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bioRxiv2020

The Forensic Landscape and the Population Genetic Analyses of Hainan Li Based on Massively Parallel Sequencing DNA Profiling

Due to the formation of the Qiongzhou Strait by climate change and marine transition, Hainan island isolated from the mainland southern China during the Last Glacial Maximum. Hainan island, located at the southernmost part of China and separated from the Leizhou Peninsula by the Qiongzhou Strait, laid on one of the modern human northward migration routes from Southeast Asia to East Asia. The Hlai-language speaking Li minority, the second largest population after Han Chinese in Hainan island, is the direct descendants of the initial migrants in Hainan island and has unique ethnic properties and derived characteristics, however, the forensic associated studies on Hainan Li population are still insufficient. Hence, 136 Hainan Li individuals were genotyped in this study using the MPS-based ForenSeq DNA Signature Prep Kit (DNA Primer Set A) to characterize the forensic genetic polymorphism landscape, and DNA profiles were obtained from 152 different molecular genetic markers (27 autosomal STRs, 24 Y-STRs, 7 X-STRs, and 94 iiSNPs). A total of 419 distinct length variants and 586 repeat sequence sub-variants, with 31 novel alleles (at 17 loci), were identified across the 58 STR loci from the DNA profiles of Hainan Li population. We evaluated the forensic characteristics and efficiencies of DAPA, it demonstrated that the STRs and iiSNPs in DAPA were highly polymorphic in Hainan Li population and could be employed in forensic applications. In addition, we set up three Datasets, which included the genetic data of (I). iiSNPs (27 populations, 2640 individuals), (II). Y-STRs (42 populations, 8281 individuals), and (III). Y-haplogroups (123 populations, 4837 individuals) along with the population ancestries and language families, to perform population genetic analyses separately from different perspectives. In conclusion, the phylogenetic analyses indicated that Hainan Li, with a southern East Asia origin and Tai-Kadai language-speaking language, is an isolated population relatively. But the genetic pool of Hainan Li influenced by the limited gene flows from other Tai-Kadai populations and Hainan populations. Furthermore, the establishment of isolated population models will be beneficial to clarify the exquisite population structures and develop specific genetic markers for subpopulations in forensic genetic fields.

genetics↗

bioRxiv2020

Accurate Estimation of Marker-Associated Genetic Variance and Heritability in Complex Trait Analyses

The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more large-effect loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability [Formula] for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and [Formula]. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and [Formula] were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and [Formula], in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and [Formula]. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance. Author summaryThe contributions of individual genes to the phenotypic variation observed for genetically complex traits has been an ongoing and important challenge in biology, medicine, and agriculture. While many genes have statistically undetectable effects, those with large effects often warrant in-depth study and can be important predictors of complex phenotypes such as disease risk in humans or disease resistance in domesticated plants and animals. The genes identified through associations with genetic markers in complex trait analyses typically account for a fraction of the heritable variation, a genetic parameter we called marker heritability. We discovered that textbook statistical methods systematically overestimate marker heritability and thus overestimate the contributions of specific genes to the phenotypic variation observed for complex traits in natural and experimental populations. We describe the source of the upward bias, validate our findings through computer simulation, describe methods for bias-correcting estimates of marker heritability, and illustrate their application through empirical examples. The statistical methods we describe supply investigators with more accurate estimates of the contributions of specific genes or networks of interacting genes to the heritable variation observed in complex trait studies.

genetics↗

bioRxiv2020

The comprehensive genetic architecture of brain white matter

Brain regions communicate with each other via tracts of myelinated axons, commonly referred to as white matter. White matter microstructure can be measured in the living human brain using diffusion based magnetic resonance imaging (dMRI), and has been found to be altered in patients with neuropsychiatric disorders. Although under strong genetic control, few genetic variants influencing white matter microstructure have ever been identified. Here we identified common genetic variants influencing white matter microstructure using dMRI in 42,919 individuals (35,741 in the UK Biobank). The dMRIs were summarized into 215 white matter microstructure traits, including 105 measures from tract-specific functional principal component analysis. Genome-wide association analysis identified many novel white matter microstructure associated loci (P < 2.3 x 10-10). We identified shared genetic influences through genetic correlations between white matter tracts and 62 other complex traits, including stroke, neuropsychiatric disorders (e.g., ADHD, bipolar disorder, major depressive disorder, schizophrenia), cognition, neuroticism, chronotype, as well as non-brain traits. Common variants associated with white matter microstructure alter the function of regulatory elements in glial cells, particularly oligodendrocytes. White matter associated genes were enriched in pathways involved in brain disease pathogenesis, neurodevelopment process, and repair of white matter damage (P < 1.5 x 10-8). In summary, this large-scale tract-specific study provides a big step forward in understanding the genetic architecture of white matter and its genetic links to a wide spectrum of clinical outcomes.

