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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Fast Computation of the Kinship Coefficients
Bonnie Kirkpatrick · 2016-02-14 · via cs.DS updates on arXiv.org

For families, kinship coefficients are quantifications of the amount of genetic sharing between a pair of individuals. These coefficients are critical for understanding the breeding habits and genetic diversity of diploid populations. Historically, computations of the inbreeding coefficient were used to prohibit inbred marriages and prohibit breeding of some pairs of pedigree animals. Such prohibitions foster genetic diversity and help prevent recessive Mendelian disease at a population level. This paper gives the fastest known algorithms for computing the kinship coefficient of a set of individuals with a known pedigree. The algorithms given here consider the possibility that the founders of the known pedigree may themselves be inbred, and they compute the appropriate inbreeding-adjusted kinship coefficients. The exact kinship algorithm has running-time $O(n^2)$ for an $n$-individual pedigree. The recursive-cut exact kinship algorithm has running time $O(s^2m)$ where $s$ is the number of individuals in the largest segment of the pedigree and $m$ is the number of cuts. The approximate algorithm has running-time $O(n)$ for an $n$-individual pedigree on which to estimate the kinship coefficients of $\sqrt{n}$ individuals from $\sqrt{n}$ founder kinship coefficients.