Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research. Genome-wide association study of renal function traits: results from the Japan Multi-institutional Collaborative Cohort study. Am. J. Nephrol. 47 , 304–316 (2018). CAS PubMed Google Scholar Lee, J. et al. Genome-wide association analysis identifies multiple loci associated with kidney disease-related traits in Korean populations. PLoS One 13 , e0194044 (2018). PubMed PubMed Central Google Scholar Mahajan, A. et al. Trans-ethnic fine mapping highlights kidney-function genes linked to salt sensitivity. Am. J. Hum. Genet. 99 , 636–646 (2016). CAS PubMed PubMed Central Google Scholar Devuyst, O. & Pattaro, C. The UMOD locus: insights into the pathogenesis and prognosis of kidney disease. J. Am. Soc. Nephrol. 29 , 713–726 (2018). CAS PubMed Google Scholar Yeo, N. C. et al. Shroom3 contributes to the maintenance of the glomerular filtration barrier integrity. Genome Res. 25 , 57–65 (2015). CAS PubMed PubMed Central Google Scholar Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70 , 214–223 (2016). PubMed Google Scholar Benner, C. et al. 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BackgroundSclerotinia Head Rot (SHR) is one of the most damaging diseases of sunflower in Europe, Argentina, and USA, causing average yield reductions of 10 to 20 %, but leading to total production loss under favorable environmental conditions for the pathogen. Association Mapping (AM) is a promising choice for Quantitative Trait Locus (QTL) mapping, as it detects relationships between phenotypic variation and gene polymorphisms in existing germplasm without development of mapping populations. This article reports the identification of QTL for resistance to SHR based on candidate gene AM.ResultsA collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum. Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napus infected with S. sclerotiorum. Associations among SHR incidence and haplotype polymorphisms in 16 candidate genes were tested using Mixed Linear Models (MLM) that account for population structure and kinship relationships. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence (P < 0.01), accounting for a SHR incidence reduction of about 20 %.ConclusionsThese results suggest that AM will be useful in dissecting other complex traits in sunflower, thus providing a valuable tool to assist in crop breeding. Association Mapping (AM) is a promising choice for Quantitative Trait Locus (QTL) mapping, as it detects relationships between phenotypic variation and gene polymorphisms in existing germplasm without development of mapping populations. This article reports the identification of QTL for resistance to SHR based on candidate gene AM. Results A collection of 94 sunflower inbred lines were tested for SHR under field conditions using assisted inoculation with the fungal pathogen Sclerotinia sclerotiorum . Given that no biological mechanisms or biochemical pathways have been clearly identified for SHR, 43 candidate genes were selected based on previous transcript profiling studies in sunflower and Brassica napus infected with S. sclerotiorum . Associations among SHR incidence and haplotype polymorphisms in 16 candidate genes were tested using Mixed Linear Models (MLM) that account for population structure and kinship relationships. This approach allowed detection of a significant association between the candidate gene HaRIC_B and SHR incidence ( P < 0.01), accounting for a SHR incidence reduction of about 20 %. sclerotiorum has been described as quantitatively inherited with predominantly additive gene action, and medium heritability [ 3 ]. Classical linkage mapping based on biparental populations was used to dissect Quantitative Trait Loci (QTL) for SHR resistance. These analyses have rendered QTL with small effects, explaining only a minor proportion of the phenotypic variance [ 9 - 15 ]. In addition, a number of studies have been done in other S. sclerotiorum host species to understand the defense mechanisms triggered in resistant genotypes [ 16 - 25 ]. However, extrapolating this information to sunflower, and using it to evaluate new sources of resistance requires the identification of orthologous genes between B. napus and H. annuus . Association Mapping (AM) was suggested as a promising alternative to