genetics↗

bioRxiv2020

Landscape genetics and species delimitation in the Andean palm rocket frog, Rheobates spp.

The complex topography of the species-rich northern Andes creates heterogeneous environmental landscapes that are hypothesized to have promoted population fragmentation and diversification by vicariance, gradients and/or the adaptation of species. Previous phylogenetic work on the Palm Rocket Frog (Anura: Aromobatidae: Rheobates spp.), endemic to mid-elevation forests of Colombia, suggested valleys were important in promoting divergence between lineages. In this study, we use a spatially, multi-locus population genetic approach of two mitochondrial and four nuclear genes from 25 samples representing the complete geographic range of the genus to delimit species and test for landscape effects on genetic divergence within Rheobates. We tested three landscape genetic models: isolation by distance, isolation by resistance, and isolation by environment. Bayesian species delimitation (BPP) and a Poisson Tree Process (PTP) model both recovered five highly divergent genetic lineages within Rheobates, rather than the three inferred in a previous study. We found that an isolation by environment provided the only variable significantly correlated with genetic distances for both mitochondrial and nuclear genes, suggesting that local adaptation may have a role driving the genetic divergence within this genus of frogs. Thus, genetic divergence in Rheobates may be driven by the local environments where these frogs live, even more so that by the environmental characteristics of the intervening regions among populations (i.e., geographic barriers).

genetics↗

bioRxiv2020

Parental influences on offspring education: indirect genetic effects of non-cognitive skills

Understanding how parents cognitive and non-cognitive skills influence offspring education is vital for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings (N=47,459), adoptees (N=6,407), and parent-offspring trios (N=2,534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36-40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents skills, facilitating future mechanistic work.

genetics↗

bioRxiv2020

Multivariate GWAS elucidates the genetic architecture of alcohol consumption and misuse, corrects biases, and reveals novel associations with disease

Genome-wide association studies (GWASs) of the Alcohol Use Disorder Identification Test (AUDIT), a ten-item screener for alcohol use disorder (AUD), have elucidated novel loci for alcohol consumption and misuse. However, these studies also revealed that GWASs can be influenced by numerous biases (e.g., measurement error, selection bias), which have led to inconsistent genetic correlations between alcohol involvement and AUD, as well as paradoxically negative genetic correlations between alcohol involvement and psychiatric disorders/medical conditions. To explore these unexpected differences in genetic correlations, we conducted the first item-level and largest GWAS of AUDIT items (N=160,824), and applied a multivariate framework to mitigate previous biases. In doing so, we identified novel patterns of similarity (and dissimilarity) among the AUDIT items, and found evidence of a correlated two-factor structure at the genetic level (Consumption and Problems, rg=.80). Moreover, by applying empirically-derived weights to each of the AUDIT items, we constructed an aggregate measure of alcohol consumption that is strongly associated with alcohol dependence (rg=.67) and several other psychiatric disorders, and no longer positively associated with health and positive socioeconomic outcomes. Lastly, by performing polygenic analyses in three independent cohorts that differed in their ascertainment and prevalence of AUD, we identified novel genetic associations between alcohol consumption, alcohol misuse, and human health. Our work further emphasizes the value of AUDIT for both clinical and genetic studies of AUD, and the importance of using multivariate methods to study genetic associations that are more closely related to AUD.

genetics↗

bioRxiv2020

The genetic and linguistic structures of Abyssinians and their neighbor reveal the historical demographic dynamics and environmental adaptation in the African Horn region.

