Thank you for visiting the GSA booth.

We’re glad you stopped by to learn about the Genetics Society of America and our journals, conferences, and career development programming. Browse this page for easy access to the things we talked about.

GSA JournalsThe Allied Genetics Conference (TAGC)
MembershipCareer Development
Wormbook at GENETICSExplore Worm Papers in the GSA Journals
Genetic Models of Rare DiseasesStatistical Genetics in Human Populations

Become part of GSA.

The Genetics Society of America is an international community of scientists who use the tools of genetics and genomics in our research.

Around half of our members are students and postdocs from more than 50 countries. These scientists hail from a wide range of disciplines— including cell biology, physiology, biochemistry, biophysics, neuroscience, developmental biology, evolutionary biology, and more.

GSA’s mission is to advance biological research by supporting professional development of scientists, communicating scientific advancements and fostering collaboration, and advocating for science and scientists. GSA achieves this through its various programming, most notably conferences focused on model organisms, and two peer-reviewed journals, GENETICS and G3: Genes|Genomes|Genetics.

GENETICS and G3: Genes|Genomes|Genetics

Publish with the GSA Journals.

The GSA Journals are peer-reviewed, peer-edited, and published in partnership with Oxford University Press.

Cover of the April 2023 issue of GENETICS depicting a 3D protein structure.

GENETICS publishes high-quality genetics and genomics research that expands scientific boundaries—we’ve been building the field since 1916. With its broad readership, rich history, and responsive editors, GENETICS brings the latest in publishing innovations to the communities it serves. We invite you to submit your research and discover the fast turnaround times and helpful review process for yourself.

Explore GENETICS External

Cover of the April 2023 issue of G3 depicting a large insect.

Get your useful data out into the world by publishing in G3: Genes|Genomes|Genetics. G3 publishes high quality foundational research, particularly studies that generate useful genetic information, such as mutant screens, single gene studies, genome maps, genome sequence data, GWAS and QTL studies, software, data resources, and new methods. The Editorial Board of G3 believes that rapid dissemination of such data lays the foundation for many important insights.

Explore G3 External

Discover Series at the GSA Journals.

As part of our mission to serve our communities, GENETICS and G3 publish thematic collections spanning a broad range of topics. Series are updated as new articles are published, and we invite additional articles on these topics on a rolling basis.

Our newest series on Genetic Models of Rare Diseases launches soon! Check out the first papers now:

GENETICS | G3: Genes|Genomes|Genetics

This series highlights ongoing advances in rare disease discovery and mechanisms by presenting key research findings, new discoveries, and reviews or perspectives. We invite high-quality submissions with a focus on model organism-human genetics, genomics, MAVE studies, and work that leverages advanced genomic tools for gene identification and editing to address the issues noted above.

Previous series include Plant Genetics and Genomics, Fungal Genetics and Genomics, Neurogenetics, Genomic Prediction, and Multiparental Populations.

Repeating row of a microscopy image of a C. elegans nematode false-colored into a variety of colors.

Wormbook at GENETICS

In March 2016, GENETICS launched WormBook: a comprehensive compendium of review articles presenting the current state of knowledge in C. elegans research. WormBook articles span the breadth of the biology, genetics, genomics, and evolution of C. elegans and are actively being published.

Explore Worm Papers at the GSA Journals

Just published in GENETICS

Highly cited in GENETICS

Just published in G3: Genes|Genomes|Genetics

Highly cited in G3: Genes|Genomes|Genetics

Bringing Genetics Together

The Allied Genetics Conference 2024

March 6–10, 2024 | Gaylord National Resort & Convention Center | Metro Washington, DC

TAGC is a unique GSA conference that brings together scientists from multiple international biological research communities to share cutting-edge science, foster new collaborations, and strengthen existing relationships. With an exciting mix of sessions that focus on advances in genetics and genomics in a variety of research organisms, TAGC 2024 is designed to shape the big picture, include diverse voices, and showcase the fundamental unity of biology—all while providing attendees the chance to spend time with old friends and valued colleagues from around the world.