classical linkage mapping to elucidate the genetic basis of complex traits [ 26 ]. The AM approach is based on the extent of Linkage Disequilibrium (LD) observed in a set of accessions that are not closely related. In contrast to classical biparental population mapping, AM is a method that detects relationships between phenotypic variation and gene polymorphisms in existing germplasm, without development of mapping populations. flowering time and aluminum tolerance in maize, resistance to late blight in potato, kernel size and milling quality in wheat, resistance to dieback in lettuce) [ 28 - 32 ]. Even though two possible strategies have been proposed, Genome Wide Association (GWA) and candidate gene This paper reports the identification of resistance QTL for SHR based on candidate gene AM. Given that no biological mechanisms or biochemical pathways have been positively identified for SHR, selection of candidate genes was based on previous transcript profiling studies in sunflower [ 36 - 38 ] and B. napus [ 25 ]. This approach resulted successful in detecting a significant association between one of the candidate genes evaluated and SHR incidence. These results suggest that AM is a useful strategy for dissecting complex traits in sunflower, thus providing a valuable tool to assist in crop breeding. In fact, 52 % of the Association Mapping Population (AMP) showed an intermediate behavior against the disease, i.e. between 40 % and 60 % (Figure 1 and Additional file 1 ). Figure 1 Sclerotinia Head Rot incidence. Phenotypic behavior of the AMP measured as the adjusted means of SHR incidence. Candidate gene selection A total of 43 genes were used as starting point for candidate gene selection. Putative orthologous sequences, either from sunflower or from other Asteraceae species were identified for 18 out of 19 A. thaliana loci selected from the work of Zhao et al. [ 25 ] using the phylogenetic approach detailed in Methods. sclerotiorum at 48 hpi, and previous literature reports [ 36 - 38 ]. After identification of polymorphisms (SNPs and indels) within the core set of 10 inbred lines, a total of 21 candidate genes were selected to be further genotyped in the AMP. This number of candidate genes has proved adequate to find significant genotype-phenotype associations for traits with different degrees of complexity. Examples include the studies carried out in A. thaliana for flowering time [ 45 ], in potato for late blight [ 30 ] and in maize for aluminum tolerance [ 31 ].

While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. When applied to a diverse selection of complex traits and disorders, including schizophrenia, GSA-MiXeR prioritizes gene sets with greater biological specificity compared to standard GSA approaches, implicating voltage-gated calcium channel function and dopaminergic signaling for schizophrenia. Such biologically relevant gene sets, often with fewer than ten genes, are more likely to provide insights into the pathobiology of complex diseases and highlight potential drug targets. GSA-MiXeR models gene heritability and variant linkage disequilibrium for improved gene set enrichment testing. GSA-MiXeR implicates relevant sets of fewer than ten genes in schizophrenia, providing more nuanced insights into trait biology. VU Research Portal Improved functional mapping of complex trait heritability with GSA-MiXeR implicates biologically specific gene sets Frei, Oleksandr; Hindley, Guy; Shadrin, Alexey A.; van der Meer, Dennis; Akdeniz, Bayram C.; Hagen, Espen; Cheng, Weiqiu; O’Connell, Kevin S.; Bahrami, Shahram; Parker, Nadine; Smeland, Olav B.; Holland, Dominic; de Leeuw, Christiaan; Posthuma, Danielle; Andreassen, Ole A.; Dale   4 While genome-wide association studies are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. Here we developed the GSA-MiXeR analytical tool for gene set analysis (GSA), which fits a model for the heritability of individual genes, accounting for linkage disequilibrium across variants and allowing the quantification of partitioned heritability and fold enrichment for small gene sets. We validated the method using extensive simulations and sensitivity analyses. Genome-wide association studies (GWAS) have discovered thousands of genomic loci associated with complex human traits and disorders, highlighting their polygenic