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bioRxiv2026

Indirect genetic effects across ontogeny in an avian cooperative breeder

Social interactions are ubiquitous in nature and have the potential to affect trait evolution, particularly in group-living animals such as cooperative breeders. Interactions among conspecific individuals can affect the amount of additive genetic variation for a trait when the phenotype of an individual is also affected by the genotype of its social partner(s) via indirect genetic effects. Thus, quantifying both direct and indirect genetic effects of social partners is critical for understanding and predicting evolutionary trajectories. While much is known about maternal indirect genetic effects, empirical estimates of indirect genetic effects from other social partners remain limited, particularly in wild populations. Here, we use animal models to assess the contribution of indirect genetic effects from all social partners in a family group (mothers, fathers, and helpers) on juvenile morphometric traits across ontogeny in the cooperatively-breeding Florida scrub-jay (Aphelocoma coerulescens). We found indirect genetic effects of helpers and fathers on nestling weight, but no indirect genetic effect of mothers. Across ontogeny, we found increasing additive genetic variation in both weight and tarsus length. Our study provides a comprehensive assessment of within-group indirect genetic effects in a cooperative breeder and highlights the importance of considering indirect genetic effects beyond maternal effects.

evolutionary biology↗

bioRxiv2026

The Flame Retardant Triphenyl Phosphate Induces Varying Responses in Genetically Diverse Mouse Induced Pluripotent Stem Cells

Genetic variation impacts biological response to chemical and environmental exposures, contributing to differences in resistance or susceptibility. Forward genetic screens of genetically diverse cell populations can be used to identify the precise genetic variants that drive response variation. Cell lines from laboratory mouse genetic reference populations like the Diversity Outbred (DO) population are well powered for genetic mapping. Diverse stem cell panels are especially beneficial for studying early developmental effects of chemical exposures like triphenyl phosphate (TPHP), an organophosphate flame retardant linked to adverse developmental effects, including altered cell cycle in stem cells. Self-renewal and pluripotency are defining properties of stem cells during development; therefore, it is important to understand how chemicals like TPHP may alter these critical characteristics. To better understand the influence of genetic variation on TPHP exposure response, we utilized a genetically diverse panel of DO induced pluripotent stem cells (DO iPSCs). Our analyses characterize interline variation in gene expression through differential gene expression analysis and expression quantitative trait locus (eQTL) mapping. We identified genomic loci that contribute to variation in gene expression following TPHP exposure, including a regulatory hotspot on chromosome 15. Gene set enrichment highlighted several pathways affected by TPHP including lipid metabolism, steroid hormone signaling, and cell cycle. Our study shows that genetic variation modulates the effects of TPHP on gene expression and cell cycle in stem cells. Additionally, our work demonstrates that genetically diverse cell panels like the DO iPSC resource offer a tractable and scalable approach for identifying the genetic determinants of chemical toxicity.

genomics↗

bioRxiv2014

Differential relationships between habitat fragmentation and within-population genetic diversity of three forest-dwelling birds

Habitat fragmentation is a major driver of environmental change affecting wildlife populations across multiple levels of biological diversity. Much of the recent research in landscape genetics has focused on quantifying the influence of fragmentation on genetic variation among populations, but questions remain as to how habitat loss and configuration influences within-population genetic diversity. Habitat loss and fragmentation might lead to decreases in genetic diversity within populations, which might have implications for population persistence over multiple generations. We used genetic data collected from populations of three species occupying forested landscapes across a broad geographic region: Mountain Chickadee (Poecile gambeli; 22 populations), White-breasted Nuthatch (Sitta carolinensis; 13 populations) and Pygmy Nuthatch (Sitta pygmaea; 19 populations) to quantify patterns of haplotype and nucleotide diversity across a range of forest fragmentation. We predicted that fragmentation effects on genetic diversity would vary depending on dispersal capabilities and habitat specificity of the species. Forest aggregation and the variability in forest patch area were the two strongest landscape predictors of genetic diversity. We found higher haplotype diversity in populations of P. gambeli and S. carolinensis inhabiting landscapes characterized by lower levels of forest fragmentation. Conversely, S. pygmaea demonstrated the opposite pattern of higher genetic diversity in fragmented landscapes. For two of the three species, we found support for the prediction that highly fragmented landscapes sustain genetically less diverse populations. We suggest, however, that future studies should focus on species of varying life-history traits inhabiting independent landscapes to better understand how habitat fragmentation influences within-population genetic diversity.

