Under the Crimson Flag: Fahle Burman's Two Trips to the Michigan Supreme Court

Erick Fahle Burman. a Swedish-born, Finnish-speaking labor and political activist, twice had cases argued on his behalf before the Michigan Supreme Court. In People vs. Burman, Burman, along with nine other defendants, had his conviction affirmed by the court and all ten were forced to pay a fine of $25 each for disturbing the peace. In People vs. Immonen, Burman and his co-defendant, Unto Immonen, had their convictions overturned because of improper evidence being admitted in their lower court trial. Though the conviction was overturned, the two men had already spent several months as prisoners at hard labor in Marquette State Prison located in Michigan's Upper Peninsula. Over twenty-five years separate Burman's two trips to Michigan's high court. On the first occasion, his arrest came less than five years after his arrival as an immigrant to the U. S. On the second occasion, his arrest came less than two years after his return to the state after being away for nearly two decades. On both occasions, Burman was arrested for his involvement with red flags. Though separated by decades, these cases, taken together, are important indicators of the state of Finnish-American radicalism in the years surrounding the red flag incidents and provide interesting insights into the delicacies of political suppression. Examination of these cases within the larger career of Fahle Burman points up his overlooked importance in the history of Finnish-American socialism and communism.

Genetic association studies under the population stratification, family pedigree and application to genome-wide association studies

This dissertation has three separate parts: the first part deals with the general pedigree association testing incorporating continuous covariates; the second part deals with the association tests under population stratification using the conditional likelihood tests; the third part deals with the genome-wide association studies based on the real rheumatoid arthritis (RA) disease data sets from Genetic Analysis Workshop 16 (GAW16) problem 1. Many statistical tests are developed to test the linkage and association using either case-control status or phenotype covariates for family data structure, separately. Those univariate analyses might not use all the information coming from the family members in practical studies. On the other hand, the human complex disease do not have a clear inheritance pattern, there might exist the gene interactions or act independently. In part I, the new proposed approach MPDT is focused on how to use both the case control information as well as the phenotype covariates. This approach can be applied to detect multiple marker effects. Based on the two existing popular statistics in family studies for case-control and quantitative traits respectively, the new approach could be used in the simple family structure data set as well as general pedigree structure. The combined statistics are calculated using the two statistics; A permutation procedure is applied for assessing the p-value with adjustment from the Bonferroni for the multiple markers. We use simulation studies to evaluate the type I error rates and the powers of the proposed approach. Our results show that the combined test using both case-control information and phenotype covariates not only has the correct type I error rates but also is more powerful than the other existing methods. For multiple marker interactions, our proposed method is also very powerful. Selective genotyping is an economical strategy in detecting and mapping quantitative trait loci in the genetic dissection of complex disease. When the samples arise from different ethnic groups or an admixture population, all the existing selective genotyping methods may result in spurious association due to different ancestry distributions. The problem can be more serious when the sample size is large, a general requirement to obtain sufficient power to detect modest genetic effects for most complex traits. In part II, I describe a useful strategy in selective genotyping while population stratification is present. Our procedure used a principal component based approach to eliminate any effect of population stratification. The paper evaluates the performance of our procedure using both simulated data from an early study data sets and also the HapMap data sets in a variety of population admixture models generated from empirical data. There are one binary trait and two continuous traits in the rheumatoid arthritis dataset of Problem 1 in the Genetic Analysis Workshop 16 (GAW16): RA status, AntiCCP and IgM. To allow multiple traits, we suggest a set of SNP-level F statistics by the concept of multiple-correlation to measure the genetic association between multiple trait values and SNP-specific genotypic scores and obtain their null distributions. Hereby, we perform 6 genome-wide association analyses using the novel one- and two-stage approaches which are based on single, double and triple traits. Incorporating all these 6 analyses, we successfully validate the SNPs which have been identified to be responsible for rheumatoid arthritis in the literature and detect more disease susceptibility SNPs for follow-up studies in the future. Except for chromosome 13 and 18, each of the others is found to harbour susceptible genetic regions for rheumatoid arthritis or related diseases, i.e., lupus erythematosus. This topic is discussed in part III.