been little research on understanding the interactions among input price risk, crop insurance, and preharvest marketing decisions of producers. A parametric stochastic simulation was used to model fertilizer, local harvest cash, and December corn future price distributions as well as county and farm yields for an Indiana producer. Various risk management strategies were analyzed and compared in their ability to reduce risk in the distribution of net income. As in previous studies, combinations of crop insurance and preharvest marketing were found to produce the largest reductions in risk compared to the naïve strategy of doing nothing. The preferred choice of insurance was dependent on the correlation and relative variability between farm and county yields. The process of simultaneously offsetting input purchases with equivalent dollar amounts of grain hedges resulted in the largest risk reductions when compared to other successful preharvest marketing options from previous literature

Incorporating Duplicate Genotype Data into Linear Trend Tests of Genetic Association: Methods and Cost-Effectiveness

The genome-wide association (GWA) study is an increasingly popular way to attempt to identify the causal variants in human disease. Duplicate genotyping (or re-genotyping) a portion of the samples in a GWA study is common, though it is typical for these data to be ignored in subsequent tests of genetic association. We demonstrate a method for including duplicate genotype data in linear trend tests of genetic association which yields increased power. We also consider the cost-effectiveness of collecting duplicate genotype data and find that when the relative cost of genotyping to phenotyping and sample acquisition costs is less than or equal to the genotyping error rate it is more powerful to duplicate genotype the entire sample instead of spending the same money to increase the sample size. Duplicate genotyping is particularly cost-effective when SNP minor allele frequencies are low. Practical advice for the implementation of duplicate genotyping is provided. Free software is provided to compute asymptotic and permutation based tests of association using duplicate genotype data as well as to aid in the duplicate genotyping design decision.

How Large Commercial Producers Choose Input Suppliers: Expendable Products from Seed to Animal Health

This study uses cluster analysis to identify buying behavior segments of commercial producers who purchase expendable products including seed, crop protection, animal health and feed. For the crop expendable products we find four buying behavior segments: Convenience, Price, Performance, and a fourth segment, called Balance buyers, who equally value the aforementioned factors as well as customer service and support services. For livestock expendable products we find three buying behavior segments: Balance, Price and Performance. We find that producers have product-specific buying behaviors and this is especially true for livestock producers. We discuss the implications of these customer segments for expendable input marketers and sales-people

High-throughput sequencing defines donor and recipient HLA B-cell epitope frequencies for prospective matching in transplantation

Compatibility for human leukocyte antigen (HLA) genes between transplant donors and recipients improves graft survival but prospective matching is rarely performed due to the vast heterogeneity of this gene complex. To reduce complexity, we have combined next-generation sequencing and in silico mapping to determine transplant population frequencies and matching probabilities of 150 antibody-binding eplets across all 11 classical HLA genes in 2000 ethnically heterogeneous renal patients and donors. We show that eplets are more common and uniformly distributed between donors and recipients than the respective HLA isoforms. Simulations of targeted eplet matching shows that a high degree of overall compatibility, and perfect identity at the clinically important HLA class II loci, can be obtained within a patient waiting list of approximately 250 subjects. Internal epitope-based allocation is thus feasible for most major renal transplant programs, while regional or national sharing may be required for other solid organs