Genetics↗

bioRxiv2017

Analysis of genetic similarity among friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health (Add Health)

Humans tend to form social relationships with others who resemble them. Whether this sorting of like with like arises from historical patterns of migration, meso-level social structures in modern society, or individual-level selection of similar peers remains unsettled. Recent research has evaluated the possibility that unobserved genotypes may play an important role in the creation of homophilous relationships. We extend this work by using data from 9,500 adolescents from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine genetic similarities among pairs of friends. While there is some evidence that friends have correlated genotypes, both at the whole-genome level as well as at trait-associated loci (via polygenic scores), further analysis suggests that meso-level forces, such as school assignment, are a principal source of genetic similarity between friends. We also observe apparent social-genetic effects in which polygenic scores of an individuals friends and schoolmates predict the individuals own educational attainment. In contrast, an individuals height is unassociated with the height genetics of peers.\n\nSignificanceOur study reported significant findings of a \"social genome\" that can be quantified and studied to understand human health and behavior. In a national sample of more than 9,000 American adolescents, we found evidence of social forces that act to make friends and schoolmates more genetically similar to one another as compared to random pairs of unrelated individuals. This subtle genetic similarity was observed across the entire genome and at sets of genomic locations linked with specific traits--educational attainment and body-mass index--a phenomenon we term \"social-genetic correlation.\" We also find evidence of a \"social-genetic effect\" such that the genetics of a persons friends and schoolmates influenced their own education, even after accounting for the persons own genetics.

genetics↗

bioRxiv2017

The genetics of the human face: identification of large effect single gene variants

In order to discover specific variants with relatively large effects on the human face we have devised an approach to identifying facial features with high heritability. This is based on using twin data to estimate the additive genetic value of each point on a face, as provided by a 3D camera system. In addition, we have used the ethnic difference between East Asian and European faces as a further source of face genetic variation. We use principal components analysis to provide a fine definition of the surface features of human faces around the eyes and of the profile, and chose upper and lower 10% extremes of the most heritable PCs for looking for genetic associations. Using this strategy for the analysis of 3D images of 1832 unique volunteers from the well characterised People of the British Isles study [1, 2] and 1567 unique twin images from the TwinsUK cohort (www.twinsuk.ac.uk), together with genetic data for 500,000 SNPs, we have identified three specific genetic variants with notable effects on facial profiles and eyes.\n\nSignificance statementThe human face is extraordinarily variable and the extreme similarity of the faces of identical twins indicates that most of this variability is genetically determined. This level of genetic variability has probably arisen through natural selection, for example, for recognition of membership of a group or as a consequence of differential mate selection with respect to facial features. We have devised an approach to identifying specific genetic effects on particular facial features. This should enable the understanding, eventually at the molecular level, of the nature of this extraordinary genetic variability, which is such an important feature of our everyday human interactions.\n\nAuthor ContributionsWFB conceived the project. BW and TD organised collection of PoBI data and KH, DJMC, DM, AB and WFB assisted in data collection. TDS, PH and AN collected TwinsUK data. WPK and WJC conducted image registration analysis under supervision of JK. DJMC analysed registered image data and genetic data under supervision of WFB. WFB and DJMC wrote the manuscript, with WJC and WPK contributing additional technical material. WFB and BW supervised the project.

genetics↗

bioRxiv2020

Temporal and genomic analysis of additive genetic variance in breeding programmes

