Identification of two mutant JASON-RELATED genes associated with unreduced pollen production in potato.

KEY MESSAGE: Multiple QTLs control unreduced pollen production in potato. Two major-effect QTLs co-locate with mutant alleles of genes with homology to AtJAS, a known regulator of meiotic spindle orientation. In diploid potato the production of unreduced gametes with a diploid (2n) rather than a haploid (n) number of chromosomes has been widely reported. Besides their evolutionary important role in sexual polyploidisation, unreduced gametes also have a practical value for potato breeding as a bridge between diploid and tetraploid germplasm. Although early articles argued for a monogenic recessive inheritance, the genetic basis of unreduced pollen production in potato has remained elusive. Here, three diploid full-sib populations were genotyped with an amplicon sequencing approach and phenotyped for unreduced pollen production across two growing seasons. We identified two minor-effect and three major-effect QTLs regulating this trait. The two QTLs with the largest effect displayed a recessive inheritance and an additive interaction. Both QTLs co-localised with genes encoding for putative AtJAS homologs, a key regulator of meiosis II spindle orientation in Arabidopsis thaliana. The function of these candidate genes is consistent with the cytological phenotype of mis-oriented metaphase II plates observed in the parental clones. The alleles associated with elevated levels of unreduced pollen showed deleterious mutation events: an exonic transposon insert causing a premature stop, and an amino acid change within a highly conserved domain. Taken together, our findings shed light on the natural variation underlying unreduced pollen production in potato and will facilitate interploidy breeding by enabling marker-assisted selection for this trait.

Smooth Descent: A ploidy-aware algorithm to improve linkage mapping in the presence of genotyping errors.

Linkage mapping is an approach to order markers based on recombination events. Mapping algorithms cannot easily handle genotyping errors, which are common in high-throughput genotyping data. To solve this issue, strategies have been developed, aimed mostly at identifying and eliminating these errors. One such strategy is SMOOTH, an iterative algorithm to detect genotyping errors. Unlike other approaches, SMOOTH can also be used to impute the most probable alternative genotypes, but its application is limited to diploid species and to markers heterozygous in only one of the parents. In this study we adapted SMOOTH to expand its use to any marker type and to autopolyploids with the use of identity-by-descent probabilities, naming the updated algorithm Smooth Descent (SD). We applied SD to real and simulated data, showing that in the presence of genotyping errors this method produces better genetic maps in terms of marker order and map length. SD is particularly useful for error rates between 5% and 20% and when error rates are not homogeneous among markers or individuals. With a starting error rate of 10%, SD reduced it to ∼5% in diploids, ∼7% in tetraploids and ∼8.5% in hexaploids. Conversely, the correlation between true and estimated genetic maps increased by 0.03 in tetraploids and by 0.2 in hexaploids, while worsening slightly in diploids (∼0.0011). We also show that the combination of genotype curation and map re-estimation allowed us to obtain better genetic maps while correcting wrong genotypes. We have implemented this algorithm in the R package Smooth Descent.