nature and the predominance of small indi- vidual effects of common genetic variants1. Gene set analysis (GSA) has become a powerful tool for understanding the biological implications of GWAS findings2. The likelihood function is also used to compute Akaike information criterion (AIC) values quantifying available evi- dence for the enrichment of individual genes and gene sets. Further details are presented in Methods. Simulation studies T o evaluate the accuracy of the GSA-MiXeR’s fold enrichment estimates and SEs, we conducted simulations by synthesizing a quantitative trait and its respective GWAS summary statistics using real UKB genotypes (N = 337,145 subjects and M = 12,926,669 variants after quality control (QC)). Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015). 12. Goeman, J. J. & Bühlmann, P. Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 23, 980–987 (2007). 13. Tashman, K. C., Cui, R., O’Connor, L. J., Neale, B. M. & Finucane, H. K. Significance testing for small annotations in stratified LD-Score regression. Preprint at medRxiv https://doi. org/10.1101/2021.03.13.21249938 (2021). 14. Speed, D., Cai, N., Johnson, M. R., Nejentsev, S. & Balding, D. J. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017). 15. Zabad, S., Ragsdale, A. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat. Genet. 49, 256–261 (2017). 24. Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018). 25. Hautakangas, H. et al. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat. Genet. 54, 152–160 (2022). 26. Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018). 27. Mishra, A. et al. & Speed, D. LDAK-GBAT: fast and powerful gene-based association testing using summary statistics. Am. J. Hum. Genet. 110, 23–29 (2023). Nature Genetics | Volume 56 | June 2024 | 1310–1318 1318 Technical Report https:/ /doi.org/10.1038/s41588-024-01771-1 40. Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017). 41. Moon, A. L., Haan, N., Wilkinson, L. S., Thomas, K. L. & Hall, J. CACNA1C: association with psychiatric disorders, behavior, and neurogenesis. Schizophr. Bull. 44, 958–965 (2018). 42. Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Burch, K. S. et al. Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes. Am. J. Hum. Genet. 109, 692–709 (2022). 48. Yao, D. W., O’Connor, L. J., Price, A. L. & Gusev, A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat. Genet. 52, 626–633 (2020). 49. Siewert-Rocks, K. M., Kim, S. S., Yao, D. W., Shi, H. & Price, A. L. Leveraging gene co-regulation to identify gene sets enriched for disease heritability. Am. J. Hum. Genet. 109, 393–404 (2022). 50. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016). 51. Zhu, X.

Litter size is a critical economic trait in the sheep industry. Like many other breeds, Duolang sheep typically produce one lamb per ewe per lambing. To date, genetic studies of this trait in that breed have largely relied on candidate gene approaches. To expand the genomic resources for this breed, we sequenced 297 genomes, generating approximately 10.52 trillion bases with an average coverage of 13.35X. High-quality alignments with a mapping rate exceeding 99% enabled the identification of 43,968,128 SNPs and 6,504,047 InDels. This dataset provides a valuable resource for identifying genetic variants associated with litter size through genome-wide association studies (GWAS) and lays the foundation for future genetic improvement efforts through genomic selection in Duolang sheep. Beyond trait mapping, the dataset also supports broader applications, including analyses of genetic diversity, phylogenetic relationships, population history, adaptive introgression, and breed-specific characteristics. Additionally, the moderate-coverage WGS data are suitable for structural variant (SV) detection and downstream analyses such as association mapping and the identification of SVs underlying phenotypic traits. Like many other breeds, Duolang sheep typically produce one lamb per ewe per lambing. To date, genetic studies of this trait in that breed have largely relied on candidate gene approaches. To expand the genomic resources for this breed, we sequenced 297 genomes, generating approximately 10.52 trillion bases with an average coverage of 13.35X. High-quality alignments with a mapping rate exceeding 99% enabled the