This study demonstrates a framework for temporal and genomic analysis of additive genetic variance in a breeding programme. Traditionally we used specific experimental designs to estimate genetic variance for a specific group of individuals and a general pedigree-based model to estimate genetic variance for pedigree founders. However, with the pedigree-based model we can also analyse temporal changes in genetic variance by summarising sampled realisations of genetic values from a fitted model. Here we extend this analysis to a marker-based model and build a framework for temporal and genomic analyses of genetic variance. The framework involves three steps: (i) fitting a marker-based model to data, (ii) sampling realisations of marker effects from the fitted model and for each sample calculating realisations of genetic values, and (iii) calculating variance of the sampled genetic values by time and genome partitions. Genome partitions enable estimation of contributions from chromosomes and chromosome pairs and genic and linkage-disequilibrium variances. We demonstrate the framework by analysing data from a simulated breeding programme involving a complex trait with additive gene action. We use the full Bayesian and empirical Bayesian approaches to account for the uncertainty due to model fitting. We also evaluate the use of principal component approximation. Results show good concordance between the simulated and estimated variances for temporal and genomic analyses and give insight into genetic processes. For example, we observe reduction of genic variance due to selection and drift and buildup of negative linkage-disequilibrium (the Bulmer effect) due to directional selection. In this study the popular empirical Bayesian approach estimated the variances well but it underestimated uncertainty of the estimates. The principal components approximation biases estimates, in particular for the genic variance. This study gives breeders a framework to analyse genetic variance and its components in different stages of a programme and over time.

genetics↗

bioRxiv2020

Whole-genome SNP analysis elucidates the genetic population structure and diversity of Acrocomia species

Acrocomia (Arecaceae) is a genus widely distributed in tropical and subtropical America that has been achieving economic interest due to the great potential of oil production of some of its species. In particular A. aculeata, due to its vocation to supply oil with the same productive capacity as the oil palm even in areas with water deficit. Although eight species are recognized in the genus, the taxonomic classification based on morphology and geographic distribution is still controversial. Knowledge about the genetic diversity and population structure of the species is limited, which has limited the understanding of the genetic relationships and the orientation of management, conservation, and genetic improvement activities of species of the genus. In the present study, we analyzed the genomic diversity and population structure of seven species of Acrocomia including 117 samples of A. aculeata covering a wide geographical area of occurrence, using single nucleotide Polymorphism (SNP) markers originated from Genotyping By Sequencing (GBS). The genetic structure of the Acrocomia species were partially congruent with the current taxonomic classification based on morphological characters, recovering the separation of the species A. aculeata, A. totai, A. crispa and A. intumescens as distinct taxonomic groups. However, the species A. media was attributed to the cluster of A. aculeata while A. hassleri and A. glauscescens were grouped together with A. totai. The species that showed the highest and lowest genetic diversity were A. totai and A. media, respectively. When analyzed separately, the species A. aculeata showed a strong genetic structure, forming two genetic groups, the first represented mainly by genotypes from Brazil and the second by accessions from Central and North American countries. Greater genetic diversity was found in Brazil when compared to the other countries. Our results on the genetic diversity of the genus are unprecedented, as is also establishes new insights on the genomic relationships between Acrocomia species. It is also the first study to provide a more global view of the genomic diversity of A. aculeata. We also highlight the applicability of genomic data as a reference for future studies on genetic diversity, taxonomy, evolution and phylogeny of the Acrocomia genus, as well as to support strategies for the conservation, exploration and breeding of Acrocomia species and in particular A. aculeata.

genetics↗

bioRxiv2021

Population-level genome-wide STR typing in Plasmodium species reveals higher resolution population structure and genetic diversity relative to SNP typing

