The need to identify large numbers of missing people from mass disasters, armed conflicts, and human rights abuses has become increasingly prevalent. DNA methods for disaster victim identification (DVI) typically rely on the identification of postmortem (PM) samples through comparison to antemortem (AM) samples from close relatives through mitochondrial or short tandem repeat (STR) profiling. There are several limitations to these tests: STRs cannot identify relatives further out than second degree and do not always provide full profiles in degraded samples; and mitochondrial DNA requires a matrilineal relative. The ForenSeq ® Kintelligence Kit was designed to interrogate 10,230 forensically relevant single-nucleotide polymorphisms (SNPs) and allows up to three samples to be sequenced simultaneously. This sequencing plexity is required to type enough SNPs to detect relationships up to fifth degree when searching DNA databases such as GEDmatch PRO™, however DVI scenarios rarely require this level of kinship matching and need a higher throughput solution. In this article, we present a novel approach for the ForenSeq Kintelligence Kit, with an increased sequencing plexity of 12 PM samples and 32 AM samples. Mock PM samples tested included bones, dental remains, and artificially degraded and low input samples. An offline model of the ForenSeq Kintelligence kinship algorithm was used to compare profiles and determine kinship. Despite lower call rates and decreased heterozygosity, relationships could be determined out to the third degree with 100% specificity and 100% sensitivity. Using the ForenSeq Kintelligence workflow in this way can facilitate higher-throughput DNA analysis and still yield a high success rate to aid cases of DVI.