identification of 43,968,128 SNPs and 6,504,047 InDels. This dataset provides a valuable resource for identifying genetic variants associated with litter size through genome-wide association studies (GWAS) and lays the foundation for future genetic improvement efforts through genomic selection in Duolang sheep. Beyond trait mapping, the dataset also supports broader applications, including analyses of genetic diversity, phylogenetic relationships, population history, adaptive introgression, and breed-specific characteristics. Additionally, the moderate-coverage WGS data are suitable for structural variant (SV) detection and downstream analyses such as association mapping and the identification of SVs underlying phenotypic traits. This adaptation makes the breed an important genetic resource not only for local production systems but also for research into environmental resilience. Although some phenotypic traits, such as coat color 7 , or meat quality 8 , have been sporadically reported, increasing litter size remains a key breeding goal. For example, an earlier study identified potential associations between FSHR polymorphisms and litter size in Duolang sheep 9 . However, despite many candidate gene and SNP-based association studies 10 , 11 , most results have yielded weak or inconsistent associations, likely due to limited statistical power, small effect sizes, or reliance on single-marker tests. With the rapid development of high-throughput sequencing technologies and reduced costs, whole-genome sequencing (WGS) has become a fundamental tool for identifying functional variants and major-effect genes. For instance, a WGS-based GWAS identified a deletion in the ABO blood group gene that influences gut microbiota composition in pigs 12 . In sheep, recent multi-omics studies incorporating WGS have linked variants in BMPR1B to litter size and PAPPA to lambing interval 13 . In addition to its use in gene discovery for economic traits, WGS has increasingly been applied to broader questions in population and functional genomics, including the role of introgression in enhancing genetic diversity 14 , the identification of PDGFD as the causal gene for the fat-rumped tail characteristic 15 , the assessment of within- and between-breed genetic variation 16 , and the reconstruction of demographic history and phylogenetic relationships 14 , 17 . Additionally, WGS, whether at moderate or high coverage, facilitates the detection of structural variants (SVs) using either direct read-based methods 18 or pangenome-guided approaches 19 , expanding the scope of genotype-phenotype association studies. Here, we present a whole-genome sequencing dataset for 297 Duolang sheep from Xinjiang of China, each with a corresponding litter size phenotype (recorded as having either a single or double litter per lambing). This dataset provides a foundational resource for SNP-based association analyses and may also support genomic selection strategies for improving reproductive traits as the reference population expands. Quality of variants After the joint calling procedure, we applied GATK’s recommended ‘hard filters’ approach, a widely accepted and robust method for reducing false positives in variant calling 31 . To assess the accuracy of variant calling, we calculated the transition-to-transversion (Ti/Tv) ratio, a commonly used indicator of variant calling quality. The calculated Ti/Tv ratio was 2.39, which falls within the range reported in previous studies on sheep 32 , 33 , suggesting that the parameters used for variant calling and filtering were appropriately calibrated. Functional annotation of the variants was performed using ANNOVAR v2016-02-01 34 . Consistent with findings from other studies in sheep 17 , 35 , the majority of variants were located in intergenic (54.39%) and intronic (39.67%) regions. Among the smaller proportion of variants found in exonic regions (2.21%), 174,533 variants resulted in amino acid changes, 3,745 caused stop codon alterations, and 35,873 led to frameshift mutations. The chromosomal distribution of variants, visualized using CMplot v4.5.1 36 was generally uniform except for chromosome 10 (Fig. 4 ), a pattern that aligns with observations in previous studies on sheep 37 , 38 . Fig. 4 Functional annotation and chromosomal distribution of detected variants.

