Linear mixed models have been formalized 60 years ago. These models allow to estimate fixed effects, as in the linear models, and random effects. First used in animal genetics, this type of modelling have been widely used in human genetics since a few years. Mixed models can be used in many genetic analysis; linkage and association studies, heritability estimations and Parent-of Origin effects studies for population or familial data.My thesis’ aim is to investigate mixed models based methods, for genetic data in population and, for familial genetic data.In the first part of my thesis, we investigated the mixed model statistical theory and their multiple uses in human genetics. We also adapted methods for our own work. An R package have been created which permits to analyze genetic data in R environment with mixed models.In a second part, we applied mixed models on Three-Cities data, a French longitudinal study, to estimate heritability of several traits. For this analysis, we have access to tag-SNPs typically used in genome-wide association studies, birthplaces and several anthropometric traits. The aim of our study is to analyze presence of population stratification and evaluate methods to correct it. In the one hand, we analyzed birthplace geographic coordinates and showed that the correction for population stratification by classical method is not sufficient in this case. In the other hand, we analyzed anthropometric traits, in particular the height for which we estimated heritability to 39% in Three-Cities study population.In the last part, we focused on family data. In a first work, we exploited familial information in causal variant research. In a second work, we explored mixed models uses for familial data, in particular association study, on Multiple Sclerosis data. We showed that mixed model methods can not be used without taking account the ascertainment scheme: in our data, all families have at least two affected sibs. To understand and correct this phenomenon, more investigations are needed.