Short tandem repeats (STRs) are highly informative genetic markers that have been used extensively in population genetics analysis. They are an important source of genetic diversity and can also have functional impact. Despite the availability of bioinformatic methods that permit large-scale genome-wide genotyping of STRs from whole genome sequencing data, they have not previously been applied to sequencing data from large collections of malaria parasite field samples. Here, we have genotyped STRs using HipSTR in more than 3,000 Plasmodium falciparum and 174 Plasmodium vivax published whole-genome sequence data from samples collected across the globe. High levels of noise and variability in the resultant callset necessitated the development of a novel method for quality control of STR genotype calls. A set of high-quality STR loci (6,768 from P. falciparum and 3,496 from P. vivax) were used to study Plasmodium genetic diversity, population structures and genomic signatures of selection and these were compared to genome-wide single nucleotide polymorphism (SNP) genotyping data. In addition, the genome-wide information about genetic variation and other characteristics of STRs in P. falciparum and P. vivax have been made available in an interactive web-based R Shiny application PlasmoSTR (https://github.com/bahlolab/PlasmoSTR). Author summaryMalaria is a severe disease caused by a genus of parasites called Plasmodium and is transmitted to humans through infected Anopheles mosquitoes. P. falciparum and P. vivax are the predominant species responsible for more than 95% of all human malaria infections which continue to pose a significant challenge to human health. Antimalarial drug resistance is a serious threat hindering the elimination of malaria. As such, it is important to understand the role of genomic variation in the development of antimalarial drug resistance. STRs are an important source of genomic variation that, from a population genetics perspective, have several advantages over SNPs, including being highly polymorphic, having a higher mutation rate, and having been widely used to study the population structure and genetic diversity. However, STRs are not routinely genotyped with bioinformatic tools across the whole genome with short read sequencing data because they are difficult to identify and genotype accurately, as they vary in size and may align poorly to the reference genome, therefore requiring rigorous quality control (QC). In this study, we genotype STRs using HipSTR[1] in more than 3,000 P. falciparum and 174 P. vivax whole-genome sequence samples collected world-wide. We develop a multivariable logistic regression model for the measurement and prediction of the quality of STRs. In addition, we use a set of genome-wide high-quality STRs to study parasite population genetics and compare them to genome-wide SNP genotyping data, revealing both high consistency with SNP based signals, as well as identifying some signals unique to the STR marker data. These results demonstrate that the identification of highly informative STR markers from large numbers of population samples is a powerful approach to study the genetic diversity, population structures and genomic signatures of selection in P. falciparum and P. vivax. Furthermore, we built an interactive web-based R Shiny application PlasmoSTR (https://github.com/bahlolab/PlasmoSTR) that includes genome-wide information about genetic variation and other characteristics of the high quality STRs identified in P. falciparum and P. vivax, allowing researchers to explore and visualize the specific STRs.

genetics↗

bioRxiv2021

Genetic diversity of wild and cultivated Coffea canephora in northeastern DR Congo and the implications for conservation.

PremiseMany cultivated coffee varieties descend from Coffea canephora, commonly known as Robusta coffee. The Congo Basin has a century long history of Robusta coffee cultivation and breeding, and is hypothesized to be the region of origin of many of the cultivated Robusta varieties. Since little is known about the genetic composition of C. canephora in this region, we assessed the genetic diversity of wild and cultivated C. canephora shrubs in the Democratic Republic of the Congo. MethodsUsing 18 microsatellite markers, we studied the genetic composition of wild and backyard-grown C. canephora shrubs in the Tshopo and Ituri provinces, and from the INERA Yangambi Coffee Collection. We assessed genetic clustering patterns, genetic diversity, and genetic differentiation between populations. Key resultsGenetic differentiation was relatively strong between wild and cultivated C. canephora shrubs, and both gene pools harbored multiple unique alleles. Strong genetic differentiation was also observed between wild populations. The level of genetic diversity in wild populations was similar to that of the INERA Yangambi Coffee Collection, but local wild genotypes were mostly missing from that collection. Shrubs grown in the backyards were genetically similar to the breeding material from INERA Yangambi. ConclusionsMost C. canephora that is grown in local backyards originated from INERA breeding programs, while a few shrubs were obtained directly from surrounding forests. The INERA Yangambi Coffee Collection could benefit from an enrichment with local wild genotypes, to increase the genetic resources available for breeding purposes, as well as to support ex situ conservation.

genetics↗

bioRxiv2022

A method for partitioning trends in genetic mean and variance to understand breeding practices