The African Horn region that includes the Abyssinian is one of the areas in the world that harbor high human genetic diversity manifesting past intermingling of people of different origins attributed to its geographic immediacy to the middle east and being historical trade and religio-cultural hub. Here, we performed a genetic structure analysis of linguistically differentiated populations of Ethiopia, South Sudan, and Somali. To get insight into the genetic landscape of the horn of Africa against the rest of the world, we leverage HapMap SNPs data from Utah residents with Northern and Western European ancestry (CEU), Maasai (MKK), and Yoruba (YRI) and analyzed for genetic admixture and diversity. The genetic and linguistic affiliations mismatch for most Cushitic and Semitic language speakers. The gradients of genetic variations among the different sub-populations within the region show gene-flow directions and past mass population movements. Ethiopians that predominately inhabited the central and northern Ethiopia harbored ~10-15% of CEU admixture. The African horn ancestral line contributed a total of ~20%, 5%, and 2% to MKK, YRI, and CEU, respectively. MKK showed a high genetic diversity comparable to the Ethiopian Cushitic, Semitic, and North Omotic language speakers. Allelic distribution frequencies among the populations at some outlier loci may also provide insight into the adaptations to critical environmental factors such as Malaria.

genetics↗

bioRxiv2020

Alignment-free methods for polyploid genomes: quick and reliable genetic distance estimation

Polyploid genomes pose several inherent challenges to population genetic analyses. While alignment-based methods are fundamentally limited in their applicability to polyploids, alignment-free methods bypass most of these limits. We investigated the use of Mash, a k-mer analysis tool that uses the MinHash method to reduce complexity in large genomic datasets, for basic population genetic analyses of polyploid sequences. We measured the degree to which Mash correctly estimated pairwise genetic distance in simulated diploid and polyploid short-read sequences with various levels of missing data. Mash-based estimates of genetic distance were comparable to alignment-based estimates, and were less impacted by missing data. We also used Mash to analyze publicly available short-read data for three polyploid and one diploid species, then compared Mash results to published results. For both simulated and real data, Mash accurately estimated pairwise genetic differences for polyploids as well as diploids as much as 476 times faster than alignment-based methods, though we found that Mash genetic distance estimates could be biased by per-sample read depth. Mash may be a particularly useful addition to the toolkit of polyploid geneticists for rapid confirmation of alignment-based results and for basic population genetics in reference-free systems with poor quality DNA.

genetics↗

bioRxiv2020

Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data: a comparison of multivariate methods

Estimation of the direct genetic effect of an individuals own genotype on their phenotype, independent of any contaminating indirect parental genetic effects is becoming increasingly important. These conditional estimates are of interest in their own right, but are also useful for downstream analyses such as intergenerational Mendelian randomization. We compare several available multivariate methods that utilize summary results statistics from genome-wide association studies to determine how well they estimate conditional direct and indirect genetic effects, while accounting for sample overlap. Using robustly associated birth weight variants and data from the UK Biobank, we contrasted the point estimates and their standard errors at each of the individual loci compared to those obtained using individual level genotype data, in addition to comparing the computational time, inflation of the test statistics and number of genome-wide significant SNPs identified by each of the methods. We show that Genomic SEM outperforms the other methods in accurately estimating conditional genetic effects and their standard errors. We subsequently applied Genomic SEM to fertility data in the UK Biobank and partitioned the genetic effect into female and male fertility in addition to a sibling specific effect. This analysis identified one novel locus for fertility and replicated seven previously identified loci. We also identified genetic correlations between fertility and educational attainment, risk taking behaviour, autism and subjective well-being. We therefore recommend Genomic SEM to be used to partition genetic effects across the genome into direct and indirect components.

genetics↗

bioRxiv2020

Identification of genetic variants regulating the abundance of clinically relevant plasma proteins using the Diversity Outbred mouse model

