The contribution of mutation and selection to multivariate quantitative genetic variance in an outbred population of Drosophila serrata

Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

Long runs of homozygosity are associated with Alzheimer’s disease

Long runs of homozygosity (ROH) are contiguous stretches of homozygous genotypes, which are a footprint of inbreeding and recessive inheritance. The presence of recessive loci is suggested for Alzheimer’s disease (AD); however, their search has been poorly assessed to date. To investigate homozygosity in AD, here we performed a fine-scale ROH analysis using 10 independent cohorts of European ancestry (11,919 AD cases and 9181 controls.) We detected an increase of homozygosity in AD cases compared to controls [βAVROH (CI 95%) = 0.070 (0.037–0.104); P = 3.91 × 10−5; βFROH (CI95%) = 0.043 (0.009–0.076); P = 0.013]. ROHs increasing the risk of AD (OR > 1) were significantly overrepresented compared to ROHs increasing protection (p < 2.20 × 10−16). A significant ROH association with AD risk was detected upstream the HS3ST1 locus (chr4:11,189,482‒11,305,456), (β (CI 95%) = 1.09 (0.48 ‒ 1.48), p value = 9.03 × 10−4), previously related to AD. Next, to search for recessive candidate variants in ROHs, we constructed a homozygosity map of inbred AD cases extracted from an outbred population and explored ROH regions in whole-exome sequencing data (N = 1449). We detected a candidate marker, rs117458494, mapped in the SPON1 locus, which has been previously associated with amyloid metabolism. Here, we provide a research framework to look for recessive variants in AD using outbred populations. Our results showed that AD cases have enriched homozygosity, suggesting that recessive effects may explain a proportion of AD heritability.

Recent Consanguinity and Outbred Autozygosity Are Associated With Increased Risk of Late-Onset Alzheimer’s Disease

Prior work in late-onset Alzheimer’s disease (LOAD) has resulted in discrepant findings as to whether recent consanguinity and outbred autozygosity are associated with LOAD risk. In the current study, we tested the association between consanguinity and outbred autozygosity with LOAD in the largest such analysis to date, in which 20 LOAD GWAS datasets were retrieved through public databases. Our analyses were restricted to eight distinct ethnic groups: African–Caribbean, Ashkenazi–Jewish European, European–Caribbean, French–Canadian, Finnish European, North-Western European, South-Eastern European, and Yoruba African for a total of 21,492 unrelated subjects (11,196 LOAD and 10,296 controls). Recent consanguinity determination was performed using FSuite v1.0.3, according to subjects’ ancestral background. The level of autozygosity in the outbred population was assessed by calculating inbreeding estimates based on the proportion (FROH) and the number (NROH) of runs of homozygosity (ROHs). We analyzed all eight ethnic groups using a fixed-effect meta-analysis, which showed a significant association of recent consanguinity with LOAD (N = 21,481; OR = 1.262, P = 3.6 × 10–4), independently of APOE∗4 (N = 21,468, OR = 1.237, P = 0.002), and years of education (N = 9,257; OR = 1.274, P = 0.020). Autozygosity in the outbred population was also associated with an increased risk of LOAD, both for FROH (N = 20,237; OR = 1.204, P = 0.030) and NROH metrics (N = 20,237; OR = 1.019, P = 0.006), independently of APOE∗4 [(FROH, N = 20,225; OR = 1.222, P = 0.029) (NROH, N = 20,225; OR = 1.019, P = 0.007)]. By leveraging the Alzheimer’s Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data, we determined that LOAD subjects do not show an enrichment of rare, risk-enhancing minor homozygote variants compared to the control population. A two-stage recessive GWAS using ADSP data from 201 consanguineous subjects in the discovery phase followed by validation in 10,469 subjects led to the identification of RPH3AL p.A303V (rs117190076) as a rare minor homozygote variant increasing the risk of LOAD [discovery: Genotype Relative Risk (GRR) = 46, P = 2.16 × 10–6; validation: GRR = 1.9, P = 8.0 × 10–4]. These results confirm that recent consanguinity and autozygosity in the outbred population increase risk for LOAD. Subsequent work, with increased samples sizes of consanguineous subjects, should accelerate the discovery of non-additive genetic effects in LOAD.

A simplified calculation of the correlations between relatives

The correlations between relatives is one of the fundamental ideas and earliest success of quantitative genetics. Whether using genomic data to infer relationships between individuals or estimating heritability from correlations of phenotypes amongst relatives, understanding the theoretical genetic correlations is a common task. Calculating the correlations between arbitrary relatives in an outbred population, however, can be a careful and somewhat complex task for increasingly distant relatives. This paper introduces an equation based method that consolidates the results of path analysis and uses easily obtainable data from non-inbred pedigrees to allow the rapid calculation of additive or dominance correlations between relatives even in more complicated situations such as cousins sharing more than two grandparents and inbreeding.

On the expression of co-operative feeding behaviour in 3rd instar Drosophila melanogaster larvae!

Under poor nutritional conditions, 3rd instar Drosophila melanogaster larvae will work collaboratively in feeding clusters to obtain resources that cannot be reached individually. To better understand the conditions that influence the expression of this behaviour we examined the frequencies, the size and the membership in vials of flies that were initially seeded with either 100 or 200 eggs each using flies from both a large, outbred population and a replicate population that was homozygous for the bw allele. Overall, more feeding clusters, containing more larval participants were observed in the higher density vials compared to the lower density vials, consistent with the idea that this social behaviour is a response to dwindling resources in the environment. The presence of the bw allele did not result in greater egg-to-adult mortality, nor did it result in lower participation in feeding clusters.

Short-term Changes in the Variance: 2. Changes in the Environmental Variance

While classical quantitative genetics usually assumes that all genotypes have the same environmental variance (the assumption of homoscedasticity), in reality, genotypes can show heteroscedasticity in the environmental variance. When such variation is heritable (i.e., has an additive variance in an outbred population), then the environmental variance can change under selection. This can either be due to an indirect response (such as during directional selection on a trait), or through direct selection to increase the homogeneity of a trait (such as for increased uniformity during harvesting). This chapter reviews the existing data on the heritability of the environmental variance and examines several different genetic models for predicting its response.