The use of genomic information in genetic evaluation has revolutionized dairy cattle breeding. It remains a major challenge to understand the genetic basis of variation for quantitative traits. Here, we study the genetic architecture for milk, fat, protein, mastitis and fertility indices in dairy cattle using NGS variants. The analysis was done using a linear mixed model (LMM) and a Bayesian mixture model (BMM). The top 10 QTL identified by LMM analyses explained 22.61, 23.86, 10.88, 18.58 and 14.83% of the total genetic variance for these traits respectively. Trait-specific sets of 4,964 SNPs from NGS variants (most ‘associated’ SNP for each 0.5 Mbp bin) explained 81.0, 81.6, 85.0, 60.4 and 70.9% of total genetic variance for milk, fat, protein, mastitis and fertility indices when analyzed simultaneously by BMM