Le psoriasis est une maladie complexe inflammatoire et chronique de la peau, due à une forte composante génétique et des facteurs environnementaux. Seule l'implication d'un locus majeur de prédisposition au psoriasis au niveau du chromosome 6p21, nommé PSORS1, a été démontrée dans diverses populations lors d'études de liaison et d'association. Ce locus ne serait responsable que de 30 à 50% de la part génétique. L'objectif de ma thèse était donc d'identifier les autres facteurs génétiques, en particulier dans un échantillon d'une population d'origine française. Pour identifier les loci, une étude de liaison a été réalisée, découvrant des régions potentielles en 12q, 14q, 16p, 20p, en plus de celle en 6p. Deux loci en 6p et 20p ont surtout été étudiés, amenant à la caractérisation de deux gènes, HLA-C et ADAM33. En parallèle, une stratégie de "gènes candidats" a été appliquée, conduisant à suspecter un rôle probable pour les gènes FLG, TGM5, CARD15 et CYLD et à confirmer celui de SLC12A8 dans l'étiologie du psoriasis. / The psoriasis is a complex chronic inflammatory skin disease due to a strong genetic component as well as environmental factors. The only major locus for psoriasis susceptibility implicated to date, named PSORS1 on chromosome 6p21, has been demonstrated in various populations in linkage and association studies. It could nevertheless be responsible for up to 50% of the genetic contribution to the disease. The aim of my thesis was thus to identify the other genetic factors, in particular in the French population. To identify the loci, a linkage study was carried out, showing possible regions on 12q, 14q, 16p, 20p, in addition to that on 6p. Two loci on 6p and 20p were studied in more detail, revealing the contribution of the two genes, HLA-C and ADAM33. In parallel, a "candidate gene" strategy was undertaken, exposing a probable role of FLG, TGM5, CARD15 and CYLD genes and a confirmed role of SLC12A8 gene in the etiology of psoriasis.

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Positive natural selection is recognized as the driving force underneath evolution. One of the surest signatures of recent positive selection is a local elevation of advantageous allele frequency and linkage disequilibrium (LD). This dissertation proposes a new test statistic to detect excess homozygosity based on a simple counting measure, which serves as a surrogate indicator of recent positive selection. Three tests are developed upon the new measure: (a) an extended genotype-based homozy- gosity test (EGHT), (b) a hidden Markov model test (HMMT), and (c) an extended haplotype-based homozygosity test (EHHT). The null hypotheses of all three tests assume random mating and Hardy-Weinberg equilibrium (HWE). They differ in how to treat LD under H0 . The EGHT assumes linkage equilibrium (LE) besides HWE while the EHHT allows arbitrary multi-locus LD. The HMMT stands between these two extremes and assumes pairwise but no higher-order disequilibrium interactions. We first conduct simulation study to compare the three score tests and verify that the EHHT is the most conservative one. We compare the performance of the EHHT with the prevailing detection methods and the EHHT has higher or similar power. We also evaluate the impact of simple demographic history on the EHHT and the simulation study suggests that the EHHT is resistant to the false-positive confounders resulting from simple demographic models. After extensive simulation studies, all three tests are then applied on HapMap Phase II data and we are able to replicate findings reported in the literature. We can also identify new candidate regions that may undergo recent selection through a set of filtering criteria including highest EHHT scores, high derived allele frequency and large population differentiation. Finally, we propose a cross-population comparison test statistic to detect chromosome regions in which there is no significant excess homozygosity in one population but homozygosity remains high in another population.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (p. 114-125).

Pattern of Linkage Disequilibrium (LD) is a major factor largely determining the power of association mapping studies. Along with nucleotide diversities and DNA polymorphism, knowledge of patterns of LD along the genome needs to be to known to effectively design association mapping studies. In this study, patterns of nucleotide diversity, population structure, LD was estimated in Norway spruce (Picea abies). The data used for this were 23 nuclear loci sequenced in around 90 individuals originating from natural populations of Norway spruce throughout the current distribution range in Sweden and Finland. The observed levels of nucleotide diversity are variable among loci varying between 0.002 and 0.008 if measured by average pairwise nucleotide diversity. Despite the samples stretching large part of Finland and Sweden there were no evidence for strong population structure. As in earlier studies LD decays fast with distance and the average pattern of the squared correlation of allele frequencies drops to less than 0.2 within 100bp. In order to put the data in perspective previously generated data sets were re-analyzed and compared to the inferred results.

