Self-compatible (SC) diploid potatoes allow innovative potato breeding. Therefore, the Sli gene, originally described in S. chacoense, has received much attention. In elite S. tuberosum diploids, spontaneous berry set is occasionally observed. We aimed to map SC from S. tuberosum origin. Two full-sib mapping populations from non-inbred diploids were used. Bulks were composed based on both pollen tube growth and berry set upon selfing. After DNA sequencing of the parents and bulks, we generated k-mer tables. Set algebra and depth filtering were used to identify bulk-specific k-mers. Coupling and repulsion phase k-mers, transmitted from the SC parent, mapped in both populations to the distal end of chromosome 12. Intersection between the k-mers from both populations, in coupling phase with SC, exposed a shared haplotype of approximately 1.5 Mb. Subsequently, we screened read archives of potatoes and wild relatives for k-mers specific to this haplotype. The well-known SC clones US-W4 and RH89-039-16, but surprisingly, also S. chacoense clone M6 were positives. Hence, the S. tuberosum source of SC seems identical to Sli. Furthermore, the candidate region drastically reduced to 333 kb. Haplotype-specific KASP markers were designed and validated on a panel of diploid clones including another renown SC dihaploid G254. Interestingly, k-mers specific to the SC haplotype were common in tetraploid varieties. Pedigree information suggests that the SC haplotype was introduced into tetraploid varieties via the founder "Rough Purple Chili". We show that Sli is surprisingly widespread and indigenous to the cultivated gene pool of potato.

The reinvention of potato, from a tetraploid clonal crop into a diploid seed-based hybrid crop, requires insight in the mutational load, recombination landscape, and the genetic basis of fertility. Genomics-based breeding and QTL discovery rely on efficient genotyping strategies such as skim sequencing, to gather genotypic information. The application of skim sequencing to full-sib population of non-inbred parents remains challenging. Here, we report on an R implementation of the OutcrossSeq pipeline for diploids. We applied this pipeline to a large diploid skim sequenced potato population. We used the resulting bin-markers for the construction of high-density parent specific linkage maps, highlighting variation in parental recombination rate and structural variations. We subsequently explored transmission ratio distortion and non-independent assortment of alleles, indicative of large-effect deleterious mutations. Finally, we identified QTLs for seedling tuber yield in pots and pollen shed. This study showcases the range of genetic analyses, from marker inference, identification of transmission ratio distortion, and linkage map construction to QTL mapping, resulting in new insights that contribute to breeding diploid potato.

The reinvention of potato, from a tetraploid clonal crop into a diploid seed-based hybrid crop, requires insight in the mutational load, recombination landscape and the genetic basis of fertility. Genomics based breeding and QTL discovery relies on efficient genotyping strategies such as skim-sequencing, to gather genotypic information. The application of skim-sequencing to full-sib population of non-inbred parents remains challenging. Here, we report on a R implementation of the OutcrossSeq pipeline for diploids and applied it to a large diploid skim-sequenced potato population. We used the resulting bin-markers for the construction of high-density parent specific linkage maps, highlighting variation in parental recombination rate and structural variations. We subsequently explored transmission ratio distortion (TRD) including epistatic ones, indicative of large effect deleterious mutations. Finally, we identified QTLs for seedling tuber yield in pots and pollen production. This study showcases the range of genetic analyses, from marker inference, TRD identification and linkage map construction to QTL mapping, resulting in new insights that contribute to breeding diploid potato.

The balanced segregation of homologous chromosomes during meiosis is essential for fertility and is mediated by crossovers. A strong reduction of crossovers leads to desynapsis, a process in which pairing of homologous chromosomes is abolished before metaphase I. This results in a random segregation of univalent and the production of unbalanced and sterile gametes. However, if desynapsis is combined with another meiotic alteration that restitutes the first meiotic division, then uniform and balanced unreduced gametes, essentially composed of non-recombinant homologs, are produced. This mitosis-like division is of interest to breeders because it transmits most of the parental heterozygosity to the gametes. In potato, desynapsis is a recessive trait that was tentatively mapped to chromosome8. In this article, we have fine-mapped the position of the desynapsis locus and identifiedStMSH4, an essential component of the class I crossover pathway, as the most likely candidate gene. A seven base-pair insertion in the second exon ofStMSH4was found to be associated with desynapsis in our mapping population. We also identified a second allele with a 3820 base-pair insertion and confirmed that both alleles cannot complement each other. Such non-functional alleles appeared to be common in potato cultivars. More than half of the varieties we tested are carriers of mutational load at theStMSH4locus. With this new information, breeders can choose to remove desynaptic alleles from their germplasm to improve fertility or to use them to produce highly uniform unreduced gametes in alternative breeding schemes.

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.