BackgroundIn breeding programmes, the observed genetic change is a sum of the contributions of different groups of individuals. Quantifying these sources of genetic change is essential for identifying the key breeding actions and optimizing breeding programmes. However, it is difficult to disentangle the contribution of individual groups due to the inherent complexity of breeding programmes. Here we extend the previously developed method for partitioning genetic mean by paths of selection to work both with the mean and variance of breeding values. MethodsWe first extended the partitioning method to quantify the contribution of different groups to genetic variance assuming breeding values are known. Second, we combined the partitioning method with the Markov Chain Monte Carlo approach to draw samples from the posterior distribution of breeding values and use these samples for computing the point and interval estimates of partitions for the genetic mean and variance. We implemented the method in the R package AlphaPart. We demonstrated the method with a simulated cattle breeding programme. ResultsWe showed how to quantify the contribution of different groups of individuals to genetic mean and variance. We showed that the contributions of different selection paths to genetic variance are not necessarily independent. Finally, we observed some limitations of the partitioning method under a misspecified model, suggesting the need for a genomic partitioning method. ConclusionWe presented a partitioning method to quantify sources of change in genetic mean and variance in breeding programmes. The method can help breeders and researchers understand the dynamics in genetic mean and variance in a breeding programme. The developed method for partitioning genetic mean and variance is a powerful method for understanding how different paths of selection interact within a breeding programme and how they can be optimised.

genetics↗

bioRxiv2022

Cross-trait assortative mating is widespread and inflates genetic correlation estimates

The observation of genetic correlations between disparate traits has been interpreted as evidence of widespread pleiotropy, altered theories of human genetic architecture, and spurred considerable research activity across the natural and social sciences. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation for observed genetic correlations. We observe that xAM is common across a broad array of phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R2 = 76%). Then, we present theoretical and simulation-based results demonstrating that, under xAM, genetic correlation estimators yield significant estimates even for traits with entirely distinct genetic bases. We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates in two large samples (N = 827,960). For example, previously reported genetic correlation estimates between many pairs of psychiatric disorders are fully consistent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using a novel approach based on cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

genetics↗

bioRxiv2022

Genetic diversity assessment of Pacific oyster Magallana gigas (Crassostrea gigas) populations from the two southern coastal farms in South Korea

South Korea is among the major producers of the Pacific oyster, Magallana gigas (Crassostrea gigas), which is one of the most valued aquaculture species. Since the early 1990s, climatic and anthropogenic factors have incurred the reduction of their wild seeds, whereby the dependence on hatchery-produced seeds has constantly increased in South Korea, thus raising concerns about losing genetic diversity and accelerating genetic deterioration. To better understand their genetic make-up, we assessed the genetic diversity of M. gigas populations from two farms (Tongyeong and Gadeokdo) in the southern coast, where about 80% of the cultivated oysters in Korea are produced. Tongyeong showed slightly higher diversity than Gadeokdo, but both populations had a similar genetic structure characterized by low nucleotide diversity. Comparative haplotype analyses provided data supporting unique genetic features of the populations that include (1) weak genotype-locality relationship, (2) low levels of gene flow between populations, and (3) seasonal fluctuation of genetic variation within a population. Furthermore, the highly alike haplotype network patterns were observed between the wild and farm populations as well as among the populations in neighboring countries, which suggests that the genetic structure is conserved between wild and hatchery populations, and geographic proximity has minimal influence on the genetic composition. These results warrant further study in biological and ecological contexts and will be invaluable in formulating genetic monitoring and sustainable long-term management of M. gigas.

genetics↗

bioRxiv2022

A comparative study of two grasswren species reveals strong genetic divergence between a peninsula and mainland population