Understanding the underlying genetic causes of adaptation to local conditions is one of the main goals of population genetics. A strong latitudinal cline in the phenotypic trait of bud set is observed in present day populations of Norway spruce (Picea abies (L.) Karst). The first step towards determining how this strong selection on adaptive traits translates at the loci underlying the trait was to use multilocus sequence data to gain information on the fundamental population genetic properties of Norway spruce. We determined that the level of LD was low and genetic diversity was in the low range. Coalescent simulations revealed a demographic scenario of a fairly old and severe bottleneck as consistent with the observed data. To examine the role of selection at genes putatively involved in the control of bud set we, again, used a multilocus data set to test for deviations from neutrality and demographic scenarios inferred from background loci. Different candidate genes were identified by using different approaches, highlighting the difficulty in predicting how local adaptation will manifest itself on different time scales and in rangewide samples. When examining properties important in the design of association studies, the inevitable next step in identifying genes involved in local adaptation, we found that previous estimates of a low level of LD were highly influenced by the joint analysis of several loci over a large distribution range and that estimates of LD was in fact heterogeneous across loci and increased within populations. In addition, we found that within species tests for deviations from neutral expectations were highly sensitive to sample size. Additional genomic sequence characterization in Norway spruce is necessary to provide more comprehensive sets of markers for association studies, also including gene promoters and non-genic regions of the genome. In the final paper we show that the HMPR method is effective in constructing libraries enriched for the single copy fraction of the genome when applied to the large and dominantly repetitive genome of Norway spruce. In summary, the studies presented in this thesis together constitute a foundation for future studies on adaptive evolution in Norway spruce.

The site frequency spectrum of mutations (SFS) and linkage disequilibrium (LD) are the two major sources of information in population genetics studies. In this study we focus on the levels of LD and the SFS and on the effect of sample size on summary statistics in 10 Scandinavian populations of Norway spruce. We found that previous estimates of a low level of LD were highly influenced by both sampling strategy and the fact that data from multiple loci were analyzed jointly. Estimates of LD were in fact heterogeneous across loci and increased within individual populations compared with the estimate from the total data. The variation in levels of LD among populations most likely reflects different demographic histories, although we were unable to detect population structure by using standard approaches. As in previous studies, we also found that the SFS-based test Tajima's D was highly sensitive to sample size, revealing that care should be taken to draw strong conclusions from this test when sample size is small. In conclusion, the results from this study are in line with recent studies in other conifers that have revealed a more complex and variable pattern of LD than earlier studies suggested and with studies in trees and humans that suggest that Tajima's D is sensitive to sample size. This has large consequences for the design of future association and population genetic studies in Norway spruce.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [205]-219).

Penalized regression methods are becoming increasingly popular in genome-wide association studies (GWAS) for identifying genetic markers associated with disease. However, standard penalized methods such as the LASSO do not take into account the possible linkage disequilibrium between adjacent markers. We propose a novel penalized approach for GWAS using a dense set of single nucleotide polymorphisms (SNPs). The proposed method uses the minimax concave penalty (MCP) for marker selection and incorporates linkage disequilibrium (LD) information by penalizing the difference of the genetic effects at adjacent SNPs with high correlation. A coordinate descent algorithm is derived to implement the proposed method. This algorithm is efficient and stable in dealing with a large number of SNPs. A multi-split method is used to calculate the p-values of the selected SNPs for assessing their significance. We refer to the proposed penalty function as the smoothed MCP (SMCP) and the proposed approach as the SMCP method. Performance of the proposed SMCP method and its comparison with a LASSO approach are evaluated through simulation studies, which demonstrate that the proposed method is more accurate in selecting associated SNPs. Its applicability to real data is illustrated using data from a GWAS on rheumatoid arthritis. Based on the idea of SMCP, we propose a new penalized method for group variable selection in GWAS with respect to the correlation between adjacent groups. The proposed method uses the group LASSO for encouraging group sparsity and a quadratic difference for adjacent group smoothing. We call it smoothed group LASSO, or SGL for short. Canonical correlations between two adjacent groups of SNPS are used as the weights in the quadratic difference penalty. Principal components are used to reduced dimensionality locally within groups. We derive a group coordinate descent algorithm for computing the solution path of the SGL. Simulation studies are used to evaluate the finite sample performance of the SGL and group LASSO. We also demonstrate its applicability on rheumatoid arthritis data.