From a love of puzzles to studies on BMI – what Mendel’s legacy means to me, and to my cat

In the first of a series of blog posts celebrating 200 years since the birth of Gregor Mendel, Lavinia Paternoster shares how learning about genetics at school shaped her future career – and introduces us to a cat called Mendel


A cat called Mendel.
A cat called Mendel

For as long as I can remember I’ve loved spotting patterns, spending hours as a child playing logic puzzles and, more recently, Sudoku. I love how just a few simple rules can be applied to break the code of seemingly complex patterns. So when I was introduced to Mendel’s pea experiments during my A-levels it was like I got to use my nerdy love of puzzles in the classroom. Compared to how hard I found languages and chemistry, I couldn’t believe that solving these little crosses to determine the genetic inheritance of pea traits counted as work. I had a very supportive biology teacher who nurtured my passion by sending me home with a jar of fruit flies over the Easter holidays to perform my own inheritance crosses (more in homage to Morgan’s drosophila crosses, but quicker and requiring less horticultural skills than growing pea plants). I was hooked and quickly signed up to study genetics at university. 

Today I still love the simplicity in the way that the laws of genetic inheritance work to influence even the most complex of human traits. Now working on human traits such as eczema, BMI and even how a disease progresses over time, most of my work involves the simplest of statistical tests (performed millions of times, in an approach called genome-wide association  studies) to identify which variants in our genomes influence these important outcomes. 

I often think about my earliest introduction to genetic inheritance and how lucky I was to find my imagination captured by those beautifully simple genetic crosses performed by Mendel. Naming my own cat in his honour, I often find myself chatting to a random passers by outside our house about Mendel and his pea experiments. Whilst glad I share some of Mendel’s (the man not the cat) love of genetic inheritance, I definitely do not also share his talent in the greenhouse, struggling to keep the most low maintenance of plants alive. But I somewhat blame Mendel’s love of digging (the cat, not the man, this time)!


  • To celebrate Mendel’s 200th birthday we are holding a two-day conference, online and in-person in Bristol on 20-21 July. For more information and to sign up, see our Mendel at 200 pages and follow #Mendel200 on social media for Mendel activities around the world.


Why siblings are interesting for genome-wide association studies

Neil Davies discusses a new paper on a genome-wide association study of almost 180,000 siblings and discusses what additional insight siblings bring to such studies.

Thousands of genome-wide association studies (GWAS) have been published, however, the vast majority have used samples of unrelated individuals. We have recently published a sibling GWAS published in Nature Genetics. In our study, we used almost 180,000 siblings across 19 studies from around the world. But why are siblings interesting for GWAS?

GWAS have already identified tens of thousands of single nucleotide polymorphisms (SNPs) related to phenotypes – using samples of unrelated individuals. However, correlation is not equal to causation. Increasing evidence suggests these associations can be driven by more than individual-level biological effects.

There can be three key sources of bias. The first potential bias is population stratification. This means the differences in the frequency of the genetic variants that relate to phenotypic differences. For example, Iron Brew consumption will associate with variants more common in Scotland. These associations are biased evidence of the causal effect of the variant on the phenotype!

The second bias is assortative mating. People don’t mate at random. For example, studies have shown that more educated people tend to have more educated and taller partners. Such trends can result in biased associations between SNPs and phenotypes in the offspring.

The third bias is indirect parental genetic effects (also known as dynastic effects).

In these, the genotype is expressed in parents, which in turn affects offspring outcomes. One example of this is that the education of parents may influence educational outcomes in the offspring, again biasing SNP-phenotype associations.

How can data from siblings help overcome these biases? Siblings inherit their genetic variants from their parents at random. They are nature’s randomized control trials. If the siblings who share the genotype have more similar trait measures, researchers can be more confident that the genotype is influencing the trait directly.

Looking at the differences between siblings controls for each of the sources of bias above.

Which phenotypes suffer most from these biases? In our Nature Genetics paper, we estimated the shrinkage from the population to sibling estimates for 25 phenotypes, to see which suffered most from these biases. We estimated this by looking at how much the associations shrunk between the population estimates (without comparing within siblings), to the within sibling estimates. The larger shrinkage in the LD-score regression plot below indicates more bias.

We found that previously reported genome-wide association study (GWAS) associations, which typically use more widely available population samples of unrelated individuals, tend to overestimate direct effects for many traits including educational attainment, cognitive ability, age when first gave birth, whether someone has ever smoked, depressive symptoms and number of children. We also found that estimates of heritability, genetic correlations and other genetic analysis methods could substantially differ when calculated using estimates from siblings.

Biases do affect genetic correlations

A major finding from our research was that these biases do affect genetic correlations. When we use sibling cohorts, the genetic correlations from LD-score regression between educational attainment and traits such as height and BMI are not detected. Note the change in power and precision in the plot below. This suggests that the correlations that are detected in population samples are unlikely to be due to a causal effect of the genetic variants in the individuals.

Are recent findings on polygenic adaption robust to these biases? Yes, height is likely to be under polygenic selection. This suggests that selective pressures in the human population have affected the number of height-associated alleles in the population. This could lead to changes in the average height of the population over multiple generations.

Are sibling samples “better” than “population” samples?

Whether sibling samples such as we use in our study are “better” than population studies depends on the question you want to look at. Large population-based samples of unrelated individuals are great if you want to discover new genetic variants associated with a disease or other outcomes, or are interested purely in prediction.

However, if you are interested in understanding why genetic variants associated with an outcome like height, BMI, or education, then family studies can provide a powerful source of evidence. In this paper, we only looked at a very small number of phenotypes, but these results suggest that these biases are more likely for social/behavioural phenotypes, and more biological ones are less likely to be biased.

What’s next? The international collaboration established for this study is continuing to work together and explore these issues further. The next steps include using bigger samples of siblings and estimating the relative contribution of these sources of bias using samples of parent-offspring trios.

A massive thanks to all our co-authors – an international group of 100 scientists were involved in this study – and many, many others. Amazing being able to work with you all!

Read the paper

Read the press release

Read our FAQs