Abstract Background/Aims Systemic lupus erythematosus (SLE) genome-wide association studies (GWAS) face multiple challenges in order to identify reliable susceptibility genes. We sought to identify non-HLA overlapping loci in SLE across ethnicities using a cluster-based approach and verify the candidate genes with expression analysis. Methods Association clustering methods such as OASIS reduce the multiple-testing burden and are more powerful than single variant analysis for identifying modest genetic effects. Here, six SLE dbGAP GWAS datasets, 4 EU and 2 Chi involving 19,710 SLE cases and 30,876 controls were analysed using OASIS. Significant variants were tested as expression quantitative trait loci (eQTLs)/splicing quantitative trait loci (sQTLs) using genotype-tissue expression (GTEx). Composite list of genes and eQTLs were checked for differential expression in SLE The Genotype-Tissue Expression (GEO) datasets (GSE30153, GSE13887 and GSE10325) with the GEO2R tool. Pathway analysis was performed using STRING. Results Top genes, common in both ethnicities, were STAT4, SMG7, IRF5, BLK, and TNFAIP3. Overall, OASIS identified 19 highly significant and 16 modestly significant (P &amp;gt; 10-8) non-HLA SLE genes common to EU and Chi ethnicities. Significant SNPs at these 35 loci were explored for eQTLs/sQTLs using GTEx. This identified 69 unique significant genes that, when matched with GEO2R results, identified 11 genes with altered expression (Table 1; bold crossed Bonferroni correction). Genes that are significant across multiple ethnicities and have the most significant variants as eQTLs/sQTLs, as well as demonstrate altered expression in multiple datasets, are believed to be reliable modulators of disease pathogenesis. Interaction of these 35 genes elucidated SLE pathways via NOD, TLR, JAK-STAT and RIG-1. Conclusion Several genes and loci were identified using this composite approach of cluster-based multi-ethnicity GWAS meta-analysis, followed by eQTL search for the most significant variants at these loci and expression analysis. This large meta-analysis has helped identify replicable pathogenic genes and pathways for SLE. Disclosure M. Khan: None.

<h4>Background</h4>Serum creatine kinase (CK) is a routinely measured biomarker of muscle damage, yet the genetic factors underlying inter-individual variation in CK levels remain poorly defined.<h4>Methods</h4>Here we present a large multi-ancestry genome-wide association meta-analysis of serum CK, comprising 237,255 participants spanning Admixed American, African American, East Asian, European and Middle Eastern populations.<h4>Findings</h4>We identify 107 independent loci at genome-wide significance (P< 5 × 10^-8), 98 of which are previously unreported, with pronounced enrichment for genes expressed in skeletal and cardiac muscle and overlap with pathways related to muscle structure and function. Notably, eight loci map to genes implicated in Mendelian myopathies, underscoring a continuum from common regulatory variation to rare pathogenic mutations. Integrative quantitative trait locus (QTL)-based Mendelian randomisation and colocalisation implicate several genes in CK regulation, most prominently SMAD3, KLF5 and STAT3 within the transforming growth factor beta signalling pathway. CK levels show positive genetic correlations with traits reflecting tissue damage as well as muscle mass and strength, and negative correlations with C-reactive protein, indicating pleiotropic effects from muscle biology and enzyme clearance.<h4>Interpretation</h4>These findings delineate the genetic architecture of serum CK across diverse populations and highlight muscle-related pathways contributing to CK variation.<h4>Funding</h4>No funding was received for this study.

Chinese indigenous sheep can be classified into three types based on tail morphology: fat-tailed, fat-rumped, and thin-tailed sheep, of which the typical breeds are large-tailed Han sheep, Altay sheep, and Tibetan sheep, respectively. To unravel the molecular genetic basis underlying the phenotypic differences among Chinese indigenous sheep with these three different tail types, we used ovine high-density 600K single nucleotide polymorphism (SNP) arrays to detect genome-wide associations, and performed general linear model analysis to identify candidate genes, using genotyping technology to validate the candidate genes. Tail type is an important economic trait in sheep. However, the candidate genes associated with tail type are not known. The objective of this study was to identify SNP markers, genes, and chromosomal regions related to tail traits. We performed a genome-wide association study (GWAS) using data from 40 large-tailed Han sheep, 40 Altay sheep (cases) and 40 Tibetan sheep (controls). A total of 31 significant (P C in exon1 of the BMP2 gene and one SNP in exon4 (rs69 C>A) of the PDGFD gene were detected. rs119 was of the TT genotype in Altay sheep, while it was of the CC genotype in Tibetan sheep. On rs69 of the PDGFD gene, Altay sheep presented with the CC genotype; however, Tibetan sheep presented with the AA genotype.

