An optimal DNA pooling strategy for progressive fine mapping

Estimating the prevalence of multiple diseases from two-stage hierarchical pooling

Testing protocols in large-scale sexually transmitted disease screening applications often involve pooling biospecimens (e.g., blood, urine, and swabs) to lower costs and to increase the number of individuals who can be tested. With the recent development of assays that detect multiple diseases, it is now common to test biospecimen pools for multiple infections simultaneously. Recent work has developed an expectation-maximization algorithm to estimate the prevalence of two infections using a two-stage, Dorfman-type testing algorithm motivated by current screening practices for chlamydia and gonorrhea in the USA. In this article, we have the same goal but instead take a more flexible Bayesian approach. Doing so allows us to incorporate information about assay uncertainty during the testing process, which involves testing both pools and individuals, and also to update information as individuals are tested. Overall, our approach provides reliable inference for disease probabilities and accurately estimates assay sensitivity and specificity even when little or no information is provided in the prior distributions. We illustrate the performance of our estimation methods using simulation and by applying them to chlamydia and gonorrhea data collected in Nebraska. Copyright © 2016 John Wiley & Sons, Ltd.

Regression analysis for multiple-disease group testing data

Group testing, where individual specimens are composited into groups to test for the presence of a disease (or other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Group testing data are unique in that only group responses may be available, but inferences are needed at the individual level. A further methodological challenge arises when individuals are tested in groups for multiple diseases simultaneously, because unobserved individual disease statuses are likely correlated. In this paper, we propose new regression techniques for multiple-disease group testing data. We develop an expectation-solution based algorithm that provides consistent parameter estimates and natural large-sample inference procedures. We apply our proposed methodology to chlamydia and gonorrhea screening data collected in Nebraska as part of the Infertility Prevention Project and to prenatal infectious disease screening data from Kenya.

Efficient mapping of a female sterile gene in wheat (Triticum aestivum L.)

Studies on inheritance of fertility are of great importance in wheat breeding. Although substantial progress has been achieved in molecular characterization of male sterility and fertility restoration recently, little effort has been devoted to female sterility. To identify the gene(s) controlling female sterility in wheat efficiently, an investigation was conducted for the seed setting ratio using a set of F2 populations derived from the cross between a female sterile line XND126 and an elite cultivar Gaocheng 8901. Bulked segregation analysis (BSA) method and recessive class approach were adopted to screen for SSR markers potentially linked to female fertility gene loci in 2005. Out of 1080 SSRs in wheat genome, eight markers on chromosome 2D showed a clear difference between two disparate bulks and small recombination frequency values, suggesting a strong linkage signal to the sterility gene. Based on the candidate linked markers, partial linkage maps were constructed with Mapmaker 3.0 (EXP) instead of whole genome maps, and quantitative trait locus (QTL) mapping was implemented with software QTLNetwork 2.0. A major gene locus designated as taf1, was located on chromosome 2DS. The above result was confirmed by the analysis for 2007 data, and taf1 was identified on the same chromosome 2DS with a confidence interval of 2.4 cM, which could explain 44.99% of phenotypic variation. These results provided fundamental information for fine mapping studies and laid the groundwork for wheat fertility genetic studies.

Combining DNA pooling with selective recombinant genotyping for increased efficiency in fine mapping

One of the key steps in positional cloning and marker-aided selection is to identify marker(s) tightly linked to the target gene (i.e., fine mapping). Selective genotyping such as selective recombinant genotyping (SRG) is commonly used in fine mapping for cost-saving. To further decrease genotyping effort and rapidly screen for tightly linked markers, we propose here a combined DNA pooling and SRG strategy. A two-stage pooled genotyping can be used for identifying recombinants between a pair of flanking markers more efficiently, and a joint use of bulked DNA analysis and two-stage pooling can also save cost for genotyping recombinants. The combined DNA pooling and SRG strategy can further be extended to fine mapping for polygenic traits. The numerical results based on hypothetical scenarios and an illustrative application to fine mapping of a mutant gene, called xl(t), in rice suggest that the proposed strategy can remarkably reduce genotyping amount compared with the conventional SRG.