Although there have been numerous expression quantitative trait loci (eQTL) studies, the effect of genetic variants on the levels of multiple plasma proteins still warrants more systematic investigation. To identify genetic modifiers that influence the levels of clinically relevant plasma proteins, we performed protein quantitative trait locus (pQTL) mapping on 92 proteins using the Diversity Outbred (DO) mouse population and identified 12 significant cis and 6 trans pQTL. Among them, we discovered coding variants in a cis-pQTL in Ahr and a trans-pQTL in Rfx1 for the IL-17A protein. Our study reports an innovative pipeline for the identification of genetic modifiers that may be targeted for drug development. Author SummaryBlood plasma is a body fluid that can be collected in a noninvasive way to detect diseases, such as autoimmune disease. However, it is known that plasma protein levels are affected by both the environment and genetic background. To determine the effect of genetics on plasma protein levels in human, one needs a rather large sample size. To overcome this critical issue, a mouse model, the Diversity Outbred (DO), was established that is genetically as diverse as the human population. In this study, we used N=140 DO mice and genotyped over 140,000 variants. In addition, we measured the levels of 92 proteins in plasma of these DO mice using Olink Proteomics technology. The proteins detected in this panel are known to be detectable in human plasma, making our study translatable to human. We identified 18 significant protein quantitative trait loci. Furthermore, we describe an analysis pipeline that allows for the detection of a single gene in the locus that is responsible for the differences in protein levels. We identified how variants in the Regulatory Factor X1 (Rfx1) gene regulates Interleukin-17A (IL-17A) plasma levels. Our study reports an innovative approach to identify genetic modifiers that may be targeted for drug development.

genetics↗

bioRxiv2021

Species ecology explains the various spatial components of genetic diversity in tropical reef fishes

Intraspecific genetic diversity should be dependent on species ecology, but the influence of ecological traits on interspecific differences in genetic variation is yet to be explored. Generating sequenced data for 20 tropical reef fish species of the Western Indian Ocean, we investigate how species ecology influences genetic diversity patterns from local to regional scales. We distinguish between the , {beta} and{gamma} components of genetic diversity, which we subsequently link to six ecological traits. In contrast to what is expected by the neutral theory of molecular evolution, we find that the and{gamma} components of genetic diversity are negatively associated with species abundance, which can be explained by larger variance in reproductive success in large populations and/or higher introgression in less frequent species. Pelagic larval duration, an important dispersal trait in marine fishes, is found to be negatively related to genetic {beta} diversity, as expected by theory. We conclude that the neutral theory of molecular evolution may not be sufficient to explain genetic diversity in tropical reef fishes and that additional processes influence those relationships.

genetics↗

bioRxiv2021

Indian genetic heritage in Southeast Asian populations

The great ethnolinguistic diversity found today in mainland Southeast Asia (MSEA) reflects multiple migration waves of people in the past. Deeply divergent East Eurasian hunter-gatherers were the first anatomically modern human population known to migrate to the region. Agriculturalists from South China migrated to the region and admixed with the local hunter-gatherers during the Neolithic period. During the Bronze and Iron Ages, the genetic makeup of people in MSEA changed again, indicating an additional influx of populations from South China. Maritime trading between MSEA and India was established at the latest 300 BCE, and the formation of early states in Southeast Asia during the first millennium CE was strongly influenced by Indian culture, and this cultural influence is still prominent today. Several ancient Indian-influenced states were located in present-day Thailand, and various populations in the country are likely to be descendants of people from those states. To systematically explore Indian genetic heritage in MSEA, we generated genome-wide SNP data (the HumanOrigins array) for 119 present-day individuals belonging to 10 ethnic groups from Thailand and co-analyzed them with published data from MSEA using the PCA, ADMIXTURE, f3-statistics, qpAdm, and qpGraph methods. We found South Asian low-level admixture in various MSEA populations which are probably descendants of people from the ancient Indian-influenced states, but failed to find a South Asian genetic component in present-day hunter-gatherer groups and relatively isolated groups from highlands in Northern Thailand. Our results also support close genetic affinity between Kra-Dai-speaking (also known as Tai-Kadai) and Austronesian-speaking populations, which fits a linguistic hypothesis suggesting cladality of the two language families. Author SummaryMainland Southeast Asia is a region with great ethnolinguistic diversity and complex population history. We studied genetic population history of present-day mainland Southeast Asian populations using genome-wide SNP data (the HumanOrigins array). We generated new data for 10 present-day ethnic groups from Thailand, which we further combined with published data from mainland and island Southeast Asians and worldwide populations. We revealed South Asian genetic admixture in various mainland Southeast Asian ethnic groups which are highly influenced by Indian culture, but failed to find it in groups who remained culturally isolated until recently. Our finding suggests that a massive migration of Indian people in the past was responsible for the spread of Indian culture in mainland Southeast Asia. We also found support for a close genetic affinity between Kra-Dai- and Austronesianspeaking populations, which fits a linguistic hypothesis suggesting cladality of the two language families.

