Human-Genetic Ancestry Inference and False Positives in Forensic Familial Searching

In forensic familial search methods, a query DNA profile is tested against a database to determine if the query profile represents a close relative of a database entrant. One challenge for familial search is that the calculations may require specification of allele frequencies for the unknown population from which the query profile has originated. The choice of allele frequencies affects the rate at which non-relatives are erroneously classified as relatives, and allele-frequency misspecification can substantially inflate false positive rates compared to use of allele frequencies drawn from the same population as the query profile. Here, we use ancestry inference on the query profile to circumvent the high false positive rates that result from highly misspecified allele frequencies. In particular, we perform ancestry inference on the query profile and make use of allele frequencies based on its inferred genetic ancestry. In a test for sibling matches on profiles that represent unrelated individuals, we demonstrate that false positive rates for familial search with use of ancestry inference to specify the allele frequencies are similar to those seen when allele frequencies align with the population of origin of a profile. Because ancestry inference is possible to perform on query profiles, the extreme allele-frequency misspecifications that produce the highest false positive rates can be avoided. We discuss the implications of the results in the context of concerns about the forensic use of familial searching.

Human-Genetic Ancestry Inference and False Positives in Forensic Familial Searching

In forensic familial search methods, a query DNA profile is tested against a database to determine if the query profile represents a close relative of a database entrant. One challenge for familial search is that the calculations may require specification of allele frequencies for the unknown population from which the query profile has originated. Allele-frequency misspecification can substantially inflate false-positive rates compared to use of allele frequencies drawn from the same population as the query profile. Here, we use ancestry inference on the query profile to circumvent the high false-positive rates that result from highly misspecified allele frequencies. In particular, we perform ancestry inference on the query profile and make use of allele frequencies based on its inferred genetic ancestry. In a test for sibling matches on profiles that represent unrelated individuals, we demonstrate that false-positive rates for familial search with use of ancestry inference to specify the allele frequencies are similar to those seen when allele frequencies align with the population of origin of a profile. Because ancestry inference is possible to perform on query profiles, the extreme allele-frequency misspecifications that produce the highest false-positive rates can be avoided. We discuss the implications of the results in the context of concerns about the forensic use of familial searching.

Swarm Quant' Intelligence for Optimizing Multi-Node OLAP Systems

Globalization and market deregulation has increased business competition, which imposed OLAP data and technologies as one of the great enterprise’s assets. Its growing use and size stressed underlying servers and forced new solutions. The distribution of multidimensional data through a number of servers allows the increasing of storage and processing power without an exponential increase of financial costs. However, this solution adds another dimension to the problem: space. Even in centralized OLAP, cube selection efficiency is complex, but now, we must also know where to materialize subcubes. We have to select and also allocate the most beneficial subcubes, attending an expected (changing) user profile and constraints. We now have to deal with materializing space, processing power distribution, and communication costs. This chapter proposes new distributed cube selection algorithms based on discrete particle swarm optimizers; algorithms that solve the distributed OLAP selection problem considering a query profile under space constraints, using discrete particle swarm optimization in its normal(Di-PSO), cooperative (Di-CPSO), multi-phase (Di-MPSO), and applying hybrid genetic operators.