TAGC 2024 Logo

Develop your skills.

GSA helps scientists develop their skills and achieve their career goals. We design programs and initiatives to provide experience, training, mentorship, and community.

Early Career Leadership Program
Develop your skills, join a thriving network, and demonstrate your abilities by participating in the GSA’s Early Career Leadership Program!  In this online program, participants work in teams to propose, develop, and implement initiatives that address unmet needs for the early career scientist community. Participants have the opportunity to work on their writing skills, which includes the option to take a writing workshop tailored for the needs of early career scientists. Early career leaders complete the program with new skills, a network of peers and mentors, and concrete deliverables that demonstrate their abilities.

Applications will reopen in Fall 2023.

Peer Review Training Program

Get real-world peer review training and experience by participating in this innovative program! Peer reviewers are vital to science. Yet early career scientists in our field rarely receive formal training in how to be a good reviewer. GSA and the GSA Journals are addressing this gap with a program that gives early career members real-world peer review experience. Participants will receive online training and advice from GENETICS and G3 editors as they become reviewers for manuscripts submitted to the journal. Early career scientists from anywhere in the world are encouraged to apply. We particularly welcome applications from members who lack opportunities to receive peer review training in their home labs or departments.

Senior graduate students, postdocs, and early career scientists within 7-years of having earned their PhD are invited to apply. Applicants should have published at least one peer-reviewed manuscript, preferably as the first author.

Participants entering the program in 2024 will review for both GENETICS and G3. Applications will open in August 2023.

Frequently Asked Questions

What does it cost to publish in the GSA Journals?

What are the benefits of a GSA membership?

How much does a GSA membership cost?

What does GSA offer for undergraduate students?

Do you have teaching materials available?

How can I tell if my work is a good fit for the GSA Journals?

How long does it take to publish at paper at the GSA Journals?

Statistical Genetics in Human Populations

GENETICS has long stood at the center of the discussion around appropriate methodology in the analysis of complex traits. From the initial mapping paper (Lander and Botstein 1989) to the critical landmark structure paper (Pritchard et al. 2000), GENETICS is not afraid to tackle hard problems and to encourage authors to consider challenging aspects of the important and difficult task of understanding complex traits in humans. This small sample of papers highlights works that both explain and bring perspective to problems like missing heritability (Stranger et al. 2011) and how methods for analyzing complex traits in different systems are interconnected (Wray et al. 2019).

Pritchard, Stephens, and Donnelly launched a field with their 2000 paper introducing the software package structure. This tool provided a practical solution to the intractable problem of population structure in genome-wide association studies (GWAS). In demonstrating what was possible, they inspired additional practical and elegant solutions to one of the fundamental challenges in GWAS, including approaches that allow for multilocus genotype data for linked loci and correlated allele frequencies (Falush et al. 2003) and increasing speed (Raj et al. 2014).

GENETICS has also been a leader in encouraging practical implementations of sophisticated Bayesian approaches (Beaumont et al. 2002, Foll and Gaggiotti 2008, Pérez-Rodríguez and de los Campos 2022, Qu et al. 2023, Chakraborty and Rannala 2023).

More on Statistical Genetics from the GSA Journals:

