Null alleles are alleles that are recessive to codominant markers without any effect on the phenotype. In SSR assays, there are several reasons for the lack of amplification at a locus: the primer does not bind well, longer fragments do not amplify due to imperfections in the PCR reaction, or the amount of DNA in the sample is insufficient. In microsatellite studies, null alleles are mostly used in pedigree analysis and population genetics calculations such as diversity estimation. Null alleles in pedigree analysis can cause rejection of the true parent; if not recognized while in population genetics they distort the results in underestimating diversity. In this review, the effects caused by null-alleles in viticultural research and its possible solutions were summarized.

Molecular markers and mapping are used to analyze an organism’s genes. They allow the selection of target genetic areas based on marker genotype (and not trait phenotype), facilitate the study of genetic variability and diversity, create linkage maps, and follow individuals or lines carrying certain genes. They may be used to select parental genotypes, remove linkage drag in back-crossing, and choose difficult-to-measure characteristics. Due to a lack of genetic variety in crops, the gene pools of wild crop relatives for future agricultural production have been examined. The invention of RFLP (Restriction Fragment Length Polymorphism) for linkage mapping allowed for the creation of other traditional approaches such as RAPD (Random Amplified Polymorphic DNA) and AFLP (Amplified Fragment Length Polymorphism). Accordingly, the need to describe the polymorphic information content (PIC) of the ideal marker has been raised. Marker selection reliability depends on the marker’s relationship to the genomic area of interest. Although informativeness must be estimated for genetic study design, there are no readily available tools. Earlier, PICcalc was developed to calculate heterozygosity (H) and PIC to simplify molecular investigations. These two values were corrected for dominant and co-dominant markers (binary and allelic data) to determine polymorphism quality. Due to the popularity of PICcalc web, we developed a downloadable version called MolMarker with extra functionality to reduce server maintenance.