Dispersal patterns dictate genetic population structure, and ultimately population resilience, through maintaining critical ecological processes and genetic diversity. Direct observation of dispersal events is not often possible, but genetic methods offer an alternative method of indirectly measuring dispersal. Here, we use 7 652 genome-wide single-nucleotide polymorphisms (SNPs) to evaluate genetic population structure and infer dispersal capabilities of the Western Grasswren (Amytornis textilis textilis; WGW) in Western Australia (n = 118), utilising a sister species, the Thick-billed Grasswren (Amytornis modestus; TBGW) as a comparison dataset (n = 80). We found genetic divergence and low genetic diversity between two populations (Hamelin and Peron) in the WGW, despite evidence of long dispersal distances within populations by females. In addition, the two WGW populations were found to be more genetically divergent than two described subspecies of TBGW, despite the WGW occurring over a smaller spatial scale. By comparing these two grasswren species, our data suggest a narrow strip of land may be acting as a geographic barrier in the WGW, limiting dispersal between a peninsula population to the mainland. We investigate if morphology aligns with genetic divergence, with some estimates of divergence between WGW populations greater than those between subspecies of TBGW. However, confidence intervals were large, preventing definitive conclusions. Our results support the hypothesis that peninsula populations of small, ground-dwelling birds are genetically isolated from adjacent mainland populations. Furthermore, there is evidence to suggest that the limited gene flow is asymmetrical, with directional dispersal occurring from the bounded peninsula population to the mainland. Our study also highlights how substantial genetic divergence does not necessarily coincide with phenotypic differences.

genetics↗

bioRxiv2022

The genetic basis of variation immune defense against Lysinibacillus fusiformis infection in Drosophila melanogaster

The genetic causes of phenotypic variation often differ depending on the population examined, particularly if the populations were founded by relatively small numbers of genotypes. Similarly, the genetic causes of phenotypic variation among similar traits (resistance to different xenobiotic compounds or pathogens) may also be completely different or only partially overlapping. Differences in genetic causes for variation in the same trait among populations suggests considerable context dependence for how selection might act on those traits. Similarities in the genetic causes of variation for different traits, on the other hand, suggests pleiotropy which also would influence how natural selection would shape variation in a trait. We characterized immune defense against a natural Drosophila pathogen, the Gram-positive Lysinibacillus fusiformis, in three different populations and found almost no overlap in the genetic architecture of variation in survival post infection. However, when comparing our results to a similar experiment with the fungal pathogen, B. bassiana, we found a convincing shared QTL peak for both pathogens. This peak contains the Bomanin cluster of Drosophila immune effectors. RNAi knockdown experiments confirms a role of some of these genes in immune defense against both pathogens. This suggests that natural selection may act on the entire cluster of Bomanin genes (and the linked region under the QTL) or specific peptides for specific pathogens. Author SummaryLike most traits, the way individuals respond to infection vary among individuals within a population. Some of this variation is caused by genetic differences in the host organism. Over the past decade, two prominent resources were developed to assess genetic variation for complex traits of the fruit fly, Drosophila melanogaster and map the genetic variants responsible. We recently described a strain of Lysinibacillus fusiformis bacteria, which was isolated from fruit flies and is moderately virulent when flies are infected. We mapped genetic variation in resistance L. fusiformis using these mapping resources. We find that among the resources, different changes were associated with immune defense. However, we also found that within a resource, the same region of the genome was associated with resistance to both L. fusiformis and a fungal pathogen. These results suggest that different populations adapt differently to the same pathogens, but two distinct pathogens share similar causes of genetic variation within a single population.

genetics↗

bioRxiv2023

Uncertainty in the mating strategy causes bias and inaccuracy in estimates of genetic parameters in honeybees