  • Anderson EC, Thompson EA. 2002. A Model-Based Method for Identifying Species Hybrids Using Multilocus Genetic Data. Genetics. 160(3):1217–1229. doi:10.1093/genetics/160.3.1217.
  • Beaumont MA, Zhang W, Balding DJ. 2002. Approximate Bayesian Computation in Population Genetics. Genetics. 162(4):2025–2035. doi:10.1093/genetics/162.4.2025.
  • Biddanda A, Steinrücken M, Novembre J. 2022. Properties of 2-locus genealogies and linkage disequilibrium in temporally structured samples. Coop G, editor. Genetics. 221(1):iyac038. doi:10.1093/genetics/iyac038.
  • Boitard S, Schlötterer C, Futschik A. 2009. Detecting Selective Sweeps: A New Approach Based on Hidden Markov Models. Genetics. 181(4):1567–1578. doi:10.1534/genetics.108.100032.
  • Booker WW, Ray DD, Schrider DR. 2023. This population does not exist: learning the distribution of evolutionary histories with generative adversarial networks. Barton N, editor. GENETICS. 224(2):iyad063. doi:10.1093/genetics/iyad063.
  • Bradburd GS, Coop GM, Ralph PL. 2018. Inferring Continuous and Discrete Population Genetic Structure Across Space. Genetics. 210(1):33–52. doi:10.1534/genetics.118.301333.
  • Cabreros I, Storey JD. 2019. A Likelihood-Free Estimator of Population Structure Bridging Admixture Models and Principal Components Analysis. Genetics. 212(4):1009–1029. doi:10.1534/genetics.119.302159.
  • Chakraborty S, Rannala B. 2023. An efficient exact algorithm for identifying hybrids using population genomic sequences. Novembre J, editor. Genetics. 223(4):iyad011. doi:10.1093/genetics/iyad011.
  • Chen C, Qi H, Shen Y, Pickrell J, Przeworski M. 2017. Contrasting Determinants of Mutation Rates in Germline and Soma. Genetics. 207(1):255–267. doi:10.1534/genetics.117.1114.
  • Chenoweth SF, Visscher PM. 2009. Association Mapping in Outbred Populations: Power and Efficiency When Genotyping Parents and Phenotyping Progeny. Genetics. 181(2):755–765. doi:10.1534/genetics.108.099218.
  • Choi SC, Hey J. 2011. Joint Inference of Population Assignment and Demographic History. Genetics. 189(2):561–577. doi:10.1534/genetics.111.129205.
  • Chundru VK, Marioni RE, Prendergast JGD, Vallerga CL, Lin T, Beveridge AJ, SGPD Consortium, Gratten J, Hume DA, Deary IJ, et al. 2019. Examining the Impact of Imputation Errors on Fine-Mapping Using DNA Methylation QTL as a Model Trait. Genetics. 212(3):577–586. doi:10.1534/genetics.118.301861.
  • Clark VJ, Ptak SE, Tiemann I, Qian Y, Coop G, Stone AC, Przeworski M, Arnheim N, Rienzo AD. 2007. Combining Sperm Typing and Linkage Disequilibrium Analyses Reveals Differences in Selective Pressures or Recombination Rates Across Human Populations. Genetics. 175(2):795–804. doi:10.1534/genetics.106.064964.
  • Colbran LL, Ramos-Almodovar FC, Mathieson I. 2023. A gene-level test for directional selection on gene expression. Hahn M, editor. GENETICS. 224(2):iyad060. doi:10.1093/genetics/iyad060.
  • Coop G, Witonsky D, Di Rienzo A, Pritchard JK. 2010. Using Environmental Correlations to Identify Loci Underlying Local Adaptation. Genetics. 185(4):1411–1423. doi:10.1534/genetics.110.114819.
  • Corander J, Waldmann P, Sillanpää MJ. 2003. Bayesian Analysis of Genetic Differentiation Between Populations. Genetics. 163(1):367–374. doi:10.1093/genetics/163.1.367.
  • Cotter DJ, Hofgard EF, Novembre J, Szpiech ZA, Rosenberg NA. 2023. A rarefaction approach for measuring population differences in rare and common variation. Ramachandran S, editor. GENETICS. 224(2):iyad070. doi:10.1093/genetics/iyad070.