genetics↗

bioRxiv2021

Marker-assisted mapping enables effective forward genetic analysis in the arboviral vector Aedes aegypti, a species with vast recombination deserts

Aedes aegypti is a major vector of arboviruses that cause dengue, chikungunya, yellow fever and Zika. Although recent success in reverse genetics has facilitated rapid progress in basic and applied research, integration of forward genetics with modern technologies remains challenging in this important species, as up-to-47% of its chromosome is refractory to genetic mapping due to extremely low rate of recombination. Here we report the development of a marker-assisted-mapping (MAM) strategy to readily screen for and genotype only the rare but informative recombinants, drastically increasing both the resolution and signal-to-noise ratio. Using MAM, we mapped a transgene that was inserted in a >100 Mb recombination desert and a sex-linked spontaneous red-eye (re) mutation just outside the region. We subsequently determined, by CRISPR/Cas9-mediated knockout, that cardinal is the causal gene of re, which is the first forward genetic identification of a causal gene in Ae. aegypti. This study provides the molecular foundation for using gene-editing to develop versatile and stable genetic sexing methods by improving upon the current re-based genetic sexing strains. MAM does not require densely populated markers and can be readily applied throughout the genome to facilitate the mapping of genes responsible for insecticide- and viral-resistance. By enabling effective forward genetic analysis, MAM bridges a significant gap in establishing Ae. aegypti as a model system for research in vector biology. As large regions of suppressed recombination are also common in other plant and animal species including those of economic significance, MAM will have broad applications beyond vector biology.

genetics↗

bioRxiv2021

Maximizing genetic representation in seed collections from populations of self and cross-pollinated banana wild relatives

BackgroundConservation of plant genetic resources, including the wild relatives of crops, plays an important and well recognised role in addressing some of the key challenges faced by humanity and the planet including ending hunger and biodiversity loss. However, the genetic diversity and representativeness of ex situ collections, especially that contained in seed collections, is often unknown. This limits meaningful assessments against conservation targets, impairs targeting of future collecting and limits their use. We assessed genetic representation of seed collections compared to source populations for three wild relatives of bananas and plantains. Focal species and sampling regions were Musa acuminata subsp. banksii (Papua New Guinea), M. balbisiana (Viet Nam) and M. maclayi s.l. (Bougainville, Papua New Guinea). We sequenced 445 samples using suites of 16-20 existing and newly developed taxon-specific polymorphic microsatellite markers. Samples of each species were from five populations in a region; 15 leaf samples and 16 seed samples from one infructescence ( bunch) for each population. ResultsAllelic richness of seeds compared to populations was 51%, 81% and 93% (M. acuminata, M. balbisiana and M. maclayi respectively). Seed samples represented all common alleles in populations but omitted some rarer alleles. The number of collections required to achieve the 70% target of the Global Strategy for Plant Conservation was species dependent, relating to mating systems. Musa acuminata populations had low heterozygosity and diversity, indicating self-fertilization; many bunches were needed (>15) to represent regional alleles to 70%; over 90% of the alleles from a bunch are included in only two seeds. Musa maclayi was characteristically cross-fertilizing; only three bunches were needed to represent regional alleles; within a bunch, 16 seeds represent alleles. Musa balbisiana, considered cross-fertilized, had low genetic diversity; seeds of four bunches are needed to represent regional alleles; only two seeds represent alleles in a bunch. ConclusionsWe demonstrate empirical measurement of representation of genetic material in seeds collections in ex situ conservation towards conservation targets. Species mating systems profoundly affected genetic representation in seed collections and therefore should be a primary consideration to maximize genetic representation. Results are applicable to sampling strategies for other wild species.

genetics↗

bioRxiv2021

The genetic origin of Daunians and the Pan-Mediterranean southern Italian Iron Age context

