Predisposed to statistical genetics
You could say it’s in his genes: when it comes to his professional proclivities, Ben Neale takes after his parents.
The trio share an interest in statistical analysis and behavioral research: Neale, an associated researcher at in the Broad Institute’s Stanley Center for Psychiatric Research and the Broad’s Program in Medical and Population Genetics, is a statistical geneticist who studies neurological disorders such as autism, attention deficit hyperactivity disorder (ADHD), and schizophrenia. His father is also a statistical geneticist as well as a behavioral geneticist, and his mother – an educational therapist by training – is assistant superintendent for instruction and federal programs for juvenile detention homes, mental health facilities, and hospitals for the state of Virginia.
“It’s the family business,” Neale says. “Psychology, psychiatry, and statistics have always been part of my scientific DNA.”
Neale even collaborated with his father on what would become the younger Neale’s first scientific paper: Neale was working in the lab of one of his father’s colleagues when they co-authored a study analyzing, of all things, nonpaternity in linkage studies. Their paper looked at how often, in genetic studies of siblings with highly differentiated traits, presumed full-siblings turned out to have different fathers.
That early work on linkage studies helped solidify Neale’s interest in statistical genetics as a profession, and his career is now flourishing. He has been at the Broad since joining the lab of Broad senior associate member Mark Daly in 2009, and he also holds appointments at Massachusetts General Hospital and Harvard Medical School. Neale studies the genetic basis of psychiatric disease and conducts statistical genetics research in other fields such as metabolism and autoimmunity. He is part of a new generation of researchers who are using the analytical tools of the genomic era to push their field forward.
Neale’s psychiatric disease research currently focuses on attention deficit hyperactivity disorder (ADHD), autism, and schizophrenia. His role often involves collecting genomic data from genetic samples – typically from the exome, the protein-coding region of the genome – and then using mathematical models to find genetic variations or mutations that may be associated with a disease.
An active member of the Psychiatric Genomics Consortium (PGC) – an international, multi-institutional collaboration that is applying genomics to psychiatric disease – Neale is helping to aggregate all of the existing exome-sequencing data collected from autism “trios” (individuals with autism and their parents). The group is using statistical models to compare the data with information on common, human genetic variation available through the 1000 Genomes Project, in an effort to identify mutations that appear in autism but not in the general population. Neale is also involved in a separate project, funded by the Stanley Center and National Human Genome Research Institute, that is using a similar pipeline to identify genes associated with schizophrenia.
Neale is also heavily involved in ADHD research. He and Daly lead the ADHD Initiative, a collaborative effort funded by the Gerstner Family Foundation that aims to discover the genomic underpinnings of the disorder. Launched in 2011, the initiative has already assembled the largest collection of genetic samples for ADHD to date. Neale and his colleagues will use the genomic data derived from those samples to conduct genome-wide association studies (GWAS), searching for genetic variations that may be at play in ADHD. Neale plays a similar role for PGC, heading their ADHD GWAS analysis group.
While psychiatric disease is the primary focus of Neale’s research, he notes that it isn’t the only thing that drew him to his field: he also has a passion for statistical problem-solving.
“What appeals to me is: how do you manipulate the data and ask the right questions statistically and mathematically to learn as much as possible about human health and epidemiology?” he explains.
That curiosity has led Neale into frequent collaborations in fields outside of psychiatric disease. Currently, he is applying his statistical know-how to a project led by Broad associate member Sekar Kathiresan that is looking into whether genetic variation in triglyceride regulation plays a role in heart disease.
“It’s one of those nice Broad projects where researchers have assembled these large datasets and are using them to make discoveries into the genetic basis of disease,” Neale says of the study.
To accumulate and sift through the data needed to conduct these genomic-wide studies, scientists rely on tools and technologies to enable their research – and Neale has a hand in that as well. He has helped develop software tools – including the popular PLINK program – that aid in the statistical analysis of GWAS. He also led the design of the exome chip, a cost-effective genotyping array that identifies coding variations in the protein-coding regions of the genome. Neale is now “knee deep in the development” of a next-generation array – one designed to make the handling of genetic samples even more efficient and cost-effective.
“And what we want are samples,” Neale explains. “If there’s one thing that’s clear in human genetics research, it’s that sample size is the best indicator of the success of GWAS. The larger the sample is, the more likely you are to identify significant, robust, replicable loci that are associated with disease.”
As a second-generation statistical geneticist, Neale’s involvement in the development of this new wave of genomic tools is only fitting, and it is paying off in psychiatric disease, which has lagged behind other medical fields in its adoption of such approaches.
“Traditionally, GWAS hasn’t been viewed as a useful approach for psychiatric illness,” Neale says, “but now that results have emerged in autism and schizophrenia that common variants can be found in psychiatric disease, I think the game is changing. How that might alter our understanding of the biology behind these conditions is only beginning to be realized.”