One of the main objectives of human genetic research is the identification of DNA variations that may be involved in the development of rare diseases. Thanks to advances in DNA sequencing technologies and to a progressive integration of the available genetic databases, it is now possible to study not only common variants, but also ones occurring at very low frequencies in the population. Despite the presence of consolidated algorithms to perform the analysis of genetic data, the major hurdle is the impossibility to efficiently process the data, and translate them into biologically meaningful information. This prevents the current solutions and architectures from scaling to growing number of individuals, both for the discovery of new variants and the adoption of these methodologies to support diagnosis and treatment of illnesses in a common clinical setting. In this scenario, Field Programmable Gate Arrays (FPGAs) provide a viable alternative to conventional software-based approaches. In particular, they have already proved to efficiently manage huge workloads while reaching outstanding performance over power consumption scores. Therefore, the purpose of this work is exploring novel computing paradigms to tackle the limitations we are facing. In particular, we present an FPGA-based acceleration of the PairHMM Forward Algorithm, the performance bottleneck in the HaplotypeCaller, a variant calling tool in the popular Genome Analysis Toolkit (GATK). Our final architecture is able to achieve 2160x speedup when compared to the Original Java version in GATK, outperforming existing implementation on both CPUs, GPUs and FPGAs.