  • Di Rienzo A, Donnelly P, Toomajian C, Sisk B, Hill A, Petzl-Erler ML, Haines GK, Barch DH. 1998. Heterogeneity of Microsatellite Mutations Within and Between Loci, and Implications for Human Demographic Histories. Genetics. 148(3):1269–1284. doi:10.1093/genetics/148.3.1269.
  • Durrant C, Mott R. 2010. Bayesian Quantitative Trait Locus Mapping Using Inferred Haplotypes. Genetics. 184(3):839–852. doi:10.1534/genetics.109.113183.
  • Falush D, Stephens M, Pritchard JK. 2003. Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies. Genetics. 164(4):1567–1587. doi:10.1093/genetics/164.4.1567.
  • Fariello MI, Boitard S, Naya H, SanCristobal M, Servin B. 2013. Detecting Signatures of Selection Through Haplotype Differentiation Among Hierarchically Structured Populations. Genetics. 193(3):929–941. doi:10.1534/genetics.112.147231.
  • Faubet P, Gaggiotti OE. 2008. A New Bayesian Method to Identify the Environmental Factors That Influence Recent Migration. Genetics. 178(3):1491–1504. doi:10.1534/genetics.107.082560.
  • Fearnhead P, Donnelly P. 2001. Estimating Recombination Rates From Population Genetic Data. Genetics. 159(3):1299–1318. doi:10.1093/genetics/159.3.1299.
  • Foll M, Gaggiotti O. 2008. A Genome-Scan Method to Identify Selected Loci Appropriate for Both Dominant and Codominant Markers: A Bayesian Perspective. Genetics. 180(2):977–993. doi:10.1534/genetics.108.092221.
  • Fraïsse C, Sachdeva H. 2021. The rates of introgression and barriers to genetic exchange between hybridizing species: sex chromosomes vs autosomes. Charlesworth B, editor. Genetics. 217(2):iyaa025. doi:10.1093/genetics/iyaa025.
  • Frichot E, Mathieu F, Trouillon T, Bouchard G, François O. 2014. Fast and Efficient Estimation of Individual Ancestry Coefficients. Genetics. 196(4):973–983. doi:10.1534/genetics.113.160572.
  • Gao Z, Waggoner D, Stephens M, Ober C, Przeworski M. 2015. An Estimate of the Average Number of Recessive Lethal Mutations Carried by Humans. Genetics. 199(4):1243–1254. doi:10.1534/genetics.114.173351.
  • Glassberg EC, Gao Z, Harpak A, Lan X, Pritchard JK. 2019. Evidence for Weak Selective Constraint on Human Gene Expression. Genetics. 211(2):757–772. doi:10.1534/genetics.118.301833.
  • Gravel S. 2012. Population Genetics Models of Local Ancestry. Genetics. 191(2):607–619. doi:10.1534/genetics.112.139808.
  • Guan Y. 2014. Detecting Structure of Haplotypes and Local Ancestry. Genetics. 196(3):625–642. doi:10.1534/genetics.113.160697.
  • Haber M, Jones AL, Connell BA, Asan, Arciero E, Yang H, Thomas MG, Xue Y, Tyler-Smith C. 2019. A Rare Deep-Rooting D0 African Y-Chromosomal Haplogroup and Its Implications for the Expansion of Modern Humans Out of Africa. Genetics. 212(4):1421–1428. doi:10.1534/genetics.119.302368.
  • Harney É, Patterson N, Reich D, Wakeley J. 2021. Assessing the performance of qpAdm: a statistical tool for studying population admixture. Novembre J, editor. Genetics. 217(4):iyaa045. doi:10.1093/genetics/iyaa045.
  • Harris AM, DeGiorgio M. 2020. Identifying and Classifying Shared Selective Sweeps from Multilocus Data. Genetics. 215(1):143–171. doi:10.1534/genetics.120.303137.
  • Harris AM, Garud NR, DeGiorgio M. 2018. Detection and Classification of Hard and Soft Sweeps from Unphased Genotypes by Multilocus Genotype Identity. Genetics. 210(4):1429–1452. doi:10.1534/genetics.118.301502.
