A major issue with association studies is a tendency to find false positives. Populations showing a desired trait also carry a specific gene variant not because the variant actually controls the trait, but due to genetic relatedness. In particular, indirect associations that are not causal will not be eliminated by increasing the sample size or the number of markers. The main sources of such false positives are linkage between causal and noncausal sites, more than one causal site and epistasis. These indirect associations are not randomly distributed throughout the genome and are less common than false positives arising from population structure. Likewise, population structure has always remained a consistent issue. Population structure leads to spurious associations between markers and the trait. This generally is not a problem in linkage analysis because researchers know the genetic structure of the family they created. But in association mapping, where relationships between diverse populations are not necessarily well understood, marker–trait associations arising from kinship and evolutionary history can easily be mistaken for causal ones. This can be accounted for with mixed models MLM. Also called the Q+K model, it was developed to further reduce the false positive rate by controlling for both population structure and cryptic familial relatedness.