The geographical location and shape of Apulia, a narrow land stretching out in the sea at the South of Italy, made this region a Mediterranean crossroads connecting Western Europe and the Balkans. Such movements culminated at the beginning of the Iron Age with the Iapygian civilization which consisted of three cultures: Peucetians, Messapians and Daunians. Among them, the Daunians left a peculiar cultural heritage, with one-of-a-kind stelae and pottery, but, despite the extensive archaeological literature, their origin has been lost to time. In order to shed light on this and to provide a genetic picture of Iron Age Southern Italy, we collected and sequenced human remains from three archaeological sites geographically located in Northern Apulia (the area historically inhabited by Daunians) and radiocarbon dated between 1157 and 275 calBCE. We find that Iron Age Apulian samples are still distant from the genetic variability of modern-day Apulians, they show a remarkable genetic heterogeneity, even though a few kilometers and centuries separate them, and they are well inserted into the Iron Age Pan-Mediterranean genetic landscape. Our study provides for the first time a window on the genetic make-up of pre-imperial Southern Italy, whose increasing connectivity within the Mediterranean landscape, would have contributed to laying the foundation for modern genetic variability. In this light, the genetic profile of Daunians may be compatible with an autochthonous origin, with plausible contributions from the Balkan peninsula.

genetics↗

bioRxiv2021

Comparison of genetic variation between rare and common congeners of Dipodomys with estimates of contemporary and historical effective population size

Organisms with low effective population sizes are at greater risk of extinction because of reduced genetic diversity. Dipodomys elator is a kangaroo rat that is classified as threatened in Texas and field surveys from the past 50 years indicate that the distribution of this species has decreased. This suggests geographic range reductions that could have caused population fluctuations, potentially impacting effective population size. Conversely, the more common and widespread D. ordii is thought to exhibit relative geographic and demographic stability. Genetic variation between D. elator and D. ordii samples was assessed using 3RAD, a modified restriction site associated sequencing approach. It was hypothesized that D. elator would show lower levels of nucleotide diversity, observed heterozygosity, and effective population size when compared to D. ordii. Also of interest was identifying population structure within contemporary samples of D. elator and detecting genetic variation between temporal samples that could indicate demographic dynamics. Up to 61,000 single nucleotide polymorphisms were analyzed. It was determined that genetic variability and effective population size in contemporary D. elator populations were lower than that of D. ordii, that there is only slight, if any, structure within contemporary D. elator populations, and there is little genetic differentiation between spatial or temporal historical samples suggesting little change in nuclear genetic diversity over 30 years. Results suggest that genetic diversity of D. elator has remained stable despite claims of reduced population size and/or abundance, which may indicate a metapopulation-like system, whose fluctuations might counteract any immediate decrease in fitness.

genetics↗

bioRxiv2021

Estimating genetic correlation jointly using individual-level and summary-level GWAS data

With the increasing accessibility of individual-level data from genome wide association studies, it is now common for researchers to have individual-level data of some traits in one specific population. For some traits, we can only access public released summary-level data due to privacy and safety concerns. The current methods to estimate genetic correlation can only be applied when the input data type of the two traits of interest is either both individual-level or both summary-level. When researchers have access to individual-level data for one trait and summary-level data for the other, they have to transform the individual-level data to summary-level data first and then apply summary data-based methods to estimate the genetic correlation. This procedure is computationally and statistically inefficient and introduces information loss. We introduce GENJI (Genetic correlation EstimatioN Jointly using Individual-level and summary data), a method that can estimate within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for another trait. Through extensive simulations and analyses of real data on within-population and transethnic genetic correlation estimation, we show that GENJI produces more reliable and efficient estimation than summary data-based methods. Besides, when individual-level data are available for both traits, GENJI can achieve comparable performance than individual-level data-based methods. Downstream applications of genetic correlation can benefit from more accurate estimates. In particular, we show that more accurate genetic correlation estimation facilitates the predictability of cross-population polygenic risk scores.

genetics↗

bioRxiv2021

A novel method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data

Cross-ancestry genetic correlation is an important parameter to understand the genetic relationship between two ancestry groups for a complex trait. However, existing methods cannot properly account for ancestry-specific genetic architecture, which is diverse across ancestries, producing biased estimates of cross-ancestry genetic correlation. Here, we present a method to construct a genomic relationship matrix (GRM) that can correctly account for the relationship between ancestry-specific allele frequencies and ancestry-specific causal effects. Through comprehensive simulations, we show that the proposed method outperforms existing methods in the estimations of SNP-based heritability and cross-ancestry genetic correlation. The proposed method is further applied to six anthropometric traits from the UK Biobank data across 5 ancestry groups. One of our findings is that for obesity, the estimated genetic correlation between African and European ancestry cohorts is significantly different from unity, suggesting that obesity is genetically heterogenous between these two ancestry groups.

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