Chromosomal inversions are ubiquitous in genomes and often coordinate complex phenotypes, such as the covariation of behavior and morphology in many birds, fishes, insects or mammals1-11. However, why and how inversions become associated with polymorphic traits remains obscure. Here we show that despite a strong selective advantage when they form, inversions accumulate recessive deleterious mutations that generate frequency-dependent selection and promote their maintenance at intermediate frequency. Combining genomics and in vivo fitness analyses in a model butterfly for wing-pattern polymorphism, Heliconius numata, we reveal that three ecologically advantageous inversions have built up a heavy mutational load from the sequential accumulation of deleterious mutations and transposable elements. Inversions associate with sharply reduced viability when homozygous, which prevents them from replacing ancestral chromosome arrangements. Our results suggest that other complex polymorphisms, rather than representing adaptations to competing ecological optima, could evolve because chromosomal rearrangements are intrinsically prone to carrying recessive harmful mutations.Hypertrophic cardiomyopathy (HCM) is a common, serious, genetic heart disorder. Rare pathogenic variants in sarcomere genes cause HCM, but with unexplained phenotypic heterogeneity. Moreover, most patients do not carry such variants. Avasimibe order We report a genome-wide association study of 2,780 cases and 47,486 controls that identified 12 genome-wide-significant susceptibility loci for HCM. Single-nucleotide polymorphism heritability indicated a strong polygenic influence, especially for sarcomere-negative HCM (64% of cases; h2g = 0.34 ± 0.02). A genetic risk score showed substantial influence on the odds of HCM in a validation study, halving the odds in the lowest quintile and doubling them in the highest quintile, and also influenced phenotypic severity in sarcomere variant carriers. Mendelian randomization identified diastolic blood pressure (DBP) as a key modifiable risk factor for sarcomere-negative HCM, with a one standard deviation increase in DBP increasing the HCM risk fourfold. Common variants and modifiable risk factors have important roles in HCM that we suggest will be clinically actionable.The heart muscle diseases hypertrophic (HCM) and dilated (DCM) cardiomyopathies are leading causes of sudden death and heart failure in young, otherwise healthy, individuals. We conducted genome-wide association studies and multi-trait analyses in HCM (1,733 cases), DCM (5,521 cases) and nine left ventricular (LV) traits (19,260 UK Biobank participants with structurally normal hearts). We identified 16 loci associated with HCM, 13 with DCM and 23 with LV traits. We show strong genetic correlations between LV traits and cardiomyopathies, with opposing effects in HCM and DCM. Two-sample Mendelian randomization supports a causal association linking increased LV contractility with HCM risk. A polygenic risk score explains a significant portion of phenotypic variability in carriers of HCM-causing rare variants. Our findings thus provide evidence that polygenic risk score may account for variability in Mendelian diseases. More broadly, we provide insights into how genetic pathways may lead to distinct disorders through opposing genetic effects.Bayes factor analysis has the attractive property of accommodating the risks of both false negatives and false positives when identifying susceptibility gene variants in genome-wide association studies (GWASs). For a particular SNP, the critical aspect of this analysis is that it incorporates the probability of obtaining the observed value of a statistic on disease association under the alternative hypotheses of non-null association. An approximate Bayes factor (ABF) was proposed by Wakefield (Genetic Epidemiology 2009;3379-86) based on a normal prior for the underlying effect-size distribution. However, misspecification of the prior can lead to failure in incorporating the probability under the alternative hypothesis. In this paper, we propose a semi-parametric, empirical Bayes factor (SP-EBF) based on a nonparametric effect-size distribution estimated from the data. Analysis of several GWAS datasets revealed the presence of substantial numbers of SNPs with small effect sizes, and the SP-EBF attributed much greater significance to such SNPs than the ABF. Overall, the SP-EBF incorporates an effect-size distribution that is estimated from the data, and it has the potential to improve the accuracy of Bayes factor analysis in GWASs.PANX1, one of the members of the pannexin family, is a highly glycosylated channel-forming protein. Recently, we identified heterozygous variants in PANX1 that follow an autosomal dominant inheritance pattern and cause female infertility characterized by oocyte death. In this study, we screened for novel PANX1 variants in patients with the phenotype of oocyte death and discovered a new type of inheritance pattern accompanying PANX1 variants. We identified two novel homozygous missense variants in PANX1 [NM_015368.4 c.712T>C (p.(Ser238Pro) and c.899G>A (p.(Arg300Gln))] associated with the oocyte death phenotype in two families. Both of the homozygous variants altered the PANX1 glycosylation pattern in cultured cells, led to aberrant PANX1 channel activation, and resulted in mouse oocyte death after fertilization in vitro. It is worth noting that the destructive effect of the two homozygous variants on PANX1 function was weaker than that caused by the recently reported heterozygous variants. Our findings enrich the variational spectrum of PANX1 and expand the inheritance pattern of PANX1 variants to an autosomal recessive mode. This highlights the critical role of PANX1 in human oocyte development and helps us to better understand the genetic basis of female infertility due to oocyte death.The unabating rise in the number of species introduced outside of their native range makes predicting the spread of alien species an urgent challenge. Most predictions use models of the ecological niche of a species to identify suitable areas for invasion, but these predictions may have limited accuracy. Here, using the global alien avifauna, we demonstrate an alternative approach for predicting alien spread based on the environmental resistance of the landscape. This approach does not require any information on the ecological niche of the invading species and, instead, uses gradients of biotic similarity among native communities in the invaded region to predict the most likely routes of spread. We show that environmental resistance predicts patterns of spread better than a null model of random dispersal or a model based on climate matching to the native range of each species. Applying this approach to simulate future spread reveals major regional differences in projected invasion risk, shaped by proximity to existing invasion hotspots as well as gradients in environmental resistance.