  • Hayeck TJ, Stong N, Baugh E, Dhindsa R, Turner TN, Malakar A, Mosbruger TL, Shaw GT-W, Duan Y, Ionita-Laza I, et al. 2022 Apr 6. Ancestry adjustment improves genome-wide estimates of regional intolerance. Novembre J, editor. Genetics.:iyac050. doi:10.1093/genetics/iyac050.
  • Hellenthal G, Pritchard JK, Stephens M. 2006. The Effects of Genotype-Dependent Recombination, and Transmission Asymmetry, on Linkage Disequilibrium. Genetics. 172(3):2001–2005. doi:10.1534/genetics.104.039271.
  • Hibbins MS, Hahn MW. 2019. The Timing and Direction of Introgression Under the Multispecies Network Coalescent. Genetics. 211(3):1059–1073. doi:10.1534/genetics.118.301831.
  • Hibbins MS, Hahn MW. 2022. Phylogenomic approaches to detecting and characterizing introgression. Turelli M, editor. Genetics. 220(2):iyab173. doi:10.1093/genetics/iyab173.
  • Holland D, Frei O, Desikan R, Fan C-C, Shadrin AA, Smeland OB, Andreassen OA, Dale AM. 2021. The genetic architecture of human complex phenotypes is modulated by linkage disequilibrium and heterozygosity. Zaitlen N, editor. Genetics. 217(3):iyaa046. doi:10.1093/genetics/iyaa046.
  • Innan H, Zhang K, Marjoram P, Tavaré S, Rosenberg NA. 2005. Statistical Tests of the Coalescent Model Based on the Haplotype Frequency Distribution and the Number of Segregating Sites. Genetics. 169(3):1763–1777. doi:10.1534/genetics.104.032219.
  • Jakobsson M, Edge MD, Rosenberg NA. 2013. The Relationship Between F ST and the Frequency of the Most Frequent Allele. Genetics. 193(2):515–528. doi:10.1534/genetics.112.144758.
  • Jeong C, Nakagome S, Di Rienzo A. 2016. Deep History of East Asian Populations Revealed Through Genetic Analysis of the Ainu. Genetics. 202(1):261–272. doi:10.1534/genetics.115.178673.
  • Jewett EM, Zawistowski M, Rosenberg NA, Zöllner S. 2012. A Coalescent Model for Genotype Imputation. Genetics. 191(4):1239–1255. doi:10.1534/genetics.111.137984.
  • Johri P, Charlesworth B, Howell EK, Lynch M, Jensen JD. 2021. Revisiting the notion of deleterious sweeps. Genetics. 219(3):iyab094. doi:10.1093/genetics/iyab094.
  • Jørsboe E, Albrechtsen A. 2022. Efficient approaches for large-scale GWAS with genotype uncertainty. Hernandez R, editor. G3 Genes|Genomes|Genetics. 12(1):jkab385. doi:10.1093/g3journal/jkab385.
  • Jouganous J, Long W, Ragsdale AP, Gravel S. 2017. Inferring the Joint Demographic History of Multiple Populations: Beyond the Diffusion Approximation. Genetics. 206(3):1549–1567. doi:10.1534/genetics.117.200493.
  • Käfer J, Lartillot N, Marais GAB, Picard F. 2021. Detecting sex-linked genes using genotyped individuals sampled in natural populations. Agrawal A, editor. Genetics. 218(2):iyab053. doi:10.1093/genetics/iyab053.
  • Kampourakis K, Peterson EL. 2023. The racist origins, racialist connotations, and purity assumptions of the concept of “admixture” in human evolutionary genetics. Barton N, editor. GENETICS. 223(3):iyad002. doi:10.1093/genetics/iyad002.
  • Kang CJ, Marjoram P. 2011. Inference of Population Mutation Rate and Detection of Segregating Sites from Next-Generation Sequence Data. Genetics. 189(2):595–605. doi:10.1534/genetics.111.130898.
  • Kessner D, Novembre J. 2015. Power Analysis of Artificial Selection Experiments Using Efficient Whole Genome Simulation of Quantitative Traits. Genetics. 199(4):991–1005. doi:10.1534/genetics.115.175075.
  • Kitada S, Kitakado T, Kishino H. 2007. Empirical Bayes Inference of Pairwise F ST and Its Distribution in the Genome. Genetics. 177(2):861–873. doi:10.1534/genetics.107.077263.