BackgroundWith the increased number of honeybee breeding plans worldwide, records from queens with diversified mating strategies need to be considered. Breeding queens might be inseminated with drones produced by a single drone-producing queen (DPQ), or by a group of sister-DPQs. Often, only the dam of DPQ(s) is reported in the pedigree. Furthermore, datasets might include colony phenotypes from DPQs that were open mated in different locations. Using simulation, we investigated the impact of the mating strategy on estimates of genetic parameters and breeding values, when the DPQs were treated in different ways in the statistical evaluation model. We quantify the bias and standard error of estimates when breeding queens are mated to a single or a group of DPQs, assuming that this information is either known or not. We also investigated two alternative strategies to accommodate phenotypes of open-mated DPQs in the genetic evaluation, adding either a dummy pseudo sire in the pedigree, or a non-genetic effect to the statistical evaluation model to account for the origin of the DPQs mates. ResultsWhen breeding queens were inseminated with semen from drones of a single DPQ and this was known, estimates of genetic parameters and genetic trends were more precise. If they were inseminated using drones from a single or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in the estimates. For colony phenotypes of open-mated DPQs, adding a dummy pseudo sire in the pedigree for each mating location led to considerable overestimation of genetic variances, while correcting for the mating area by adding a non-genetic effect in the evaluation model gave unbiased estimates. ConclusionsKnowing only the dam of the DPQ(s) in the mating may lead to erroneous assumptions on how DPQs were used and cause severe biases in estimates of genetic parameters and genetic trends. Therefore, keeping track in the pedigree of which DPQ(s), and not only which dam of DPQ(s) are used, is recommended. Records from DPQ colonies with queens open mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

genetics↗

bioRxiv2023

Estimation of indirect genetic effects and heritability under assortative mating

Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects -- effects of alleles in an individual (e.g. parents) on another individual (e.g. offspring) -- can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components. We extend this theory to show that popular methods for estimating indirect genetic effects or genetic nurture through analysis of parental and offspring polygenic predictors (called polygenic indices or scores -- PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating. We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies. We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes. We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.

genetics↗

bioRxiv2023

Development of selection strategies for genetic improvement in production traits of Mecheri sheep based on a Bayesian multi trait evaluation

The progression of genetic selection techniques to enhance farm animal performance traits is guided by the present level of genetic variation and maternal impact in each trait, as well as the genetic association between traits. This study was conducted on a population of Mecheri sheep maintained from 1980 to 2018 at Mecheri Sheep Research Station, Pottaneri, India, to determine variance and covariance components, as well as genetic parameters for various production performance traits. The production traits of Mecheri sheep assessed include: birth weight (BW), weaning weight (WW), six-month weight (SMW), nine-month weight (NMW) and yearling weight (YW). The Bayesian approach using the Gibbs sampler analyzed six animal models with different combinations of additive direct and maternal additive effects was used. Direct genetics, maternal genetics and residual effects models were the major significantly contributors to total phenotypic variation for all the production traits studied. Direct heritability estimates of birth weight, WW, SMW, NMW and YW were 0.25, 0.20, 0.12, 0.14 and 0.13, respectively. The maternal heritability estimated for BW, WW, SMW, NMW and YW were 0.17, 0.10, 0.12, 0.14 and 0.14, respectively. The maternal effects had a major impact on the pre-weaning production traits. The genetic correlations estimated between different pairs of production traits studied ranged from 0.19 to 0.93. The body weight at birth exhibited higher genetic relationship with weaning weight than post-weaning growth characteristics, and the genetic correlation between weaning weight and post-weaning attributes was moderate to high (0.52 to 0.72). Based on the additive genetic variance in weaning weight and the correlation estimates of weaning weight with post-weaning traits, the weaning weight was proposed as a selection criterion for improving growth traits in Mecheri sheep.

genetics↗

bioRxiv2024

A geographic history of human genetic ancestry

Describing the distribution of genetic variation across individuals is a fundamental goal of population genetics. In humans, traditional approaches for describing population genetic variation often rely on discrete genetic ancestry labels, which, despite their utility, can obscure the complex, multifaceted nature of human genetic history. These labels risk oversimplifying ancestry by ignoring its temporal depth and geographic continuity, and may therefore conflate notions of race, ethnicity, geography, and genetic ancestry. Here, we present a method that capitalizes on the rich genealogical information encoded in genomic tree sequences to infer the geographic locations of the shared ancestors of a sample of sequenced individuals. We use this method to infer the geographic history of genetic ancestry of a set of human genomes sampled from Europe, Asia, and Africa, accurately recovering major population movements on those continents. Our findings demonstrate the importance of defining the spatial-temporal context of genetic ancestry to describing human genetic variation and caution against the oversimplified interpretations of genetic data prevalent in contemporary discussions of race and ancestry.

genetics↗