Genetic markers have long been used for characterization of plant genetic diversity and exploitation in crop improvement. The advent of DNA marker technology (during 1980s) has revolutionized crop breeding research as it has enabled the breeding of elite cultivars with targeted selection of desirable gene or gene combinations in breeding programmes. DNA markers are considered better over traditional morphology and protein-based markers because they are abundant, neutral, reliable, convenient to automate and cost-effective. Over the years, DNA marker technology has matured from restriction based to PCR based to sequence based and to eventually the sequence itself with the emergence of novel genome sequencing technologies. Trait mapping has been the foremost application of molecular markers in plant breeding. Genomic locations of numerous genes or quantitative trait loci (QTLs) associated with agronomically important traits have been determined in several crop plants using linkage or association mapping approaches. Plant breeders always look for an easy, rapid and reliable method of selection of desirable plants in breeding populations. Conventionally, desirable plants are selected based on phenotypic observations. The phenotypic selection for complex agronomic traits is difficult, unpredictable and challenging. Once the marker-trait association is correctly established, the gene- or QTL-linked markers can be used to select plants carrying desirable traits, the process called m

Gestational diabetes mellitus (GDM) affects ~14% of pregnancies and increases maternal type 2 diabetes mellitus (T2DM) risk. The GenDiP Consortium presents trans-generational, multi-ancestry genome-wide association study meta-analyses of GDM and pregnancy glycemic traits in up to 38,305 GDM cases and 776,145 controls. We identify 37 GDM-associated loci (7 novel) and five novel loci for pregnancy glycemic traits, all operating through the maternal genome. We classify 12 GDM variants with stronger effects in GDM than T2DM into five biologically informed categories, revealing pleiotropy patterns, pregnancy-dependent effect modification, and diagnostic heterogeneity. While all these loci overlap with T2DM and/or non-pregnant glycaemic traits, four (G6PC2, CAST-PCSK1, HKDC1, FOXA2) lack genome-wide-significant T2DM associations; GCK shows distinct causal variants for GDM, and MTNR1B exhibits pregnancy-amplified effects. Our findings provide new genetic insights into GDM and highlight the need for larger, ancestrally diverse studies of GDM and glycaemic traits during pregnancy to understand potential pregnancy-specific effects.

Rare-variant association studies typically perform gene-level tests in which coding variants are filtered (or 'masked') and aggregated based on functional annotation and allele frequency. Through a systematic literature review, we cataloged 664 masks used across 234 studies and found that masking strategies (that is, sets of masks) rarely repeat across studies and are rarely justified. To quantify their impact on association results, we applied all previously employed strategies to 54 traits within 189,947 UK Biobank exomes. Here we find that the number of significant associations greatly depends on the masking strategy (ranging from 58 to 2,523 associations), which is a key reason for the modest overlap (<30%) of associations between separate published analyses of this dataset. We empirically determine masking strategies with high discovery power for low-frequency and rare variant gene-level associations across numerous datasets and traits, and we use these to explore the impact of other factors on burden test results. These findings offer a baseline strategy in burden tests to increase study power and replicability, addressing one source of inconsistency in previous studies.

Network Evolution of Body Plans Segmentation in arthropod embryogenesis represents a well-known example of body plan diversity. Striped patterns of gene expression that lead to the future body segments appear simultaneously or sequentially in long and short germ-band development, respectively. Regulatory genes relevant for stripe formation are evolutionarily conserved among arthropods, therefore the differences in the observed traits are thought to have originated from how the genes are wired. To reveal the basic differences in the network structure, we have numerically evolved hundreds of gene regulatory networks that produce striped patterns of gene expression. By analyzing the topologies of the generated networks, we show that the characteristics of stripe formation in long and short germ-band development are determined by Feed-Forward Loops (FFLs) and negative Feed-Back Loops (FBLs) respectively. Network architectures, gene expression patterns and knockout responses exhibited by the artificially evolved networks agree with those reported in the fly Drosophila melanogaster and the beetle Tribolium castaneum.