  • Lander ES, Botstein D. 1989. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 121(1):185–199. doi:10.1093/genetics/121.1.185.
  • Laval G, Patin E, Boutillier P, Quintana-Murci L. 2021. Sporadic occurrence of recent selective sweeps from standing variation in humans as revealed by an approximate Bayesian computation approach. Novembre J, editor. Genetics. 219(4):iyab161. doi:10.1093/genetics/iyab161.
  • Li N, Stephens M. 2003. Modeling Linkage Disequilibrium and Identifying Recombination Hotspots Using Single-Nucleotide Polymorphism Data. Genetics. 165(4):2213–2233. doi:10.1093/genetics/165.4.2213.
  • Liang M, Nielsen R. 2014. The Lengths of Admixture Tracts. Genetics. 197(3):953–967. doi:10.1534/genetics.114.162362.
  • Lin K, Futschik A, Li H. 2013. A Fast Estimate for the Population Recombination Rate Based on Regression. Genetics. 194(2):473–484. doi:10.1534/genetics.113.150201.
  • Lloyd-Jones LR, Robinson MR, Moser G, Zeng J, Beleza S, Barsh GS, Tang H, Visscher PM. 2017. Inference on the Genetic Basis of Eye and Skin Color in an Admixed Population via Bayesian Linear Mixed Models. Genetics. 206(2):1113–1126. doi:10.1534/genetics.116.193383.
  • Lloyd-Jones LR, Robinson MR, Yang J, Visscher PM. 2018. Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio. Genetics. 208(4):1397–1408. doi:10.1534/genetics.117.300360.
  • Loh P-R, Lipson M, Patterson N, Moorjani P, Pickrell JK, Reich D, Berger B. 2013. Inferring Admixture Histories of Human Populations Using Linkage Disequilibrium. Genetics. 193(4):1233–1254. doi:10.1534/genetics.112.147330.
  • Lohmueller KE, Bustamante CD, Clark AG. 2009. Methods for Human Demographic Inference Using Haplotype Patterns From Genomewide Single-Nucleotide Polymorphism Data. Genetics. 182(1):217–231. doi:10.1534/genetics.108.099275.
  • Martin SH, Van Belleghem SM. 2017. Exploring Evolutionary Relationships Across the Genome Using Topology Weighting. Genetics. 206(1):429–438. doi:10.1534/genetics.116.194720.
  • Meisner J, Albrechtsen A. 2018. Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data. Genetics. 210(2):719–731. doi:10.1534/genetics.118.301336.
  • Meyer WK, Arbeithuber B, Ober C, Ebner T, Tiemann-Boege I, Hudson RR, Przeworski M. 2012. Evaluating the Evidence for Transmission Distortion in Human Pedigrees. Genetics. 191(1):215–232. doi:10.1534/genetics.112.139576.
  • Milligan WR, Amster G, Sella G. 2022. The impact of genetic modifiers on variation in germline mutation rates within and among human populations. Martin G, editor. Genetics. 221(4):iyac087. doi:10.1093/genetics/iyac087.
  • Montinaro F, Busby GBJ, Gonzalez-Santos M, Oosthuitzen O, Oosthuitzen E, Anagnostou P, Destro-Bisol G, Pascali VL, Capelli C. 2017. Complex Ancient Genetic Structure and Cultural Transitions in Southern African Populations. Genetics. 205(1):303–316. doi:10.1534/genetics.116.189209.
  • Mott R, Fischer C, Prins P, Davies RW. 2020. Private Genomes and Public SNPs: Homomorphic Encryption of Genotypes and Phenotypes for Shared Quantitative Genetics. Genetics. 215(2):359–372. doi:10.1534/genetics.120.303153.
  • Mughal MR, DeGiorgio M. 2022. Properties and unbiased estimation of F – and D -statistics in samples containing related and inbred individuals. Browning S, editor. Genetics. 220(1):iyab090. doi:10.1093/genetics/iyab090.
  • Nøhr AK, Hanghøj K, Garcia-Erill G, Li Z, Moltke I, Albrechtsen A. 2021. NGSremix: a software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data. G3 Genes|Genomes|Genetics. 11(8):jkab174. doi:10.1093/g3journal/jkab174.
