Mark P. Nelson

Association mapping of disease loci, by use of a pooled DNA genomic screen.

Genomic screening to map disease loci by association requires automation, pooling of DNA samples, and 3,000-6,000 highly polymorphic, evenly spaced microsatellite markers. Case-control samples can be used in an initial screen, followed by family-based data to confirm marker associations. Association mapping is relevant to genetic studies of complex diseases in which linkage analysis may be less effective and to cases in which multigenerational data are difficult to obtain, including rare or late-onset conditions and infectious diseases. The method can also be used effectively to follow up and confirm regions identified in linkage studies or to investigate candidate disease loci. Study designs can incorporate disease heterogeneity and interaction effects by appropriate subdivision of samples before screening. Here we report use of pooled DNA amplifications-the accurate determination of marker-disease associations for both case-control and nuclear family-based data-including application of correction methods for stutter artifact and preferential amplification. These issues, combined with a discussion of both statistical power and experimental design to define the necessary requirements for detecting of disease loci while virtually eliminating false positives, suggest the feasibility and efficiency of association mapping using pooled DNA screening.

PyPop: a software framework for population genomics: analyzing large-scale multi-locus genotype data.

Software to analyze multi-locus genotype data for entire populations is useful for estimating haplotype frequencies, deviation from Hardy-Weinberg equilibrium and patterns of linkage disequilibrium. These statistical results are important to both those interested in human genome variation and disease predisposition as well as evolutionary genetics. As part of the 13th International Histocompatibility and Immunogenetics Working Group (IHWG), we have developed a software framework (PyPop). The primary novelty of this package is that it allows integration of statistics across large numbers of data-sets by heavily utilizing the XML file format and the R statistical package to view graphical output, while retaining the ability to inter-operate with existing software. Largely developed to address human population data, it can, however, be used for population based data for any organism. We tested our software on the data from the 13th IHWG which involved data sets from at least 50 laboratories each of up to 1000 individuals with 9 MHC loci (both class I and class II) and found that it scales to large numbers of data sets well.

Locus and population specific evolution in HLA class II genes

The population genetics of the HLA class II loci was studied with reference to variation in the frequency of (a) alleles at a locus and (b) amino acids at specific sites. Variation was surveyed at 4 loci (DRB1, DQA1, DQB1, and DPB1) in 22 populations from the Twelfth International Histocompatibility Workshop (Saint‐Malo, 1996). Allele and amino acid variation was measured by computing heterozygosity and the effective number of alleles. Substantial variations in polymorphism were observed among the various populations and loci studied. In the majority of the populations, DRB1 has the highest heterozygosity and effective number of alleles. As previously shown, the Amerindian populations have lower levels of allelic diversity when compared to other populations. At the amino acid level, DRB1 antigen recognition sites (ARS) have the highest heterozygosities and effective number of alleles. For the other loci (DPB1, DQA1, and DQB1) for which there is no crystal structure and for which ARS sites were inferred from DRB1, non‐ARS sites were often among the sites with highest levels of variation. It is possible that these putative non‐ARS sites do play a role in antigen presentation.

Lightning talk: PyPop – a software pipeline for large-scale multilocus population genomics

PyPop (Python for Population Genomics) is an open-source framework for performing large-scale population genetic analyses on multilocus genotype and allele frequency data. It computes tests and measures of Hardy-Weinberg equilibrium (locus-level and individual genotype-level), linkage disequilibrum, and selection, and estimates multi-locus haplotypes. PyPop supplements and extends existing population genetic software incorporating them as modules, modified to accommodate highly polymorphic data, rather than reimplementing them from scratch. It facilitates evolutionary analyses by integrating population genetic statistics within and across populations. Originally developed to analyze the highly polymorphic genetic data of the human leukocyte antigen region of the human genome, PyPop has applicability to any kind of multilocus genetic data. It was the primary platform for evolutionary analysis of data collected for a major NIH-funded collaborative grant that included over 30 laboratories and 200 populations (Lancaster et al., 2007a,b). PyPop has also been successfully used in studies by our group, with collaborators, and in publications by many independent research teams in over 70 peer reviewed papers. PyPop deploys a standard Extensible Markup Language (XML) output format and integrates the results of multiple analyses on various populations that were performed at different times into a common output format that can be read into a spreadsheet. The XML output format allows PyPop to be embedded as part of larger analysis pipelines. It also features an Application Programming Interface (API) allowing functionality to be incorporated into other programs. This talk will focus on recent features of PyPop which include the prefiltering of the input genotype data and the ability to translate arbitrary allele names into full amino acid or nucleotide sequences. All code is made available under the terms of the GNU General Public License (GNU GPL):