skater: an R package for SNP-based kinship analysis, testing, and evaluation

Identifying distant relatives using benchtop-scale sequencing

The genetic component of forensic genetic genealogy (FGG) is an estimate of kinship, often conducted at genome scales between a great number of individuals. The promise of FGG is substantial: in concert with genealogical records and other nongenetic information, it can indirectly identify a person of interest. A downside of FGG is cost, as it is currently expensive and requires chemistries uncommon to forensic genetic laboratories (microarrays and high throughput sequencing). The more common benchtop sequencers can be coupled with a targeted PCR assay to conduct FGG, though such approaches have limited resolution for kinship. This study evaluates low-pass sequencing, an alternative strategy that is accessible to benchtop sequencers and can produce resolutions comparable to high-pass sequencing. Samples from a three-generation pedigree were augmented to include up to 7th degree relatives (using whole genome pedigree simulations) and the ability to recover the true kinship coefficient was assessed using algorithms qualitatively similar to those found in GEDmatch. We show that up to 7th degree relatives can be reliably inferred from 1 × whole genome sequencing obtainable from desktop sequencers.

Elucidating the process of SNPs identification in non-reference genome crops

Advances in the next generation sequencing technologies, genome reduction techniques and bioinformatics tools have given a big impetus to the identification of genome-wide single nucleotide polymorphisms (SNPs) in crops. NGS technologies can make available a large amount of sequence data in a short span of time. The huge data requires detailed bioinformatics analysis steps, including preprocessing, mapping, and identification of sequence variants. A plethora of available software meant for sequence analysis is used for different sequence analysis steps. However, SNPs identification is far more challenging for orphaned crops or non-reference genome crops. The current article reports different steps for in silico SNPs identification in a sequential manner and proposes some mapping approaches using CLC Genomics software that could provide an alternative method for SNPs identification in orphan crops having no reference genome. The three mapping approaches: Common reference map from progenitor genomes (CRMPG), step-wise use of progenitor genomes (SWPG) and de novo assembly of sequence read (DASR) were validated with the dd-RAD sequenced data of two genotypes from Brassica juncea.Communicated by Ramaswamy H. Sarma.