  • Novembre J. 2014. Variations on a Common STRUCTURE: New Algorithms for a Valuable Model. Genetics. 197(3):809–811. doi:10.1534/genetics.114.166264.
  • Novembre J. 2016. Pritchard, Stephens, and Donnelly on Population Structure. Genetics. 204(2):391–393. doi:10.1534/genetics.116.195164.
  • Novembre J, Barton NH. 2018. Tread Lightly Interpreting Polygenic Tests of Selection. Genetics. 208(4):1351–1355. doi:10.1534/genetics.118.300786.
  • Ortega-Del Vecchyo D, Lohmueller KE, Novembre J. 2022. Haplotype-based inference of the distribution of fitness effects. Gravel S, editor. Genetics. 220(4):iyac002. doi:10.1093/genetics/iyac002.
  • Osmond MM, Coop G. 2020. Genetic Signatures of Evolutionary Rescue by a Selective Sweep. Genetics. 215(3):813–829. doi:10.1534/genetics.120.303173.
  • Padhukasahasram B, Rannala B. 2011. Bayesian Population Genomic Inference of Crossing Over and Gene Conversion. Genetics. 189(2):607–619. doi:10.1534/genetics.111.130195.
  • Patterson N, Moorjani P, Luo Y, Mallick S, Rohland N, Zhan Y, Genschoreck T, Webster T, Reich D. 2012. Ancient Admixture in Human History. Genetics. 192(3):1065–1093. doi:10.1534/genetics.112.145037.
  • Paul JS, Song YS. 2010. A Principled Approach to Deriving Approximate Conditional Sampling Distributions in Population Genetics Models with Recombination. Genetics. 186(1):321–338. doi:10.1534/genetics.110.117986.
  • Paul JS, Steinrücken M, Song YS. 2011. An Accurate Sequentially Markov Conditional Sampling Distribution for the Coalescent With Recombination. Genetics. 187(4):1115–1128. doi:10.1534/genetics.110.125534.
  • Pérez-Rodríguez P, de los Campos G. 2022. Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package. Chesler E, editor. Genetics. 222(1):iyac112. doi:10.1093/genetics/iyac112.
  • Peter BM. 2016. Admixture, Population Structure, and F -Statistics. Genetics. 202(4):1485–1501. doi:10.1534/genetics.115.183913.
  • Popescu A-A, Harper AL, Trick M, Bancroft I, Huber KT. 2014. A Novel and Fast Approach for Population Structure Inference Using Kernel-PCA and Optimization. Genetics. 198(4):1421–1431. doi:10.1534/genetics.114.171314.
  • Pritchard JK, Stephens M, Donnelly P. 2000. Inference of Population Structure Using Multilocus Genotype Data. Genetics. 155(2):945–959. doi:10.1093/genetics/155.2.945.
  • Przeworski M. 2002. The Signature of Positive Selection at Randomly Chosen Loci. Genetics. 160(3):1179–1189. doi:10.1093/genetics/160.3.1179.
  • Przeworski M. 2003. Estimating the Time Since the Fixation of a Beneficial Allele. Genetics. 164(4):1667–1676. doi:10.1093/genetics/164.4.1667.
  • Ptak SE, Voelpel K, Przeworski M. 2004. Insights Into Recombination From Patterns of Linkage Disequilibrium in Humans. Genetics. 167(1):387–397. doi:10.1534/genetics.167.1.387.
  • Qu J, Runcie D, Cheng H. 2023. Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits. Endelman J, editor. Genetics. 223(3):iyac183. doi:10.1093/genetics/iyac183.
  • Racimo F. 2016. Testing for Ancient Selection Using Cross-population Allele Frequency Differentiation. Genetics. 202(2):733–750. doi:10.1534/genetics.115.178095.
  • Racimo F, Berg JJ, Pickrell JK. 2018. Detecting Polygenic Adaptation in Admixture Graphs. Genetics. 208(4):1565–1584. doi:10.1534/genetics.117.300489.
  • Raj A, Stephens M, Pritchard JK. 2014. fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets. Genetics. 197(2):573–589. doi:10.1534/genetics.114.164350.
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